FUNDAMENTALS AND APPLICATION OF POROUS MEDIA...

Fundamentals and Application of Porous Media Filtration forthe Removal of Nanoparticles from Industrial Wastewater

Item Type text; Electronic Dissertation

Authors Rottman, Jeffrey J.

Publisher The University of Arizona.

Rights Copyright © is held by the author. Digital access to this materialis made possible by the University Libraries, University of Arizona.Further transmission, reproduction or presentation (such aspublic display or performance) of protected items is prohibitedexcept with permission of the author.

Download date 14/07/2018 22:54:58

Link to Item http://hdl.handle.net/10150/255157

http://hdl.handle.net/10150/255157

FUNDAMENTALS AND APPLICATION OF POROUS MEDIA FILTRATION FOR

THE REMOVAL OF NANOPARTICLES FROM INDUSTRIAL WASTEWATER

by

Jeffrey Joseph Rottman

____________________

A Dissertation Submitted to the Faculty of the

DEPARTMENT OF CHEMICAL AND ENVIRONMENTAL ENGINEERING

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

WITH A MAJOR IN CHEMICAL ENGINEERING

In the Graduate College

THE UNIVERSITY OF ARIZONA

2012

2

THE UNIVERSITY OF ARIZONA

GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation

prepared by Jeffrey Joseph Rottman entitled FUNDAMENTALS AND APPLICATION

OF POROUS MEDIA FILTRATION FOR THE REMOVAL OF NANOPARTICLES

FROM INDUSTRIAL WASTEWATER and recommend that it be accepted as fulfilling

the dissertation requirement for the Degree of Doctor of Philosophy

_________________________________________________ Date: 11/7/12

Farhang Shadman

_________________________________________________ Date: 11/7/12

Reyes Sierra-Alvarez

_________________________________________________ Date: 11/7/12

Craig Aspinwall

Final approval and acceptance of this dissertation is contingent upon the candidate’s

submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and

recommend that it be accepted as fulfilling the dissertation requirement.

_________________________________________________ Date: 11/7/12

Dissertation Director: Farhang Shadman

3

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of requirements for an

advanced degree at the University of Arizona and is deposited in the University Library

to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided

that accurate acknowledgment of source is made. Requests for permission for extended

quotation from or reproduction of this manuscript in whole or in part may be granted by

the author.

SIGNED: Jeffrey Joseph Rottman

4

ACKNOWLEDGEMENTS

This accomplishment would not have been possible if it were not for the support of many

people. First, I would like to thank my advisor, Dr. Farhang Shadman, for all his support

and guidance over the past years. He has been instrumental in my development as an

engineer and researcher; ever pressing for fundamental understanding and critical

evaluation. I would also like to thank Dr. Reyes Sierra for her tireless instruction and

direction in experimental design and effective communication. The two of you have

provided an excellent example of principled scientific inquiry and have personally been

both encouraging and caring over the course of my studies. I would also like to thank Dr.

Roberto Guzman and Dr. Craig Aspinwall for their probing questions and helpful

direction as members of my graduate committee. I am very thankful for all those at SRC

who were always available for questions and who work diligently to support us graduate

students. Thank you to all of my fellow graduate students, especially Anand, Rahul,

Ming, David, Janae, and Dave, whom I have been able to both celebrate and commiserate

with over the past years. Finally, thank you to all the staff from the ERC and Chemical

Engineering Department, namely Karen, Ali, Jo and Arla for their friendship and their aid

in all of my complicated administrative matters.

I would specifically like to thank the many friends outside of the University who have

loved and supported me: Ryan & Yunuen Jankowski, Liam & Natalie Grimes, Luke &

Marianne Evans, Ben & Lis Richards, Matt & Dana McReynolds, Tim & Nikki

Finnegan, Bret & Val Holley, Dave & Christina Jorg and David Ritsema. You have all

been like family. I would also like to thank Rincon Mountain Presbyterian Church and

Pastor Phil Kruis for all the worship and fellowship shared over my time here in Tucson.

To my parents, Greg and Suzanne, and my brother and sister, Steven and Kim, thank you

for all your encouragement. Finally, I would like to thank my loving wife, Beth, and our

daughter, Clara, without whom this would not have been possible. I have enjoyed

sharing every minute of this with you.

Above all, I give thanks and glory to my Heavenly Father, whom through His Son, Jesus,

has given me life. I am as grass and my glories are as the flowers of the field; the grass

withers and the flowers fall, but the word of our Lord stands forever.

5

DEDICATION

To my wonderful wife, Beth, and little Clara

I love you more each day

6

TABLE OF CONTENTS

LIST OF TABLES ....................................................................................................................... 10

LIST OF FIGURES ..................................................................................................................... 11

ABSTRACT .................................................................................................................................. 16

CHAPTER I INTRODUCTION ................................................................................................ 19

1.1. Introduction to Nanoparticles .......................................................................................... 19

1.2. Nanoparticle Release and Exposure ................................................................................ 20

1.3. Human Toxicity and Ecotoxicity of Nanoparticles ........................................................ 21

1.4. Nanoparticle Stability in Aqueous Medium ................................................................... 23

1.5. Nanoparticle Abatement .................................................................................................. 30

1.5.1. Targeted Nanoparticle Abatement ............................................................................... 30

1.5.2. Primary Wastewater Treatment ................................................................................... 31

1.5.3. Secondary Wastewater Treatment ............................................................................... 33

1.5.4. Porous Media Filtration ............................................................................................... 35

1.6. Scope of the Work ............................................................................................................. 39

CHAPTER II REAL-TIME MONITORING OF NANOPARTICLE RETENTION IN

POROUS MEDIA ........................................................................................................................ 41

Abstract ..................................................................................................................................... 41

2.1. Introduction ....................................................................................................................... 42

2.2. Experimental ..................................................................................................................... 44

7

2.2.1. Materials ...................................................................................................................... 44

2.2.2. Experimental Setup ...................................................................................................... 44

2.2.3. Analysis........................................................................................................................ 46

2.3. Results and Discussion ...................................................................................................... 47

2.3.1. TiO2 Nanoparticles ....................................................................................................... 47

2.3.2. Apparatus Performance ................................................................................................ 47

2.4. Conclusions ........................................................................................................................ 53

CHAPTER III APPLICATION OF FLUORESCENT CORE-SHELL SILICA

NANOPARTICLES AS TRACERS IN POROUS MEDIA FILTRATION ........................... 54

Abstract ..................................................................................................................................... 54

3.1. Introduction ....................................................................................................................... 56

3.2. Materials and Methods ..................................................................................................... 59

3.2.1. Fluorescent Nanoparticle Synthesis ............................................................................. 59

3.2.2. Filtration Media ........................................................................................................... 61

3.2.3. Column Experiments ................................................................................................... 61

3.2.4. Imaging of Nanoparticles ............................................................................................. 62

3.2.5. Particle Size Distribution and Zeta Potential ............................................................... 63

3.2.6. Analysis ........................................................................................................................ 63

3.2.7. Chemicals ..................................................................................................................... 63


TABLE OF CONTENTS - Continued

8

3.3.1. Nanoparticle Synthesis ................................................................................................. 64

3.3.2. Fluorescent SiO2 Nanoparticles as Tracers in Porous Media Column Experiments:

Effect of Particle Size and Concentration .............................................................................. 72


Effect of Porous Media and Flow Rate .................................................................................. 76

3.4 Conclusions ......................................................................................................................... 79

CHAPTER IV REMOVAL OF TiO2 NANOPARTICLES BY POROUS MEDIA: EFFECT

OF FILTRATION MEDIA AND WATER CHEMISTRY ...................................................... 80

Abstract ..................................................................................................................................... 80

4.1. Introduction ....................................................................................................................... 82

4.2. Materials and Methods ..................................................................................................... 85

4.2.1. Materials ...................................................................................................................... 85

4.2.2. Porous Media and Nano-TiO2 Characterization ........................................................... 86

4.2.3. Adsorption Isotherms ................................................................................................... 87

4.2.4. Flow-through Column Experiments ............................................................................. 88

4.2.5. Chemical Analysis ....................................................................................................... 90


4.3.1. Porous Media and Nano-TiO2 Characterization ........................................................... 90

4.3.2. Adsorption Isotherms ................................................................................................... 99

4.3.3. Effect of Porous Media on Nano-TiO2 Transport ...................................................... 102


9

4.3.4. Effect of Solution Contaminants on Nano-TiO2 Transport ........................................ 106

4.3.5. Environmental and Industrial Implications ................................................................ 108

4.4. Conclusions ...................................................................................................................... 110

CHAPTER V MODELING NANOPARTICLE TRANSPORT AND RETENTION IN

POROUS MEDIA ...................................................................................................................... 111

Abstract ................................................................................................................................... 111

5.1. Introduction ..................................................................................................................... 112

5.2. Model Description ........................................................................................................... 114

5.3. Results and Discussion .................................................................................................... 119

5.3.1. Porous Media Column ............................................................................................... 119

5.3.2 Modeling Results ........................................................................................................ 121

5.4. Conclusions ...................................................................................................................... 129

CHAPTER VI ACTIVATED SLUDGE TREATMENT OF NANOPARTICLES ............. 130

6.1. Introduction ..................................................................................................................... 130

6.2. Materials and Methods ................................................................................................... 133

6.3. Results and Discussion .................................................................................................... 135

6.4. Conclusions ...................................................................................................................... 144

CHAPTER VII CONCLUSIONS ............................................................................................. 145

CHAPTER VIII CONTINUATION OF WORK .................................................................... 148

REFERENCES ........................................................................................................................... 149


10

LIST OF TABLES

Table 4.1. Material characteristics for porous media used in column experiments .........93

Table 4.2. Zeta potential of TiO2 nanoparticles in tested dispersions at pH 7.0. .............97

Table 4.3. Fit constants for batch isotherms of TiO2 nanoparticles on selected

filtration media. ...........................................................................................................101

Table 5.1. Physical parameters of the porous media columns .......................................117

Table 5.2. Model constants for best fit approximation of NP transport in varying

porous media. ..............................................................................................................122

Table 6.1. Fitting constants for Freundlich and Langmuir NP association isotherms,

including goodness of fit as determined by the coefficient of determination, R2. ......140

11

LIST OF FIGURES

Figure 1.1. Illustration of the electric double layer surrounding particles in

aqueous solution. ...........................................................................................................23

Figure 1.2. Interaction energy as a function of separation distance as predicted by

the DLVO theory. Upper inset denotes critical points of the force curve. Lower

inset displays the shape of the curve for varying conditions: (a) highly charged

surface, weak ionic strength; (b) highly charged surface, stronger ionic strength;

(c) moderate surface charge, stronger ionic strength; (d) moderate surface

charge, high ionic strength; (e) little to no surface charge. Obtained from

Israelachvili [1]. ............................................................................................................28

Figure 1.3. Mechanisms of nanoparticle removal in primary (left) and secondary

(right) wastewater treatment. ........................................................................................33

Figure 1.4. Mechanisms of nanoparticle capture in porous media filtration:

sedimentation (a), interception (b), straining (c), and diffusion/adsorption (d). ...........36

Figure 2.1. Online experimental apparatus including continuously-stirred

nanoparticle suspension [1], peristaltic pump [2], UV-Vis spectrophotometer

[3], flow through cuvettes for influent [4] and effluent [6], and glass column

packed with porous media [5]. This apparatus provides fully online data of

nanoparticle retention in the column without the need for sampling or further

sample preparation prior to measurement. ....................................................................45

Figure 2.2. Particle size distribution of the TiO2 nanoparticle dispersions with no

additive (—) and with synthetic dispersant, Dispex (- - -). ...........................................48

12

LIST OF FIGURES - Continued

Figure 2.3. Transmission electron microscopy image of the TiO2 nanoparticles

utilized in this study. .....................................................................................................49

Figure 2.4. Breakthrough curves of TiO2 NPs with no additives (A) and with the

presence of a synthetic dispersant Dispex (B) in beds packed with sand (○) and

GAC (□). NP dispersion (pH 7) was introduced at 2.6 mL min-1

. Error bars

indicate standard deviation for three runs. ....................................................................50

Figure 3.1. Fluorescent dyes, NHSF (a) and RITC (b), and their respective

conjugations with APTES, (c) and (d). .........................................................................61

Figure 3.2. TEM images (left) and the corresponding particle size distributions

(right) of first generation n-SiO2 synthesized with NHSF dye shown for Fl-S

(A), Fl-L (B), and Fl-XL (C). ........................................................................................66

Figure 3.3. Zeta potential of silica dispersions as a function of pH for a

commercial n-SiO2 (♦) and for the synthesized Fl-S (●), Fl-L (▲), and Fl-XL

(■) n-SiO2 particles.......................................................................................................67

Figure 3.4. Fluorescence calibration curves for Fl-L (▲) and Fl-XL (■) particles

are best fit by (R2 = 0.9996) and

(R2 = 0.9993), respectively. ..........................................................................................67

Figure 3.5. TEM images (left) and the corresponding particle size distributions

(right) of first generation synthesized n-SiO2 with RITC dye shown for Rh-S

(A), Rh-L (B), and Rh-XL (C). .....................................................................................71

13


Figure 3.6. Zeta potential of silica dispersions as a function of pH for a

commercial n-SiO2 (♦) and for the synthesized Rh-S (●), Rh-L (▲), and Rh-XL

(■) particles. ..................................................................................................................71

Figure 3.7. Fluorescence calibration curves for Rh-S (●), Rh-L (▲), and Rh-XL

(■) particles are best fit by (R2 = 0.995),

(R2 = 0.999), and (R

2 = 0.999), respectively. .............71

Figure 3.8. Relative effluent concentration of fluorescent-core n-SiO2 as a function

of the number of DE bed volumes processed for the synthesized Rh-S at 1 mg-

SiO2 L-1

(●), Rh-L at 10 mg-SiO2 L-1

(■), Rh-L at 50 mg-SiO2 L-1

(▲), and Rh-

XL at 10 mg-SiO2 L-1

(♦) particles. The n-SiO2 dispersions were introduced at

2.6 mL min-1

. Error bars represent the standard deviation of duplicate

measurements. ...............................................................................................................74

Figure 3.9. Bed capacities of DE for Rh-S at 1 mg-SiO2 L-1

(■), Rh-L at 10 (■)

and 50 mg-SiO2 L-1

(■) and Rh-XL at 10 mg-SiO2 L-1

(■) based on mass

concentration (A) and number concentration (B). Error bars represent the

standard deviation of duplicate measurements. .............................................................75

Figure 3.10. Column effluent concentration as a function of bed volumes

processed for fluorescent n-SiO2 (Fl-L, 109±1 nm) at 84 mg-SiO2 L-1

introduced

at 2.6 mL min-1

in both activated carbon (—) and sand (- - -). .....................................77

14


Figure 3.11. The effect of feed flow rate on activated carbon column effluent

concentration as a function of bed volumes processed for fluorescent n-SiO2 (Fl-

L, 109±1 nm) at 84 mg-SiO2 L-1

and two different flow rates: 2.6 mL min-1

(—)

and 5.7 mL min-1

(- - -) .................................................................................................78

Figure 4.1. Scanning electron microscopy images of sand (A), activated carbon

(B), and diatomaceous earth (C). ..................................................................................92

Figure 4.2. Surface charge density (σ) as a function of pH for sand ( ),

activated carbon ( ), and diatomaceous earth ( ). ........................................94

Figure 4.3. Particle size distribution of the nano-TiO2. ...................................................95

Figure 4.4. Zeta potential of n-TiO2 as a function of pH. ................................................96

Figure 4.5. Transmission electron microscopy image of the n-TiO2. ..............................96

Figure 4.6. Average hydrodynamic diameter of n-TiO2 aggregates as a function of

time for the cases of no contaminant (●) and lysozyme (■). Standard deviations

of triplicate measurements are shown as error bars. Average sizes of n-TiO2

dispersions containing Dispex and glycine did not differ notably from the no

contaminant case. ..........................................................................................................98

Figure 4.7. Association isotherms for n-TiO2 onto three bed media: sand (A),

activated carbon (B), and diatomaceous earth (C). Error bars shown for

duplicate measurements. Additionally, Henry (─ ∙ ─), Freundlich (---), and

Langmuir (─) isotherm fits are provided. ...................................................................100

15


Figure 4.8. Relative effluent n-TiO2 concentration as a function of the number of

bed volumes processed for sand (A), activated carbon (B) and diatomaceous

earth (C). Plots for dispersions with no contaminant (─) and for dispersions

amended with Dispex (---), lysozyme (─ ∙ ─), and glycine (∙∙∙). Dispersions at

pH 7 were introduced at 2.6 mL min-1

. .......................................................................103

Figure 4.9. TiO2 nanoparticle concentrations associated with porous media as a

function of bed depth for sand (A), activated carbon (B), and diatomaceous earth

(C). Four cases shown: no contaminant ( ), dispex ( ), lysozyme (

) and glycine ( ). All suspensions (pH 7) were introduced at 2.6 mL

min-1

............................................................................................................................104

Figure 5.1. NP effluent concentration relative to the inlet concentration as a

function of bed volumes processed for sand (●), AC (■), and DE (▲). NP

dispersions at pH 7 were introduced at 2.6 mL min-1

. Error bars indicate

standard deviation for triplicate measurements...........................................................119

Figure 5.2. Retained NP concentration as a function of relative bed length for sand

( ), AC ( ), and DE ( ). Error bars indicate standard deviation for

triplicate measurements. ..............................................................................................121

Figure 5.3. Sand model results for the relative effluent NP concentration (C),

above, and the relative retained NP concentration (S), below. Data (○) is shown

alongside the model (—). Error bars indicate standard deviation for triplicate

measurements ..............................................................................................................125

16


Figure 5.4. AC model results for the relative effluent NP concentration (C), above,

and the relative retained NP concentration (S), below. Data (□) is shown

alongside the model (—). Error bars indicate standard deviation for triplicate

measurements. .............................................................................................................126

Figure 5.5. DE model results for the relative effluent NP concentration (C), above,

and the relative retained NP concentration (S) at 30 bed volumes processed,

below. Data (∆) is shown alongside the model (—). Error bars indicate

standard deviation for triplicate measurements...........................................................127

Figure 5.6. DE model results (—) for the relative retained NP concentration (S)

after 485 bed volumes processed. ...............................................................................128

Figure 6.1. TEM images of Al2O3 (A), CeO2 (B), and SiO2 (C) nanoparticles. ............136

Figure 6.2. Association isotherms for Al2O3 (A), CeO2 (B), and SiO2 (C).

Experimental results (♦) are presented along with graphical representations of

the Freundlich (—) and Langmuir (---) isotherms. Error bars included but not

visible due to size of points .........................................................................................137

Figure 6.3. TEM (left) and SEM (right) images of microorganisms in activated

sludge after exposure to Al2O3 (A), CeO2 (B), and SiO2 (C) nanoparticles

(denoted by arrows).....................................................................................................142

Figure 6.4. SEM-EDS analysis of microorganisms in activated sludge after

exposure to Al2O3 (A) and CeO2 (B) NPs. Red or blue dots represent presence

of Al or Ce, respectively. ............................................................................................143

17

ABSTRACT

Increasing use of engineered nanomaterials presents concerns as some

nanoparticles appear to be harmful to both human health and the environment. Effective

treatment methods are required to remove problematic nanoparticles from (waste)water

streams. Porous media filtration, commonly used for the removal of particulate matter,

shows promise for nanoparticle treatment. The goal of this work is to investigate the

potential of porous media filtration for the abatement of nanoparticles from aqueous

waste streams. To this end, an automated method was developed that allows real-time

and in-situ monitoring of nanoparticle transport and retention in porous media using

online measurement of UV-visible absorbance or fluorescence.

Development of fluorescent-core nano-silica (n-SiO2) in controllable sizes

provided an excellent tracer for nanoparticle transport in porous media. Measurement of

n-SiO2 by destructive techniques is complicated by high natural Si background levels.

Fluorescence monitoring enables real-time measurement, facilitating rapid evaluation of

n-SiO2 transport. Synthesized n-SiO2 remain in their primary sizes making an evaluation

of the behavioral change of particles due to transition into the “nano” range possible. A

comparison of the role of particle size on transport in porous media displayed the

importance of particle number concentration as the dominance of site-specific adsorption

may be obscured by simple mass concentration evaluation.

The effectiveness of different bed materials, namely, sand, activated carbon (AC),

and diatomaceous earth (DE), for the removal of TiO2 nanoparticles (n-TiO2) from

aqueous streams was investigated. DE proved promising for n-TiO2 capture shown by its

18

high bed capacity (33.8 mg TiO2 g-1

medium) compared to AC (0.23 mg TiO2 g-1

medium) or

sand (0.004 mg TiO2 g-1

medium). The presence of organic and synthetic contaminants

produced varying effects on n-TiO2 retention, mostly due to either enhanced electrostatic

or steric interactions.

Application of a process simulator combining physical straining with site-specific

interactions, delineating physisorption from chemisorption and diffusion limited

interactions, enabled the accurate fit of n-TiO2 transport in sand, AC and DE. The fitting

process revealed the advantage of DE due to increased physisorption and physical

straining of n-TiO2. Modeling of this system afforded the elucidation of controlling

retention mechanisms and provides a basis for future scaling and system design.

19

CHAPTER I

INTRODUCTION

1.1. Introduction to Nanoparticles

Nanotechnology is the utilization and manipulation of matter in the nanoscale.

Nanoparticles, defined as particles with at least one dimension in the range of 1 to 100

nm, form an important sector of nanotechnology [2]. Nanoparticles draw such interest

from their unique properties which result from their size approaching atomic dimensions.

As opposed to their larger counterparts, nanoparticles have a much larger surface area,

are typically much more reactive, and have properties that are adjustable with size [3-5].

Controlling these properties at such small scales, nearing the atomic, is what provides for

their applicability to various industries and technologies. Development in the

understanding and control of nano-properties has allowed for many advances in

consumer technology including nano-additives enabling lighter weight polymers and

better performing cosmetics [2]. Nanomaterials are also integral to the electronics

industry, with nano-scale transistors enabling smaller, more efficient devices and

nanoparticles allowing precise semiconductor manufacturing [6-7].

Nanotechnology has many environmental benefits including material weight

reduction, which leads to lighter airplanes and vehicles and thus less energy consumption,

enhancement of energy generation, such as improving photovoltaic efficiency or battery

capacity, and replacement of hazardous materials, such as the use of nano-titanium

dioxide or nano-silicon dioxide as flame retardants in place of bromine [8].

20

Nanoparticles may also aid in water purification, especially through the use of

nanosorbents which have been shown to have a high capacity for metal ions [9]. The

applications of nanoparticles continues to increase, with uses ranging from textiles to bio-

medical applications [10].

Nanoparticles are generally classified into two groups: carbon-based structures

such as fullerenes and carbon nanotubes and inorganic nanoparticles such as quantum

dots, metals and metal oxides. Metal oxide nanoparticles, such as titanium dioxide

(TiO2), cerium dioxide (CeO2) and silicon dioxide (SiO2), are of particular interested due

to their wide commercial and industrial application, from use in sunscreens and cosmetics

to use as abrasives in slurries used for semiconductor manufacturing [10-11].

1.2. Nanoparticle Release and Exposure

Nanoparticles (NPs) are released to the environment in a variety of ways, both

intentionally and unintentionally. One such example of intentional NP release is the

injection of nano-sized zero-valent iron into wells of groundwater contaminated with

chlorinated solvents [12]. Unintentional release is of much greater concern; resulting both

from point and non-point sources. Non-point sources are typically found from

commercial products. These products might include brake pads on cars, paint, fabrics,

sunscreen and cosmetics; the associated NP release is dependent on their use [13-14]. NP

point sources are typically industrial manufacturing facilities, but also include landfills

and wastewater treatment plants [13, 15-16]. Modeling of environmental concentrations

of TiO2 NPs produced an estimate of over 1,500 tons per year entering sewage treatment

plants, with the majority of release being divided between the soil (~48%) and surface

21

water (~24%) [17]. These levels of release are expected to only increase with continued

increase in production. A recent study puts the upper bounds of yearly TiO2 NP

production at approximately 2.5 million metric tons by 2025 [18]. In fact, the “nano”

market is expected to be worth approximately $1 trillion annually by 2015 [19-20].

Understanding how these releases and exposures influence both human and ecological

heath is critical.

1.3. Human Toxicity and Ecotoxicity of Nanoparticles

While NPs offer many positive contributions [21], concerns arise over their

increasing application due to the potential negative effects of NPs on human and

environmental health [22]. Metal and metal oxide NPs have been shown to have

inflammatory and toxic effects on cells [23-25]. Human bronchoalveolar carcinoma-

derived cells were exposed to SiO2 NPs of 15 nm and 46 nm and the cytotoxicity due to

oxidative stress was found to be essentially equal [26]. This finding seems to be in

opposition to the commonly held theory that smaller particles are more toxic [27-28],

however the particles were significantly aggregated (590 and 617 nm, respectively)

which may explain the similarity in toxicity. Zinc oxide (ZnO) NPs were shown to

reduce MTT function in mitochondrial cells [29] as well as reduce cell functions at only

3.75 mg L-1

, likely attributable to the release of Zn2+

ions [30]. Iron oxide (Fe2O3) NPs

have been shown to be lethal to human mesothelioma cells at 7.5 mg L-1

; showing similar

toxicity to asbestos [30]. Investigation of the effect of 20 nm CeO2 NPs on human lung

cancer cells revealed a time-dependent and dose-dependent toxic effect due to oxidative

stress as well as lipid peroxidation and cell membrane damage [31]. TiO2 NPs displayed

22

photogenotoxicity as DNA damage occurred during light exposure but no damage was

observed in protected samples [32]. Additionally, TiO2 NPs have added to these previous

concerns about human interaction by displaying neuro-toxicity toward dorsal root

ganglion cells, even with commonly applied inorganic coatings [33]. Human toxicity is

not the only concern, as the environmental impact of engineered nanomaterials is also of

interest.

Ecotoxicity is also concerning as the influence these new nanomaterials have on

natural systems is still being determined [15]. As a basis of ecosystems, microorganisms

are of great importance and the effect of NPs on them may provide insight on the total

environmental impact. TiO2 NPs have been found to be damaging toward both Bacillus

subtilis and Escherichia coli, possibly due to the generation of reactive oxygen species

[34]. CeO2 has also been shown to have an antimicrobial effect on E. coli [35]. Daphnia

magna, a fresh water crustacean, is commonly used as a target organism in ecotoxicity

testing. It has been found that TiO2 (25 nm) was more toxic to D. magna than its 100 nm

counterpart [36]. Also, 30-nm TiO2 NPs induced 100% mortality of D. magna at 10 mg

L-1

and a LC50 (median lethal concentration) of 5.5 mg L-1

[37]. The effect of TiO2 NPs

on larger rainbow trout was studied and exposure was found to cause decreased Na+K

+-

ATPase activity in the gills and intestine but overall is not expected to be a major

ionoregulatory toxicant for concentrations less than 1.0 mg L-1

[38]. A study on the

phytotoxicity of alumina (Al2O3) NPs displayed root growth inhibition for 13 nm samples

but not for the larger 200 – 300 nm sample [39]. It is debated whether surface free

hydroxyl groups [39] or aluminum solubility [40] is the cause of this inhibition.

23

These results highlight the need to understand NP behavior in the environment as

these NPs have potentially far reaching effects for both human health and environmental

sustainability. Understanding NP behavior in water establishes the principles by which

evaluation of NP transport in the environment as well as efficiencies of treatment

technologies may be accomplished.

1.4. Nanoparticle Stability in Aqueous Medium

In order to effectively design a treatment technique for NPs and to evaluate

efficiencies of current treatment strategies, an understanding of the principles governing

NPs in water solutions is necessary. Metal oxide NPs in water solutions form what is

known as an electric double layer (EDL). The EDL, illustrated in Figure 1.1, is

comprised of two sections: the Stern or Helmholtz layer and the diffuse layer. The Stern

Stern Layer

Particle Surface

Diffuse Layer

Bulk Solution

Figure 1.1. Illustration of the electric double layer surrounding particles in aqueous

solution.

24

or Helmholtz layer is the layer of ions of opposite charge than the particle surface which

adhere to the surface via electrostatic and physical adsorption. Beyond this layer there is

a concentrated cloud of ions, the majority of which are charge opposites of the particle,

which serve to satisfy electroneutrality. A common measurement used to characterize the

EDL thickness is the Debye length, which is the distance from the particle surface at

which the apparent charge is about 37% that of the surface [41]. While this is just an

appoximation of the EDL, it does provide information on how far into solution the

particle’s charge will be “felt” thus influencing stability. Larger Debye lengths are

associated with enhanced stability.

In order to measure surface charge a zeta potential value is often used. The zeta

potential is the potential between the shear (or slipping) plane and the bulk solution. The

shear plane exists in the diffuse layer just beyond the Stern layer and is defined as the

division between those ions which travel with the particle and the bulk solution. This

surface charge measurement is highly useful in determining NP stability, as high-

magnitude like-charges produce strong repulsive forces between the NPs. Zeta potentials

greater than 20 mV and less than -20 mV are expected to produce highly stable NP

dispersions [42]. The zeta potential of the NPs, and by extension the dispersion stability,

is determined by many factors including pH, ionic strength, and counterion valence.

Metal oxides can become charged by adsorption of hydrogen (H+) or hydroxide

ions (OH־) and the relative amounts is influenced by the pH of the solution. Recall that a

zeta potential of large magnitude (either positive or negative) will result in a highly stable

solution. However, the zeta potential decreases from positive to negative values with

25

increasing pH thus implying a point at which the zeta potential reaches zero, known as

the isoelectric point (pHIEP) or point of zero charge (pHPZC). Zeta potential measurements

as a function of pH have been performed for a variety of metal oxide NPs including FeO

(20 nm) [43], SiO2 (15 nm) [44], Al2O3 (11, 44, and 190 nm) [45], and CeO2 (24.5 nm)

[46]. The IEP of each was determined to be ~7.8, 1.6, ~9, and ~8, respectively. The pH

region immediately surrounding the pHIEP therefore is a region of low stability and high

agglomeration rates.

According to the Sogami-Ise theory an increase in the ionic strength (salt

concentration) results in a decrease in the Debye length and interparticle distance through

a condensing of the diffuse layer [47]. This compression of the diffuse layer retards the

interparticle repulsion resulting in agglomeration [48]. French and coworkers studied the

aggregation of titanium dioxide NPs (4-5 nm) at ionic strengths similar to those

characteristic of soils and found that even at low ionic strengths (1 mM), aggregation was

enhanced [48]. The observed effect of ionic strength change on zeta potential is a

reduction in zeta potential magnitude at pH values far above and below the IEP, lending

the solution toward aggregation in a wider range of pH [45]. Many studies have been

performed on various NPs and have yielded similar results [45-46, 48-50].

The contribution of cation valence to the aggregation characteristics of NPs

relates closely with ionic strength effects. A study of cation valence in solution and its

effects on NP aggregation, specifically TiO2 (4-5 nm), was performed by French and

coworkers [48]. The study monitored the particle size distribution when NaCl or CaCl2

salt was added while pH was held relatively constant at 4.5 – 4.8. The particles reached

26

micron-sized aggregates in less time in the solution containing Ca2+

(ionic strength of

12.8 mM) than in the solution containing Na+ (ionic strength of 12.5 mM). Also, the

final aggregates in the calcium solution showed similar size to those in the 16.5 mM Na+

solution which proves that the enhanced aggregation is due to other factors than mere

ionic strength. Their conclusion was that the divalent ions caused a shortening of the

Debye length and thus reduced the electrostatic repulsion.

Thus far, only electrostatic or double-layer forces have been addressed. Another

significant force that must be taken into consideration is the van der Waals force. The

van der Waals force is a combination of dipole-dipole, dipole-induced dipole, and

induced dipole-induced dipole interactions. The last of these, induced dipole-induced

dipole or dispersion force, is typically dominant and results in strong attraction. This

force, however, is an inverse function of the sixth power of distance from the particle and

thus is only relevant over short distances. Normally van der Waals attraction is

dominated by electrostatic repulsion due to the EDL reaching further into solution;

particles are repelled before they are close enough for attractive van der Waals forces to

take over. The combination of the van der Waals force and the electrostatic force is

common, and has resulted in a centralized theory of particle stability.

This centralized theory is known as the DLVO theory after the originators

Derjaguin and Landau [51] and Verwey and Overbeek [52] who later improved upon it.

The DLVO theory allows for the prediction of NP stability from intrinsic properties of

the materials. A typical force curve produced from DLVO calculations is shown in

Figure 1.2. The main force curve displays the summation of electrostatic repulsion and

27

van der Waals attraction over the separation distance between the particles. The upper

inset notes the characteristic points of the force curve. The lower inset displays the

shifting of the force curve for differing conditions. For a highly charged surface in a

weak ionic strength solution (Fig. 1.2a), the particles will strongly repel one another

leading to a stable dispersion. As the ionic strength is increased a secondary minimum

forms (Fig. 1.2b). Here the particles may come into equilibrium if the well is deep

enough. Decreasing the surface charge (Fig. 1.2c) leads to a decreased energy barrier, so

while the particles may rest in equilibrium in the secondary minimum, there will likely be

some aggregation as thermal energy carries the particles into a primary minimum. The

final cases both result in aggregation of the particles; the secondary minimum occurring

for high ionic strength (Fig. 1.2d) simply slows the transition to a fully aggregated state

which would occur rapidly in the absence of surface charge (Fig. 1.2e). While the DLVO

theory is applicable in a wide range of situations and is helpful in predicting NP

dispersion stability, it is only accurate over large separation distances and can break down

at distances approaching a few molecular diameters [1].

At small separation distances two additional interactions become important:

solvation and steric interactions. Solvation interactions refer to how solvent molecules

arrange near the particle surfaces and how the arrangement is altered as two surfaces

approach one another [1]. These solvation interactions, also known as hydration,

hydrogen-bonding, or Lewis acid-base interactions, have been encompassed in what is

known as the extended DLVO (XDLVO) theory [53-54]. This theory provides more

28

accurate prediction for strongly hydrophobic or hydrophilic surfaces in water. In water,

the hydration forces arise due to the binding of water molecules to surface groups and

Figure 1.2. Interaction energy as a function of separation distance as predicted by the

DLVO theory. Upper inset denotes critical points of the force curve. Lower inset

displays the shape of the curve for varying conditions: (a) highly charged surface,

weak ionic strength; (b) highly charged surface, stronger ionic strength; (c) moderate

surface charge, stronger ionic strength; (d) moderate surface charge, high ionic

strength; (e) little to no surface charge. Obtained from Israelachvili [1].

29

thus the repulsion between two surfaces is due to the breaking of this water structure and

essential dehydration of the two surfaces [1]. A study of the stability of TiO2 (rutile)

colloids showed that at ionic strengths greater than 20 mM the repulsive hydration force

produced greater stability than expected by the simple DLVO theory and that the energy

barrier could be calculated over a wide range of ionic strengths [55]. Similarly, a study

of SiO2 proved that the dispersion remained relatively stable even in highly unfavorable

conditions such as the pHIEP, and this was attributed to a hydration force inherent to the

SiO2 [56]. Additionally, the solvation forces between oxide surfaces are dependent on

the specific counter-ions in solution, not simply the charge of those ions [57].

Steric interactions arise from polymer or organic adsorption onto the surface.

When two coated surfaces are in close proximity, the polymer or organic structures begin

to overlap. This typically results in repulsion due to the free energy of compression of

these structures to the surface [1]. This stabilization has been shown to be the case for

fulvic acid adsorbed onto TiO2 NPs [58], a non-ionic surfactant adsorbed onto TiO2 NPs

[59], and zero-valent iron NPs coated with guar gum [60]. In some circumstances,

typically at low coverages, the attached structures can produce an attractive force

attributable to interparticle bridging as was the case for Al2O3 NPs with the addition of

humic acids at acidic pH [61]. Comprehension of these competing forces provides a

basis for the evaluation and enhancement of treatment technologies for NP abatement.

30

1.5. Nanoparticle Abatement

1.5.1. Targeted Nanoparticle Abatement

There are very few studies on targeted removal of NPs from aqueous waste

streams. Two common techniques used to remove particulate matter are coagulation and

membrane filtration. Coagulation can be accomplished by a variety of means: chemical

coagulation, electrocoagulation, or thermal coagulation. Chemical coagulation involves

the addition of an ionic species, typically cationic, to induce aggregation of negatively

charged particles. Flocculants, polymeric structures, are then added to bridge the

coagulants and aid in sedimentation. An investigation of the effluents from an industrial

park in Taiwan showed the addition of polyaluminum chloride (3-5 mg L-1

as Al) was

ineffective in removing NPs of 90 nm but effective for the 2 nm particles [62]. This

result was not altered by increasing the coagulant dose, but was aided by increased

residence times. An evaluation of colloidal silica removal by coagulation and

flocculation provided low specific silica removal capacities for both polyaluminum

chloride and alum and no significant removal for the cationic and anionic flocculants:

polydiallyl dimethylammonium chloride and polyacrylamide, respectively [63].

Electrocoagulation has had limited application but shows some promise for NP removal.

A study of SiO2 NPs (68 – 120 nm) treated by electrocoagulation with an Al/Fe electrode

produced an average particle size of 16.8 μm with no discernible particles less than 100

nm [64].

Membrane filtration is a process by which particles are separated by allowing

water to pass through a membrane with a pore size smaller than the particles.

31

Ultrafiltration is the term given to membrane filtration processes which operate in a size

range applicable to NP retention. Membrane filtration is more complicated than simple

size exclusion; NP interaction with the membrane structure and filtration orientation

(either dead-end or cross flow) are significant factors. A study using a Pall Corporation

ultrafiltration system called Microza observed complete removal of solids from a NP-

containing semiconductor processing effluent by the double-skinned hollow fiber

membrane [65]. Another study evaluated the removal of Au, Ag, and SiO2 NPs ranging

from 5 – 150 nm by a filter made of carbonaceous nanofibers and provided a highly

selective size cutoff dependant on the filter structure which retained all but the 5 nm

particles [66]. The main drawback of NP treatment by membrane filtration is the

decreased permeability due to membrane fouling. A study of polystyrene and magnetite

NPs (20 – 250 nm) concluded that the permeability drop was significantly more

substantial for the 20 – 30 nm particles compared to the 100 – 250 nm equivalents [67].

This fouling is a significant hindrance to membrane filtration applications for NP

abatement. As there are little to no currently utilized NP-specific treatment techniques,

an evaluation of common wastewater treatment for the removal of NPs is necessary.

1.5.2. Primary Wastewater Treatment

One major point source for NP release to the environment is the wastewater

treatment plant. It has been noted that many industrially utilized NPs proceed to

municipal wastewater treatment [68]. Wastewater treatment plants have two main

segments: primary and secondary treatment. Primary treatment consists of

sedimentation of suspended solids sometimes with the addition of coagulants to aid in the

32

process. Secondary treatment is a biological treatment mainly designed to decompose

organic matter. Figure 1.3 provides an overview of the mechanisms working toward NP

retention in each segment. In primary treatment, the kinetics of NP settling in

sedimentation tanks is described by Stoke’s Law where settling velocity is a function of

the particle mass (density) and proportional to the square of the particle size [69]. This

implies that NPs will have settling velocities that are orders of magnitude less than

micron-sized chemical equivalents and therefore primary NPs, with diameters of 1 – 100

nm by definition, are unlikely to be removed by simple settling. The mechanism is

complicated however by the addition of coagulants and the tendency of NPs to

agglomerate in wastewater streams [46]. The addition of coagulants provides an

adsorption site for the NPs which could then be removed. Additionally, the flocs formed

by coagulants in the wastewater can act as filters through entrainment of the NPs as they

settle more rapidly.

The tendency of NPs to agglomerate can be attributed to numerous properties

(pH, ionic strength) as well as typical components of wastewater such as proteins, humic

acids, etc. [43, 48, 50, 61, 70]. NP dispersion stability related to pH, as discussed

previously, involves the relative proximity of the pHIEP. Accepted pHIEP values for

different metal oxide NPs such as Al2O3, CeO2, and SiO2, are 7–9, 6–8, and 2-3,

respectively. [44, 46, 71] It can be seen that the differing pHIEP will greatly affect the

aggregation behavior of these NPs at typical wastewater pH of seven to eight.

Aggregation of NPs changes the effective diameter as well as the density which

significantly affects the settling velocity of these particles. The increased velocity due to

33

the enlarged size outweighs the decrease due to the reduced density caused by the loose

packing of the aggregate thus resulting in increased removal of the NPs. Uncoated SiO2

NPs, in fact, have been shown to not settle during typical residence times, while those

coated with a non-ionic surfactant were more effectively removed [7]. The expectation

of NPs to mostly pass through primary treatment unretained shifts the potential of

removal to biological treatment.

1.5.3. Secondary Wastewater Treatment

Interactions of NPs with the biosolids present in secondary wastewater treatment

can be investigated in three parts: physical, electrostatic and chemical interactions.

Physically, due to their small size, NPs preferentially diffuse to surfaces more readily

than their larger counterparts [72]. This can be generalized by evaluating the diffusion

coefficient, which is inversely proportional to particle diameter. Additionally, the NPs

Figure 1.3. Mechanisms of nanoparticle removal in primary (left) and secondary

(right) wastewater treatment.

Settling

Aggregation

Flocculation

Primary Treatment

Secondary Treatment

Physical

Diffusion or

Entrapment

Electrostatic Chemical

34

could become physically entrapped in biological flocs. Secondly, bacteria commonly

used in wastewater treatment have a net negative surface charge which may lead to

electrostatic interactions contributing to the removal of certain NPs [35, 73]. Inorganic

oxides have varying surface charges in solution at circum-neutral pH and thus will show

varying degrees of attraction to the biological surface. It has been shown, for example

with CeO2 NPs, that the electrostatic interactions play a main role in their adhesion to E.

coli [35]. Finally, interferences of other wastewater components on the partitioning play

a key role in NP removal. Studies of the influence of polyelectrolytes on the adsorption

of NPs to bacteria show the order of addition of the two components strongly affects the

adsorption of the NPs [74-75]. Few studies have been performed on the ability of

biological wastewater treatment to remove NPs from waste streams. Two studies have

been performed on CeO2 NPs in model secondary treatment with similar results of

significant retention greater than 94% [46, 76]. This retention though, was found to be

highly dependent on NP destabilization due to wastewater conditions. Another study

investigated titanium nanomaterial removal in wastewater treatment and found that only

23% of TiO2 NPs were removed during exposure to wastewater biomass [77]. Biological

aeration has been shown to be ineffective at removing NPs from true wastewater at an

industrial park in Taiwan, showing no change in the particle size distribution post

treatment [62]. The high variability of NP retention as well as the influence of the

myriad of contaminants found in wastewater streams highlights the importance of

improved NP treatment.

35

1.5.4. Porous Media Filtration

The study of the transport of NPs in porous media is important in understanding

their environmental fate. Porous media is used to model soil systems, helping to

determine NP impact on the food chain and groundwater. Additionally, porous media

can be used as a depth filter to remove particulate matter. Comprehension and

subsequent manipulation of dominant transport mechanisms can allow for the targeted

removal of NPs from aqueous waste streams. Porous media filtration is a process mainly

utilized in water treatment but its use is increasing in wastewater treatment schemes [69].

It is designed to remove colloidal substances, which by definition are those between one

and 1,000 nm, thus showing potential for targeted NP treatment.

1.5.4.1. Mechanisms of Nanoparticle Capture in Porous Media

There are four main mechanisms of NP capture in porous media filtration outlined

in Figure 1.4: sedimentation, interception, straining, and diffusion/adsorption. The first

three are physical interactions having to do with the structure and packing of the porous

material. Sedimentation involves heavy particle breaking from streamlines and settling

onto the porous media [78]. Sedimentation (Fig. 1.4a) is governed by the Stokes settling

velocity of a spherical particle which has a 2nd

order dependence on particle diameter.

Therefore, sedimentation is unlikely to play a significant role in NP capture as small size

of the particles precludes them from settling across streamlines. Interception (Fig. 1.4b)

is a process by which particulate matter comes into contact with the porous media surface

simply because the streamline it is traveling in passes in close enough proximity to the

media surface [78]. Interception is a function of the tortuosity of the porous media bed

36

and is controlled by the average size of that media. Straining (Fig. 1.4c) is a mechanism

by which particulate matter is retained due to its size relative to the pore space [79].

Straining is a function of the relative sizes of the colloidal matter (dc) and porous media

(dpm) with a critical dc/dpm ratio of 0.0017 [80], above which straining occurs, and

straining increasing with increasing dc/dpm [81].

Diffusion is the final capture mechanism in porous media filtration, and is often

paired with adsorption. Diffusion (Fig. 1.4d) is the transport across streamlines due to

Brownian motion of the particles [78]. Diffusion is expected to be the most significant

mechanism for NP capture as it is inversely related to particle size. After moving to the

surface, the colloidal matter may be held by chemical or electrostatic interactions.

Figure 1.4. Mechanisms of nanoparticle capture in porous media filtration:

sedimentation (a), interception (b), straining (c), and diffusion/adsorption (d).

(a)

(b)

(c)

(d)

Collector

Media

Collector

Media

Collector

Media

Collector

Media

37

Chemical interactions include any interactions between surface groups and may become

dominant when specific additives are introduced to influence NP transport and retention.

Electrostatic interactions are generally understood through the previously described

DLVO or XDLVO theory. Balancing these mechanisms is a complicated combination of

a myriad of factors influencing retention.

1.5.4.2. Factors Influencing Retention in Porous Media

There are numerous factors influencing retention in porous media including

porous media characteristics, NP characteristics, solution chemistry, and flow conditions.

The physical structure of the porous media has an obvious impact on retention. As the

average size of the porous media decreases straining becomes a much more dominant

factor [82]. Additionally, the shape of the media influences the tortuosity of the bed,

complicating fluid streamlines and increasing interception. Finally, the surface chemistry

of the porous media plays a significant role in NP retention. Most investigations of NP

retention in porous media have used sand or glass beads which have a negative surface

charge [83-85]. This highly negative surface will have strong electrostatic interactions

with charged NPs. A study on the transport of rutile TiO2 NPs in sand columns found no

retention due to electrostatic repulsion between the similarly charged TiO2 NPs and sand

[86].

The size of the NP contributes to its retention in porous media. According to the

Tufenkji-Elimelech filtration model, 1 μm is the optimum size for colloid transport in

typical water treatment systems [87]. A recent study of two SiO2 NPs (8 and 52 nm) in

sand found increased retention of the 8 nm NP at all ionic strengths tested [88].

38

However, a study of latex colloids (50, 110, and 1500 nm) found that attachment

efficiency increased with increasing particle size defying the DLVO theory [89]. While

the effect of primary particle size is still being determined, another complicating factor is

the proclivity of NPs to aggregate in aqueous solutions [61, 90]. The aggregation of TiO2

NPs, in one study, had competing effects as the aggregated particles were better retained,

however, size exclusion prevents the aggregated particles from a large fraction of the

media surface area [84]. The NP surface chemistry is also important, but this is often

controlled by solution chemistry, especially for metal oxide NPs.

The role of surface chemistry in NP retention can be generally divided into three

segments: pH, ionic strength and contaminants. The pH of the solution dominates the

electrostatic interactions between the NP and porous media. Metal oxide NPs have a

wide range of pHIEP, which is the pH at which the surface charge of the NP approaches

zero. At a pH above the pHIEP the surface will be negatively charged and the opposite

below. Therefore NPs such as SiO2, with a pHIEP around 2 – 3 [46], and TiO2, with a

pHIEP around 4 – 5 [91], will generally be negatively charged at circumneutral pH.

Alternatively, Al2O3 (pHIEP = 7.9) and ZnO (pHIEP = 9.2) will be positively charged at

environmental pH values [50, 61]. The influence of ionic strength on the electrostatic

interactions of the NPs, as discussed previously, shortens the Debye length, reducing the

distance into solution the charge effects are observed. An investigation of TiO2 NPs in

quartz sand found a strong correlation between increased ionic strength and increased NP

elution [92]. Similar results were found for other metal oxide NPs (Fe3O4, TiO2, CuO

and ZnO) with increasing ionic strength leading to enhanced NP deposition [83].

39

Increasing ionic strength has the added complication of enhancing aggregation of NPs

leading to retention by many other mechanisms including straining and interception [48].

Organic contaminants can affect NP transport in a variety of ways. Various surfactants

have been shown to produce a stabilizing effect either due to electrostatic stabilization or

steric hindrances, increasing NP elution from porous media columns [59, 85, 93].

Similarly natural organic matter has been shown to decrease retention of NPs in porous

media [83, 89, 94].

Finally, flow conditions can influence NP deposition. Increased flow velocity

may aid in the elution of NPs [59, 95]. This, however, might not be a general rule as

fullerene-based NPs were found to have a slight increase in retention with increased flow

rate while SiO2 and TiO2 NPs showed no change, with no hypothetical mechanism

provided [96]. Overall, numerous factors influence NP retention in porous media and the

interdependence of these factors should be an area of focused study.

1.6. Scope of the Work

The scope of this work stems from a desire to develop treatment schemes

specifically designed for removing NPs from aqueous waste streams. As current

treatment techniques have shortcomings regarding NP retention, porous media filtration

shows promise as a simple yet robust technique for targeted NP treatment. In order to

evaluate the application of porous media filtration to NP abatement, a four-fold approach

was developed.

First, a system was developed to rapidly evaluate NP transport behavior in porous

media with varying solution and bed media conditions. This system was then

40

implemented to determine granular materials and conditions under which NP retention

can be optimized. Thirdly, fluorescent-cored silica NPs were developed and tested as

tracer NPs in system evaluation. Finally, a process model was proposed to further

elucidate controlling mechanisms as well as provide information necessary for process

optimization and industrial scaling.

41

CHAPTER II

REAL-TIME MONITORING OF NANOPARTICLE RETENTION IN

POROUS MEDIA

Abstract

Nanoparticles are not specifically targeted in conventional treatment schemes;

consequently, typical wastewater treatment systems are ineffective for nanoparticles

removal. With rapidly increasing concern over their health effects, improved

understanding of nanoparticle transport and retention in porous media filters is critical

because of its application in new wastewater treatment methods and for assessment of the

fate of the discharged nanoparticles in soil. In this study a unique and robust integrated

method is developed and validated. Experimentally, this approach uses an on-line, real-

time, and in-situ method for measuring nanoparticle retention dynamics, eliminating the

laborious and less accurate sampling and off-line analysis. The data analysis part is a

process simulator which provides both kinetic properties of the retention process as well

as the overall capacity and loading. This technique is validated by application to the

transport and retention of TiO2 nanoparticles in two vastly different porous filtration

media – activated carbon and sand. TiO2 retained concentrations ranged from 0.24-0.37

mg/g for activated carbon and 0.01-0.014 mg/g for sand. The integrated method

presented here is useful for both comparison of the filtration effectiveness of various

42

porous materials as well as for process optimization and scale-up for industrial

applications.

2.1. Introduction

The use of nanoparticles in manufacturing continues to increase [97], raising

concerns over their environmental and health effects [22, 98]. Inorganic oxide

nanoparticles, in particular, have growing applications in catalysis, polymers, coatings,

etc. A large amount of these nanoparticles are contained in wastewater streams [68].

Released nanoparticles can be exposed to porous media through water treatment

techniques, such as slow or rapid sand filtration, or during transport through soil or

sediments. Understanding transport and removal mechanisms in porous media is of

utmost importance to develop treatment technologies specifically designed for the

removal of nanomaterials as well as to determine environmental fate.

The transport, deposition, and retention of nanoparticles in saturated porous media

have been examined in recent studies [83-84, 99-100]. Nanoparticle behavior is highly

influenced by solution chemistry, such as pH, ionic strength, and valence and

concentration of ionic species [48, 101]. Mechanistic studies have shown that electrical

double layer interactions strongly influence the retention of inorganic oxide

nanoparticles. With the significant variance of influencing factors, a fast, simple,

accurate technique for the measurement of nanoparticle retention in porous media would

be highly profitable.

The most common technique for measuring the concentration of nanoparticles in

porous media filtration experiments are UV-Vis spectrophotometry [96, 102-103] or

43

inductively coupled plasma atomic emission spectroscopy (ICP-AES) analysis of acid-

digested samples [83-84]. A common problem associated with both of these techniques

is sample collection and time-consuming preparation and analysis procedures. Sampling

provides opportunities for error in mass balance, especially in the case of ICP-AES

measurement, as a digestion step is often necessary prior to analysis. Measurement delay

could also result in the aggregation or settling of nanoparticles which could interfere with

UV-Vis measurements. Finally, sampling adds unnecessary complexity to the

experiment and digestion and ICP-AES analysis is laborious and more costly. Online

measurement is advantageous due to its simplicity and improved accuracy.

In this work, an approach to monitoring nanoparticle retention in porous media

utilizing online UV-Vis absorbance measurements is proposed and demonstrated. The

ability to rapidly obtain data on nanoparticle transport under various conditions using the

proposed apparatus, provides a basis for developing more effective strategies for the

treatment of effluents containing nanoparticles.

44

2.2. Experimental

2.2.1. Materials

Nano-TiO2 (Aeroxide P25, average primary particle size = 25 nm) was obtained

from Evonik Industries (Essen, Germany). Two types of porous media were used, sand

and granular activated carbon (GAC). The quartz sand (Acros Organics, Geel, Belgium)

had a size range of 149 to 400 µm. Sand particles were washed prior to use with 10%

HNO3, rinsed with deionized water and dried at 105˚C. The GAC (KCI-40 AD, KC

International, Thousand Palms, CA) ranged in particle size from 400 to 1,680 µm. The

GAC was rinsed thoroughly with deionized water under ultrasonic agitation to remove

fines and then saturated with deionized water prior to column packing.

TiO2 suspensions (50 mg L-1

) were prepared in a phosphate buffer (0.5 mM, pH 7,

1 mM ionic strength). Dispersions were sonicated before the start of an experiment using

a Cole-Parmer ultrasonic processor (Vernon Hills, IL) at 65% intensity for 5 min.

2.2.2. Experimental Setup

Figure 2.1 shows a framework of the experimental apparatus. The glass column

(Diameter = 15 mm, Length = 150 mm, Omnifit Benchmark, Diba Industries, Danbury,

CT) was connected to flow-through quartz cuvettes (10 mm path length, Starna Cells

Inc., Atascadero, CA) using PTFE tubing. The nanoparticle dispersion was fed using a

Micropuls3 peristaltic pump (Gilson Inc., Middleton, WI). Absorbance at 300 nm was

monitored for the inlet and outlet of the column at 10 sec intervals using a UV-Vis

spectrophotometer (UV 1800, Shimadzu Corporation, Kyoto, Japan).

45

Column preparation varied slightly between sand and activated carbon. Sand

columns were dry packed with 36.5 g of sand under agitation from an ultrasonic bath.

The sand column was then filled from the bottom with deionized water to ensure wetting

of the bed and facilitate removal of trapped air. GAC columns were wet packed using

previously rinsed GAC (10.5 g dry weight). The columns were then rinsed with

deionized water in an ultrasonic bath to ensure elution of any remaining fines. Column

Figure 2.1. Online experimental apparatus including continuously-

stirred nanoparticle suspension [1], peristaltic pump [2], UV-Vis

spectrophotometer [3], flow through cuvettes for influent [4] and

effluent [6], and glass column packed with porous media [5]. This

apparatus provides fully online data of nanoparticle retention in the

column without the need for sampling or further sample preparation

prior to measurement.

Pump Waste

[1]

[2] [3]

[4] [6]

[5]

46

porosity was determined by measuring the entrained water mass after rinsing. A

phosphate buffer (0.5 mM, pH 7, 1 mM ionic strength) was prepared and ammonium

polyacrylate dispersant (Dispex A40, BASF Chemical Co., Freeport, TX), if used, was

added at 0.1 g L-1

prior to final pH adjustment. A portion of this solution was then

separated to pre-rinse the column, displacing 5 bed volumes, so that the pore solution in

the column was identical to that used in the nanoparticle suspension.

The nanoparticle suspension was pumped through the column at a rate of 2.6 mL

min-1

for 30 bed volumes. Inlet and outlet dispersion samples were collected at 10 bed

volume intervals and tested for size distribution and zeta potential.

2.2.3. Analysis

TiO2 NPs were imaged by transmission electron microscope (TEM) using a

Hitachi H8100 (Hitachi High-Technologies Corp., Tokyo, Japan) at 200 keV. The zeta

potential of nanoparticle dispersions was measured immediately after sampling by a

ZetaSizer Nano ZS (Malvern, Inc., Sirouthborough, MA) using laser doppler

velocimetry. Particle size distribution measurements were conducted by dynamic light

scattering using the same instrument. Elemental analysis of the samples for titanium

content was performed in two steps. First, the samples were digested with equal parts

HNO3 (70%) and H2SO4 (95%) in a microwave-assisted extraction system (120˚C, 45

min, MARS Xpress, CEM Corp, Matthews, NC). The samples were then diluted and

analyzed by ICP-AES (334.94 nm, Optima 2100DV, Perkin Elmer, Waltham, MA).

47

2.3. Results and Discussion

2.3.1. TiO2 Nanoparticles

TiO2 nanoparticle size distributions were obtained in the buffer solution with and

without added dispersant. The average particle sizes came to 194 and 200 nm, as seen in

the particle size distributions shown in Figure 2.2. Additionally, particle morphology

was determined by TEM imaging. The particles are nearly spherical and crystalline as

shown in Figure 2.3.

2.3.2. Apparatus Performance

The proposed technique allows rapid, simultaneous measurement of the

nanoparticle concentration in both the influent and effluent of the column by utilizing

flow-through cuvettes in the reference and measurement cells of a UV-Vis

spectrophotometer. The absorption difference between the two cells is constantly

measured, resulting in detailed breakthrough curves, as shown in Figure 2.4A for

nanoparticles in columns packed with clean sand or GAC. The small standard deviation

of the triplicate measurements emphasizes the high reproducibility and precision of the

technique.

Sand showed a very poor affinity for the nanoparticles and complete breakthrough

was observed in just over two bed volumes. This is likely due to the large electrostatic

repulsion between the similarly charged nanoparticles and sand. The surface charge of

the TiO2 dispersion was highly negative, with a zeta potential of -45 mV. Sand also has a

highly negative surface charge at pH 7, thus making it a poor sorbent. This result

48

matches well with the findings of other investigations of inorganic oxide interaction with

sand, with total breakthrough attained around two bed volumes [84, 99]. Activated

carbon, alternatively, showed a higher affinity for nano-TiO2.

Figure 2.2. Particle size distribution of the TiO2 nanoparticle dispersions with no

additive (—) and with synthetic dispersant, Dispex (- - -).

0

2

4

6

8

10

12

14

1 10 100 1000 10000

Inte

ns

ity %

Diameter (nm)

49

Figure 2.3. Transmission electron microscopy image of the TiO2 nanoparticles

utilized in this study.

50

Figure 2.4. Breakthrough curves of TiO2 NPs with no additives (A) and with

the presence of a synthetic dispersant Dispex (B) in beds packed with sand (○)

and GAC (□). NP dispersion (pH 7) was introduced at 2.6 mL min-1

. Error bars

indicate standard deviation for three runs.

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10

Rela

tive

Co

nce

ntr

ati

on

(C

/Co)

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10

Rela

tive

Co

nce

ntr

ati

on

(C

/Co)

Bed Volumes

51

After an initial steep rise to approximately 60% influent concentration, a much slower

retention mechanism became dominant. This increased capture affinity is likely due to

the complex pore network typical of GAC. Nanoparticles may be able to diffuse into the

GAC pore network; however, this hypothesis would require further investigation.

Currently there are no published studies considering the use of activated carbon as an

adsorptive media for nanoparticles.

The addition of a dispersant, Dispex A40, effectively stabilized the nanoparticles

through increasing the negative charge density on the surface and led to rapid

nanoparticle breakthrough both in sand and GAC packed columns (Figure 2.4B). The

zeta potential of the TiO2 dispersions supplied with Dispex decreased to -52 mV, thus

further diminishing the association of the nanoparticles with sand. In the case of GAC,

the increased electrostatic repulsion between the nanoparticles likely hindered their

migration into the more constricted pore space. Coating of the nanoparticle surface with a

highly hydrophilic dispersant may have also contributed to reduce nanoparticle affinity

for the hydrophobic GAC, resulting in full breakthrough in fewer than two bed volumes.

The negative impact of synthetic dispersants on NP retention by porous media has been

reported in previous studies [85, 93, 104]. This result displays the sensitivity of

nanoparticle retention resulting from a change in solution characteristics.

Overall, it is shown that the online measurement of nanoparticle retention is

highly sensitive, allowing for detailed contours of the breakthrough curve, as well as

rapid and highly repeatable, as evidenced by the low standard deviations between

replicates. The retention measurements at short time intervals also allow this technique

52

to fully capture the dynamics of NP capture, which is often difficult with off-line

sampling as a preset sampling frequency often limits the number of data points obtained

during the important transition from full retention to full breakthrough. Characterization

of the capture dynamics is of utmost importance for future system design. The basis of

this technique, online monitoring of inlet and outlet NP concentrations, has the added

benefit of providing the flexibility to adapt to the evaluation other treatment techniques

such as membrane filtration. Finally, the rapid evaluation of the effect of numerous flow

and solution conditions eases parameter studies necessary for model development,

another key aspect of system design and scaling. This method provides a foundation for

enhanced comprehension of transport and retention of NPs in porous media systems,

which enables the design of enhanced abatement technologies and the prediction of NP

fate in the environment.

53

2.4. Conclusions

The method developed and validated in this study provides a rapid, robust

approach for monitoring nanoparticle retention in porous media. The major advantage

and uniqueness of this method is in using an on-line, real-time, and in-situ method for

measuring nanoparticle transport and retention dynamics; this eliminates the complexity

and errors of sampling and off-line analysis. The method was applied and tested for TiO2

nanoparticles in two porous filtration media, GAC and sand, which provided retained

concentrations of 0.24-0.37 mg/g and 0.01-0.014 mg/g, respectively. The method is

useful for determining the role of various transport mechanisms, process bottlenecks, and

values of fundamental process parameters.

54

CHAPTER III

APPLICATION OF FLUORESCENT CORE-SHELL SILICA

NANOPARTICLES AS TRACERS IN POROUS MEDIA

FILTRATION

Abstract

Engineered nanomaterials have provided many benefits for manufacturing but

their increased utilization has also led to environmental and health issues as some

nanoparticles appear to be harmful to both human health and the environment. The

release of nanoparticles into the environment highlights the importance of understanding

the transport and retention of nanoparticles in porous media as these will govern the fate

of engineered nanomaterials in the subsurface soil environment and will also determine

the potential application of granular filtration as a targeted technique for nanoparticle

removal. Despite the increased usage of silica nanoparticles (n-SiO2) little is known

about their behavior in porous media, likely due to the lack of suitable methods that

would allow their detection in real time. Utilization of fluorescent-cored n-SiO2 provides

a rapid method of determining transport in porous media. Presented is the synthesis and

application of custom, fluorescent, core-shell n-SiO2 in three sizes: 20-25 nm, 110-120

nm, and 700-850 nm. The selection of the fluorescent dye proved important as

rhodamine isothiocyanate was found to avoid the dye leakage problem associated with

NHS-fluorescein. A thorough understanding of the impact of nanoparticle size is critical

55

in the evaluation of how transport differs for nanoparticles as opposed to their larger

counterparts. A comparison of the transport of the three types of n-SiO2 in diatomaceous

earth columns displayed the importance of evaluation of particle number capacity as well

as mass capacity. Results indicate that the interaction between the SiO2 nanoparticles and

diatomaceous earth is governed by site-specific adsorption.

56

3.1. Introduction

Many industries have benefited greatly from the advent of nanotechnology and

the use of engineered nanoparticles (NPs) [6-7, 16]. Nano-sized silicon dioxide (n-SiO2)

has become one of the most commonly used nanoparticles, with applications ranging

from fabric additive to abrasive in semiconductor manufacturing [105]. Utilization of n-

SiO2 is also increasing in biomedical applications including cancer therapy and drug

delivery [106-107]. Increased usage potentially results in a greater release of n-SiO2,

with the international Organization for Economic Cooperation and Development (OECD)

listing n-SiO2 as a nanoscale material of interest.

Limited studies of n-SiO2 transport in porous media have been performed.

Torkzaban and coworkers investigated the n-SiO2 transport in quartz sand columns and

found insignificant deposition at both 5 and 10 mM Ca2+

unlike the quantum dots and

carboxylate-modified latex NPs, which were adsorbed via bridging by the Ca2+

[108].

Another study by Wang and coworkers evaluated n-SiO2 (8 and 52 nm) retention in sand

by fraction collection and inductively coupled plasma – optical emission spectroscopy

(ICP-OES) [88]. They found that the smaller NPs were better retained than the larger

particles due to enhanced energetics of the interaction between the smaller NPs with the

sand. Both studies relied on fraction collection and offline analysis of the n-SiO2

concentration.

Investigation of the environmental release of n-SiO2 is difficult due to the

omnipresence of SiO2 in the environment. Destructive analysis techniques, such as ICP-

OES, therefore, require very clean samples with low levels of background silicon in order

57

to obtain good results. Light absorption techniques have been shown to be applicable for

nanoparticles (NPs) with a high optical density, such as zero-valent iron and titanium

dioxide [102-103]. However, for less opaque NPs such as SiO2, a large concentration is

necessary for adequate detection. The use of fluorescently modified n-SiO2 provides

increased sensitivity according to dye concentration and flexibility in that numerous

fluorescent dyes may be utilized.

Attaching amine-reactive fluorescent tags onto the surface of n-SiO2 is a

relatively simple process involving the silanization of the silica surface followed by

reaction with the dye [109]. However, the large fluorescent molecule on the surface may

greatly influence the n-SiO2 behavior in the environment as seen with other large organic

molecules [89, 110]. Embedding the fluorescent dye within the framework of the NP

provides increased photo-stability, leading to greater sensitivity, and allows for natural

SiO2 surface chemistry to dictate environmental behavior.

The sol-gel synthesis of colloidal SiO2 was introduced by the ground-breaking

work of Stöber and his coworkers in 1968 [111]. This process, which takes place in a

water-doped ethanol solution using ammonia as a catalyst, produces electrostatically

stabilized particles ranging from hundreds of nanometers to single micrometers by

hydrolysis (Eq. 3.1) and condensation (Eq. 3.2) of tetraethoxysilane. Later, van

Blaaderen and coworkers expanded this work to include the addition of organic

Si(OR)4 + 4H2O → Si(OH)4 + 4ROH (3.1)

Si(OH)4 → SiO2 + 4H2O (3.2)

58

molecules in different locations from the surface to throughout the entirety of the particle

[112]. This group introduced fluorescein isothiocyanate as a fluorescent marker and

found that any amine-reactive fluoropore could be adequately substituted. The Weisner

group has since furthered the production of fluorescent-core n-SiO2 to provide for the

synthesis of fluorescent NPs, pushing the applicable size range down to the order of 10-

15 nm [113]. These particles had an incorporated fluoropore in the “core” of the particle

and a “shell” of pure SiO2. This particle structure, notably the siliceous shell, provided

enhanced photo-stability as well as increased brightness. Since then, SiO2 NPs of

varying sizes with imbedded dyes have been produced using numerous colored and

fluorescent dyes [114-116].

The purpose of this work is to demonstrate the applicability of custom synthesized

fluorescent core SiO2 NPs as tracers for NP transport in porous media. This was

accomplished first by the synthesis of multiple sizes of fluorescent cored n-SiO2. These

fluorescent NPs were utilized to investigate NP transport in porous media under varying

conditions. The simple SiO2 surface chemistry along with the ability to be tracked at low

concentrations provided a basis for their use as representative nanoparticles for

environmental analysis.

59

3.2. Materials and Methods

3.2.1. Fluorescent Nanoparticle Synthesis

Three sizes of n-SiO2 (S, L, and XL) were synthesized with target sizes of 25, 100

and 850 nm, and they are named for the fluorescent dye used, either N-

hydroxysuccinimide fluorescein (NHSF) or rhodamine B isothiocyanate (RITC), as Fl- or

Rh-. All particles were synthesized at room temperature (23±2°C). The smallest

nanoparticles, Fl-S and Rh-S, were synthesized by initial conjugation of the selected

fluorescent dye with 3-aminopropyl-triethoxysilane (APTES) at a molar ratio of 1:50.5

(dye:APTES) in ethanol for a final dye concentration of 4.2 mM. This conjugation is

illustrated in Figure 3.1. An aliquot (1.01 mL) of the conjugated dye solution was then

added to a 250 mL core-formation solution with an ethanol base and 0.20 M ammonia,

0.86 M deionized (DI) water, and 0.05 M tetraethyl orthosilicate (TEOS). The

components were added in the following order: ammonia, DI water, conjugated dye,

TEOS. The solution was then stirred for at least 8 h using a magnetic stir bar at 300 rpm.

In order to coat the NPs with a shell of pure silica, 12-0.5 mL aliquots of TEOS were

added every 15 min, allowing 30 min after the final addition.

The larger nanoparticles, Fl-L and Rh-L, were synthesized by conjugation of the

selected dye with APTES at a molar ratio of 1:3.8 (dye:APTES) in ethanol. The core

preparation was accomplished by addition of the following, in the same order as above, to

final concentrations of 0.995 M ammonium hydroxide, 1.272 M DI water, 0.173 M

TEOS as well as 5.0 mL of the conjugated dye. This solution was stirred for at least 8 h

60

using a magnetic stir bar at 300 rpm. The silica shell was formed by addition of 5.1 mL

of TEOS and 0.85 mL of DI water every 2 h for six total additions.

The sub-micron particles, Fl-XL and Rh-XL, utilized the same initial procedure

for dye conjugation as for the larger NPs. However, for the core preparation the reagent

concentrations were 2.64 M ammonium hydroxide, 0.83 M DI water, and 0.16 M TEOS.

The solution was stirred similarly and the shell was produced by addition of 3.6 mL of

TEOS and 0.58 mL of DI water every 2 h for two total additions.

All formed particles were cleaned using a sequential centrifugation technique.

Samples (40 mL) were added to 50 mL centrifuge tubes and they were centrifuged at

(a) (b)

(c)

(d)

Figure 3.1. Fluorescent dyes, NHSF (a) and RITC (b), and their respective

conjugations with APTES, (c) and (d).

61

5,000 rpm for 8 h. The supernatant was discarded and the particles were resuspended in

40 mL ethanol by sonication using an ultrasonic processor (Cole-Parmer, Vernon Hills,

IL, USA, 70% intensity, 5 min). Centrifugation and resuspension were repeated once

using ethanol and twice resuspending in water. The final resuspension was done using

only 20 mL of DI water in order to obtain a concentrated stock solution.

3.2.2. Filtration Media

Three column bed materials were examined: quartz sand (Acros Organics, Geel,

Belgium) with an average diameter of 190 μm, activated carbon (AC) (KCI-40AD, KC

International, Thousand Palms, CA, USA), an acid washed activated carbon with 1,000

μm average diameter, and diatomaceous earth (DE) (Celite 545, Sigma Aldrich, St.

Louis, MO, USA) with a reported size of < 125.3 μm. DE is a siliceous compound with

varied deposits of other compounds. According to the manufacturer, the batch of DE

utilized contained: silicon dioxide (89.0%), aluminum oxide (1.0%), calcium oxide

(6.7%), ferric oxide (0.46%), as well as sodium oxide and potassium oxide (1.9%

combined). The DE was sieved and the material retained on a 200 mesh (74 μm) sieve

was separated for use. Sand and DE were washed prior to use: soaked in diluted

hydrochloric acid (HCl, 5%), rinsed by deionized water, and dried in an oven for 8 h

(105˚C). The AC was rinsed with deionized water to remove associated fines.

3.2.3. Column Experiments

Experiments were performed using a glass column (diameter = 15 mm, length =

62

150 mm, Omnifit Benchmark, Diba Industries, Danbury, CT, USA) packed with the

porous medium. A flow-through quartz cuvette with a 10 mm path length, 4 x 11 mm

excitation window, and 10 x 11 mm emission window (Starna Cells, Inc., Atascadero,

CA, USA) was connected to the column effluent using 0.159-cm diameter PTFE tubing.

A fluorescence spectrophotometer (LS 55, Perkin Elmer, Waltham, MA, USA) provided

fluorescence data, at 494/518 nm (excitation/emission) for NHSF and at 530/580 nm

(excitation/emission) for RITC, monitored by an attached computer at 1 sec intervals. All

measurements were taken with 10-mm excitation and emission slit widths. All

experiments were performed at room temperature (23±2°C) and flow rate, 2.6 mL min-1

unless otherwise noted, was achieved using a peristaltic pump (Micropuls3, Gilson, Inc.,

Middleton, WI, USA).

Sand or DE columns were dry-packed with 36.5 g or 8.5 g of pre-washed sand or

DE, respectively, under agitation from an ultrasonic bath. The column was then filled

from the bottom with deionized water at a rate of 2.6 mL min-1

for 30 min in an ultrasonic

bath to ensure wetting of the bed. AC columns were wet packed using previously rinsed

AC to a final dry activated carbon weight of 10.5 g. The AC columns were then rinsed

with deionized water for 30 min in an ultrasonic bath to ensure elution of any remaining

fines.

3.2.4. Imaging of Nanoparticles

The synthesized NPs were imaged by transmission electron microscope (TEM) using a

Hitachi H8100 (Hitachi High-Technologies Corp., Tokyo, Japan) at 200 keV. Samples

were placed on Formvar coated copper grids and allowed to dry prior to imaging.

63

3.2.5. Particle Size Distribution and Zeta Potential

The zeta potential and particle size distribution of NP dispersions were measured with a

ZetaSizer Nano ZS (Malvern, Inc., Sirouthborough, MA, USA) using laser doppler

velocimetry and dynamic light scattering, respectively. The refractive index used for

SiO2 was 1.475 and all size distributions were obtained from light intensity distributions.

The isoelectric point (pHIEP) of the synthesized nanoparticles was determined using the

above instrument with pH control provided by an autotitrator accessory (MPT-2,

Malvern, Inc.) using 1.0 M HCl and NaOH solutions.

3.2.6. Analysis

Elemental analysis of the samples for silicon content was performed in two steps. First,

the samples were digested with HF (1%) in a microwave-assisted extraction system

(MARS Xpress, CEM Corp, Matthews, NC) for 45 min at 120˚C. The samples were then

diluted to 0.1% HF and analyzed by ICP-AES (251.611 nm, Optima 2100DV, Perkin

Elmer).

3.2.7. Chemicals

The fluorescent dyes used included NHSF (Thermo-Scientific, Waltham, MA, USA) and

RITC (Sigma Aldrich, St. Louis, MO, USA). The APTES (≥ 98%), ethanol (200 proof,

anhydrous) and TEOS (reagent grade, 98%) were all obtained from Sigma Aldrich.

Ammonia addition for the smallest nanoparticles was achieved using a pre-diluted

ammonia solution (2.0 M in ethanol, Sigma Aldrich), while ammonium hydroxide (ACS

64

reagent, Sigma Aldrich) was used for the larger particles. Commercial n-SiO2 (10-20

nm) was obtained from Sigma Aldrich.


3.3.1. Nanoparticle Synthesis

The first generation of the core-shell SiO2 NPs utilized NHSF as the fluorescent

dye. Nanoparticles with three different sizes were successfully produced. The average

particle sizes determined by dynamic light scattering were 24±1 nm (Fl-S), 109±1 nm

(Fl-L), and 848±20 nm (Fl-XL). The polydispersity of the Fl-S, L and XL particles was

0.122, 0.189, and 0.103, respectively. TEM images of each of these batches of particles,

along with their corresponding particle size distributions, are shown in Figure 3.2.

Determination of the average particle size by TEM analysis resulted in sizes for Fl-S, L

and XL of 21±3 nm, 69±10 nm, and 634±36 nm, respectively. All particles presented

smaller in TEM analysis due to the fact that light scattering methods determine the

hydrodynamic diameter of the particles. Dehydration of the samples for TEM imaging

eliminates this hydrated layer. One notable feature of these particles is the relative

uniformity in size as all distributions had less than 3% standard deviation, and the smaller

two batches had standard deviations less than 0.6%. To compare the surface behavior of

the synthesized n-SiO2 in comparison to commercial n-SiO2, their zeta potential was

determined as a function of pH. The resulting titration curves are shown in Figure 3.3.

While the pHIEP was not fully reached, the value can be safely assumed to be close to 2

65

for all samples. All synthesized particles showed remarkable agreement with the

commercial SiO2 sample implying a successful coating of the surface with pure silica.

The calibration curves fluorescence (Fl) vs. concentration (C, mg-SiO2 L-1

) for the

Fl-L and Fl-XL particles are shown in Figure 3.4. For the Fl-L particles, a calibration

curve can be plotted ( , R2

= 0.9996) providing a limit of detection

of 10 mg-SiO2/L. Similarly the calibration curve for the Fl-XL particles (

, R2 = 0.9993) provides a limit of detection of approximately 10 mg-SiO2 L

-1.

Fl-S NPs were not included in this calibration due to significant dye leakage. On

average, approximately 80% of the fluorescein dye leaked from the Fl-S particle matrix

after only 5 d. The Fl-L and Fl-XL particles were not immune from this effect, with

average dye leakage amounts of approximately 30% after 5 d.

66

Figure 3.2. TEM images (left) and the corresponding particle size distributions (right)

of first generation n-SiO2 synthesized with NHSF dye shown for Fl-S (A), Fl-L (B),

and Fl-XL (C).

0

4

8

12

16

1 100 10000

Inte

nsi

ty %

Diameter (nm)

0

2

4

6

8

10

12

14

1 100 10000

Inte

nsi

ty %

Diameter (nm)

0

5

10

15

20

1 100 10000

Inte

nsi

ty %

Diameter (nm)

A

B

C

67

Figure 3.3. Zeta potential of silica dispersions as a function of pH for a commercial

n-SiO2 (♦) and for the synthesized Fl-S (●), Fl-L (▲), and Fl-XL (■) n-SiO2 particles.

-60

-50

-40

-30

-20

-10

0

1 3 5 7 9 11 13

Zet

a P

ote

nti

al

(mV

)

pH

Figure 3.4. Fluorescence calibration curves for Fl-L (▲) and Fl-XL (■) particles are

best fit by (R2

= 0.9996) and (R2 =

0.9993), respectively.

0

100

200

300

400

500

600

700

0 20 40 60 80 100

Flu

ore

scen

ce

Concentration (mg-SiO2/L)

68

The dye leakage was constant over the time frame of all experiments with the

various particles; however Fl-S nanoparticles were not used in further work due to the

exceedingly high leakage amount. This leakage may be due to the overall negative

charge of fluorescein, which may hinder engrafting into the silica matrix. In order to

address the dye leakage problem, another dye was utilized: RITC. RITC was selected as

it possesses a positive charge, potentially enhancing the incorporation into the silica

matrix. Additionally, RITC is less susceptible to photo-bleaching than NHSF, providing

a more stable fluorescent signature [117]. All particles utilizing rhodamine had dye

leakage amounts of less than 0.5% constant over one month of storage. Similarly, three

batches of RITC n-SiO2 were synthesized with average particle sizes (based on dynamic

light scattering) of 26±1 nm (Rh-S), 118±2 nm (Rh-L) and 720±4 nm (Rh-XL). The

polydispersity values of the Rh-S, L, and XL particles were 0.120, 0.044, and 0.186,

respectively. TEM analysis was paired with dynamic light scattering to investigate the

uniformity of the particles (Figure 3.5). Similarly to the particles made with NHSF, the

particle sizes calculated using the TEM images were smaller than the dynamic light

scattering calculations. TEM analysis resulted in average particle sizes of 18±4 nm (Rh-

S), 96±14 nm (Rh-L) and 577±57 nm (Rh-XL).

69

Figure 3.5. TEM images (left) and the corresponding particle size distributions (right) of

first generation synthesized n-SiO2 with RITC dye shown for Rh-S (A), Rh-L (B), and Rh-

XL (C).

0

4

8

12

16

1 100 10000

Inte

nsi

ty %

Diameter (nm)

0

5

10

15

20

25

30

1 100 10000

Inte

nsi

ty %

Diameter (nm)

0

5

10

15

20

1 100 10000

Inte

nsi

ty %

Diameter (nm)

A

B

C

70

Characterization of the SiO2 surface was done by zeta potential titration. The

titration of Rh-S, Rh-L and Rh-XL was shown to be in agreement with the commercially

obtained standard (Figure 3.6), again leading to the conclusion that the surface properties

of these NPs were similar to that of SiO2. The pHIEP of the Rh-S, L and XL particles

were 2.94, 2.85, and 2.96, respectively. A detection limit of these particles was

determined via a fluorescent calibration curve shown in Figure 3.7. The Rh-S NPs

produced the most steep calibration curve ( , R2 = 0.995)

incurring a detection limit of just 1 mg-SiO2 L-1

. Rh-L particles produced a higher

detection limit of 5 mg SiO2 L-1

with its calibration curve ( , R2 =

0.999). Rh-XL particles resulted in a higher still limit of detection of 10 mg SiO2 L-1

with

its calibration curve ( , R2 = 0.999). The high sensitivity and low

detection limit of the Rh-S NPs is due to a larger dye concentration relative to SiO2 in the

particles. The transition to RITC as the fluorescent dye produced highly stable particles,

eliminating the issue of dye leakage.

71

Figure 3.6. Zeta potential of silica dispersions as a function of pH for a commercial

n-SiO2 (♦) and for the synthesized Rh-S (●), Rh-L (▲), and Rh-XL (■) particles.

-70

-60

-50

-40

-30

-20

-10

0

10

1 3 5 7 9 11 13

Zet

a P

ote

nti

al

(mV

)

pH

Figure 3.7. Fluorescence calibration curves for Rh-S (●), Rh-L (▲), and Rh-XL (■)

particles are best fit by (R2 = 0.995),

(R2 = 0.999), and (R

2 = 0.999), respectively.

0

100

200

300

400

500

600

700

800

900

0 50 100 150 200

Flu

ore

scen

ce

Concentration (mg-SiO2/L)

72


Effect of Particle Size and Concentration

An important question in the field of NP research is how the reduction in size into

the “nano” range changes the behavior of the otherwise well characterized bulk material.

One of the major benefits of these synthesized fluorescent-core SiO2 nanoparticles is that

they remain as primary particles in solution. This is due to the fact that they are never

dried and thus avoid the agglomeration issues with re-exposure to water which plague

most NP investigations [50]. Breakthrough curves for Rh-S, L, and XL NPs in columns

packed with DE are shown in Figure 3.8. Rh-S NPs were introduced at 1 mg-SiO2/L and

Rh-XL particles were introduced at 10 mg-SiO2 L-1

. Rh-L NPs were introduced at two

different concentrations in order to facilitate capacity comparisons: 10 and 50 mg-SiO2

L-1

. Rh-XL particles escaped the column starting at approximately 140 bed volumes, but

took until 300 bed volumes to come to full breakthrough. Rh-L NPs at 10 mg-SiO2 L-1

began breakthrough at 150 bed volumes reaching completion around 260 bed volumes

while at 50 mg-SiO2 L-1

initial escape began at 130 bed volumes and full breakthrough

occurred at approximately 230 bed volumes, a similar transition time. The steeper

breakthrough curves for the smaller particles is not consistent with a change in

dispersion, which is typically a function of fluid velocity and porous media grain size, but

may be explained by differences in surface interactions. The breakthrough curve for Rh-

S NPs, produced a different shape. There was an initial steep rise to approximately 70%

followed by a more gradual approach to full breakthrough. This is similar to results

obtained for TiO2 NPs in activated carbon columns [118]. This curve was explained by a

73

diffusion limited adsorption into the pore network of the activated carbon. This may

explain the current situation, as the DE pores are possibly smaller and thus this

interaction only became evident with the Rh-S NPs. While evaluation of the curves

provides a modicum of insight into the NP transport mechanisms, assessment of the bed

capacities produced insightful comparisons.

Comparison of the DE bed capacities, calculated from mass balances on the

breakthrough curves, for the fluorescent n-SiO2 is presented in Figure 3.9. The mass

capacities (Figure 3.9A) are relatively constant between the Rh-L at 10 mg-SiO2 L-1

(15±1 g-SiO2 g-1

DE) and Rh-XL (18±1 g-SiO2 g-1

DE), but show a steep decrease for the

Rh-S NPs (2±1 g-SiO2 g-1

DE). This finding is consistent with other studies evaluating the

effect of particle size on retention: smaller NPs display less retention than their larger

counterparts [101, 103, 119]. This conclusion breaks down, however with evaluation of

the number capacities (Figure 3.9B). The number concentrations were determined

assuming uniformly spherical particles of constant density. Comparing the same three

curves shows greater numbers of Rh-S NPs (9.5±0.4 x 1015

particles g-1

DE) retained than

that of Rh-L at 10 mg-SiO2 L-1

(9.1±0.6 x 1014

particles g-1

DE) or Rh-XL (7.9±0.3 x 1012

particles g-1

DE). To further evaluate the role of particle number as opposed to mass

concentration additional experiments were performed utilizing the Rh-L NPs at 50 mg-

SiO2/L, which produces a similar number concentration to that of the Rh-S NPs (3.1 x

1012

vs. 5.7 x 1012

particles L-1

, respectively). A comparison of these trials shows highly

similar number capacities and highly disparate mass capacities. This result indicates that

the interaction of n-SiO2 with DE is largely dominated by site-specific interactions with a

74

limited number of sites throughout the porous media. Thus, an evaluation of solely the

superior mass capacity of the Rh-L NPs at 50 mg-SiO2 L-1

over that of the Rh-S NPs may

lead to the inaccurate conclusion that the Rh-L NPs are better retained. A study of n-

SiO2 transport in sand columns has found that evaluations of NP retention in porous

media must be careful to inspect number concentrations as well as mass concentrations

before drawing conclusions on relative utility [88]. The site-specific adsorption of n-

SiO2 on DE also aids in the modeling of this system for scale-up and design.

Figure 3.8. Relative effluent concentration of fluorescent-core n-SiO2 as a function of

the number of DE bed volumes processed for the synthesized Rh-S at 1 mg-SiO2 L-1

(●), Rh-L at 10 mg-SiO2 L-1

(■), Rh-L at 50 mg-SiO2 L-1

(▲), and Rh-XL at 10 mg-

SiO2 L-1

(♦) particles. The n-SiO2 dispersions were introduced at 2.6 mL min-1

. Error

bars represent the standard deviation of duplicate measurements.

0

0.2

0.4

0.6

0.8

1

1.2

0 50 100 150 200 250 300 350 400 450

Rel

ati

ve

Con

cen

trati

on

(C

/Co)

Bed Volumes Processed

75

Figure 3.9. Bed capacities of DE for Rh-S at 1 mg-SiO2 L-1

(■), Rh-L at 10 (■) and

50 mg-SiO2 L-1

(■) and Rh-XL at 10 mg-SiO2 L-1

(■) based on mass concentration (A)

and number concentration (B). Error bars represent the standard deviation of duplicate

measurements.

0

10000

20000

30000

40000

50000

60000

70000

Rh-S Rh-L-10 Rh-L-50 Rh-XL

Cap

aci

ty (

mg

-SiO

2/g

DE

)

1E+10

1E+11

1E+12

1E+13

1E+14

1E+15

1E+16

1E+17

1E+18

Rh-S Rh-L-10 Rh-L-50 Rh-XL

Cap

aci

ty (

#/g

DE

)

A

B

76


Effect of Porous Media and Flow Rate

As the utilization of the fluorescent NPs as tracers in porous media filtration was

the goal of the synthesis, application of the particles involved passing them through

various porous media columns while monitoring the effluent fluorescence signal.

Preliminary experiments comparing NP transport in two different porous media, namely

AC and sand, provided information on the retention of the NPs and the sensitivity of the

technique (Figure 3.10). Note that all concentration data regarding Fl-L and Fl-XL

particles is corrected for 30% dye leakage. It is readily apparent that there was increased

NP retention in the AC column, as the sand column attained full breakthrough almost

immediately. The difference in these curves may be due to the complex pore network of

the AC and the slow diffusion of the NPs into the pores. Additionally, there is

electrostatic repulsion between the n-SiO2 and the sand surface limiting adsorption as

seen in other studies [108]. The smooth nature of both curves indicated a strong signal to

noise ratio, giving credence to the results.

The effect of feed flow rate in AC columns is displayed in Figure 3.11. A study

on the velocity effects of NP deposition in porous media noted the importance of the

relative transport rates [96]. The slow diffusion of NPs into the AC pore structure is

dominated by convective transport down the column length under higher flow rates thus

decreasing the time to breakthrough. Similarly, another study of flow rate of ferrihydrite

NPs in sand found that generally as flow rate increased, retention decreased [120].

Observed again is the sensitivity of the technique and the applicability of the synthesized

77

particles for investigation of n-SiO2 retention in porous media. Continued application of

these particles may contribute to understanding the transport of n-SiO2 in porous media

as well as aid in the modeling of this complex system.

Figure 3.10. Column effluent concentration as a function of bed volumes processed for

fluorescent n-SiO2 (Fl-L, 109±1 nm) at 84 mg-SiO2 L-1

introduced at 2.6 mL min-1

in

both activated carbon (—) and sand (- - -).

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50

Rel

ati

ve

Con

cen

trati

on

(C

/Co)


78

Figure 3.11. The effect of feed flow rate on activated carbon column effluent

concentration as a function of bed volumes processed for fluorescent n-SiO2 (Fl-L,

109±1 nm) at 84 mg-SiO2 L-1

and two different flow rates: 2.6 mL min-1

(—) and 5.7

mL min-1

(- - -).

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50

Rel

ati

ve

Con

cen

trati

on

(C

/Co)


79

3.4 Conclusions

Fluorescent, core-shell silica NPs have been synthesized in selectable sizes with

tight distributions. The fluorescent dye selection was displayed to be of significant

importance as substantial leaking of NHSF from the silica structure was observed with

time. Replacement with the fluorescent dye RITC solved this problem and additionally

provided enhanced photostability. One of the main advantages of these particles is the

propensity to remain in an unagglomerated state in water which allowed for a true size

comparison and elucidation of the “nano” effect. These synthesized NPs allowed for a

rapid, in-situ, and real-time assessment of the fate of n-SiO2 in porous media.

Comparisons of retention based on NP size showed that in the nano-range the particle

number concentration is important in drawing conclusions on the value of a particular

porous media. While larger NPs displayed vastly greater mass capacities, the particle

number capacities were very similar leading to the conclusion of site-specific adsorption

on the DE surface. This insight provides a basis for future media evaluation as well as

supports the modeling of this process for future scale-up and application.

80

CHAPTER IV

REMOVAL OF TiO2 NANOPARTICLES BY POROUS MEDIA:

EFFECT OF FILTRATION MEDIA AND WATER CHEMISTRY

Abstract

The use of nanoparticles in manufacturing as well as commercial products

continues to rise despite concerns over the environmental release and potentially negative

ecological and health effects. Some aqueous waste streams carry a large fraction of

released nanoparticles and thus should be targeted for treatment. Conventional porous

media filtration has focused on sand as the bed material with discouraging results. This

study investigated the effectiveness of three different bed materials, namely, sand,

activated carbon, and diatomaceous earth, on the removal of nano-TiO2 from aqueous

streams. Additionally, the impact of solution chemistry (a commercial dispersant and the

two organic compounds lysozyme and glycine) on nanoparticle retention by the various

bed materials was evaluated. Diatomaceous earth displayed great promise in nanoparticle

capture, providing full retention of a 50 mg TiO2 L-1

stream for the 30 bed volumes tested

as compared to zero and only 20% TiO2 capture for sand and activated carbon,

respectively. Batch isotherms showed that diatomaceous earth, with specific loading

capacities exceeding 25 mg TiO2 g-1

medium, has a high affinity for nano-TiO2. This

loading capacity is 20- and 1000-fold higher compared to activated carbon and sand,

respectively. The solution contaminants investigated had varying effects on nano-TiO2

81

retention depending on the bed material, indicating the need for investigation of co-

contaminants and their role on nanoparticle filtration. This study demonstrates the

superiority of DE as a filtration material compared to conventional sand and indicates its

suitability as a new material for the removal of nanoparticles in porous media filtration.

82

4.1. Introduction

Nanoparticles (NP) form the basis for development of new nano-enabled

technology, which is expected to be worth approximately $1 trillion annually by 2015

[1]. Metal oxide NPs, such as nano-titanium dioxide (n-TiO2), are of particular interest

due to their use in varied commercial products, from sunscreens and cosmetics to

abrasives in slurries used for semiconductor manufacturing [2]. A recent study puts the

upper bounds of yearly n-TiO2 production at approximately 2.5 million metric tons by

2025 [3]. Evaluating the health and environmental risks of these emerging contaminants

is an active area of research [4]. Effects on human health are of great concern with n-

TiO2 being shown to display neuro-toxicity toward dorsal root ganglion cells, even with

commonly applied inorganic coatings [5] and to bring about apoptosis and necrotic death

in human umbilical vein endothelial cells [6]. Ecotoxicity is also concerning as n-TiO2

has been found to be damaging toward both B. subtilis and E. coli, possibly due to

reactive oxygen species production [7]. The numerous exposure pathways and potential

toxicity leads to concerns about the release of these nanomaterials [8-9].

Wastewater streams have been specifically found to be potential point sources of

NP release [10]. A recent modeling of environmental concentrations of n-TiO2 produced

an estimate of over 1,500 tons of n-TiO2 per year entering sewage treatment plants in the

United States, with the majority of release being divided between the soil (~48%) and

surface water (~24%) [11]. While no current treatment techniques are utilized that

primarily target NP removal, the effectiveness of standard wastewater treatment

operations has been evaluated in a few studies. Flocculation and sedimentation processes

83

used in primary wastewater treatment were shown to be ineffective in removing SiO2

NPs due to their stability and slow settling rate [12]. Treatment by activated sludge, the

most common element of secondary wastewater treatment, was shown to be more

effective at removing NPs, however significant amounts of n-TiO2 remain in effluents of

these conventional wastewater treatment plants [13]. Additional procedures must be

instated to address the treatment of this emerging nano-scale contaminant. Porous media

filtration is a standard treatment technique for removing colloidal and particulate matter

from water streams and is becoming more common for wastewater treatment [14]. The

effectiveness of porous media filtration to remove colloidal substances, which by

definition are those between 1 and 1,000 nm, indicates the potential of this technique to

remove engineered nanomaterials. Understanding the role of this established technique

in NP removal is important for potential optimization and targeted use.

The most common type of porous media filtration used in wastewater treatment is

sand filtration [14]. The majority of research performed on NP removal has focused on

the use of sand as the bed material [15-17]. Investigations of NP retention in saturated

sand media have shown the importance of solution chemistry and its effects on

electrostatic interactions between the NPs and the sand surface. Quartz sand typically has

a point of zero charge (pHpzc) of approximately 3 and thus will be negatively charged at

circumneutral pH values [18]. The pHpzc of TiO2 ranges from 3.9 to 7.2 [19], thus the

surface of n-TiO2 typically carries a negative to near neutral charge near pH 7. This

electrostatic repulsion typically leads to significant and rapid elution of TiO2 NPs in sand

columns [16, 20-21]. In addition to pH, ionic strength and cation valence can have a

84

great impact on NP aggregation and NP retention by porous media filtration. Deposition

of TiO2 NPs onto sand has been shown to increase with both ionic strength and cation

valence [20]. A mechanistic study of TiO2 NPs in sand columns determined that

electrostatic interactions between the NPs and the sand surface were a significant factor

in NP capture. However, site blocking due to adsorbed NPs contributed significantly in

electrostatically favorable conditions and physical straining was notable during

aggregation inducing conditions [22]. These studies illustrate significant NP elution as

well as low loading capacities for the tested sand bed material.

The majority of studies have investigated NP retention in columns packed with

glass beads or quartz sand. Investigation of more efficient adsorptive media capable of

attaining high loading capacities is necessary to promote the potential use of porous

media filtration as a technique for NP abatement in aqueous waste streams. The

objective of this study is to determine a suitable bed media for the abatement of TiO2 NPs

and determine the major mechanisms guiding NP-media interactions, ultimately leading

to an improved treatment design. In order to attain this objective, this study evaluated

three different filtration materials including sand as a reference material, as well as

activated carbon and diatomaceous earth, which have never previously been applied to

NP filtration schemes. In addition, the impact of three model water contaminants, an

anionic dispersant and two organic compounds with disparate points of zero charge, on n-

TiO2 fate and transport in porous media was evaluated in order to evaluate potential

aqueous contaminants.

85


4.2.1. Materials

The NPs used were TiO2 (Aeroxide P25, Evonik Industries, Essen, Germany)

with a reported primary particle size of 21 nm. Aeroxide P25 is a well documented

composite of both anatase and rutile forms. A recent study found that composition

ranged from 73 - 85 % anatase, 14 - 17 % rutile and 0 - 18 % amorphous TiO2 [23].

Three bed materials were examined: quartz sand (Acros Organics, Geel,

Belgium) with an average diameter of 190 μm, activated carbon (AC) (KCI-40AD, KC

International, Thousand Palms, CA, USA), an acid washed activated carbon with 1,000

μm average diameter, and diatomaceous earth (DE) (Celite 545, Sigma Aldrich, St.

Louis, MO, USA) with a reported size of < 125.3 μm. DE is a siliceous compound with

varied deposits of other compounds. According to the manufacturer, the batch of DE

utilized contained: silicon dioxide (89.0%), aluminum oxide (1.0%), calcium oxide

(6.73%), ferric oxide (0.46%), as well as sodium oxide and potassium oxide (1.88%

combined). The DE was sieved and the material retained on a 200 mesh (74 μm) sieve

was separated for use. Sand and DE were washed prior to use: soaked in diluted

hydrochloric acid (HCl, 5%), rinsed by deionized water, and dried in an oven for 8 h

(105˚C). The AC was rinsed with deionized water to remove associated fines.

Three contaminants were used to simulate varying contaminants commonly found

in aqueous streams. An ammonium polyacrylate surfactant (Dispex A40, BASF,

Freeport, TX, USA) was selected as a model surfactant and dispersant. Lysozyme and

86

glycine, both obtained from Sigma Aldrich, are two model organic compounds with

disparate pHpzc: 9.60 and 5.97, respectively [24-25].

4.2.2. Porous Media and Nano-TiO2 Characterization

Scanning electron microscopy imaging of the porous media was done on a Hitachi

S-4800 field-emission SEM (Hitachi, Ltd., Tokyo, Japan) at 5 keV after fixing as

previously reported [26]. TiO2 NPs were imaged by transmission electron microscope

using a Hitachi H8100 at 200 keV. Surface area measurements were obtained by nitrogen

gas adsorption using a Beckman Coulter SA 3100 (Beckman Coulter, Inc., Brea, CA) and

the pore distribution data was deduced using a cylindrical pore model.

The measurement of net surface charge of the various media in relation to pH was

determined using an acidimeteric-alkalimeteric titration [27]. Vials with a 50 mL

capacity were filled with 20 mL of 0.01 M NaCl solution and 0.2 g of media. Seven vials

had aliquots of HCl (0.1 M) ranging from 0.05 to 1.5 mL added and equal additions of

NaOH (0.1 M) were added to seven additional vials. One vial was left with no addition

giving a total of 15 samples. The equilibrium pH was measured after 24 h shaking at

room temperature (23±2°C). The surface charge was determined using a site balance

[27].

The zeta potential and particle size distribution of NP dispersions was measured

immediately after sampling with a ZetaSizer Nano ZS (Malvern, Inc., Sirouthborough,

MA, USA) using laser doppler velocimetry and dynamic light scattering (DLS),

respectively. The hydrodynamic diameter of the NPs was calculated as the intensity

mean. The pHpzc of n-TiO2 was determined using the above instrument with pH control

87

provided by an autotitrator accessory (MPT-2, Malvern, Inc.). All measurements were

conducted at 25°C.

4.2.3. Adsorption Isotherms

Batch experiments for determining equilibrium isotherms of the TiO2 NPs with

the three bed materials were conducted in duplicate using a weak phosphate buffer

solution (0.5 mM, pH 7, 1.0 mM ionic strength) in glass serum flasks (166 mL) at room

temperature (23±2°C). The solution volume was 50 mL and the initial NP concentration

ranged from 5 – 200 mg L-1

. From 0.1 to 1.0 g of bed material was added to each flask.

NP-free and porous media-free controls were performed concurrently to account for any

titanium (Ti) leached from the porous media and for any TiO2 removal mechanisms not

mediated by the media, respectively. Samples were taken of the supernatant both initially

and after 3 days of stirring at 150 rpm, which a kinetic study proved to be sufficient time

to reach equilibrium. Samples for titanium analysis were taken after allowing the

suspensions to rest for 30 min to ensure the settling of the adsorptive media. The amount

of TiO2 adsorbed was determined by mass balance upon correction for any TiO2 settling

observed in the media-free control. An alternate method was used for DE due to

significant settling which occurred in these batches. The amount of n-TiO2 associated

with DE was determined by direct analysis. This was accomplished by filtering the

settled DE through a 0.45-µm membrane filter. This filtration ensured the elution of any

suspended n-TiO2 while capturing the DE, which was subsequently dried, weighed and

digested. Samples were then analyzed for Ti content. Settling of n-TiO2 in assays

88

amended with DE was possibly due to calcium dissolving from the DE as divalent cations

have been shown to cause significant instability in NP dispersions [28].

Three traditional isotherm models, Henry, Freundlich and Langmuir, were utilized

to quantify the equilibrium data. These models consider the relationship between the

equilibrium liquid concentration (Ce) and the media-associated concentration (Cs) of a

sorbate. The Henry isotherm is a linear isotherm with a slope fit to parameter KH (L g-

1medium) (Eq. 4.1). The Freundlich isotherm is fitted using the parameters Kf (mg

1-(1/n)

L(1/n)

g-1

medium) and n (Eq. 4.2), with the isotherm becoming linear when n = 1. The

Langmuir isotherm is fit by the parameters a (L g-1

medium) and b (L mg-1

) (Eq. 4.3).

4.2.4. Flow-through Column Experiments

Experiments were performed in similar fashion to previously published methods

[29] using a glass column (inner diameter = 15 mm, length = 150 mm, Omnifit

Benchmark, Diba Industries, Danbury, CT, USA) packed with the porous medium at

room temperature (23±2°C). Two flow-through quartz cuvettes with 10 mm path lengths

(Starna Cells, Inc., Atascadero, CA, USA) were connected to the column influent and

effluent using 0.159 cm diameter PTFE tubing. A UV-Vis spectrophotometer (UV 1800,

Shimadzu Corporation, Kyoto, Japan) provided absorption data, at 300 nm, monitored by

⁄

(4.2)

(4.3)

(4.1)

89

an attached computer at 10 sec intervals. Flow rate control was achieved using a

peristaltic pump (Micropuls3, Gilson, Inc., Middleton, WI, USA).

Sand or DE columns were dry-packed with 36.5 g or 8.5 g of pre-washed sand or

DE, respectively, under agitation from an ultrasonic bath. The column was then filled

from the bottom with deionized water at a rate of 2.6 mL min-1

for 30 min in an ultrasonic

bath to ensure wetting of the bed. AC columns were wet packed using previously rinsed

AC to a final dry activated carbon weight of 10.5 g. The AC columns were then rinsed

with deionized water for 30 min in an ultrasonic bath to ensure elution of any remaining

fines.

A weak phosphate buffer (0.5 mM, pH 7, 1 mM ionic strength) was prepared and

any contaminants, when applicable, were added prior to final pH adjustment. Both pH

and conductivity measurements were taken for each preparation. A portion of this

solution was then separated to pre-rinse the column, displacing 5 bed volumes, so that the

conditions on the column were identical to those in the NP suspension.

Suspensions of n-TiO2 (50 mg L-1

) were prepared in the previously prepared

buffer solution by adding the appropriate amount of NPs to 50 mL centrifuge tubes filled

with approximately 45 mL of the background solution. These were then sonicated

(Ultrasonic Processor, Cole-Parmer, Vernon Hills, IL, USA, 65% intensity, 5 min) and

recombined under constant stirring. Both pH and conductivity measurements were again

performed to ensure continuity between experimental runs.

The NP suspension was pumped through the column at a rate of 2.6 mL min-1

for

30 bed volumes. Concurrently, samples were taken at 10 bed volume intervals and tested

90

for size distribution and zeta potential. After 30 bed volumes, the column was rinsed for

5 bed volumes with the background solution. Samples of the column media were then

taken at five locations equidistant throughout the column starting at the inlet in order to

determine the amount of retained NPs associated with the column media. These

experiments were performed in triplicate.

4.2.5. Chemical Analysis

Elemental analysis of the samples for titanium content was performed in two

steps. First, the samples were digested with equal parts HNO3 (70%) and H2SO4 (95%)

in a microwave assisted extraction system (MARS Xpress, CEM Corp, Matthews, NC,

USA, 120˚C, 45 min). The samples were then diluted and analyzed by ICP-AES (334.94

nm, Optima 2100DV, Perkin Elmer, Waltham, MA, USA). The detection limit for Ti was

0.1 μg L-1

.


4.3.1. Porous Media and Nano-TiO2 Characterization

4.3.1.1. Media Properties

In porous media filtration, the physical structure of the media is a major factor in

determination of retention mechanisms. A more porous material may allow for

additional surface area or more dead volume for the NPs to become trapped in. Also, a

rougher material provides a more tortuous path for the NPs which increases physical

entrapment. Figure 3.1 shows a scanning electron microscopy image of the bed materials

91

utilized in this study. The differences in the surface of the various materials are readily

apparent: sand has a smooth surface and is non-porous (Fig. 4.1A), AC has a planarized

surface with micropores (Fig. 4.1B), and DE displayed a variety of shapes and is very

tortuous (Fig. 4.1C). Table 4.1 chronicles the inherent characteristics of the three

materials. The difference in surface area is of particular importance for this study. As

expected, AC has the largest surface area; approximately 1,000 times that of DE. The

surface area of sand was below the threshold of the instrument and therefore is not

reported. The pore volume distributions give additional data on the surface availability of

the materials with approximately 20% of the pore volume lying between 20 and 100 nm

for AC, excluding much of the surface area from exposure to NPs. DE was only slightly

better with approximately 30% of the pore volume lying in this range. Also of note is the

bed porosity for the various materials, of which DE is the most porous due to its intricate

structure and small size. These three materials were not only selected for their variance

in physical structure, but also for their varied chemical composition and surface

characteristics.

92

Figure 4.1. Scanning electron microscopy images of sand (A), activated carbon (B),

and diatomaceous earth (C).

93

Table 4.1. Material characteristics for porous media used in column experiments.

Material

Average

Diameter

(µm)

pHIEP

Surface

Chargea

(C m-2

)

Surface

Area, BET

(m2 g

-1)

VPore

<20nm

(%)

VPore

<100nm

(%)

Column

Porosity

Sand 190 3.5 -21.7 <0.05 - - 0.214

Activated

Carbon 1000 8.5 +0.02 965±13 78.3 97.2 0.482

Diato-

maceous

Earth

100 8.7 +1.8 1.0±0.1 61.4 93.5 0.660

a Surface charge measured at pH 7.

The pHpzc of each material was determined from the surface charge curves given

in Figure 4.2. Here it is seen that the apparent surface charge of sand is negative across

circumneutral and high pH ranges, while DE follows a classic profile with an extended

range near neutral between pH 4 and 9, and AC is relatively neutral across the entire

tested range. The surface charge at pH 7 is of particular interest as that is the operating

pH of the column experiments. At that pH value, sand has a highly negative surface

charge (-21.7 C m-2

), while AC is essentially neutral (0.02 C m-2

), and DE is only slightly

positive (1.8 C m-2

).

94

4.3.1.2. Nanoparticle Properties

The size and zeta potential of the n-TiO2 greatly affects their transport in porous

media. NPs have been shown to readily aggregate in water [21, 28, 30] and the average

aggregate size of the NPs helps to determine the elution or retention proclivity by

determining the effective retention mechanisms [20, 22]. The zeta potential of the NPs

allows for determination of interactions between the NPs and porous media typically

governed by the conventional Derjaguin-Landau-Verwey-Overbeek (DLVO) theory of

colloidal stability [31-32]. This theory combines electrostatic interactions with attractive

van der Waals interactions to produce a unified force curve and has been extensively

applied to NP interaction with porous media [20, 33-34]. The particle size distribution of

n-TiO2 in aqueous solution at pH 7 is provided in the Figure 4.3. At the same pH, the

Figure 4.2. Surface charge density (σ) as a function of pH for sand ( ), activated

carbon ( ), and diatomaceous earth ( ).

-100

-80

-60

-40

-20

0

20

40

60

2 4 6 8 10 12

σ (

C m

-2)

pH

95

average particle size of n-TiO2 was 200 ± 2 nm, and the zeta potential -42 ± 4 mV. The

pH dependence of the zeta potential (Fig. 4.4) was measured and the pHpzc was

determined to be 4.1. Transmission electron microscopy imaging of the n-TiO2 displayed

crystalline and nearly spherical particles (Fig 4.5).

Figure 4.3. Particle size distribution of the nano-TiO2.

0

2

4

6

8

10

12

14

1 10 100 1000 10000

Inte

nsi

ty %

Diameter (nm)

96

Figure 4.4. Zeta potential of n-TiO2 as a function of pH.

-60-50-40-30-20-10

01020

2 7 12

Zet

a P

ote

nti

al

(mV

)

pH

Figure 4.5. Transmission electron microscopy image of the n-TiO2.

97

The three tested materials selected as model contaminants included a commercial

polyacrylate dispersant (Dispex) used to stabilize inorganic oxide NP dispersions, and

two other model organic compounds with differing pHpzc: lysozyme (9.60) and glycine

(5.97). The choice of organic compounds with respective pHpzc values above and below

the tested pH of 7.0 provided information that can be used to predict the interaction of

positively and negatively charged organic molecules with n-TiO2. Table 4.2 displays the

zeta potential and average particle size for n-TiO2 after addition of the three model

contaminants. When no contaminant was added, the n-TiO2 showed a consistent average

particle size of 200 ± 2 nm. While NP dispersions amended with Dispex and glycine

displayed little departure from the virgin material, lysozyme showed significant potential

for inducing n-TiO2 aggregation. The change in average particle size with time

determined for n-TiO2 in the absence and presence of lysozyme is displayed in Figure

4.6. The NP dispersion amended with lysozyme rapidly aggregated to approximately 350

nm and then slowly trended toward 500 nm. The explanation for the rapid aggregation

with lysozyme can be found in the zeta potential shift induced by the protein. While the

Table 4.2. Zeta potential of TiO2 nanoparticles in tested dispersions at pH 7.0.

Solution Zeta Potential (mV) Average Particle Size (nm)

No Contaminant -42 ± 4 200 ± 2

Dispex -51 ± 3 195 ± 2

Lysozyme 18 ± 3 513 ± 36

Glycine -43 ± 3 203 ± 3

98

zeta potential was -42 ± 4 mV for no contaminant, for lysozyme there was a positive shift

to 18 ± 3 mV due to its high pHpzc of 9.60 which produces a net positive charge over the

particle with absorbed lysozyme. It is generally held that NP suspensions with a zeta

potential less than 20 mV in magnitude will readily aggregate [35]. The instability

caused by the adsorption of lysozyme onto the surface of the NPs induces aggregation.

Dispex caused only a small reduction in the average size of TiO2, 195 ± 2 nm, which

corresponds to the further reduction in zeta potential, -51 ± 3 mV. Glycine addition only

caused a slight decrease in zeta potential (-43 ± 3 mV). This decrease corresponds with

the pHpzc of glycine, 5.97, which would be slightly negative at pH 7. Thus, the net charge

on the NP with absorbed glycine would be slightly reduced.

Figure 4.6. Average hydrodynamic diameter of n-TiO2 aggregates as a function of

time for the cases of no contaminant (●) and lysozyme (■). Standard deviations of

triplicate measurements are shown as error bars. Average sizes of n-TiO2 dispersions

containing Dispex and glycine did not differ notably from the no contaminant case.

0

200

400

600

0 50 100 150 200Av

era

ge

Pa

rtic

le D

iam

eter

(nm

)

Time (min)

99

4.3.2. Adsorption Isotherms

There are four major mechanisms for NP capture in porous media: sedimentation,

interception, straining, and diffusion or selective adsorption [36]. The first three are

physical interactions having to do with the structure and packing of the porous material,

while diffusion or selective adsorption is controlled by surface interactions. In order to

separate physical interactions from surface adsorption, batch isotherms were performed

with n-TiO2 and the three tested bed materials. Figure 4.7 shows these batch isotherms

for each of the three materials, plotting the surface concentration of n-TiO2 as a function

of equilibrium liquid concentration. The differences between the materials can clearly be

seen in the vertical scale of the graphs. For example, at an equilibrium concentration of

approximately 50 mg TiO2 L-1

, the n-TiO2 loadings determined for sand, AC and DE

were approximately 0.02, 0.7, and 10 mg TiO2 g-1

medium, respectively. DE displayed a

much greater affinity for the n-TiO2 under batch conditions which indicates a superior

retention capacity in dynamic column filtration as the inherent association between the n-

TiO2 and DE is supplemented by physical interactions or straining. The isotherm

equation fits also illuminate the superiority of DE in the capture of n-TiO2 (Fig. 4.7C).

100

B

Figure 4.7. Association isotherms for n-TiO2 onto three bed media: sand (A),

activated carbon (B), and diatomaceous earth (C). Error bars shown for duplicate

measurements. Additionally, Henry (─ ∙ ─), Freundlich (---), and Langmuir (─)

isotherm fits are provided.

0

0.01

0.02

0.03

0 20 40 60 80

Cs

(mg T

iO2 g

-1m

ed

ium

)

0

0.3

0.6

0.9

1.2

1.5

0 50 100 150 200 250

Cs

(mg T

iO2 g

-1m

ed

ium

)

0

10

20

30

0 50 100 150 200 250

Cs (m

g T

iO2 g

-1m

ed

ium

)

Ce (mg TiO2/L)

A

B

C

101

Table 4.3 provides the isotherm fit data for each material with three isotherm

types, i.e., Langmuir, Freundlich and Henry models as described in Section 4.2.3.

Adsorption onto sand and AC are both fit best by the Langmuir isotherm, with AC also

being fit equally well by the Freundlich curve. This fit provides for the conclusion that

an adsorption maximum is very near or has been met, limiting the usefulness of sand and

AC. DE, however, is fit best by a Henry isotherm. The near linear isotherm determined

for DE even at aqueous equilibrium concentrations as high as 235 mg TiO2 L-1

suggests a

very high maximum absorbance capacity, providing great promise for use of this granular

material in the capture of NPs. As all isotherms approach Henry’s isotherm at low

concentration, it is likely that absorption of n-TiO2 onto DE follows a Langmuir isotherm

similar to sand or AC, but the equilibrium concentrations evaluated did not reach the

maximum absorbed concentration. Considering the slightly positively charged DE

surface at pH 7, electrostatic interactions may be important in the retention of the highly

Table 4.3. Fit constants for batch isotherms of TiO2 nanoparticles on selected filtration

media.

Material

Henry Freundlich Langmuir

KHa

R2 Kf

b n R

2 a

c b

d R

2

Sand 0.001 -3.53 0.01 7.96 0.32 0.002 0.09 0.94

Activated

Carbon 0.006 0.44 0.12 2.26 0.99 0.05 0.03 0.98

Diatomaceous

Earth 0.098 0.88 0.04 0.82 0.83 0.16 0.003 0.84

a KH [L g

-1medium]

b Kf [mg

1-(1/n) L

(1/n) g

-1medium]

c a [L g

-1medium]

d b [L mg

-1]

A

102

negative n-TiO2. This surface charge may provide additional information into n-TiO2

capture. Since the DE was primarily SiO2 (89.0%), the surface charge would be expected

to be similar to that of sand. Instead, DE is slightly positive at pH 7, which may be due

to the Al2O3 and Fe2O3 impurities that have pHpzc of 8-9 and 6.5-6.8, respectively [19].

The influence of these impurities elucidates the importance of surface heterogeneity, as

this likely determines the abundance of active sites suitable for NP capture. DE has been

shown to be better equipped for n-TiO2 capture due to enhanced surface reactions and

adsorption, likely due to its heterogeneous composition.

4.3.3. Effect of Porous Media on Nano-TiO2 Transport

In order to compare the effectiveness of the three selected media to remove n-

TiO2 from aqueous streams, flow-through column experiments were performed and the

effluent concentration of n-TiO2 was monitored for 30 bed volumes after which the bed

was sampled for retained n-TiO2. Figure 4.8 compares the relative effluent NP

concentration with respect to time determined for n-TiO2 dispersions in flow-through

column experiments using sand, AC or DE. Plots of the NP concentrations associated

with the filtration medium as a function of relative bed depth are shown in Figure 4.9.

Sand was highly ineffective as an absorbent material, with breakthrough being reached in

less than two bed volumes (Fig. 4.8A). Sand had a very small surface area and very little

surface roughness, so physical interactions are unlikely to play a major role in retention

under these conditions. This curve does match well with DLVO predictions, with the

repulsive electrostatic interaction between the negatively charged n-TiO2 and the

negatively charged sand surface greatly outweighing the attractive van der Waals

103

Figure 4.8. Relative effluent n-TiO2 concentration as a function of the number of bed

volumes processed for sand (A), activated carbon (B) and diatomaceous earth (C).

Plots for dispersions with no contaminant (─) and for dispersions amended with

Dispex (---), lysozyme (─ ∙ ─), and glycine (∙∙∙). Dispersions at pH 7 were introduced

at 2.6 mL min-1

.

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30

Rel

ati

ve

Eff

luen

t

Con

cen

trati

on

(C

/Co)

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30

Rel

ati

ve

Eff

luen

t

Con

cen

trati

on

(C

/Co)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30

Rel

ati

ve

Eff

luen

t

Con

cen

trati

on

(C

/Co)

Bed Volumes

A

B

C

104

Figure 4.9. TiO2 nanoparticle concentrations associated with porous media as a

function of bed depth for sand (A), activated carbon (B), and diatomaceous earth (C).

Four cases shown: no contaminant ( ), dispex ( ), lysozyme ( ) and

glycine ( ). All suspensions (pH 7) were introduced at 2.6 mL min-1

.

0.001

0.01

0.1

1

0 0.2 0.4 0.6 0.8 1

Ass

oci

ate

d N

P

Con

cen

trati

on

(m

g T

iO2 g

-1m

ed

ium

)

0.01

0.1

1

0 0.2 0.4 0.6 0.8 1

Ass

oci

ate

d N

P

Con

cen

trati

on

(mg T

iO2 g

-1m

ed

ium

)

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1

Ass

oci

ate

d N

P

Con

cen

trati

on

(mg T

iO2 g

-1m

ed

ium

)

Relative Bed Depth

A

B

C

105

interactions. The captured NP concentration is extremely low; around 0.01 mg TiO2 g-

1sand for the entire bed length (Fig. 4.9A). Overall, the physical characteristics of sand do

not aid in the retention of the n-TiO2 and the repulsive electrostatic interactions dominate

resulting in essentially zero retention [16, 20-21].

The effluent concentration curve of n-TiO2 in an AC bed displayed a distinct

behavior with an initial steep rise to just over 60% effluence followed by a slow approach

to breakthrough (Fig. 4.8B). This is possibly attributable to the slow diffusion of the NPs

into the complex pore structure of the AC. Taking into regard the charge profile for AC

which displays essential neutrality around pH 7 (Fig. 4.2), it is unlikely that electrostatic

interactions play a significant role in NP retention on AC. Physical interactions are likely

to be the major cause of any NP capture by AC due to the large surface area and intricate

pore network. NP retention profiles in AC beds are mostly flat and lay in the range of

approximately 0.01 to 0.10 mg TiO2 g-1

AC (Fig. 4.9B). The flat profile supports the

theorem that diffusion plays a significant role as retention is very steady down the length

of the column. AC showed marked improvement in n-TiO2 retention in this novel

application, exemplified by a larger NP retention capacity. AC enhanced physical

interactions while eliminating the repulsive electrostatic hindrances; however, it still

lacked the ability to achieve full retention.

The effluent concentration profile of n-TiO2 for DE is in stark contrast to those

obtained for sand or AC beds (Fig. 4.8C). Here full retention was achieved over the entire

30 bed volumes tested. DE adsorption of n-TiO2 is also significantly higher, with

retention generally ranging between 0.1 and 1.0 mg TiO2 g-1

DE (Fig. 4.9C). The

106

exponential decay observed in the linear decrease across the bed length lends to the idea

that physical straining plays a significant role. DE provides a large surface area like AC,

but a pore size distribution more available to n-TiO2. Additionally, the highly porous bed

provides a tortuous path through the filtration media. The enhanced physical

contributions to retention provided by DE taken with its increased surface adsorption

displayed in the batch isotherms show the superiority of DE as an adsorbent material for

porous media filtration.

4.3.4. Effect of Solution Contaminants on Nano-TiO2 Transport

As no true filtration process runs solely under ideal conditions, investigation of

the role of solution chemistry on NP retention is necessary for all real-world applications

of porous media filtration. The model dispersant, Dispex, proved to be very effective in

reducing n-TiO2 retention during bed filtration. In Figure 3.8, the effluent concentration

of n-TiO2 in dispersions amended with Dispex may be compared to that of unamended n-

TiO2. No departure from the baseline lacking Dispex can be observed for the sand bed

(Fig. 4.8A) due to the overall poor retention of sand. The impact of Dispex on NP

retention can be more clearly observed in the AC bed, with immediate full breakthrough

for n-TiO2 dispersions amended with the dispersant (Fig. 4.8B). This may be due to

steric hindrances to pore diffusion caused by the adsorbed dispersant, or by resistance due

to the dispersant’s hydrophilicity as compared to the hydrophobic AC surface. Other

dispersants have been shown to decrease NP retention [33, 37] and studies have

concluded this to be due to steric hindrances due to the adsorbed species [21, 38]. Dispex

was even able to prevent retention in the highly effective DE bed enabling full

107

breakthrough in just under five bed volumes (Fig. 4.8C). Figure 4.9 displays the profiles

of n-TiO2 retained in the bed. This data supports the effluent concentration curves as the

dispersant-contaminated n-TiO2 is consistently the least retained in all bed materials.

The organic molecules provided interesting influences on n-TiO2 capture in the

various beds. Addition of glycine did not lead to significant departure from the baseline

case without contaminants in size or zeta potential, nor did it change the retention

behavior in sand or AC beds. In the DE bed, however, glycine supplementation

increased the mobility of n-TiO2 and lead to NP breakthrough around 10 bed volumes. In

contrast, lysozyme greatly influenced n-TiO2 retention in all beds. Results for lysozyme

addition to the NP dispersion with sand as the filtration medium provide an excellent

model case for filter ripening (Fig. 4.8A). Here, as the lysozyme-coated NPs associate

with the sand surface, the NPs themselves, destabilized by the addition of lysozyme onto

their surface, become more efficient collectors than the bare sand surface, providing the

characteristic “hump” in the breakthrough curve. While destabilization due to lysozyme

addition did add to NP retention, a significant fraction of the n-TiO2 eluted from the

column. The retained n-TiO2 bed profile for lysozyme contamination on the sand bed

displayed a linear decrease over the bed length (Fig. 4.9A), which exposes an exponential

decay characteristic of strong interactions between adsorbent and adsorbate. This could

be either due to capture approaching capacity before moving down the column in the

classical “front” or due to physical straining occurring near the inlet of the column. Due

to the highly aggregated state of the lysozyme coated n-TiO2 (> 500 nm); physical

straining is the more likely cause. Lysozyme addition aided in NP capture in the AC bed

108

(Fig. 4.8B) due likely to NP destabilization and physical straining. Lysozyme,

interestingly, did not aid in NP retention with DE (Fig. 4.8C). Instead, there is gradual

progress toward breakthrough possibly due to steric hindrances of the aggregated n-TiO2

coated with a large organic molecule. This steric hindrance and subsequent increased

mobility due to absorbed organic material has been shown for addition of humic acid [34,

39]. An exponential decay is again observed in the retained n-TiO2 bed profile for

lysozyme on DE, providing further support for the hypothesis that physical straining is

likely the dominant mechanism (Fig. 4.9C). The combined results of the contaminants

and the variability in their effects displays the importance of a comprehensive

investigation of any targeted waste stream to determine the competing roles the varying

contaminants contained will play.

4.3.5. Environmental and Industrial Implications

The results of n-TiO2 transport in porous media display the variation in mobility

of NPs in different media. This variation will have a major impact on natural soil

systems exposed to NP-contaminated groundwater. A more thorough understanding of

capture mechanisms, delineating the role of physical capture from surface adsorption,

enables the prediction of the transport behavior in common sediments and soils. The

stabilization and elution differences due to solution chemistry also display the sensitivity

of NP transport in regard to absorbed species. It is unlikely that released NPs will have

bare surfaces and results indicate a significant disparity in behavior of n-TiO2 dispersions

amended with two common contaminants: a dispersant and lysozyme. Thus, the history

of the released NP is a large factor in determining its environmental fate.

109

A recent study has predicted that almost 3,000 tons of n-TiO2 are released yearly

from production, manufacturing and consumption in the United States alone [11]. This,

combined with the desire of the EPA to regulate NP release [40], bolsters the importance

of addressing NP release in industry. The development of simple, inexpensive, easily

implemented techniques for NP removal from aqueous waste streams is necessary. This

study furthers the investigation into suitable materials for porous media filtration,

demonstrating the superiority of DE for NP retention. Continued evaluation and design

of this treatment scheme will provide for future system implementation.

110

4.4. Conclusions

In conclusion, this study confirmed that both solution chemistry and the choice of

filtration media have a strong impact on the transport and retention of TiO2 NPs during

porous media filtration. DE displayed great promise in the capture of n-TiO2, providing

full NP retention of a dispersion containing 50 mg TiO2 L-1

for over 30 bed volumes.

The potential of DE as a granular media for the removal of NPs was also confirmed by

batch isotherm results which confirmed the high NP loading capacity of DE (> 25 mg

TiO2 g-1

DE) in comparison to that of the classic adsorbent sand (0.025 mg TiO2 g-1

sand).

The presence of organic and synthetic contaminants drastically altered the retention of n-

TiO2 in porous media filtration. Continued investigation of common contaminants as

well as competing effects is necessary to propel the introduction of porous media

filtration as a NP-targeted treatment technique. This study has demonstrated the

applicability of DE as a promising new material for the removal of NPs as well as the

inferiority of conventional filtration materials such as sand and AC. The optimization of

DE could potentially provide a superior material for the implementation of porous media

filtration in the treatment of NPs in aqueous waste streams.

111

CHAPTER V

MODELING NANOPARTICLE TRANSPORT AND RETENTION IN

POROUS MEDIA

Abstract

With the continued rise in the use of nanoparticles in manufacturing, concerns

arise over the associated environmental exposure due to the lack of targeted treatment

techniques. Many of the released nanoparticles exist in aqueous waste streams, thus

modification of a conventional particulate matter removal technique, porous media

filtration, may provide a simple, easily implemented nanoparticle specific treatment

system. Necessary for the design of such a system is a comprehensive kinetic model of

nanoparticle retention for various media types. Herein, an amalgam of physisorption and

chemisorption deposition including the influence of physical straining is applied to the

filtration of titanium dioxide nanoparticles through three porous media: sand, activated

carbon, and diatomaceous earth. Diatomaceous earth proved to be the superior material,

as evaluation of the best fit (R2 ≥ 0.97) model parameters show that it displays both

increased physisorption and straining. Application of this model provides vital

information on the dominant interaction mechanisms which enable future enhancement of

the technique for nanoparticle abatement.

112

5.1. Introduction

The use of nanoparticles (NPs) in manufacturing initiatives continues to increase

[97], thus raising concerns over the environmental, safety, and health effects of these

materials. It has been shown that a large amount of these NPs exit to the environment

through wastewater streams [68, 121]. Understanding the transport and retention

mechanisms of NPs in porous media provides a two-fold benefit. First, it provides

information on how NPs released to the environment would interact with soils. Second,

it provides a basis upon which a targeted treatment technique could be established for the

specific retention of NPs in aqueous waste streams. A fundamental element of the latter

benefit is the development of a process model to elucidate dominant mechanisms and

enable process design.

Modeling of NP transport in porous media is a developing field. The basic

filtration model developed by Yao, et al. [78] has been the basis of this work.

Improvements in the form of accounting for effects of neighboring media and van der

Waals forces as well as including Happel’s sphere-in-cell approach [122] were

introduced by Rajagopalan and Tien [123]. Tufenkji and Elimelech further added to the

field by incorporating the effect of hydrodynamic retardation on the diffusion coefficient

using a purely numerical solution [87]. While these formulations are effective at

predicting attachment to porous media under favorable conditions, each is limited by its

inability to predict deposition under repulsive (unfavorable) conditions.

In order to account for removal under these conditions, removal due to wedging

or straining, Bradford developed a model utilizing a clean-bed filtration model [124] as

113

well as a first-order, depth dependent straining factor [125]. This formulation provided

improved fit of the bed profiles of the retained colloids. These utilize a constant first-

order attachment rate coefficient to describe NP retention. While these models

adequately describe attachment to clean, homogeneous porous media, a process model

must provide information for more complex porous media selections as well as

interactions as bed saturation is approached.

This work details a conceptual model which incorporates porous media site

heterogeneity and kinetic differences in transport processes. This formulation is then

applied to breakthrough curves of TiO2 NPs (n-TiO2) in three vastly different porous

media: sand, activated carbon, and diatomaceous earth.

114

5.2. Model Description

The convection-dispersion equation for flow in porous media is shown in

Equation 5.1 where ϵ is the column porosity, CL (g L-1

) is the liquid NP concentration, t

(s) is time, D (m2 s

-1) is the dispersion coefficient, v (m s

-1) is the interstitial fluid

velocity, ρ (g m-3

) is the porous media bulk density, and CS (g kg-1

) is the NP

concentration on the porous media.

This can then be simplified by the assumption of uniform one-dimensional flow in the

direction of column length, neglecting end effects (Eq. 5.2).

This equation describes the transport of the NPs through the porous media column. The

first two terms on the right side of the equation represent the dispersive and convective

flux. The final term on the right describes the interactions of the NPs with the porous

media surface. This final term is divided into two parts, one describing chemisorbed sites

(CS1) and one describing physisorbed sites (CS2), as shown in Eq. 5.3.

This combined approach adsorption model was first described by Cameron and Klute

[126] to describe chemical adsorption in soil columns. It was established to account for

surface heterogeneity on the soil surface. Cameron and Klute reasoned that there could

be vastly different interaction rates for differing surface sites with some being seemingly

(1)

(5.3)

(1)

(5.2)

(1)

(5.1)

115

instantaneous (physisorbed) and some reacting much slower (chemisorbed). The

interactions of the latter type also may include any physisorption interactions inhibited by

slow diffusion into pore structures. A linear Freundlich equilibrium model was utilized

(Eq. 5.4) where k4 is an equilibrium constant, however any equilibrium model may be

substituted.

The kinetic portion of the surface interactions is defined in Equation 5.5. It is divided

into three parts.

The first defining is a first order approximation of the combined non-equilibrium surface

reactions and diffusion inhibited physisorption reactions (k1, s-1

), and the third governs

desorption (k3, s-1

), which make up a first order reversible kinetic model. The second

term describing NP retention due to straining was described by Bradford [125] where k2

(s-1

) is a first order straining coefficient (Eq. 5.6) and Ψ2 is a dimensionless straining

function (Eq. 5.7) [81].

In these equations dC (m) is the average NP diameter and d50 (m) is the average porous

media grain diameter. The term β is a fitting parameter determined to be 0.432. Taking

the derivative of (5.4) with respect to time and inserting it, along with (5.5), into (5.3)

produces the combined surface interactions (Eqn. 5.8).

(

)

(1)

(5.7)

( )

(1)

(5.6)

(1)

(5.5)

(1)

(5.4)

116

Combining (5.8) with (5.2) and simplifying:

These two equations, (5.12) and (5.5), form a system of equations describing NP

transport in the porous media with the following boundary conditions:

Here CL0 (g m-3

) is the inlet NP concentration and L (m) is the length of the column.

In order to facilitate numerical solving of the above set of equations, COMSOL

Multiphysics 4.3 was utilized. COMSOL Multiphysics is simulation software which uses

finite element analysis discretized by backward differentiation to solve the coupled

partial differential equations. The selection of a 2D model with axial symmetry provided

the basis to build a representation of the porous media column. Two interfaces were

selected to model the system: the Transport of Diluted Species interface provided the

(5.16)

(5.15)

(5.14)

(5.13)

( )

(1)

(5.12)

( )

[

]

(1)

(5.11)

[

]

(1)

(5.10)

[

]

(1)

(5.9)

(1)

(5.8)

117

convective-dispersive equation (5.12) and the General Form PDE interface was edited for

the surface concentration (5.5). The constants used to model the various systems are

described in Table 1. For all systems the average NP diameter (dc) was set at 2.0 x 10-7

m. The effect of straining was determined by (5.6) and (5.7). The model was fit to the

effluent curve and bed concentration data by manipulating four variables: D, k1, k3, and

k4.

Table 5.1. Physical parameters of the porous media columns

Sand Activated Carbon Diatomaceous

Earth

ϵ 0.21 0.48 0.66

ρ (kg m-3) 1922 480 2300

d50 (m) 1.9 x 10-4

1.0 x 10-3

1.0 x 10-4

v (m s-1

) 1.15 x 10-3

5.09 x 10-4

3.72 x 10-4

In order to simplify the equations, they can be made dimensionless by substituting

the following variables:

(5.24)

(5.23)

(5.22)

(5.21)

(5.20)

(5.19)

(5.18)

(5.17)

118

Substituting equations (5.17-26) into equations (5.12), (5.5), and (5.13-16) results in the

following:

(5.32)

(5.31)

(5.30)

(5.29)

(1)

(5.28)

( )

(5.27)

(5.26)

(5.25)

119


5.3.1. Porous Media Column

The selection of a well suited porous medium is vital to the performance of the

filter, as evidenced in Figure 5.1. Column effluent and retained NP concentration data

was obtained as described in Chapter 4 Section 2. Results indicate that sand is a poor

collector with almost instantaneous breakthrough. This is most likely due to the highly

uniform and relatively smooth surface. AC followed a similar trend until it reached an

inflection point at approximately 70% effluence.

Figure 5.1. NP effluent concentration relative to the inlet concentration as a function

of bed volumes processed for sand (●), AC (■), and DE (▲). NP dispersions at pH 7

were introduced at 2.6 mL min-1

. Error bars indicate standard deviation for triplicate

measurements.

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 25 30

Rel

ati

ve

Eff

luen

t C

on

cen

trati

on

(C/C

o)


120

After this inflection, there was a slow, steady climb toward full breakthrough

likely due to the delayed transport of the NPs into the complex pore structure. DE

presented full retention of the NPs over the entire 30 bed volumes tested. This is

hypothesized to be due to the heterogeneous nature of the DE surface, allowing for site-

specific preferential adsorption.

Evaluation of the retained NP bed profiles (Fig. 5.2) shows that adsorption is

uniform across the column length for both sand and AC. The superior retention of AC

over sand is, again, likely due to the complexity of the AC pore network providing

increased number of entrapment sites. DE displayed different retention with an

exponential decay from the inlet of the column. This curvature suggests that straining is

a significant factor in NP retention in DE. This is to be expected as it has the smallest

porous media grain diameter and the highest porosity which provides for a highly

tortuous flow path. Modeling of these curves further elucidates the retention mechanisms

of each media type.

121

5.3.2 Modeling Results

Application of the previously described model to the data obtained from the three

porous media selected provided the results outlined in Table 5.2. The effect of straining

was calculated utilizing equations (6-7) for DE systems as it was the only where dc/d50

was above 0.0017, which is a critical ratio determined by Bradford [80]; for sand and AC

k2 was set to zero.

Figure 5.2. Retained NP concentration as a function of relative bed length for sand

( ), AC ( ), and DE ( ). Error bars indicate standard deviation for

triplicate measurements.

1

10

100

1000

10000

100000

0 0.25 0.5 0.75 1

Ret

ain

ed N

P C

on

cen

trati

on

(m

g/k

g)

Relative Bed Distance

122

Table 5.2. Model constants for best fit approximation of NP transport in varying porous

media.

Sand AC DE

Pe 421 38 5580

K1 0.01 0.35 8.06

K3 0.005 0.074 0.323

K4 0.7 0.0 300

Porous media columns packed with sand provided the least retention at less than

2%. Results from the best fit model for this system are provided in Figure 5.3. The

model provides a highly accurate fit of the effluent NP concentration curve (R2 = 0.990)

but underestimates the retained NP concentration (R2 = -7.805). The best fit constants

help explain the poor retention observed. Adsorption is controlled mainly by

physisorption as K4 is more than an order of magnitude greater than K1, which represents

chemisorption and diffustion-limited physisorption processes. Desorption is only slightly

influential with K3 being about half the value of K1. The model is able to accurately

predict adsorption along the full length of the column with straining not being a

significant retention mechanism. Due to these results, sand, with some affinity for n-

TiO2, could be useful as a prefilter but would be expected to be ineffective as a primary

filtration material.

Modeling results for AC as the porous media are shown in Figure 5.4. AC

columns retained a greater fraction of NPs from the inlet stream than sand as shown by

the 4 – 5 fold increase in the relative retained NP concentration, S. This increase,

123

however, was not due to an increase in preferential adsorption as the physisorbtion

interactions, represented by K4, were decreased to zero. The increased adsorption was

due to an increase in non-equilibrium deposition, K1. This fits with the theory that the NP

retention in AC columns was due to diffusion into the porous structure and not due to

site-specific adsorption as posited previously. The slow transport into the pore network

would be encompassed in K1. While desorption, K3, is greater than in the case of sand, it

remains an order of magnitude less than the forward adsorption process, K1. The model

is able to accurately describe (R2 = 0.977) the non-equilibrium process which produces a

slow approach to full breakthrough. Again, the retained NP (S) profile is relatively flat

showing no significant straining, and the model is able to reflect this (R2 = 0.965). AC

was more successful at retaining the n-TiO2 than sand, however the dominance of a non-

equilibrium retention mechanism (large K1) paired with no specific surface affinity (K4 =

0) presents little promise for application in a targeted NP treatment filter.

In order to model the DE curve, identical experiments were run at longer times in

order to find the breakthrough point. Figure 5.5 shows the model fit of the breakthrough

curve for DE. Figure 5.5 also shows the retained NP bed profile at 30 bed volumes

processed as well as the model fit. The model provided an excellent fit for both the

effluent NP curuve (R2 = 0.971) and retained bed profile (R

2 = 0.997) well. It can be seen

in the comparison of the interaction constants that the interaction of the n-TiO2 with DE

is dominated by physisorption with K4 having a value almost four orders of magnitude

greater than that of sand. There is also larger adsorptive and desorptive constants (K1 and

K3) suggesting that there may be pore space that can retain NPs however that pore space

124

does not adequately prevent desorption as was the case in AC. Examination of the

retained bed profile shows that straining is also dominant at the entrance to the column

early in the process. Comparing this with the model prediction for the full breakthrough

time in Figure 5.6 shows that physisorption equilibrium dominates in all other areas of

the column.

In conclusion, DE shows great promise for application as a porous media

designed for NP retention. The large physisorption, presumably due to site-specific

adsorption of the n-TiO2, should be further evaluated to exploit the interaction and

increase the number of active sites on the DE surface. The large straining component

may be a concern as it may lead to significant pressure drops as the streamlines become

clogged with NPs. This may be alleviated by the aforementioned use of sand as a

prefilter to aid in the removal of any aggregated NPs, lessening their exposure to the

more tortuous DE.

125

Figure 5.3. Sand model results for the relative effluent NP concentration (C), above,

and the relative retained NP concentration (S), below. Data (○) is shown alongside the

model (—). Error bars indicate standard deviation for triplicate measurements.

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4

C

(Rel

ati

ve

Eff

luen

t C

on

cen

trati

on

)

T (Bed Volumes)

0

1

2

3

4

0 0.25 0.5 0.75 1

S

(Rel

ati

ve

Ret

ain

ed N

P C

on

cen

trati

on

)

z (Relative Bed Distance)

126

Figure 5.4. AC model results for the relative effluent NP concentration (C), above,

and the relative retained NP concentration (S), below. Data (□) is shown alongside the

model (—). Error bars indicate standard deviation for triplicate measurements.

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10

C

(Rel

ati

ve

Eff

luen

t C

on

cen

trati

on

)

T (Bed Volumes)

0

2

4

6

8

10

0 0.25 0.5 0.75 1

S

(Rel

ati

ve

Ret

ain

ed N

P C

on

cen

trati

on

)


127

Figure 5.5. DE model results for the relative effluent NP concentration (C), above,

and the relative retained NP concentration (S) at 30 bed volumes processed, below.

Data (∆) is shown alongside the model (—). Error bars indicate standard deviation for

triplicate measurements.

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500

C

(Rel

ati

ve

Eff

luen

t C

on

cen

trati

on

)

T (Bed Volumes)

0

400

800

1200

1600

0 0.25 0.5 0.75 1

S

(Rel

ati

ve

Ret

ain

ed N

P C

on

cen

trati

on

)


128

Figure 5.6. DE model results (—) for the relative retained NP concentration (S) after

485 bed volumes processed.

0

400

800

1200

1600

2000

2400

0 0.25 0.5 0.75 1

S

(Rel

ati

ve

Ret

ain

ed N

P C

on

cen

trati

on

)


129

5.4. Conclusions

The model developed in this study provided an accurate formulation for

describing NP retention in porous media. The flexibility of the method is shown in its

applicability to three vastly different materials: sand, AC, and DE. Separation of

physisorption interactions and physical straining from the typical first order adsorption-

desorption model provides a basis for more accurate fitting of both the effluent

breakthrough curves and the retained NP bed profiles. Coefficients of determination (R2)

for all curves were greater than 0.96 with the exception of the sand bed profile.

Investigation into the best fit parameters displayed the superiority of DE for targeted NP

retention in porous media as it provided the greatest physisorbed interactions and

enhanced straining due to its small size and high porosity. Further refinement of this

model will provide an accurate foundation for future system design.

130

CHAPTER VI

ACTIVATED SLUDGE TREATMENT OF NANOPARTICLES

6.1. Introduction

The commercial production of nanoparticles (NPs), defined as particles with at

least one dimension in the range of 1 to 100 nm, is rising [97], with an expected market

value for nano-products in 2011 to 2015 of around $1 trillion yearly [20]. Facilitating

this growth is the diversity of use in a variety of fields, from textiles to semiconductor

manufacturing [10]. While nanomaterials offer many positive contributions [21],

concerns arise over their increasing application due to the potential negative effects of

NPs on human and environmental health [22]. In fact, many nanomaterials have been

labeled as possible emerging contaminants by the Organization for Economic

Cooperation and Development (OECD), for example, cerium oxide (CeO2), aluminum

oxide (Al2O3), and silicon oxide (SiO2), among others. Due to these developments,

understanding the potential impact of these materials on environmental and human health

is of utmost importance.

Nanoparticles (NPs) may be introduced to the environment in many ways

including purposefully, such as the use of zero-valent iron NPs in remediation [102], or

through waste streams, both industrial [127] and municipal [16]. Many NPs used

industrially proceed to municipal water treatment [68]. Additionally, studies have shown

that significant amounts of TiO2 [128] as well as CeO2 NPs [46] remain in effluents of

conventional wastewater treatment plants (WWTPs) and that the removed fraction is

131

concentrated in the settled solids which are typically applied agriculturally, disposed of in

landfills, or incinerated [129]. Therefore, WWTPs are a potentially large point source of

NPs. Determining the fate of NPs in common wastewater treatment operations will help

direct further research into the impact of these novel materials.

Activated sludge, the most commonly applied process in secondary wastewater

treatment, involves a bioreactor filled with bacterial biosolids or “sludge”, with a main

function of degrading organic matter. NPs can interact with biosolids both physically and

chemically. In general, NPs diffuse to surfaces more readily than their larger

counterparts due to their small size [72]. Secondly, microorganisms commonly found in

WWTPs have a net negative surface charge which leads to electrostatic interactions

contributing to the removal of NPs [35, 73]. Inorganic oxides have varying surface

charges in solution at circum-neutral pH and thus will show varying degrees of attraction

to the biological surface. It has been shown, for example with CeO2 NPs, that the

electrostatic interactions play a main role in their adhesion to E. coli [35]. Additionally,

the influence of background constituents may greatly affect the association of NPs with

biosolids, shown by the effect of the order of addition of polyelectrolytes and bacteria on

NP partitioning [74-75]. Finally, NPs may undergo physical entrapment during the

settling phase of treatment and thus the settling process step is important in the evaluation

of NP removal. Understanding the interactions of NPs with biosolids will significantly

add to the work regarding the fate of NPs in waste streams.

This work will study the interactions of three inorganic oxide nanoparticles,

CeO2, Al2O3, and SiO2, with sewage biosolids. The goal is to elucidate the significance

132

of biosorption as a mechanism contributing to the removal of NPs during activated sludge

treatment.

133


CeO2 (50 nm, 99.95% purity), Al2O3 (< 50 nm, 99%), and SiO2 (10-20 nm,

99.5%) NPs were obtained from Sigma-Aldrich (St. Louis, MO). Stock dispersions (2 g

NP/L) were prepared in deionized water. For alumina and ceria, the pH was titrated to

4.5 with 2 mM HCl to enhance dispersion stability. All dispersions were sonicated using

an ultrasonic processor (DEX® 130, 130 Watts, 20 kHz) at 65% amplitude for 5 min, and

then allowed to settle for 4 d. Next, the supernatant was transferred into 50-mL vials for

future use. Dispersions were stored at 4°C and sonicated for 5 min before use.

Sorption isotherm experiments were conducted in duplicate using glass flasks

(166 mL) supplemented with return activated sludge and a known volume of 2 mM

phosphate buffer (pH 7.5) spiked with the target nanomaterial. The solution volume was

50 mL and the final NP concentrations ranged from 0.5-200 mg L-1

. The concentration of

sludge was 0.50, 0.33, and 0.19 g-volatile suspended solids (VSS) L-1

for the CeO2,

Al2O3 and SiO2 isotherms, respectively. Controls lacking either NP or sludge addition

were run in parallel in order to correct for the background levels of Si, Ce or Al in the

sludge and for potential NP losses not mediated by interactions with the sludge. All flasks

were sealed and stirred at 22±1°C in an orbital shaker (150 rpm) for 24 h. Kinetic

measurements demonstrated that 24 h was sufficient to attain equilibrium. Subsequently,

they were allowed to settle for 1 hour. Samples of the supernatant were taken for

analysis and associated concentrations were determined by mass balance. Sludge was

obtained from a local municipal WWTP. The sludge was rinsed in order to minimize

interference by background contaminants.

134

Particle size distribution measurements were determined by dynamic light

scattering using a Zetasizer Nano ZS (Malvern Inc., Sirouthborough, MA). Samples for

analysis of CeO2, SiO2 and Al2O3 were digested by microwave assisted extraction

(MDS2100, CEM Corp., Matthews, NC). CeO2 samples (1 mL) were mixed with 8 mL

of nitric acid (70%) and 2 mL of H2O2(30%) and digested at 70 psi for 30 min. SiO2 or

Al2O3 digestion utilized 10 mL HF (10%) or 10 mL HCl (6.75 M), respectively, at 70 psi

for 45 min. Concentrations were then determined by inductively coupled plasma optical

emission spectroscopy using an 2100DV Optima instrument (Perkin Elmer, Shelton, CT).

All other chemical analysis, including pH and VSS, were conducted according to

standardized methods [130].

NPs were examined by transmission electron microscopy (TEM) to gain

information on particle morphology and size. TEM images were acquired on a Hitachi

H8100 (Hitachi High Technologies, Schaumburg, IL). Biosolids exposed to NPs were

imaged by TEM and scanning electron microscopy-electron dispersion spectroscopy

(SEM-EDS). Samples of the biosolids and NP suspensions were filtered through 0.2 µm

polycarbonate filter (Whatman, Kent, UK) and fixed prior to SEM-EDS analysis as

previously reported [76]. SEM imaging was done on a Hitachi S-4800 field-emission

SEM at 5 keV. Sample preparation for TEM imaging was accomplished by fixing,

following the procedure above for SEM, and then embedding prior to imaging.

135


TEM images of the Al2O3, CeO2, and SiO2 NPs utilized in this study are shown in

Figure 6.1. Al2O3 NPs exhibited a rod-like structure and CeO2 displayed sharper edges,

primarily triangular or rectangular in shape, while the SiO2 was more spherical, slightly

amorphous, and almost cloudlike. All images support the particle sizes reported by the

manufacturer. The average particle size of Al2O3, CeO2 and SiO2 was 175, 132 and 368

nm, respectively. These values are 3-30 times higher compared to the primary particle

size values determined by TEM imaging of the NPs in dry form. This disharmony shows

the rapid tendency of these particles to agglomerate in aqueous solution, even in

conditions favorable for stability (Zhang et al., 2008).

The interaction behavior of Al2O3, CeO2, and SiO2 with the sludge varied widely as

shown in Figure 6.2. These isotherm plots compare the concentration of the NPs

associated with the biosolids (Cs) with the concentration of NPs free in solution (Ce) at

equilibrium. Al2O3 was the only nanomaterial tested that displayed any settling apart

from interaction with the biosolids. On average, approximately 29% of the Al2O3 NPs

settled out of dispersion. Other than this, the behavior of the Al2O3 and CeO2 NPs was

relatively similar, both showing a strong tendency to associate with the sludge. CeO2 had

the highest affinity for the biosolids, as evidenced by the large amount of this

nanomaterial associated with the biosolids at similar solution concentrations and the

steepness of the curve. SiO2 NPs, on the other hand, showed a very low affinity for the

biosolids. On average, SiO2 NP association was 5-10 times less than that of Al2O3 or

CeO2 at similar equilibrium concentrations in dispersion. As an example, at the

136

maximum Ce values tested (75 to 92 mg L-1

, depending on the NP), the concentration of

Al2O3, CeO2 and SiO2 in the sludge (Cs) was 137, 238, and 28 mg g-1

VSS.

Figure 6.1. TEM images of Al2O3 (A), CeO2 (B), and SiO2 (C) nanoparticles.

137

Figure 6.2. Association isotherms for Al2O3 (A), CeO2 (B), and SiO2 (C).

Experimental results (♦) are presented along with graphical representations of the

Freundlich (—) and Langmuir (---) isotherms. Error bars included but not visible due

to size of points.

0

40

80

120

160

200

0 20 40 60 80 100

Cs

(mg

/g-V

SS

)

A

0

100

200

300

0 20 40 60 80

Cs

(mg

/g-V

SS

)

B

0

10

20

30

40

0 20 40 60 80 100

Cs

(mg

/g-V

SS

)

Ce (mg/L)

C

138

The different behavior observed for these NPs can be attributed to electrostatic

interactions between the NPs and biosolids, as seen in other studies [35, 131]. As

mentioned previously, biosolids typically have a negative surface charge. The surface

charge of the three oxides is mainly a factor of pH, as the pH being above or below their

respective isoelectric points (IEPs) will cause the NPs to have either a negative or

positive surface charge, respectively. The IEPs of Al2O3, CeO2, and SiO2 generally range

from 7-9, 6-8, and 2-3, respectively [44, 46, 71]. Therefore, at the tested pH of 7.5 both

Al2O3 and CeO2 are expected to have a neutral to slightly positive surface charge, while

SiO2 will have a strongly negative surface charge. The similar and disparate charge

interactions account for the variation between the poorly adsorbed SiO2 and the more

strongly adsorbed Al2O3 and CeO2, respectively.

The calculated removal efficiencies, i.e., the amount of NPs associated with the

sludge as a percentage of the total NP concentration, averaged across the tested values

were 30±5, 51±3, and 8±2% for Al2O3, CeO2, and SiO2, respectively. Moderately higher

removal levels (21%) were found in a study with fluorescently-labeled SiO2 NPs after

exposure to activated sludge [77]. High levels of CeO2 removal (95-98%) were also

reported in a model WWTP fed with synthetic wastewater [46]. While this study focused

on primary interactions between NPs and sewage sludge, further investigation is needed

to evaluate the effects of solution chemistry, as this will greatly affect NP aggregation

behavior. Two studies have been done integrating the effects of background constituents;

however both used synthetic wastewater [46, 128]. Synthetic wastewater is unlikely to

fully reproduce the complex chemistry in municipal wastewater.

139

Two traditional isotherm models, Freundlich and Langmuir, were utilized to

analyze experimental equilibrium results. These models consider the relationship between

the equilibrium liquid concentration (Ce) and the biosolid-associated concentration (Cs) of

a sorbate. The Freundlich isotherm is fitted using the parameters Kf (mg1-(1/n)

L g-1

VSS)

and n (Eq. 6.1), with the isotherm becoming linear when n = 1. The Langmuir isotherm

is fit by the parameters a (L mg-1

) and b (gVSS mg-1

) (Eq. 6.2). The fit constants

determined using these models, as well as the R2 values are shown in Table 6.1, while the

graphical representations of the models for Al2O3, CeO2, and SiO2 are shown in Figure

6.2(A-C).

Overall, the Langmuir model more closely described the interaction behavior of

NPs and biosolids, being both visually and statistically superior in the case of Al2O3 and

SiO2. While for CeO2 both models were equally accurate due to the high linearity

(Freundlich n = 1.04) across the equilibrium concentrations evaluated (up to 75 mg L-1

).

The Langmuir model suggests monolayer adsorption; however, additional inspection with

electron microscopy provides a clearer image of the NPs on the biosolid surface.

⁄

(6.1)

(6.2)

140

Figure 6.3 shows the SEM and TEM images of activated sludge after exposure to

the various NPs. The majority of the filtrate imaged was in the form of biological flocs

with NPs entrained within the extracellular material; however the selected images of the

individual bacteria help to elucidate the nature of the interaction between the NPs and the

sludge. The NPs seemed to interact only with the surface of the microorganisms and no

definitive evidence of internalization is observed. Generally, the NPs appeared to

associate with the microbial surface in the form of aggregates. This is expected as the

common state of NPs in solution is aggregated. Figure 6.4 shows SEM-EDS analysis of

biosolid samples exposed to Al2O3 or CeO2 NPs and gives further information into the

mode of association between NPs and biosolids. SEM-EDS results for biosolids exposed

to SiO2 NPs were not conclusive due to interference of background silicon with the

detection of the added SiO2. Figure 6.4 clearly indicates that both cerium and aluminum

Table 6.1. Fitting constants for Freundlich and Langmuir NP association isotherms,

including goodness of fit as determined by the coefficient of determination, R2.

Nano-

particle

Freundlich Fit Langmuir Fit

Kf

(mg1-(1/n)

L

g-1

VSS)

n R2

a

(L mg-1

)

b

(gVSS mg-1

) R

2

Al2O3 2.8±0.7 1.1±0.1 0.85 0.02±0.01 0.004±0.002 0.96

CeO2 3.6±0.5 1.0±0.1 0.99 0.001±0.003 0.0002±0.0008 0.99

SiO2 1.0±3.4 1.3±0.2 0.91 0.03±0.02 0.03±0.01 0.97

141

appear to be ubiquitous; however there appears no difference in concentration between

the surface of the bacteria and that of the background filter on which the samples were

fixed. This leads to the determination that the coverage of NPs across the sample was

due to the post-run filtering process and not to preferential association. Thus, the

previous assertion that the NPs are predominately associated with the biosolids as large

aggregates is maintained.

142

Figure 6.3. TEM (left) and SEM (right) images of microorganisms in activated sludge

after exposure to Al2O3 (A), CeO2 (B), and SiO2 (C) nanoparticles (denoted by

arrows).

143

Figure 6.4. SEM-EDS analysis of microorganisms in activated sludge after exposure

to Al2O3 (A) and CeO2 (B) NPs. Red or blue dots represent presence of Al or Ce,

respectively.

144

6.4. Conclusions

The significance of activated sludge-nanoparticle interactions as a removal

mechanism for NPs released into municipal WWTPs was evaluated. The removal

efficiency of three common nanomaterials, Al2O3, CeO2, and SiO2, varied widely. Due to

the high removal efficiency and shape of the association isotherm, CeO2 and Al2O3 are

expected to be significantly removed by activated sludge treatment. In contrast, poor

removal of SiO2 is anticipated given the low affinity of this oxide for association with the

biosolids. In conclusion, these results show that sludge-nanoparticle interactions play an

important role in the removal of NPs from aqueous waste streams, and that activated

sludge treatment is reasonably effective in retaining certain NPs.

145

CHAPTER VII

CONCLUSIONS

The method developed and validated in this study provides a rapid, robust

approach for monitoring nanoparticle retention in porous media. The major advantage

and uniqueness of this method is in using an online, real-time and in-situ method for

measuring nanoparticle transport and retention dynamics; this eliminates the complexity

and errors of sampling and off-line analysis.

Additionally, fluorescent, core-shell silica nanoparticles (n-SiO2) were

synthesized in sizes ranging from 25 to 850 nm. These particles were shown to be highly

useful as tracers in porous media filtration. Their application provided for a retention

comparison based on size alone. Evaluation of the breakthrough curves displayed the

importance of particle number concentration on retention in diatomaceous earth (DE)

beds. Larger n-SiO2 provided significantly larger mass capacities, which would normally

lead to the conclusion that they were better retained by the porous media. However,

evaluation of the number capacities showed similar values. The dependence on particle

number concentration suggests site-specific adsorption on the DE surface.

The method was applied to evaluate TiO2 nanoparticles (n-TiO2) in three varying

porous media: sand, activated carbon (AC), and DE. DE displayed great promise in the

capture of n-TiO2, providing full NP retention of a dispersion containing 50 mg TiO2 L-1

for over 250 bed volumes. The potential of DE as a granular media for the removal of

nanoparticles was also confirmed by batch isotherm results which confirmed the high

146

nanoparticle loading capacity of DE (> 25 mg TiO2 g-1

DE) in comparison to that of the

classic filtration material sand (0.025 mg TiO2 g-1

sand). In fact, evaluation of the bed

capacities after full breakthrough bolstered the superiority of DE as its capacity was

determined to be 33.8 mg TiO2 g-1

DE as compared to that of AC (0.23 mg TiO2 g-1

AC) or

sand (0.004 mg TiO2 g-1

sand). The presence of organic and synthetic contaminants

drastically altered the retention of n-TiO2 in porous media filtration. In conclusion, DE

has been presented by this study to be a promising material for the retention of

nanoparticles while conventional filtration materials such as sand and AC have been

proven to be lacking. Isolation and optimization of specific surface interactions of DE

could provide an avenue for the implementation of porous media filtration for the

treatment of nanoparticles in aqueous waste streams. Continued investigation of common

contaminants as well as competing effects is necessary to propel the introduction of

porous media filtration as a nanoparticle-targeted treatment technique.

Modeling of this system utilized a combination of site delineation, which

separates physisorbed sites from chemisorbed, and physical straining. This enabled the

accurate fitting of the breakthrough curves and retained nanoparticle concentration

profiles for sand, AC and DE with coefficients of determination (R2) greater than 0.96 for

all curves save that for the nanoparticles retained in the sand bed. Best fit parameters of

this model indicated that DE provided superior retention mostly due to increased

physisorption, confirming the previous hypothesis of site-specific adsorption. Continued

refinement will lead to a process model useful in both system scaling and design.

147

Evaluation of the interactions of three nanoparticles, cerium oxide (n-CeO2),

aluminum oxide (n-Al2O3), and n-SiO2, with activated sewage sludge displayed the

disparity of retention based on nanoparticle type. The n-CeO2 and n-Al2O3 were both

significantly retained by the sludge while n-SiO2 was poorly removed. This was largely

due to electrostatic interactions between the sludge and nanoparticles as n-SiO2 was

highly negatively charged at circumneutral pH while n-CeO2 and n-Al2O3 were neutral to

slightly positively charged. This result reinforces the need for targeted nanoparticle

retention techniques, as conventional treatment techniques may be very ineffective for

nanoparticle abatement.

The use of porous media filtration has been shown to be effective for the purpose

of removing nanoparticles from aqueous waste streams. A novel technique for

monitoring retention of n-TiO2 and the synthesis of fluorescent-cored n-SiO2 provided

pathways for rapid evaluation of nanoparticle transport in porous media under varying

conditions. DE was proved to be an effective material which may be combined with

other materials such as sand to form a composite porous media filtration apparatus which

could be designed to perform under specific stream conditions and to target specific

nanoparticles. Application of a process simulator clarified controlling retention

mechanisms and enables future scale-up and process design. Continued investigation

into the role of solution contaminants as well as optimization of the porous media may

provide an easily implemented, nanoparticle-specific porous media treatment technique

for the abatement of nanoparticles.

148

CHAPTER VIII

CONTINUATION OF WORK

In the continuation of this study, the active site or sites on diatomaceous earth

(DE) which are conducive to nanoparticle capture must be conclusively determined. This

will allow for a selection of a DE which has an abundance of these sites. The role of DE

grain size must also be established in order to balance nanoparticle retention with

pressure drop across the column. This will be very important when scaling to industrial

dimensions. Integration of different fluorescent dyes into the n-SiO2 may allow for

simultaneous monitoring of multiple nanoparticles, providing the ability to study site

competition between nanoparticle sizes. It would also allow for concurrent studies of

nanoparticles coated with a contaminant versus the virgin nanoparticles. Finally, further

refinement of the process model is necessary. The more accurate Danckwert’s boundary

condition should be used at the column inlet as opposed to the simpler constant-

concentration condition used. Delineation between the chemisorbed and diffusion-

limited physisorbed interactions would also be helpful for determining regeneration

capability.

149

REFERENCES

1. Israelachvili, J.N. Intermolecular and surface forces. 1985, London: Academic Press Inc.

2. National Nanotechnology Initiative. Nanotechnology 101. 2012. www.nano.gov.

3. Schmid, G. Nanoparticles: From theory to application. 2nd ed. 2010, Weinheim: Wiley-

VCH.

4. Poole, C.P. and F.J. Owens. Introduction to nanotechnology. 2003, Hoboken, NJ: J.

Wiley.

5. Niemeyer, C.M. Nanoparticles, proteins, and nucleic acids: Biotechnology meets

materials science. Angewandte Chemie-International Edition, 2001. 40(22): 4128-4158.

6. Guzman, K.A.D., M.R. Taylor, and J.F. Banfield. Environmental risks of

nanotechnology: National nanotechnology initiative funding, 2000-2004. Environmental

Science & Technology, 2006. 40(5): 1401-1407.

7. Jarvie, H.P., H. Al-Obaidi, S.M. King, M.J. Bowes, M.J. Lawrence, A.F. Drake, M.A.

Green, and P.J. Dobson. Fate of silica nanoparticles in simulated primary wastewater

treatment. Environmental Science & Technology, 2009. 43(22): 8622-8628.

8. GreBler, S. and M. Nentwich. Nano and the environment - part 1: Potential

environmental benefits and sustainablity effects. NanoTrust-Dossier, 2012(26).

9. Li, Y.H., J. Ding, Z.K. Luan, Z.C. Di, Y.F. Zhu, C.L. Xu, D.H. Wu, and B.Q. Wei.

Competitive adsorption of Pb2+

, Cu2+

and Cd2+

ions from aqueous solutions by

multiwalled carbon nanotubes. Carbon, 2003. 41(14): 2787-2792.

10. Tsuzuki, T. Commercial scale production of inorganic nanoparticles. International

Journal of Nanotechnology, 2009. 6(5-6): 567-578.

11. Ju-Nam, Y. and J.R. Lead. Manufactured nanoparticles: An overview of their chemistry,

interactions and potential environmental implications. Science of the Total Environment,

2008. 400(1-3): 396-414.

12. Zhang, W.X. Nanoscale iron particles for environmental remediation: An overview.

Journal of Nanoparticle Research, 2003. 5(3-4): 323-332.

13. Wiesner, M.R., G.V. Lowry, P. Alvarez, D. Dionysiou, and P. Biswas. Assessing the

risks of manufactured nanomaterials. Environmental Science & Technology, 2006.

40(14): 4336-4345.

14. Biswas, P. and C.Y. Wu. 2005 critical review: Nanoparticles and the environment.

Journal of the Air & Waste Management Association, 2005. 55(6): 708-746.

150

15. Klaine, S.J., P.J.J. Alvarez, G.E. Batley, T.F. Fernandes, R.D. Handy, D.Y. Lyon, S.

Mahendra, M.J. McLaughlin, and J.R. Lead. Nanomaterials in the environment:

Behavior, fate, bioavailability, and effects. Environmental Toxicology and Chemistry,

2008. 27(9): 1825-1851.

16. Nowack, B. and T.D. Bucheli. Occurrence, behavior and effects of nanoparticles in the

environment. Environmental Pollution, 2007. 150(1): 5-22.

17. Gottschalk, F., T. Sonderer, R.W. Scholz, and B. Nowack. Modeled environmental

concentrations of engineered nanomaterials (TiO2, ZnO, Ag, CNT, fullerenes) for

different regions. Environmental Science & Technology, 2009. 43(24): 9216-9222.

18. Robichaud, C.O., A.E. Uyar, M.R. Darby, L.G. Zucker, and M.R. Wiesner. Estimates of

upper bounds and trends in nano-TiO2 production as a basis for exposure assessment.

Environmental Science & Technology, 2009. 43(12): 4227-4233.

19. Navarro, E., A. Baun, R. Behra, N.B. Hartmann, J. Filser, A.J. Miao, A. Quigg, P.H.

Santschi, and L. Sigg. Environmental behavior and ecotoxicity of engineered

nanoparticles to algae, plants, and fungi. Ecotoxicology, 2008. 17(5): 372-386.

20. Roco, M.C. Environmentally responsible development of nanotechnology.

Environmental Science & Technology, 2005. 39(5): 106A-112A.

21. Savage, N. and M.S. Diallo. Nanomaterials and water purification: Opportunities and

challenges. Journal of Nanoparticle Research, 2005. 7(4-5): 331-342.

22. Handy, R.D. and B.J. Shaw. Toxic effects of nanoparticles and nanomaterials:

Implications for public health, risk assessment and the public perception of

nanotechnology. Health Risk & Society, 2007. 9(2): 125-144.

23. Peters, K., R.E. Unger, C.J. Kirkpatrick, A.M. Gatti, and E. Monari. Effects of nano-

scaled particles on endothelial cell function in vitro: Studies on viability, proliferation

and inflammation. Journal of Materials Science-Materials in Medicine, 2004. 15(4): 321-

325.

24. Cheng, M.D. Effects of nanophase materials (<= 20 nm) on biological responses. Journal

of Environmental Science and Health Part a-Toxic/Hazardous Substances &

Environmental Engineering, 2004. 39(10): 2691-2705.

25. Rahman, Q., M. Lohani, E. Dopp, H. Pemsel, L. Jonas, D.G. Weiss, and D. Schiffmann.

Evidence that ultrafine titanium dioxide induces micronuclei and apoptosis in syrian

hamster embryo fibroblasts. Environmental Health Perspectives, 2002. 110(8): 797-800.

26. Lin, W., Y.-w. Huang, X.-D. Zhou, and Y. Ma. In vitro toxicity of silica nanoparticles in

human lung cancer cells. Toxicology and Applied Pharmacology, 2006. 217(3): 252-259.

151

27. Kipen, H.M. and D.L. Laskin. Smaller is not always better: Nanotechnology yields

nanotoxicology. American Journal of Physiology - Lung Cellular and Molecular

Physiology, 2005. 289(5): L696-L697.

28. Nel, A., T. Xia, L. Mädler, and N. Li. Toxic potential of materials at the nanolevel.

Science, 2006. 311(5761): 622-627.

29. Jeng, H.A. and J. Swanson. Toxicity of metal oxide nanoparticles in mammalian cells.

Journal of Environmental Science and Health Part a-Toxic/Hazardous Substances &

Environmental Engineering, 2006. 41(12): 2699-2711.

30. Brunner, T.J., P. Wick, P. Manser, P. Spohn, R.N. Grass, L.K. Limbach, A. Bruinink, and

W.J. Stark. In vitro cytotoxicity of oxide nanoparticles: Comparison to asbestos, silica,

and the effect of particle solubility. Environmental Science & Technology, 2006. 40(14):

4374-4381.

31. Lin, W., Y.-w. Huang, X.-D. Zhou, and Y. Ma. Toxicity of cerium oxide nanoparticles in

human lung cancer cells. International Journal of Toxicology, 2006. 25(6): 451-457.

32. Nakagawa, Y., S. Wakuri, K. Sakamoto, and N. Tanaka. The photogenotoxicity of

titanium dioxide particles. Mutation Research-Genetic Toxicology and Environmental

Mutagenesis, 1997. 394(1-3): 125-132.

33. Bolis, V., C. Busco, M. Ciarletta, C. Distasi, J. Erriquez, I. Fenoglio, S. Livraghi, and S.

Morel. Hydrophilic/hydrophobic features of TiO2 nanoparticles as a function of crystal

phase, surface area and coating, in relation to their potential toxicity in peripheral nervous

system. Journal of Colloid and Interface Science, 2012. 369: 28-39.

34. Adams, L.K., D.Y. Lyon, and P.J.J. Alvarez. Comparative eco-toxicity of nanoscale

TiO2, SiO2, and ZnO water suspensions. Water Research, 2006. 40(19): 3527-3532.

35. Thill, A., O. Zeyons, O. Spalla, F. Chauvat, J. Rose, M. Auffan, and A.M. Flank.

Cytotoxicity of CeO2 nanoparticles for escherichia coli. Physico-chemical insight of the

cytotoxicity mechanism. Environmental Science & Technology, 2006. 40(19): 6151-

6156.

36. Hund-Rinke, K. and M. Simon. Ecotoxic effect of photocatalytic active nanoparticles

TiO2 on algae and daphnids. Environmental Science and Pollution Research, 2006. 13(4):

225-232.

37. Lovern, S.B. and R. Klaper. Daphnia magna mortality when exposed to titanium dioxide

and fullerene (C-60) nanoparticles. Environmental Toxicology and Chemistry, 2006.

25(4): 1132-1137.

38. Federici, G., B.J. Shaw, and R.D. Handy. Toxicity of titanium dioxide nanoparticles to

rainbow trout (oncorhynchus mykiss): Gill injury, oxidative stress, and other

physiological effects. Aquatic Toxicology, 2007. 84(4): 415-430.

152

39. Yang, L. and D.J. Watts. Particle surface characteristics may play an important role in

phytotoxicity of alumina nanoparticles. Toxicology Letters, 2005. 158(2): 122-132.

40. Murashov, V. Comments on "Particle surface characteristics may play an important role

in phytotoxicity of alumina nanoparticles" By Yang, L., Watts, D.J., Toxicology Letters,

2005, 158, 122-132. Toxicology Letters, 2006. 164(2): 185-187.

41. Debye, P. and E. Huckel. The theory of the electrolyte II - The border law for electrical

conductivity. Physikalische Zeitschrift, 1923. 24: 305-325.

42. Riddick, T.M. Control of colloid stability through zeta potential. 1968, New York: Zeta-

Meter, Inc.

43. Baalousha, M. Aggregation and disaggregation of iron oxide nanoparticles: Influence of

particle concentration, pH and natural organic matter. Science of the Total Environment,

2009. 407(6): 2093-2101.

44. Fisher, M.L., M. Colic, M.P. Rao, and F.F. Lange. Effect of silica nanoparticle size on

the stability of alumina/silica suspensions. Journal of the American Ceramic Society,

2001. 84(4): 713-718.

45. Jailani, S., G.V. Franks, and T.W. Healy. Zeta potential of nanoparticle suspensions:

Effect of electrolyte concentration, particle size, and volume fraction. Journal of the

American Ceramic Society, 2008. 91(4): 1141-1147.

46. Limbach, L.K., R. Bereiter, E. Mueller, R. Krebs, R. Gaelli, and W.J. Stark. Removal of

oxide nanoparticles in a model wastewater treatment plant: Influence of agglomeration

and surfactants on clearing efficiency. Environmental Science & Technology, 2008.

42(15): 5828-5833.

47. Sogami, I. and N. Ise. On the electrostatic interaction in macroionic solutions. Journal of

Chemical Physics, 1984. 81(12): 6320-6332.

48. French, R.A., A.R. Jacobson, B. Kim, S.L. Isley, R.L. Penn, and P.C. Baveye. Influence

of ionic strength, pH, and cation valence on aggregation kinetics of titanium dioxide

nanoparticles. Environmental Science & Technology, 2009. 43(5): 1354-1359.

49. Lu, J.J., X.M. Yan, Y.M. Zhou, B.Y. Shi, X.P. Ge, and H.X. Tang. Comparative study of

the adsorption capacity of peat humic acid (PHA) and soil humic acid on nano-SiO2 and

nano-kaolin. Fresenius Environmental Bulletin, 2009. 18(3): 346-352.

50. Zhang, Y., Y.S. Chen, P. Westerhoff, K. Hristovski, and J.C. Crittenden. Stability of

commercial metal oxide nanoparticles in water. Water Research, 2008. 42(8-9): 2204-

2212.

51. Derjaguin, B.V. and L. Landau. Theory of the stability of strongly charged lyophobic sols

and of the adhesion of strongly charged particles in solution of electrolytes. Acta

Physicochim URSS, 1941. 14: 633-662.

153

52. Verwey, E. and J. Overbeek. Theory of stability of lyophobic colloids. 1948, Amsterdam:

Elsevier.

53. van Oss, C.J. Acid-base interfacial interactions in aqueous-media. Colloids and Surfaces

a-Physicochemical and Engineering Aspects, 1993. 78: 1-49.

54. van Oss, C.J. Interfacial forces in aqueous media. 1994, New York: Marcel Dekker.

55. Yotsumoto, H. and R.H. Yoon. Application of extended DLVO theory 1. Stability of

rutile suspensions. Journal of Colloid and Interface Science, 1993. 157(2): 426-433.

56. Yotsumoto, H. and R.H. Yoon. Application of extended DLVO theory 2. Stability of

silica suspensions. Journal of Colloid and Interface Science, 1993. 157(2): 434-441.

57. Bostrom, M., V. Deniz, G.V. Franks, and B.W. Ninham. Extended DLVO theory:

Electrostatic and non-electrostatic forces in oxide suspensions. Advances in Colloid and

Interface Science, 2006. 123: 5-15.

58. Domingos, R.F., N. Tufenkji, and K.J. Wilkinson. Aggregation of titanium dioxide

nanoparticles: Role of a fulvic acid. Environmental Science & Technology, 2009. 43(5):

1282-1286.

59. Godinez, I.G. and C.J.G. Darnault. Aggregation and transport of nano-TiO2 in saturated

porous media: Effects of pH, surfactants and flow velocity. Water Research, 2011. 45(2):

839-851.

60. Tiraferri, A. and R. Sethi. Enhanced transport of zerovalent iron nanoparticles in

saturated porous media by guar gum. Journal of Nanoparticle Research, 2009. 11(3):

635-645.

61. Ghosh, S., H. Mashayekhi, B. Pan, P. Bhowmik, and B.S. Xing. Colloidal behavior of

aluminum oxide nanoparticles as affected by pH and natural organic matter. Langmuir,

2008. 24(21): 12385-12391.

62. Chang, M.R., D.J. Lee, and J.Y. Lai. Nanoparticles in wastewater from a science-based

industrial park - coagulation using polyaluminum chloride. Journal of Environmental

Management, 2007. 85(4): 1009-1014.

63. Chuang, S.H., T.C. Chang, C.F. Ouyang, and J.M. Leu. Colloidal silica removal in

coagulation processes for wastewater reuse in a high-tech industrial park. Water Science

and Technology, 2007. 55(1-2): 187-195.

64. Lai, C.L. and S.H. Lin. Treatment of chemical mechanical polishing wastewater by

electrocoagulation: System performances and sludge settling characteristics.

Chemosphere, 2004. 54(3): 235-242.

154

65. Browne, S., V. Krygier, J. O'Sullivan, and E.L. Sandstrom. Treating wastewater from

CMP using ultrafiltration. Micro, 1999. 17(3): 77.

66. Liang, H.W., L. Wang, P.Y. Chen, H.T. Lin, L.F. Chen, D.A. He, and S.H. Yu.

Carbonaceous nanofiber membranes for selective filtration and separation of

nanoparticles. Advanced Materials, 2010. 22(42): 4691.

67. Lippa, P., U. Muller, B. Hetzer, and T. Wagner. Characterization of nanoparticulate

fouling and breakthrough during low-pressure membrane filtration. Desalination and

Water Treatment, 2009. 9(1-3): 234-240.

68. Boxall, A.B., K. Tiede, and Q. Chaudhry. Engineered nanomaterials in soils and water:

How do they behave and could they pose a risk to human health? Nanomedicine, 2007.

2(6): 919-927.

69. Metcalf & Eddy., G. Tchobanoglous, F.L. Burton, and H.D. Stensel. Wastewater

engineering : Treatment and reuse. 4th ed. McGraw-Hill series in Civil and

Environmental Engineering. 2003, Boston: McGraw-Hill. xxviii, 1819 p.

70. Chen, K.L. and M. Elimelech. Influence of humic acid on the aggregation kinetics of

fullerene (C-60) nanoparticles in monovalent and divalent electrolyte solutions. Journal

of Colloid and Interface Science, 2007. 309(1): 126-134.

71. Hosokawa, M. Nanoparticle technology handbook. 2007, Amsterdam, Netherlands;

Boston, Mass.: Elsevier.

72. Brar, S.K., M. Verma, R.D. Tyagi, and R.Y. Surampalli. Engineered nanoparticles in

wastewater and wastewater sludge - evidence and impacts. Waste Management, 2010.

30(3): 504-520.

73. Neal, A.L. What can be inferred from bacterium-nanoparticle interactions about the

potential consequences of environmental exposure to nanoparticles? Ecotoxicology, 2008.

17(5): 362-371.

74. Wang, Z.S., M.T. Hung, and J.C. Liu. Sludge conditioning by using alumina

nanoparticles and polyelectrolyte. Water Science & Technology, 2007. 56(8): 125-132.

75. Sennerfors, T. and F. Tiberg. Adsorption of polyelectrolyte and nanoparticles at the

silica-aqueous solution interface: Influence of the history of additions of the two

components. Journal of Colloid and Interface Science, 2001. 238(1): 129-135.

76. Gómez-Rivera, F., J.A. Field, D. Brown, and R. Sierra-Alvarez. Fate of cerium dioxide

(CeO2) nanoparticles in municipal wastewater during activated sludge treatment.

Bioresource Technology, 2012. 108: 300-304.

77. Kiser, M.A., H. Ryu, H.Y. Jang, K. Hristovski, and P. Westerhoff. Biosorption of

nanoparticles to heterotrophic wastewater biomass. Water Research, 2010. 44(14): 4105-

4114.

155

78. Yao, K.M., M.M. Habibian, and C.R. Omelia. Water and waste water filtration - concepts

and applications. Environmental Science & Technology, 1971. 5(11): 1105.

79. McDowellboyer, L.M., J.R. Hunt, and N. Sitar. Particle-transport through porous-media.

Water Resources Research, 1986. 22(13): 1901-1921.

80. Bradford, S.A., S.R. Yates, M. Bettahar, and J. Simunek. Physical factors affecting the

transport and fate of colloids in saturated porous media. Water Resources Research,

2002. 38(12): 12.

81. Bradford, S.A. and M. Bettahar. Concentration dependent transport of colloids in

saturated porous media. Journal of Contaminant Hydrology, 2006. 82(1–2): 99-117.

82. Bradford, S.A., J. Simunek, M. Bettahar, M.T. van Genuchten, and S.R. Yates.

Significance of straining in colloid deposition: Evidence and implications. Water

Resources Research, 2006. 42(12): 16.

83. Ben-Moshe, T., I. Dror, and B. Berkowitz. Transport of metal oxide nanoparticles in

saturated porous media. Chemosphere, 2010. 81(3): 387-393.

84. Solovitch, N., J. Labille, J. Rose, P. Chaurand, D. Borschneck, M.R. Wiesner, and J.Y.

Bottero. Concurrent aggregation and deposition of TiO2 nanoparticles in a sandy porous

media. Environmental Science & Technology, 2010. 44(13): 4897-4902.

85. Tian, Y.A., B. Gao, C. Silvera-Batista, and K.J. Ziegler. Transport of engineered

nanoparticles in saturated porous media. Journal of Nanoparticle Research, 2010. 12(7):

2371-2380.

86. Chen, G., X. Liu, and C. Su. Transport and retention of TiO2 rutile nanoparticles in

saturated porous media under low-ionic-strength conditions: Measurements and

mechanisms. Langmuir, 2011. 27(9): 5393-5402.

87. Tufenkji, N. and M. Elimelech. Correlation equation for predicting single-collector

efficiency in physicochemical filtration in saturated porous media. Environmental


88. Wang, C., A.D. Bobba, R. Attinti, C.Y. Shen, V. Lazouskaya, L.P. Wang, and Y. Jin.

Retention and transport of silica nanoparticles in saturated porous media: Effect of

concentration and particle size. Environmental Science & Technology, 2012. 46(13):

7151-7158.

89. Pelley, A.J. and N. Tufenkji. Effect of particle size and natural organic matter on the

migration of nano- and microscale latex particles in saturated porous media. Journal of

Colloid and Interface Science, 2008. 321(1): 74-83.

156

90. Hotze, E.M., T. Phenrat, and G.V. Lowry. Nanoparticle aggregation: Challenges to

understanding transport and reactivity in the environment. Journal of Environmental

Quality, 2010. 39(6): 1909-1924.

91. Kosmulski, M. Chemical properties of material surfaces. 2001: Marcel Dekker, Inc.

92. Chowdhury, I., Y. Hong, R.J. Honda, and S.L. Walker. Mechanisms of TiO2 nanoparticle

transport in porous media: Role of solution chemistry, nanoparticle concentration, and

flowrate. Journal of Colloid and Interface Science, 2011. 360(2): 548-555.

93. Jeong, S.W. and S.D. Kim. Aggregation and transport of copper oxide nanoparticles in

porous media. Journal of Environmental Monitoring, 2009. 11(9): 1595-1600.

94. Jaisi, D.P., N.B. Saleh, R.E. Blake, and M. Elimelech. Transport of single-walled carbon

nanotubes in porous media: Filtration mechanisms and reversibility. Environmental


95. Weronski, P., J.Y. Walz, and M. Elimelech. Effect of depletion interactions on transport

of colloidal particles in porous media. Journal of Colloid and Interface Science, 2003.

262(2): 372-383.

96. Lecoanet, H.F. and M.R. Wiesner. Velocity effects on fullerene and oxide nanoparticle

deposition in porous media. Environmental Science & Technology, 2004. 38(16): 4377-

4382.

97. U.S.EPA. Nanotechnology whitepaper (Report No. EPA 100/b-07/001). Science Policy

Council, Nanotechnology Workgroup, U.S. Environmental Protection Agency, 2007.

98. Auffan, M., J. Rose, J.Y. Bottero, G.V. Lowry, J.P. Jolivet, and M.R. Wiesner. Towards a

definition of inorganic nanoparticles from an environmental, health and safety

perspective. Nature Nanotechnology, 2009. 4(10): 634-641.

99. Chen, G.X., X.Y. Liu, and C.M. Su. Transport and retention of TiO2 rutile nanoparticles

in saturated porous media under low-ionic-strength conditions: Measurements and

mechanisms. Langmuir, 2011. 27(9): 5393-5402.

100. Godinez, I.G. and C.J.G. Darnault. Aggregation and transport of nano-TiO2 in saturated

porous media: Effects of pH, surfactants and flow velocity. Water Research, 2011. 45(2):

839-851.

101. Dunphy Guzman, K.A., M.P. Finnegan, and J.F. Banfield. Influence of surface potential

on aggregation and transport of titania nanoparticles. Environmental Science &

Technology, 2006. 40(24): 7688-7693.

102. Kim, H.J., T. Phenrat, R.D. Tilton, and G.V. Lowry. Fe-0 nanoparticles remain mobile in

porous media after aging due to slow desorption of polymeric surface modifiers.


157

103. Lecoanet, H.F., J.Y. Bottero, and M.R. Wiesner. Laboratory assessment of the mobility

of nanomaterials in porous media. Environmental Science & Technology, 2004. 38(19):

5164-5169.

104. Lin, D.H., X.L. Tian, F.C. Wu, and B.S. Xing. Fate and transport of engineered

nanomaterials in the environment. Journal of Environmental Quality, 2010. 39(6): 1896-

1908.

105. Napierska, D., L.C.J. Thomassen, D. Lison, J.A. Martens, and P.H. Hoet. The nanosilica

hazard: Another variable entity. Particle and Fibre Toxicology, 2010. 7.

106. Hirsch, L.R., R.J. Stafford, J.A. Bankson, S.R. Sershen, B. Rivera, R.E. Price, J.D. Hazle,

N.J. Halas, and J.L. West. Nanoshell-mediated near-infrared thermal therapy of tumors

under magnetic resonance guidance. Proceedings of the National Academy of Sciences of

the United States of America, 2003. 100(23): 13549-13554.

107. Slowing, II, J.L. Vivero-Escoto, C.W. Wu, and V.S.Y. Lin. Mesoporous silica

nanoparticles as controlled release drug delivery and gene transfection carriers. Advanced

Drug Delivery Reviews, 2008. 60(11): 1278-1288.

108. Torkzaban, S., J.M. Wan, T.K. Tokunaga, and S.A. Bradford. Impacts of bridging

complexation on the transport of surface-modified nanoparticles in saturated sand.

Journal of Contaminant Hydrology, 2012. 136: 86-95.

109. Jung, H.S., D.S. Moon, and J.K. Lee. Quantitative analysis and efficient surface

modification of silica nanoparticles. Journal of Nanomaterials, 2012.

110. Chen, G.X., X.Y. Liu, and C.M. Su. Distinct effects of humic acid on transport and

retention of TiO2 rutile nanoparticles in saturated sand columns. Environmental Science

& Technology, 2012. 46(13): 7142-7150.

111. Stober, W., A. Fink, and E. Bohn. Controlled growth of monodisperse silica spheres in

micron size range. Journal of Colloid and Interface Science, 1968. 26(1): 62.

112. Van Blaaderen, A. and A. Vrij. Synthesis and characterization of colloidal dispersions of

fluorescent, monodisperse silica spheres. Langmuir, 1992. 8(12): 2921-2931.

113. Ow, H., D.R. Larson, M. Srivastava, B.A. Baird, W.W. Webb, and U. Wiesner. Bright

and stable core-shell fluorescent silica nanoparticles. Nano Letters, 2005. 5(1): 113-117.

114. Burns, A., H. Ow, and U. Wiesner. Fluorescent core-shell silica nanoparticles: Towards

"Lab on a particle" Architectures for nanobiotechnology. Chemical Society Reviews,

2006. 35(11): 1028-1042.

115. Song, X., F. Li, J. Ma, N. Jia, J. Xu, and H. Shen. Synthesis of fluorescent silica

nanoparticles and their applications as fluorescence probes. Journal of Fluorescence,

2011. 21(3): 1205-1212.

158

116. Choi, J.H., A.A. Burns, R.M. Williams, Z.X. Zhou, A. Flesken-Nikitin, W.R. Zipfel, U.

Wiesner, and A.Y. Nikitin. Core-shell silica nanoparticles as fluorescent labels for

nanomedicine. Journal of Biomedical Optics, 2007. 12(6).

117. Feng, W., T. Wee Beng, Z. Yong, F. Xianping, and W. Minquan. Luminescent

nanomaterials for biological labelling. Nanotechnology, 2006. 17(1).

118. Rottman, J., R. Sierra-Alvarez, and F. Shadman. Real-time monitoring of nanoparticle

retention in porous media. Environmental Chemistry Letters, (in press).

119. Darlington, T.K., A.M. Neigh, M.T. Spencer, O.T. Nguyen, and S.J. Oldenburg.

Nanoparticle characteristics affecting environmental fate and transport through soil.

Environmental Toxicology and Chemistry, 2009. 28(6): 1191-1199.

120. Tosco, T., J. Bosch, R.U. Meckenstock, and R. Sethi. Transport of ferrihydrite

nanoparticles in saturated porous media: Role of ionic strength and flow rate.


121. Tiede, K., M. Hassellov, E. Breitbarth, Q. Chaudhry, and A.B.A. Boxall. Considerations

for environmental fate and ecotoxicity testing to support environmental risk assessments

for engineered nanoparticles. Journal of Chromatography A, 2009. 1216(3): 503-509.

122. Happel, J. Viscous flow in multiparticle systems - slow motion of fluids relative to beds

of spherical particles. Aiche Journal, 1958. 4(2): 197-201.

123. Rajagopalan, R. and C. Tien. Trajectory analysis of deep-bed filtration with sphere-in-

cell porous-media model. Aiche Journal, 1976. 22(3): 523-533.

124. Logan, B.E., D.G. Jewett, R.G. Arnold, E.J. Bouwer, and C.R. Omelia. Clarification of

clean-bed filtration models. Journal of Environmental Engineering-Asce, 1995. 121(12):

869-873.

125. Bradford, S.A., J. Simunek, M. Bettahar, M.T. Van Genuchten, and S.R. Yates. Modeling

colloid attachment, straining, and exclusion in saturated porous media. Environmental


126. Cameron, D.R. and A. Klute. Convective-dispersive solute transport with a combined

equilibrium and kinetic adsorption model. Water Resources Research, 1977. 13(1): 183-

188.

127. Benn, T.M. and P. Westerhoff. Nanoparticle silver released into water from commercially

available sock fabrics. Environmental Science & Technology, 2008. 42(11): 4133-4139.

128. Kiser, M.A., P. Westerhoff, T. Benn, Y. Wang, J. PeÌ•rez-Rivera, and K. Hristovski.

Titanium nanomaterial removal and release from wastewater treatment plants.


159

129. Ozsoy, G., F.B. Dilek, and F.D. Sanin. An investigation of agricultural use potential of

wastewater sludges in Turkey - case of heavy metals. Water Science and Technology,

2006. 54(5): 155-161.

130. APHA. Standard methods for the examination of water and wastewater. 20th ed. 1998,

Washington, DC: American Public Health Association.

131. Morones, J.R., J.L. Elechiguerra, A. Camacho, K. Holt, J.B. Kouri, J.T. Ramirez, and

M.J. Yacaman. The bactericidal effect of silver nanoparticles. Nanotechnology, 2005.

16(10): 2346-2353.

FUNDAMENTALS AND APPLICATION OF POROUS MEDIA...

Documents

Transcript of FUNDAMENTALS AND APPLICATION OF POROUS MEDIA...