Scott dissertation 2015

To the University of Wyoming:

The members of the Committee approve the dissertation of Brandon L. Scott presented on May

13, 2015.

Dr. Keith T. Carron, Chairman

Dr. David T. Anderson

Dr. Jing Zhou

Dr. Franco Basile

Dr. James L. Caldwell

APPROVED:

Dr. Keith Carron, Department Chair, Chemistry

Dr. Paula M. Lutz, Dean, College of Arts and Sciences

Scott, Brandon L., Dynamic Signal Processing for the Characterization of SERS-Active

Nanoparticles, Ph.D., Department of Chemistry, August 2015

Abstract

Since its discovery in the 1970’s, Surface-Enhanced Raman Scattering (SERS) has aided

the development of analytical methods for a wide variety of applications. Raman scattering

enhancements of up to 7 orders of magnitude permit trace detection and identification of

analytes. Furthermore, the ease of use, affordability, and portability of modern Raman

instrumentation makes it a viable candidate for analytical chemistry.

We developed a new direct and indirect SERS assay with buoyant silica microspheres,

termed Lab-on-a-Bubble. Direct assays involve coating silica bubbles with nanoparticles and

indirect assays pair bubbles with Raman reporters in a sandwich assay. These assays have the

unique advantage of buoyancy-driven detection and selection of analytes in solution. To evaluate

these assays we looked at cyanide and 5,5’-dithiobis(2-nitrobenzoic acid) (direct) and cholera

(indirect).

The second part of this dissertation relates to particle aggregation. This work follows a

report from Wustholz et al. that suggested SERS enhancement occurs near gap regions in

nanoparticle aggregates, termed hotspots. Aggregates are difficult to study due to their small

size. They can be probed in vacuum by electron microscopy but they cannot be observed directly

with light microscopy in solution. We developed a statistical method for specific extraction of

SERS signals from colloidal SERS active nanoparticles, termed dynamic SERS (DSERS). Our

first study examined a strongly coordinating monolayer, 4-mercaptopyridine, which exhibits

1

unique SERS spectra in acid and base but invariant DSERS spectra. Our interpretation was that

DSERS results showed only molecules in the gap region between nanoparticles.

Continued work examined a non-coordinating (thiophenol) and a weakly coordinating (4-

mercaptophenol) monolayer and their role in aggregation of NPs. Thiophenol was observed to

not produce unique DSERS spectra as a function of pH. In contrast to 4-mercaptopyridine, we

found that 4-mercaptophenol produced different DSERS spectra as a function of pH. We also

developed additional statistical methods to complement DSERS results: correlograms and

frequency shift histograms.

In addition to these studies we began looking at viologen-functionalized SERS substrates

for the detection of polycyclic aromatic hydrocarbons and chiral molecules. While this work is

very preliminary we observed differences in SERS spectra of (DL)-, (D)- and (L)-cysteine

adsorbed to silver nanoparticles coated with chiral viologen. We also observed adsorption of

polycyclic aromatic hydrocarbons on these substrates.

2

DYNAMIC SIGNAL PROCESSING FOR THE CHARACTERIZATION OF SERS-

ACTIVE NANOPARTICLES

by

Brandon Scott

A dissertation submitted to the Department of Chemistry and the Graduate School of the University of Wyoming in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

in

CHEMISTRY

Laramie, Wyoming

August 2015

Acknowledgements

I would like to thank my graduate advisor, Dr. Keith Carron, for his support and encouragement

throughout my undergraduate and graduate career. Special thanks to research collaborators Dr.

Richard Martoglio, Dr. Virginia Schmit, Dr. Aaron Strickland, Dr. Ed Clennan, Xiaoping Zhang,

and Jacob Williams. Thanks to all of the teachers and professors that inspired me to pursue

scientific research, my friends and my family. I would not be where I am today without all of the

people in my life who believe in me.

ii

Table of Contents

Abstract

Acknowledgements

Table of Contents

1 Introduction............................................................................................................................1

1.1 Raman Spectroscopy.........................................................................................................3

1.2 Surface-Enhanced Raman Scattering (SERS)...................................................................4

1.3 Salt Enhancement of SERS...............................................................................................5

1.4 Lab-on-a-Bubble...............................................................................................................7

1.5 Dynamic SERS...............................................................................................................12

1.6 References.......................................................................................................................14

2 Lab-on-a-Bubble (LoB): Synthesis, Characterization, and Evaluation of Buoyant Gold Nanoparticle-Coated Silica Spheres..........................................................................16

2.1 Introduction.....................................................................................................................16

2.2 Experimental Methods....................................................................................................18

2.2.1 Silanization of Glass Bubbles..................................................................................18

2.2.2 Preparing and Shelling Gold Nanoparticles (AuNPs).............................................18

2.2.3 Modification of Glass Bubbles with AuNPs...........................................................18

2.2.4 Concentration of AuNP-Coated Glass Bubbles.......................................................19

2.2.5 Instrumentation........................................................................................................19

2.2.6 UV-vis Spectroscopy...............................................................................................20

2.2.7 SERS of AuNPs Added to Aqueous Cyanide (CN-) Solutions...............................20

2.2.8 SERS of AuNP-Coated Glass Bubbles Added to Aqueous CN- Solutions.............21

2.2.9 SERS of Varying Amounts of AuNP-Coated Glass Bubbles Added to CN- Solutions of Constant Concentration.......................................................................21

2.3 Results and Discussion....................................................................................................22

2.4 References.......................................................................................................................30

3 Lab-on-a-Bubble Surface Enhanced Raman Indirect Immunoassay for Cholera.........32

3.1 Introduction.....................................................................................................................32

3.2 Materials and Methods....................................................................................................35

iii

3.2.1 LoB Activation and Antibody Attachment..............................................................35

3.2.2 Dynamic Light Scattering (DLS).............................................................................35

3.2.3 Raman Reporter Synthesis.......................................................................................36

3.2.4 Preparing and Shelling AuNPs................................................................................40

3.2.5 LoB Immunoassay...................................................................................................41

3.2.6 Data Acquisition and Analysis................................................................................41

3.3 Results.............................................................................................................................42

3.4 Acknowledgements.........................................................................................................48

3.5 References.......................................................................................................................48

4 Dynamic SERS: Extracting SERS from Normal Raman Scattering...............................51

4.1 Introduction.....................................................................................................................51


4.2.1 SERS Signal Extraction...........................................................................................52

4.2.2 Sites Selective Spectroscopy...................................................................................55

4.3 Conclusion......................................................................................................................60

4.4 Acknowledgements.........................................................................................................61

4.5 References.......................................................................................................................61

5 Statistical Analysis of 4-Mercaptophenol and Thiophenol on Gold Nanoparticles.......63

5.1 Introduction.....................................................................................................................63

5.2 Materials..........................................................................................................................67

5.3 Experimental...................................................................................................................68

5.4 Instrumentation...............................................................................................................68

5.5 Data Analysis..................................................................................................................68

5.6 Raman Modes.................................................................................................................70

5.7 Results.............................................................................................................................71

5.7.1 4-Mercaptophenol Analysis.....................................................................................71

5.7.2 Thiophenol Analysis................................................................................................78

5.7.3 4-Mercaptopyridine Analysis..................................................................................82

5.8 Summary.........................................................................................................................84

5.9 Conclusion......................................................................................................................85

5.10 References.......................................................................................................................86

iv

6 Clennan Group Collaboration: Viologen-Functionalized SERS Substrates for the Detection of Polycyclic Aromatic Hydrocarbons and Chiral Molecules.........................88

6.1 Introduction.....................................................................................................................88

6.2 Silver Nanoparticle (AgNP) Synthesis...........................................................................88

6.3 Instrumentation...............................................................................................................89

6.4 Experimental...................................................................................................................89


6.6 References.......................................................................................................................99

v

1 Introduction

Since its discovery, analytical assays based on surface-enhanced Raman scattering

(SERS) have developed using wide variety of methods for several applications. Our research

group continued the development of SERS assays by implementing buoyant bubbles with unique

advantages and with dynamic Raman scattering (DRS) to detect very low concentrations of

SERS particles.

The first goal was to optimize SERS enhancement by inducing hotspots via addition of

electrolytes to SERS substrates. Although these solutions showed significant SERS

enhancement, the stability of the SERS substrate was compromised due to rapid aggregation.

This led us to examine methods of improving the stability of colloidal SERS substrates.

Ultimately this gave rise to two novel SERS detection methods. Both methods utilized buoyant

silica microspheres which float to the surface of a solution. The first method involves adsorbing

gold colloids to the microsphere surface, effectively controlling aggregation effects while

maintaining SERS activity. The second method involves coupling SERS-active colloids coated

in silica (Raman reporters) to buoyant silica microspheres via antigen-antibody binding. The

novelty in these two methods came from pairing SERS substrates to the buoyant silica

microspheres to effectively concentrate the SERS-analyte complex to the surface of the aqueous

solution. The term Lab on a Bubble (LoB) was coined to describe this technique and is described

in detail in chapters 2 and 3.

However, both of these techniques affect SERS hotspot phenomena by permanently

fixing colloids to a surface or within a silica shell. Several research groups showed that SERS

hotspots occur in colloidal solutions between coalesced nanoparticles, albeit at very low

1

concentrations relative to nanoparticle monomers. This led to our attempts to develop a new data

analysis technique to distinguish between normal and hotspot-enhanced SERS signaling within a

colloidal solution. We began by monitoring signal fluctuations between multiple spectra of dilute

colloidal SERS substrates collected in rapid succession. Fluctuations in the SERS signal were

found to be inversely proportional to nanoparticle concentration; a result of noise created by

fewer particles passing through the detection beam by Brownian motion. The term Dynamic

SERS (DSERS) was coined to describe this technique. Analytes with pH-dependent SERS

substrate binding sites were examined to induce hotspot formation via chemical bonding and

DSERS results were compared. DSERS provided a tool to investigate shifts in vibrational modes

and anomalous SERS signals due to hotspots that are otherwise lost in conventional SERS

analysis.

We are including a brief collaboration with Dr. Clennan’s group to implement a chiral

viologen they synthesized into achiral SERS assays. Similar research demonstrated the ability of

viologen-functionalized SERS substrates to detect polycyclic aromatic compounds (PAHs) that

are otherwise undetectable by conventional SERS methods and we generated similar results. Our

preliminary results look promising but this topic of research will require further investigation.

Raman spectroscopy and surface-enhanced Raman scattering are the backbone for

modern SERS assays. We finish the chapter by introducing signal enhancement techniques and a

novel SERS detection application. We describe what they are, why they are important to the

technique, and their potential for further advancement. The next three chapters describe

completed work submitted for publication; lab-on-a bubble assays and dynamic SERS,

respectively. The final two chapters describe work to be submitted for publication.

2

1.1 Raman Spectroscopy

Raman spectroscopy is a technique to measure the rotational and vibrational modes of

molecules. Unlike infrared spectroscopy, which involves a change in the dipole moment of a

molecule excited from the ground vibrational state to an excited state, Raman spectroscopy

involves an induced dipole moment that leads to the scattering of light from a vibrational state.

The resulting scattered photon can either have a frequency less than or greater than the incident

light frequency, known as Stokes or anti-Stokes scattering, respectively, as shown in Figure 1.1.

For both cases this inelastically scattered light (Raman scattering) can be separated from the

dominant elastically scattered light (Rayleigh scattering) by dispersion from the spectrum before

a detector. The amount of deformation of the electron cloud of a molecule with respect to the

vibrational coordinate determines the strength of the Raman effect1. Raman spectroscopy and IR

spectroscopy produce similar, but sometimes complementary, results.

3

Excited electronic state

Virtual states

Ground electronic states

v = 0

v = 1

v = 2

Vibrational levels

Infrared Absorption

Stokes

Rayleigh Anti-Stokes

Figure 1.1: Electronic state diagram showing Stokes, anti-Stokes, and Rayleigh scattering events for a molecule interacting with light of suitable frequency.

1.2 Surface-Enhanced Raman Scattering (SERS)

Surface-enhanced Raman scattering (SERS) enhances Raman scattering from molecules

adsorbed to a rough metal surface by up to seven orders of magnitude. This gives it the potential

to be a sensitive tool for analytical chemistry. The phenomenon was first observed by

Fleischmann, et al.2 in 1974 and explained by Jeanmaire and Van Duyne3 three years later. There

are two proposed theories for describing the SERS effect: electromagnetism and the formation of

charge-transfer complexes. The electromagnetic theory attributes the light-induced interaction

between adsorbed molecules and the localized surface plasmon resonance of certain metals, such

as gold and silver, to explain the large enhancement factor of SERS. When metal nanoparticles

are much smaller than the wavelength of incident light, the individual atoms undergo concerted

dipolar electric field oscillations to produce the LSPR phenomenon. This phenomenon can be

explained by the solution for the response of a dielectric sphere in a uniform electric field4

E¿=3 ε (ω )

ε (ω )+2E0

Where Ein is the electric field near the particle, ε(ω) is the dielectric function of the particle

material, and E0 is the electric field of the light incident on the sphere. In the case of free electron

metals, such as copper, silver and gold, the dielectric function has a negative real and small

imaginary component, which correspond to the storage and dissipation of energy within the

medium, respectively. As ε(ω) approaches -2 a resonance occurs and the electric field inside the

particle increases dramatically.

4

These dipolar plasmon oscillations produce an enhancement of the electric field of both the

incident light as well as the scattered Raman light, to produce a combined E4 signal

enhancement. Gold and silver nanoparticles are typically used for SERS since their plasmon

resonance frequencies lie within the visible and near-infrared region.

The electromagnetic theory can be used to explain most of the SERS enhancement of any

species of molecule either chemisorbed or physisorbed to a metal surface. However the charge-

transfer complex theory, or chemical theory, can be used to explain SERS enhancement larger

than the E4 predicted by the electromagnetic theory. Molecules containing lone electron pairs are

capable of forming chemical bonds with the metal surface that may lead to a charge-transfer

(CT) complex. The CT complex may have absorption in the visible region that lead to resonance

Raman.

Recently, SERS signal enhancement due to hotspots has been investigated by several

research groups. By combining experimental and modeling experiments Van Duyne’s research

group determined that hotspots are located near interparticle gap regions where two particles are

in subnanometer proximity or have coalesced to form crevices. SERS signal enhancements of 108

were determined for aggregated nanoparticles containing hotspots.

1.3 Salt Enhancement of SERS

SERS enhancement of analytes adsorbed to gold nanoparticles may be further increased

by the addition of a weak electrolyte solution to the sample matrix (Figure 1.2). This

phenomenon was examined with NaCl, NaF, KBr, NaI and NaBr by adding 250 uL of varying

concentrations of each salt solution to a mixture of 250 uL gold colloids with Nile Blue as our

probe. Salt concentrations 500mM, 250mM, 125mM, 62.5 mM, and 31.3 mM with a constant

5

concentration of Nile Blue-adsorbed nanoparticles in every sample were analyzed. Spectra were

collected for each sample and the Nile Blue peak heights at 589 cm-1 were plotted as a function

of salt concentration. Figure 1.3 indicated a signal increase at low salt concentrations with 62.5

mM NaCl producing the largest enhancement and a reduction of signal enhancement at higher

concentrations. The sample with the highest salt concentration had a weaker signal than the

sample containing no salt indicating degradation of the SERS-active complexes most likely

results from particle aggregation within the sample matrix. Results for NaF, KBr, NaI, and NaBr

experiments showed similar behavior.

Figure 1.2: Raman spectra of colloids in 1.6 µM nile blue (red); colloids in 1.6 µM nile blue and 31.3 mM NaCl (blue).

6

400 450 500 550 600 650 700 400 450 500 550 600 650 700

Inte

nsi

ty

Wavenumber

0 100 200 300 400 500 60012000

13000

14000

15000

16000

17000

18000

NaCl titration of 1 µM NB colloids

[NaCl] (mM)

peak

hei

ght

Figure 1.3: Nile blue peak height at 589 cm-1 vs. NaCl concentration (mM)

While it is clear that the addition of a weak electrolyte solution to a SERS-active

substrate can be used to optimize SERS enhancement controlling this phenomenon is not

reported. Two possible explanations for salt SERS enhancement are an increase in the LSPR due

to Van Der Waals forces from the salt ions or the promotion of hotspot-containing nanoparticle

multimers from the electrolyte-induced reduction in the repelling force of negatively charged

gold nanoparticles. However, it is clear that the stability of the colloidal solution is compromised

by the addition of electrolytes ultimately leading to particle aggregation and precipitation of the

SERS substrate.

1.4 Lab-on-a-Bubble

The use and effectiveness of SERS assays have been demonstrated in a wide variety of

applications5. Common techniques involve either direct detection of analyte-bound nanoparticles

suspended in a colloidal solution or indirect detection of analytes bound to Raman-active

7

nanoparticles via ligand binding interactions. Although somewhat effective, both of these

techniques have major drawbacks. Direct SERS assays often have large limit of detection (LOD)

values due to a small amount of analyte present in a large sampling volume and poor binding

affinity of some analytes to gold nanoparticles. The second problem can be overcome by

implementing indirect techniques if the analyte of interest binds to the modified nanoparticle but

this technique also has its shortfalls, including detection of false positives.

Our work on SERS immunoassays yielded interesting results by sandwiching analytes

between Raman-active nanoparticles and paramagnetic microparticles via antigen-antibody

interactions and concentrating the analyte-bound complex within the sample by introducing a

magnetic field6. Although this method improves the LOD and reduces the detection of false

positives it too has a problem. The attractive magnetic force between a permanent magnet and a

paramagnetic particle decreases exponentially with increasing distance. This requires using

powerful magnets and small sample vials to conduct such paramagnetic pull-down sandwich

immunoassays.

Our research group proposed a solution for both direct and indirect methods by

implementing buoyant silica microspheres into the assays7. The resulting assay is referred to as

Lab-on-a-bubble, or LoB. Figure 1.4 illustrates the concept of a direct LoB assay along with

representative scanning electron micrographs and Raman data acquired from LoB reagents. In a

typical LoB assay, LoB reagents comprised of buoyant SiO2 bubbles and Au or Ag nanoparticles

(NPs) are combined to provide a SERS active particle platform (Figure 1.4A-B) for the

detection of target analytes by localizing them close to the bubble-NP composite (Figure 1.4B-

C). Bubble flotation drives the complex to a specified point in a reaction vessel where the

analyte is selectively detected as a concentrated LoB complex as illustrated in Figure 1.4C. For

8

the current study AuNP-coated LoBs were prepared by activating buoyant silica bubbles (3M

Corporation) with aminopropyltriethoxysilane (APTES) following a standard protocol for glass

coating (Figure 1.4A ,B).8,9 Colloidal gold was incubated with the activated bubbles to adsorb

AuNP aggregates onto the bubble surface (Figure 1.4B, D); aggregates of gold and silver

nanoparticles are known to exhibit strong enhancements in the Raman signal of adsorbed

analytes.10,11 Figure 1.4E shows spectra resulting from AuNP-coated LoBs in the presence

(black spectrum) and absence (red spectrum) of 5 μM 5,5’-dithiobis(2-nitrobenzoic acid)

(DTNB). These spectra were collected by combining SERS active LoBs with DTNB analyte,

allowing the buoyant LoBs to float to the top of a vessel, and collecting the Raman data using an

808 nm Sierra Raman spectrometer (SnRI LLC). Figure 1.4C and the inset in Figure 1.4E

demonstrate a detection scheme for the LoB assay. AuNP coated LoBs were optimized for

SERS activity by starting with a known bubble quantity and saturating the bubble surface using a

progressively larger volumes of colloidal AuNP.

9

Figure 1.4: A-C) The basic components of a Lab on a Bubble (LoB) assay for SERS-based detection of a analyte. (D) Representative scanning electron micrographs of SERS-active AuNP-coated LoBs. (E) Representative Raman spectra of ‘naked’ LoBs, and LoBs in the presence of DTNB. The inset shows picture of SERS-active buoyant LoBs in a microcentrifuge tube.

LoB materials serve as a convenient platform for the detection of analytes in solution and

offer several advantages over traditional colloidal gold and planar SERS substrates. Chapter 3

describes a LoB-based cyanide assay. Cyanide bound to gold-coated LoBs was detected directly

from the corresponding SERS signal. Detection of cyanide in gold colloid is comparable to that

in the presence of LoBs, with a detection limit of ~170 part-per-trillion determined for both

10

cases. Prevention of aggregation common to colloidal nanoparticles is also discussed in relation

to an assay for 5 μM 5,5’-dithiobis(2-nitrobenzoic acid) (DTNB). However, the sensitivity of this

technique still depends on the binding equilibrium between the analyte and LoBs, which limits

the improvement of detection to tightly binding analytes. To overcome this obstacle, our research

group has developed an additional analytical method, known as dynamic SERS, which is

detailed in the following section.

An improved SERS sandwich assay was developed using buoyant silica microspheres,

described above, coated with antibodies against the B subunit of the cholera toxin (CT), and gold

nanoparticles tagged with a Raman reporter, shelled with silica and coated with antibodies

against the B subunit of CT12. Together these components couple to form a sandwich which, after

incubation, floats on the surface of the sample. The buoyant silica microparticle / nanoparticle

reporter combination has been coined a Lab on a Bubble (LoB). LoB materials may provide a

platform for rapid detection of antigen in solution and offers advantages over lateral flow or

magnetic pull-down assays. The Raman reporter provides a unique and intense signal to indicate

a positive analysis. Our limit of detection for the beta subunit of the CT in a buffer based system

is 1100 ng.

11

Figure 1.5: Comparison of LoB sandwich assay (A) and paramagnetic pull-down assay (B)

1.5 Dynamic SERS

Although SERs-based assays have proven to be effective analytical tools, there is still

speculation as to what actually causes enhancement and detection within a sample solution. It is

widely accepted that an analyte binds to gold nanoparticles as predicted by an isotherm model

with a monolayer leading to the greatest signal enhancement. However, inconsistencies of SERS

enhancement between different nanoparticle species within a colloidal solution have been

demonstrated. Such inconsistencies often arise between single nanoparticles and clusters of

nanoparticles. Other researchers demonstrated that clusters of two or more nanoparticles lead to

the largest amount of SERS enhancement due to the presence of so-called “hotspots”13. Hotspots

are regions where two nanoparticles are in close proximity with one another. The Van Duyne

group investigated nanoparticle hotspot regions using a combination of SEM and LSPR

spectroscopy on adjoined nanoparticle pillars14. Other research groups developed high hotspot-

yielding nanoparticle complexes either by novel synthesis or filtration methods.

12

Particle Pairs Float to Surface

Particle Pairs Pulled by Magnet

LDN Assay

Paramagnetic Pull-down

Assay

A B

Our research group developed a much simpler approach to detecting hotspot-containing

nanoparticle complexes involving the time-dependent data analysis of multiple SERS spectra15.

Similarly, time correlation of fluorescence spectroscopy was shown to distinguish instantaneous

light scattering events and delayed fluorescence signals. We demonstrated that the standard

deviation of SERS signal intensity increases as the concentration of nanoparticles in a sample

solution decreases7 due to individual nanoparticles passing through the Raman laser beam as

dictated by Brownian motion within the sample medium. By taking a large number of SERS

spectra in a short amount of time and subtracting the average spectrum from the normalized

standard deviation spectrum we generated unprecedented solvent noise reduction. The result is a

spectrum containing the signal produced specifically by the nanoparticle complexes within the

sample. Furthermore, correlating the data set at specific spectral peaks revealed the presence and

movement of individual nanoparticle-analyte complexes of varying SERS enhancement.

Figure 1.6: (Left) Illustration of an analyte-adsorbed AuNP dimer with a hotspot. (Middle) comparison of SERS spectrum vs. DSERS-corrected spectrum. (Right) Stochastic motion of AuNP complexes within the detection beam.

13

Stochastic Nanoparticle Motion

Analyte monolayer

HotspotS

σWavenumbers

SERS

DSERS

Our first example of shelled nanoparticles at very low concentrations, explained in

further detail in chapter 4, confirmed the benefit of DSERS for removal of an overwhelmingly

strong solvent spectral interference. The second benefit, site selection, was demonstrated with 4-

mercaptopyridine on bare Au nanoparticles to observe a small population of molecules that were

spectroscopically unique from the large population of molecules on the particles. The DSERS

spectrum originated from excess variance between a small population of adsorbates on the

ensemble of nanoparticles. We demonstrated two significant benefits of dynamic SERS

(DSERS) measurements: removal of instrumental and normal Raman interferences in SERS

spectroscopy; and site selective spectroscopy of adsorbate populations on SERS active particles.

1.6 References

1. Raman, C. V.; Krishnan, K. S., A New Type of Secondary Radiation. Nature 1928, 121, 501-502.

2. Fleischmann, M.; Hendra, P. J.; McQuilla.Aj, Raman-Spectra of Pyridine Adsorbed at a Silver Electrode. Chem. Phys. Lett. 1974, 26 (2), 163-166.

3. Jeanmaire, D. L.; Van Duyne, R. P., Surface Raman Spectroelectrochemistry. Part 1. Heterocyclic, Aromatic, and Aliphatic-Amines Adsorbed on the Anodized Silver Electrode. J. Electroanal. Chem. 1977, 84 (1), 1-20.

4. Van de Hulst, H. C., Light Scattering by Small Particles. Dover Publications, Inc.: New York, 1981; p 71.

5. Driscoll, A. J.; Harpster, M. H.; Johnson, P. A., The Development of Surface-Enhanced Raman Scattering as a Detection Modality for Portable In Vitro Diagnostics: Progress and Challenges. Physical chemistry chemical physics : PCCP 2013, 15 (47), 20415-33.

6. Lu, Y.; Yin, Y. D.; Mayers, B. T.; Xia, Y. N., Modifying the Surface Properties of Superparamagnetic Iron Oxide Nanoparticles through a Sol-Gel Approach. Nano Lett. 2002, 2 (3), 183-186.

7. Schmit, V. L.; Martoglio, R.; Scott, B.; Strickland, A. D.; Carron, K. T., Lab-on-a-Bubble: Synthesis, Characterization, and Evaluation of Buoyant Gold Nanoparticle-Coated Silica Spheres. J. Am. Chem. Soc. 2012, 134 (1), 59-62.

14

8. Freeman, R. G.; Grabar, K. C.; Allison, K. J.; Bright, R. M.; Davis, J. A.; Guthrie, A. P.; Hommer, M. B.; Jackson, M. A.; Smith, P. C.; Walter, D. G.; Natan, M. J., Self-Assembled Metal Colloid Monolayers: An Approach to SERS Substrates. Science 1995, 267, 1629-1632.

9. Karrasch, S.; Dolder, M.; Schabert, F.; Ramsden, J.; Engel, A., Covalent Binding of Biological Samples to Solid Supports for Scanning Probe Microscopy in Buffer Solution. Biophys. J. 1993, 65 (6), 2437-2446.

10. Pierre, M. C. S.; Mackie, P. M.; Roca, M.; Haes, A. J., Correlating Molecular Surface Coverage and Solution-Phase Nanoparticle Concentration to Surface-Enhanced Raman Scattering Intensities. J. Phys. Chem. C 2011, 115 (38), 18511-18517.

11. Wang, H.; Levin, C. S.; Halas, N. J., Nanosphere Arrays with Controlled Sub-10-Nm Gaps as Surface-Enhanced Raman Spectroscopy Substrates. J. Am. Chem. Soc. 2005, 127 (43), 14992-14993.

12. Schmit, V. L.; Martoglio, R.; Carron, K. T., Lab-on-a-Bubble Surface Enhanced Raman Indirect Immunoassay for Cholera. Anal. Chem. 2012, 84 (9), 4233-4236.

13. Chen, G.; Wang, Y.; Yang, M. X.; Xu, J.; Goh, S. J.; Pan, M.; Chen, H. Y., Measuring Ensemble-Averaged Surface-Enhanced Raman Scattering in the Hotspots of Colloidal Nanoparticle Dimers and Trimers. J. Am. Chem. Soc. 2010, 132 (11), 3644-+.

14. Wustholz, K. L.; Henry, A. I.; McMahon, J. M.; Freeman, R. G.; Valley, N.; Piotti, M. E.; Natan, M. J.; Schatz, G. C.; Van Duyne, R. P., Structure-Activity Relationships in Gold Nanoparticle Dimers and Trimers for Surface-Enhanced Raman Spectroscopy. J. Am. Chem. Soc. 2010, 132 (31), 10903-10910.

15. Scott, B. L.; Carron, K. T., Dynamic Surface Enhanced Raman Spectroscopy (SERS): Extracting SERS from Normal Raman Scattering. Anal. Chem. 2012, 84 (20), 8448-51.

15

2 Lab-on-a-Bubble (LoB): Synthesis, Characterization, and Evaluation of Buoyant

Gold Nanoparticle-Coated Silica Spheres1

2.1 Introduction

Micro and Nano – Electro – Mechanical systems MEMS and NEMS have made

significant impacts on chemical sensors. For example, the technology behind Lab-on-a-Chip

(LOC) has emerged into a large market defining Point-of-Care (POC) diagnostics2. These novel

systems represent combinations of miniaturized chemical separation methods and a variety of

detection schemes. The drive to miniaturized instrumentation and straightforward single-step

assays has brought about the growth of these research efforts. One example, of a nano-powered

engine is separations that use nanoparticulate paramagnetic materials to couple to analytes. The

paramagnetic engines are powered by external magnets that concentrate the assay results into a

small localized volume for more sensitive analysis. This scheme works well in small sample

volumes and with sufficient time for exponentially decaying magnetic fields to impel the

majority of the particles. In this article we will present a different method of nanopropulsion –

buoyancy from a hollow silica ‘bubble’ to produce a Lab on a Bubble (LoB).

Our initial work with paramagnetic nanoparticles was driven by a fundamental limitation

to Surface Enhanced Raman Scattering (SERS) analysis with colloidal nanoparticles. This

limitation originates with dispersive Raman instruments and the property of étendue. Succinctly,

étendue describes the inverse relationship between spectral resolution and a spectrometer’s

optical throughput. When sampling a nanoparticle solution étendue coupled with a reasonable

spectral resolution requires a focused beam from the excitation laser. Likewise, the colloidal

nature of nanoparticles in a solution requires that they be continually propelled by Brownian

16

motion and thus individual particles are moving into and out of the focused laser beam. It is

often desirable to use a small quantity of nanoparticles to maximize the surface coverage of a

strongly adsorbing analyte; this leads to fluctuations in the SERS signal due to the Brownian

motion induced fluctuation of particles within the focal volume. A chemical analysis for analyte

concentration will be limited by these fluctuations. It is desirable to have the noise in an

experiment be limited by shot noise of the detector, but as we will report, the noise in our

colloidal nanoparticle experiments far exceeds the detector’s shot noise.

SERS active nanoparticles provide valuable information about species in aqueous media.

However their widespread use is limited by their instability. Recently, Pierre et al.3 have shown

the affect of nanoparticle instability on Au nanoparticle (AuNP) assays. They demonstrated the

loss of signal due to changes in AuNP surface as a result of adsorption of a neutral thiol species.

Aggregation is also caused by changes in pH, ionic strength, and mixing parameters. The

limitations of signal noise in excess of the detection system and the instability of nanoparticles

under adsorptive processes is a critical problem for viable SERS diagnostics.

In this study we report results from a different approach to solution phase analysis with

SERS active nanoparticles that combines the separation mechanism directly coupled to the

detection method. This LoB concept is centered on a low density particle that utilizes a buoyant

force to drive assay separation, while Au nanoparticles (AuNP) coupled to the buoyant particles

act as SERS nanosensors. Addition of a selective coating on the AuNP creates the potential for

smart sensors. In the current study we report the detection of a generic thiol containing Raman

active small molecule, and cyanide which is a relevant model analyte in environmental testing.

17

2.2 Experimental Methods

2.2.1 Silanization of Glass Bubbles

0.3g of S60/10000 3M Glass bubbles (average diameter 30 μm, density 0.6 g/mL) were

added to 10N H2SO4 overnight to activate the glass surface. 4 Silanization of the activated glass

bubbles was achieved via exposure to a 10% solution (v/v) of 3-aminopropyltriethoxysilane in

methanol overnight with constant rocking. The glass bubbles were subsequently washed 6 times

with methanol and re-suspended in 3 mL HPLC grade H2O for future use. 5

2.2.2 Preparing and Shelling Gold Nanoparticles (AuNPs)

AuNPs were prepared by the Frens method.6 200mL of HPLC grade H2O was added to a

beaker and warmed on a hot plate. Once the water was warmed to approximately 30˚C, 20 mg of

HAuCl4 was added to the solution and brought to a rolling boil. 1200 μL of 1% (wt/vol)

Na3C6H5O7 was then added all at once. The solution boiled for one hour with a watch glass

placed over the beaker. The solution was then removed from the heat and allowed to cool to

room temperature prior to storage. This method of synthesis produced AuNPs with an average

diameter of approximately 50 nm as determined by SEM. The concentration of the AuNP

solution was 6.0 x 1010 AuNPs/mL by a method similar to that of Haiss et. al.7

2.2.3 Modification of Glass Bubbles with AuNPs

Immediately following sufficient agitation of the silane-treated glass bubble solution, 200

μL was added to a 1.75 mL Eppendorf tube. The glass bubbles were allowed to float to the

surface and the supernatant was removed with a 1 mL syringe and 26-gauge needle. The glass

bubbles were rinsed at least 5 times with 200 μL of 50% (v/v) MeOH solution in water to

18

remove excess APTES: For each wash, 200 μL of the MeOH solution was added and the sample

was agitated at room temperature for ca. 2 minutes. 1 mL of HPLC H2O was then added to the

glass bubble MeOH solution to facilitate floatation of the glass bubbles. The supernatant was

carefully removed and the rinse procedure was performed at least 4 more times with the

supernatant being completely removed on the final rinse. Next, 200 μL of Au nanoparticles

(AuNPs) were added to these rinsed glass bubbles. The mixture was agitated at room

temperature until the solution became almost clear. The glass bubbles were allowed to float to

the top of the solution and the supernatant was removed. AuNPs were added in 200 μL volumes

and agitated until the solution remained purple. The resulting Au coated glass bubbles were re-

suspended in 500 μL of HPLC grade H2O.

2.2.4 Concentration of AuNP-Coated Glass Bubbles

10 μL of the Au-coated glass bubble solution was added to a microscope slide and

allowed to dry. An Olympus BX51 microscope was used to determine the counting area of the

bubble solution and the bubbles in this area were enumerated. Based on the total area of the

solution and the numbers of bubbles counted, we approximated the concentration of Au-coated

glass bubbles to be 1 x 105 bubbles/mL.

2.2.5 Instrumentation

All spectroscopic data was collected using a Snowy Range Instruments IM 52 808 nm laser

Raman system with rastering capability. The rastering addition maintains small laser spot size

while averaging over an elliptical area of ca. 2 mm x 0.5 mm.

19

2.2.6 UV-vis Spectroscopy

UV-vis spectra of aqueous gold nanoparticles can be used to determine the concentration

of the colloidal solution if the approximate nanoparticle diameter is known, as described by

Haiss et. al7. The size of nanoparticles affects how the colloidal solution scatters incident light.

Thus, the wavelength of maximum absorbance changes as a function of nanoparticle diameter.

The amount of relative absorbance at a given wavelength is a function of nanoparticle

concentration, as described by Beer’s law. Although TEM or SEM imaging can be used to

simultaneously determine nanoparticle size and concentration, this technique is much faster and

easier to implement. Gold nanoparticle solutions synthesized using the Frenz citrate method

described in section 2.1 have a concentration of about 0.1 nM.

2.2.7 SERS of AuNPs Added to Aqueous Cyanide (CN-) Solutions

30 μL of AuNPs (1.8 x 109 nanoparticles) were added to an equal volume of sodium

cyanide solution buffered at pH = 9 (4:1 (v/v) 0.1M NaHCO3:0.1M Na2CO3 buffer). Cyanide

solutions of varying concentrations (200 parts per million (ppm) to 2 parts per billion (ppb))

were titrated while maintaining constant volumes from sample to sample. Upon addition of

AuNPs to the CN- solutions, each sample was incubated for 5 minutes with gentle agitation at

room temperature. The entire volume was pipetted onto a steel substrate for interrogation with

the laser. Each spectrum was acquired for 0.5 sec and the intensity was plotted against the

cyanide concentration. Each data point was replicated 5 times for the same integration time and

error bars on graph are +/- 1 standard deviation of all 5 replicates.

20

2.2.8 SERS of AuNP-Coated Glass Bubbles Added to Aqueous CN- Solutions

10 μL of Au-coated glass bubble solution (1.5 x 106 Au-coated glass bubbles) was added

to 40 μL of sodium cyanide solution buffered at pH = 9 (4:1 0.1M NaHCO3:0.1M Na2CO3

buffer). Cyanide solutions of varying concentrations (200 parts per million (ppm) to 2 parts per

billion (ppb)) were titrated while maintaining constant volumes from sample to sample. Samples

were incubated for 5 minutes with gentle agitation at room temperature. The entire volume was

pipetted onto a steel substrate for interrogation with the laser. The Au-coated glass bubbles were

allowed to float to the top of each sample prior to analysis and they formed a small circular

island in the middle of each sample. Once this was observed, each spectrum was acquired for 0.1

sec and the intensity was plotted against the cyanide concentration. Each data point was

replicated 5 times for the same integration time and error bars on graph are +/- 1 standard

deviation of all 5 replicates.

2.2.9 SERS of Varying Amounts of AuNP-Coated Glass Bubbles Added to CN- Solutions

of Constant Concentration

In each trial, the CN- concentration was held at 1 ppm. The amounts of Au-coated glass

bubbles were varied, but the amount of solution containing the Au-coated glass bubbles was held

constant for each sample. Dilutions of the Au-coated glass bubbles were made as follows from

500 μL of the Au-coated glass bubble stock solution: 80 μL stock solution was added to 20 μL

H2O, 60 μL stock was added to 40 μL H2O, 40 μL stock was added to 60 μL H2O, and 20 μL

stock was added to 80 μL H2O. 10 μL of each dilution was added to 30 μL of 1ppm CN-

solution. 10uL of the undiluted stock solution was also added to 30 μL of 1 ppm CN- solution,

and 10 μL water was added to 30 μL of 1 ppm CN as a negative control. Samples were mixed

21

with gentle agitation for 3 minutes at room temperature. The entire volume was pipetted onto a

steel substrate for interrogation with the laser. The Au-coated glass bubbles were allowed to float

to the top of each sample prior to analysis and they formed a small circular island in the middle

of each sample. Each spectrum was acquired for 0.5 sec and the intensity was plotted against the

Au-coated glass bubble concentration. Each data point was replicated 5 times for the same

integration time and error bars on graph are +/- 1 standard deviation of all 5 replicates.

2.3 Results and Discussion

Figure 2.1 illustrates the dynamic properties of AuNP-coated LoBs as compared to

AuNPs in a solution. In Figure 2.1A we illustrate that as the number of nanoparticles in a

focused laser beam decreases the relative error of a measurement sharply increases due to

Brownian motion. Statistically this is expected to follow a Poisson distribution and to increase

according to 1/N1/2 as the number of nanoparticles (N) decrease. The data in Figure 2.1A was

collected with a shot-noise limited detector (Andor) cooled to -80°C (New Dimension Raman

Microscope (SnRI, LLC). SEM analysis of the particles indicated that the average size was

approximately 50 nm and UV-Vis indicated a stock concentration of 6.4 x 1010 AuNP/mL. Our

probe in this study was adsorbed cyanide from a sodium cyanide solution at 1 ppm and pH = 9.

With 16 AuNP in the focal volume of ~ 8 nL the variation in the signal is 24 times that predicted

by a shot-noise limited detection system.

22

Figure 2.1: A) The increase in noise as a function of colloidal AuNP concentration. B) The increase in noise as a function of LoB concentration. The noise is determined by the relative standard deviation from 10 measurements. In both measurements a focus beam was used to collect the data.

A goal in chemical analysis is to reduce the variation in signals such that the limit of

detection (LOD) will decrease. The LOD is defined as: LOD = 3σ/m, where σ is the standard

deviation and m is the slope. Figure 2.1B shows our results with LoB particles. Figure 2.1B

demonstrates the large difference in σ for the static LoBs as compared to colloidal AuNPs; where

σ(LoB) is 0.05 for 1 LoB particle compared to 1.0 for 16 AuNPs in the beam.

We also performed an experimental determination of the isotherm for cyanide adsorption

for on AuNPs and AuNP coated LoBs. The isotherm for cyanide on AuNPs shown in Figure

2.2A exhibits a combination of Frumkin behavior associated with adsorption of charged species

23

at a charged surface, and loss of gold due to dissolution. Figure 2.2B shows the isotherm we

observed for cyanide on our AuNP coated LoBs. Both isotherms have a similar shape with

slightly different dependencies on the cyanide concentration.

Figure 2.2: Cyanide adsorption isotherms for colloidal AuNP (top) and LoB particles (bottom). The k values are calculated from the slope between the first and second data points. The LOD was detected from 3 σ/m.

We found the adsorption coefficient, k, to be quite different from the 0.16 ppb-1 reported

by Tessier, et al.8 Our values calculated from the slope at low concentrations for AuNPs and

LoBs are 0.0059 ppb-1 and 0.0051 ppb-1, respectively. The 30 smaller values for the cyanide

adsorption on our particles may be explained by their surface structure and the pH difference of 9

in our study and 10 in their study. The pKa is 9.5 for HCN and this favors a high pH to keep the

24

solution species as CN-. However, Tessier reported similar k values for both low and high pH

values since the adsorption process is for CN-. Additionally, the Au surface developed by

Tessier is a planar substrate with AuNP coated polystyrene spheres. While Tessier et al. did not

discuss other materials on their AuNPs we observed strongly bound citrate that did not change

intensity through our isotherm titrations.

The zeta potential of our nanoparticles created using the Frens6 protocol is approximately

-35 mV indicating strongly adsorbed citrate. The strong negative charge will repel CN- causing

k to be lower than that from a neutral surface. This may contribute to the smaller k values we

observed. The CN peak we observed is at the same location as reported elsewhere, 2125 cm-

1,8,9,10 and the citrate peaks we observed were also located at the same wavenumbers that other

groups had observed.11,12 Our spectra, shown in Figure 2.3, have citrate peaks at the same

locations noted by Siiman et al.,12 who also reported that the citrate is strongly adsorbed and did

not change in composition or intensity over pH ranges from 2.8 to 9.9. Clearly the saturation of

our surfaces does not represent 100% of the surface coated with cyanide, but rather, the fraction

that is not covered with citrate. Repulsion of CN- by our citrate coated AuNPs may be the best

explanation for the difference in our observed k values relative to the study by Tessier et al.

Tessier et al. reported LOD values of 210 parts-per-trillion (ppt) at high pH. Our values

are similar with 180 ppt for colloidal AuNPs and 173 ppt for LoBs. The sharp drop off of CN-

coverage at the < 100 ppb solution concentration level will dictate the LOD in terms of the slope.

However, Figure 2.1A demonstrates that the σ value increases exponentially for AuNPs. To

alleviate this problem we performed these experiments with a relatively high AuNP

concentration (1.8 x 109 AuNP/mL) and we used a Raman system with a large 1 mm raster area

25

(Sierra ORS, Snowy Range Instruments) to eliminate noise created by dynamic AuNP motion.

The isotherm in Figure 2.2B was collected with identical acquisition parameters and 1000 LoBs.

The cyanide system used in this study demonstrates LoB assays with a fairly weak

reversibly binding species. An examination of the theoretical intensities predicted for colloidal

AuNPs demonstrates a further advantage of the LoB assay. This can be seen from the following

derivation:

I = FΘN

where I (photons/sec) describes the SERS intensity from an analyte from an AuNP colloid with a

fractional analyte coverage of θ and N nanoparticles/mL. F is a factor which converts coverage

into Raman intensity. Assuming a Langmuir isotherm and solving this equation for I as a

function of the number of nanoparticles provides a model to better understand AuNP SERS

assays. Of particular interest are the cases when the analyte concentration c0 is low and the

adsorption coefficient, k, is large. In this case θ is no longer dictated by c0 as the amount of

material adsorbed onto the surface becomes a significant fraction of the total amount of analyte

in the solution. We solved for I as a function of c0 and produced an equation to calculate the

effect of analyte depletion by the AuNPs.

26

Figure 2.3: SERS spectra of cyanide and citrate on LoB and AuNPs. These spectra indicate that citrate is not being displaced by the adsorption of cyanide.

Figure 2.4 illustrates the interplay between k and θ as a function of the number of

particles present. As the concentration of nanoparticles decreases it can be seen that the

coverage increases and as k increases the coverage increases. Intuitively this result is not

surprising; but since θ increases with fewer colloidal AuNPs this result dramatically illustrates

the difficulty of colloidal AuNP assays. For example, the data in Figure 2.1A begins at 3.2 x 106

AuNP/mL and it already is showing significant fluctuations due to dynamic motion into and out

of the laser beam. This simple model predicts that a fundamental limitation occurs as noise

increases while surface coverage increases. Although this may not be observed in a system

examining fairly high concentrations, it will be the fundamental limit of a system examining

trace levels of materials.

27

Figure 2.4: An illustration of the theoretical coverage vs. k. The curves relate to the concentration of nanoparticles in a given sample.

To demonstrate the value of LoBs with a neutral adsorbate and a high k, we chose the

popular tag, 5,5’-dithiobis(2-nitrobenzoic acid) (DTNB). Grubisha, et al.13 reported femtomolar

detection of prostate-specific antigen with the succinimide derivative of DTNB. Specifically,

Grubisha used immobilized particles on a glass slide to avoid aggregation effects from AuNPs in

solution and their ultimate detection limit was detected hypothetically by looking at a ratio of the

22 micron laser beam spot and the 5 mm spot of immobilized AuNPs used in the study. Our

experiment with DTNB consists of a comparison of colloidal AuNPs and LoBs. Figure 2.5

illustrates the signal difference from LoBs and colloidal AuNPs under conditions with an

equivalent amount of AuNP in both analyses. In other words, this demonstrates the

concentration benefit of detecting a single LoB rather than colloidal nanoparticles in a small

beam volume. At 5 μM DTNB we observe a signal that is 28x larger on the LoB than the

colloidal AuNPs. We also do not observe citrate at this concentration as it is displaced from the

AuNP surface by the strongly binding DTNB. This difference can be easily understood from the

28

study by Pierre et al.3 using 2-naphthalene thiol (2-NT). In their study with 2-NT Pierre et al.

found that displacement of the citrate by the strong thiol adsorption led to a time-dependent

signal due to aggregation. The LoB has a stable aggregated surface of AuNPs and through

agitation has the ability to interrogate the solution for DTNB. The colloidal AuNPs are stable

when citrate is strongly adsorbed, but rapidly aggregate and fall out of solution as DTNB is

adsorbed and AuNP surface charge is neutralized.

Figure 2.5: Representative SERS spectra of DTNB at equal concentration on a mass equivalent amount of 50 nm AuNPs. The LoB bound AuNPs do not aggregate and fall out of solution. The colloidal AuNP particles do aggregate and their signal is lost.

The number of LoBs observed in our DTNB experiment is 1. Our 25 μm laser beam is

smaller than a single LoB. We used 200 LoBs on our experiment and made two observations:

we can translate across the surface of our droplet and see signal variations that indicate we are

detecting individual LoBs; and we examined the droplet with a light microscope and found that

our 200 LoBs were uniformly distributed in a monolayer. The localization of our LoB particles

at the top of a droplet is equivalent to the creation of a pellet by a paramagnetic pull-down. The

ability to mix large volumes of samples with a small number of LoBs which localize rapidly

29

through their buoyant force could be advantageous over the paramagnetic counterpart which

requires an external magnetic force that decays rapidly with the distance from the magnetic.

Further, the available chemistries for Au surface modification present many opportunities for the

LoB concept in sensing applications.

2.4 References


2. Mallouk, T. E.; Sen, A., Powering Nanorobots. Sci.Am. 2009, 300 (5), 72-77.

3. Pierre, M. C. S.; Mackie, P. M.; Roca, M.; Haes, A. J., Correlating Molecular Surface Coverage and Solution-Phase Nanoparticle Concentration to Surface-Enhanced Raman Scattering Intensities. J. Phys. Chem. C 2011, 115 (38), 18511-18517.

4. Aebersold, R. H.; Teplow, D. B.; Hood, L. E.; Kent, S. B. H., Electroblotting onto Activated Glass. The Journal of Biological Chemistry 1986, 261 (9), 4229-4238. S-1.

5. Freeman, R. G.; Grabar, K. C.; Allison, K. J.; Bright, R. M.; Davis, J. A.; Guthrie, A. P.; Hommer, M. B.; Jackson, M. A.; Smith, P. C.; Walter, D. G.; Natan, M. J., Self-Assembled Metal Colloid Monolayers - an Approach to SERS Substrates. Science 1995, 267 (5204), 1629-1632.

6. Frens, G., Controlled Nucleation for Regulation of Particle-Size in Monodisperse Gold Suspensions. Nature-Physical Science 1973, 241 (105), 20-22.

7. Haiss, W.; Thanh, N. T. K.; Aveyard, J.; Fernig, D. G., Determination of Size and Concentration of Gold Nanoparticles from UV-Vis spectra. Anal. Chem. 2007, 79 (11), 4215-4221.

8. Tessier, P. M.; Christesen, S. D.; Ong, K. K.; Clemente, E. M.; Lenhoff, A. M.; Kaler, E. W.; Velev, O. D., On-Line Spectroscopic Characterization of Sodium Cyanide with Nanostructured Gold Surface-Enhanced Raman Spectroscopy Substrates. Appl. Spectrosc. 2002, 56 (12), 1524-1530.

9. Premasiri, W. R.; Clarke, R. H.; Londhe, S.; Womble, M. E., Determination of Cyanide in Waste Water by Low-Resolution Surface Enhanced Raman Spectroscopy on Sol-Gel Substrates. Journal of Raman Spectroscopy 2001, 32 (11), 919-922.

10. Shelton, R. D.; Haas, J. W.; Wachter, E. A., Surface-Enhanced Raman Detection of Aqueous Cyanide. Appl. Spectrosc. 1994, 48 (8), 1007-1010.

30

11. Kerker, M.; Siiman, O.; Bumm, L. A.; Wang, D. S., Surface Enhanced Raman-Scattering (SERS) of Citrate Ion Adsorbed on Colloidal Silver. Appl. Optics 1980, 19 (19), 3253-3255.

12. Siiman, O.; Bumm, L. A.; Callaghan, R.; Blatchford, C. G.; Kerker, M., Surface-Enhanced Raman-Scattering by Citrate on Colloidal Silver. J. Phys. Chem. 1983, 87 (6), 1014-1023.

13. Grubisha, D. S.; Lipert, R. J.; Park, H. Y.; Driskell, J.; Porter, M. D., Femtomolar Detection of Prostate-Specific Antigen: an Immunoassay Based on Surface-Enhanced Raman Scattering and Immunogold Labels. Anal. Chem. 2003, 75 (21), 5936-5943.

31

3 Lab-on-a-Bubble Surface Enhanced Raman Indirect Immunoassay for Cholera1

3.1 Introduction

Surface Enhanced Raman Scattering (SERS) assays are effective analytical methods due

to the robustness of properly prepared nanoparticle materials2; the large dynamic range of single

molecules to high analyte concentrations3; the selectivity of Raman spectroscopy; and

development of small portable Raman devices to read the assays4. We recently demonstrated an

interesting direct SERS assay that employed buoyant silica bubbles derivatized with gold

nanoparticles (AuNP)5. It was demonstrated that the buoyancy could pull the AuNP coated silica

bubbles, coined Lab-on-a-Bubble (LoB), from the sample volume to a compact monolayer of

LoBs on the surface of the sample.

Direct SERS assays have been demonstrated with colloidal AgNP or AuNP, SERS active

substrates, and with AuNP modified paramagnetic particles. Many schemes have been used to

enhance the adsorption of analytes to the fairly unreactive noble metal surfaces. The

significance of the LoB direct assay concept stems largely from the stability of the nanoparticle

coating in contrast to the inherent instability of colloidal particles.

32

Figure 3.1: Conceptualization of an indirect LoB assay for cholera. The components (left) consist of a cholera-antibody derivatized silica bubble (LoB), the cholera-antigen (CT-AG), and an antibody derivatized silica shelled AuNP reporter. For this project, the Raman reporter is 1,2-bis(4-pyridyl)ethylene (BPE). The resulting reaction between antigen and the LoB components is illustrated to the right.

The relative dimensions are exaggerated to show the AuNP reporters. Multiple reporters/bubbles are possible and were observed by SEM imaging.

Figure 3.1 illustrates a LoB indirect assay. This assay, rather than utilizing AuNP coated

LoBs, has LoBs that are coated with an analyte binding reagent. The analyte contains multiple

binding sites such that it can also bind to an AuNP reporter (NPR) coated with analyte binding

reagents. The NPR consists of an AuNP core, single or multiple AuNPs, covered with a

submonolayer coating of a coupled strong Raman scatterer, and a protective shell of SiO2. The

NPRs have the advantage of robustness in comparison to a colloidal AuNP. The relative area of

33

AuNP

CT-AG

LoB

Components Results

the of the silica bubble to the shell nanoparticle is about 4 x 104, making it likely that multiple

analyte bindings can occur at a single LoB.

We chose cholera as the model system to demonstrate a LoB indirect assay. Vibrio

cholerae is the causative agent of cholera, a highly contagious and commonly fatal bacterial

infection of the gastrointestinal tract. Death can occur within hours of infection if not treated

immediately and is usually due to hypovolemic shock or acidosis6. Individuals infected and

actively shedding V. cholerae routinely demonstrate 107 to 108 colony forming units (CFU)/mL

feces. The most common method of identifying cholera in environmental samples is traditional

microbiology: enrich samples for infectious agents by growing them on selective media, and

further selection and identification of a serotype through a series of biochemical tests which take

approximately 8 days for a conclusive determination7. Other tests have been introduced in the

search for a quick and effective V. cholerae identification: Polymerase Chain Reaction (PCR)

following enrichment steps6, direct cell duplexing PCR for immediate identification of infectious

strains8, Digoxigenin labeling (DIG) or radioactive hybridization of colonies for selection of

infectious strains after initial colony growth7, and various immunoassays of V. cholerae colonies

directly imaged by microscopy or Western Blotting9. The US Food and Drug Administration

couples bacterial enrichment steps to PCR identification of pathogenic strains10. A rapid,

accurate diagnostic assay for the presence of CT in either a water sample or a patient sample

would significantly benefit those in outbreak areas.

34

3.2 Materials and Methods

3.2.1 LoB Activation and Antibody Attachment

LoBs (3M S60 glass bubbles) were activated with 10 N sulfuric acid overnight. Bubbles

were silanized with 1:10 3-aminopropyltriethoxysilane APTES in methanol overnight and

washed extensively in methanol (MeOH). Bubbles were resuspended in 3 mL HPLC grade

water. Following APTES silanization, antibodies were activated with the carbodiimide EDC. 1

μg CT Subunit B antibody (anti CT antibody) (Abcam 34992) was added to the reaction with

EDC and activated and silanized LoB solution.

3.2.2 Dynamic Light Scattering (DLS)

Colloidal nanoparticle solutions remain homogeneous for several months due to

Brownian motion of individual particles. The velocity of particles within a solution is a function

of nanoparticle size. Dynamic light scattering determines average particle velocity by measuring

time-correlated fluctuations in the average amount of light scattered by the colloidal solution,

which can be used to calculate nanoparticle diameter. This technique is also useful for

determining the polydispersity of a colloidal sample. DLS measurements were made for bare

colloids and silica-coated nanoparticles. Dynamic light scattering measurements were made

using a ZetaPALs DLS instrument (Brookhaven instruments).

35

Figure 3.2: Example of DLS measurement results, showing the average diameter and polydispersity of a colloidal sample solution.

3.2.3 Raman Reporter Synthesis

Although gold nanoparticle solutions remain stable for several months, addition of

analytes can lead to rapid and irreversible particulate precipitation. One solution to this problem

is to glass-coat the molecule-adsorbed nanoparticles. The result is a stable solution of Raman

reporters which can be further modified (e.g. antibody attachment) for more complex research

applications. The wide array of antibody-antigen combinations permits countless research

possibilities, including pathogen detection, blood glucose monitoring, and detection of

primordial life molecules.

A known method for coating gold nanoparticles with amorphous silica11 was tested using

the synthesized colloids. DLS results showed an increase in particle diameter (~155 nm) and a

36

decrease in polydispersity, indicating successful synthesis of core-shell colloids. The next step

was to coat analyte-adsorbed nanoparticles with silica to make a stable Raman reporter. Thiol

species form a strong bond to metal nanoparticles12 that is unaffected by the silation reaction

process, making it a suitable tag for the core-shell particles. A final diameter of 130-150 nm was

desired to ensure complete silica coverage of the thiophenol-adsorbed nanoparticles while

maintaining SERS properties. Incubation time was adjusted to achieve the desired particle size.

To make these tagged colloids, 4 µL of 1 mM thiophenol (or BPE) was added to 4 mL

gold nanoparticles. This solution was added to 16 mL of 2-propanol while stirring. 500 µL of

ammonia hydroxide, followed by 16 µL of tetraethyl orthosilicate (TEOS) was added to the

reaction mixture to initiate the silation process. After one hour of stirring, the reaction product

was centrifuged for 10 min at 7,200 rpm. The supernatant was poured off and the pellet was re-

suspended in 250 µL H2O. DLS was used to determine the diameter (142 nm) of the thiol-coated,

shelled nanoparticles (Figure 3.3) and a Raman spectrum verified the presence of a strong

analyte peak signature (Figure 3.4) that persisted for several weeks (Figure 3.5).

37

Figure 3.3: Dynamic light scattering results of bare nanoparticles (top) and Raman reporter particles (bottom). Shelled Raman reporters exhibit a larger diameter than bare NPs with little change to the polydispersity.

38

Figure 3.4: Raman spectra of shelled (red) and unshelled (green) colloids in 5 µM BPE.

39

200 400 600 800 1000 1200 1400 1600 1800

Inte

nsi

ty

Wavenumber

Figure 3.5: Raman spectra of thiophenol-adsorbed coated colloids taken on 10/1 (blue), 10/8 (green), and 10/29 (red).

3.2.4 Preparing and Shelling AuNPs

Gold nanoparticles were prepared using the citrate reduction method described by Frens

in 197313. Colloids were sized using SEM and were an average of 50 nm in diameter.

Nanoparticle concentration was determined as described by Haiss et. al14. After UV-vis

spectroscopy and the calculations from that work, we determined the concentration of our

nanoparticles to be 6.02 x 1010 nanoparticles per mL. 4mL fresh colloids were labeled with 50

nM 1,2-bis(2-pyridyl) ethylene (BPE) and added to 20 mL isopropanol (99%) at room

temperature while stirring. Colloids were shelled with silica as detailed in Lu et al.11, 15. The

40

200 400 600 800 1000 1200 1400 1600

Inte

nsity

Wavenumber

SEM image in Figure 3.6D shows that many of the NPR are paired AuNPs. This is significant

as it has been demonstrated that paired AuNPs provide larger enhancements16.

Figure 3.6: SEM images of a positive LoB assay. Images A, B, and C are acquired with refelected electrons to enhance the physical structure of the LoB materials. Image D used backscattered electron detection to visualize the captured AuNP particles. Note that many are AuNP combinations.

3.2.5 LoB Immunoassay

Antibody conjugated LoBs were blocked with nonfat dry milk in PBS and incubated for

10 minutes shaking at room temperature prior to addition to reaction. Shelled, tagged colloids

were incubated with a 1:500 dilution in PBS anti CT antibody (original concentration 1 mg / mL)

and incubated for 20 minutes shaking at room temperature to allow antibodies to adsorb to the

silica surface. Following antibody adsorption, colloids were blocked with nonfat dry milk in

PBS and incubated for 10 minutes shaking at room temperature prior to adding the colloid

component to reaction. Recombinant beta subunit CT (concentration: 1 mg / mL) (Sigma

41

Aldrich C9903) was added at varying concentrations to each reaction. The standard addition

experiment antigen addition description is as follows: (1) Unknown concentration of CT (final

volume in this reaction is 50 μL), (2) Unknown + 2500 ng CT, (3) Unknown + 5000 ng CT.

Antibodies were attached to LoBs in Eppendorf low binding tubes (cat # 0030 108.116) using

EDC. Prior to each assay, antibodies were adsorbed to shelled nanoparticle reporters (NPRs) in

low binding tubes. The LoBs and the NPRs were each added to the reaction tube which was also

a low binding tube. The reactions were incubated shaking for 20 minutes. Following incubation,

the entire reaction volume (85 μL) was transferred to a polished aluminum surface where the

LoBs were allowed to rise to the surface (approximately 5 minutes)5. We did not observe

problems related to evaporation of the droplet in the ~ 5 minute time for LoB floatation and

Raman collection.

3.2.6 Data Acquisition and Analysis

Data were acquired on a Snowy Range Instruments Sierra Raman ORSTM instrument with

an 808 nm rastering laser. By rastering the laser beam over a 2 x 0.5 mm area, the laser spot size

remains small which is a requirement for selectivity in Raman spectroscopy while a larger area is

sampled allowing averaging of possible inhomogeneity. The SEM images in Figure 3.6

illustrates that with the current design, the LoBs appear to have locations where there are many

and few NPRs. This problem is averaged out with the rastering laser. One of the signature peaks

of each Raman tag was chosen for analysis (1600 cm-1 BPE), and 1000 cm-1 glass as an internal

standard was chosen to standardize each data point. The internal standard was a fluorescence

peak generated from the glass of the LoBs. The intensity of the peak from the Raman tag was

divided by the intensity of the internal standard peak to arrive at a standardized intensity for each

42

sample point. This eliminates variations in intensity due to differences in focus in individual

samples. Data from each sample was acquired 5 times to ascertain the standard deviation of the

LoB assay.

3.3 Results

Our Raman measurements were made with an 808 nm Sierra Raman ORS system

(Snowy Range Instruments). This system is capable of maintaining a high etendue with a tightly

focused laser beam, yet it can be adjusted to examine a large sample area. We found that our

LoBs were static and formed a monolayer at the top of the sample droplet, Figure 3.7. Our

focused laser beam’s diameter was approximately 30 µm or about the size of one silica bubble.

We performed a mock assay and obtained a micrograph of the bubbles. We counted the bubbles

in the assay and found a monolayer of ~1000 bubbles. In a monolayer, this equates to a diameter

of 1 mm. We tuned our raster circuitry to produce a spherical pattern of slightly larger than 1

mm to capture the signal from all of the LoBs.

The cholera assay was performed on a droplet placed on an aluminum surface to create a

curved surface to focus the LoBs at the surface, see Figure 3.7A. The underlying concept is that

the indirect LoB assay is to concentrate the positive assays, bubbles conjugated to shelled NPRs,

and to separate the signals from the conjugated NPRs from the unconjugated. Our shelled NPRs

have a density of 2.95 g/cm3, using 200 nm for the SiO2 shell diameter and 50 nm for the AuNP

particle diameter. This causes them to rapidly sink and interfere with the results of a

paramagnetic or centrifugal pull-down assay. Our optical method scans the top of the droplet

and locates the positive LoBs. The focus of the beam and the opacity of the LoBs differentiates

between the silica bubbles on top of the droplet and the material near the bottom. Figure 3.7B

43

illustrates that the focusing of the particles will also produce a spatial differentiation as the

unbound NPRs will disperse to a larger area in the sample.

Figure 3.7: Schematic of the Raman measurement method used in our assay. A) side view illustrating the spatial separation between LoBs and unconjugated AuNPRs. B) Top view illustrating further spatial separation between the focused LoBs and the dispersed AuNPRs.

SEM analysis of the assay materials demonstrates that the assay consists of multiple

AuNPs in each shell and that a single silica bubble binds with multiple NPRs, see Figure 3.6. An

SEM/Raman study by Wustholz et al. demonstrated that the local surface plasmon resonance

(LSPR) responsible for the SERS enhancement shifts with the number of AuNPs and their

orientation16. Their assumption is that the large SERS signals observed from dimers and

multimers stem from single molecules in the AuNP junctions. Our shelled NPRs also show a

large number of dimers and multimers; Figure 2D has 3 monomers, 2 dimers, and one

quadramer.

44

Figure 3.8A is the spectrum obtained from 1 x 104 ng of CT in a LoB assay. The peak

around 1000 cm-1 is due to luminescence from the silica bubbles. We observed this peak in silica

with NIR excitation and it is very strong with 808 nm excitation. We used this as an internal

control to account for the number of LoBs at the droplet’s apex. This accounts for LoBs lost

during the assay development and transfer to the sampling surface. The 1600 cm-1 peak stems

from the reporter molecule, 1,2-Bis(2-Pyridyl) Ethylene (BPE).

Figure 3.8: Assay results for CT. A) Raman spectrum from 10 μg CT pulled out with LoBs and NPRs. B) Standard addition plot with calculated limit of detection.

Cholera detection is commonly required in water supplies or stool samples. Both cases

present a complex sample matrix. Additionally, the CT antigen used for assay development

contains stabilizers and preservatives that affect our assays. We used standard additions to

45

account for interactions between the matrix and the analyte. Figure 3.8B is the standard addition

graph obtained from our experiments. The value of the unknown is found by:

[c] = b/m

where [c] is the unknown concentration, b is the y-intercept, and m is the slope.

The y axis in our plot is the ratio of the silica emission peak around 1000 cm-1 and our

reporter molecule, BPE, peak around 1600 cm-1. Using this method and a linear regression, we

found our predicted unknown to be 3700 ng (actual 5000 ng). The limit of detection (LOD) was

found to be 1100 ng from the linear regressions predicted error in the y-intercept and the slope:

LOD = 3 (σ/m)

where σ in this case is the predicted error in the y-intercept. This may slightly overestimate the

LOD as the calculated predicted error in the y-intercept includes the errors of all the data and

since we see significant heteroscedasticity in the data. However, the calculation provides a

reasonable approximation.

The heteroscedasticity is interesting. It is nearly 20 times larger than the predicted

spectroscopic noise from the signals. We suggest that it is due to the variations in the signals

due to loss of particles during the assay and the transfer of particles to the sampling surface.

This error should be larger when the silica LoBs contain more NPRs. In other words, the loss of

10% of the highly positive LoBs will result in a larger error than 10% of a low positive assay.

All results are discussed as mass rather than concentration since the buoyant LoBs enable

us to detect mass independent of volume. The LoBs will concentrate on top of whatever volume

46

is in the sample. We see this as a significant benefit as the concentration (analyte/volume)

should be very low for samples with large volumes.

Diagnostic assays are not commonly used in developing countries. Reagents are often

refrigerated, trained personnel must operate the instruments, and much laboratory equipment is

required to run diagnostic tests. The LoB platform for the sandwich assay frees the tests from

any volume limitation that the magnet strength would dictate in traditional paramagnetic assays.

It also decreases the likelihood of finding false positives from contamination of the sample to be

interrogated with the NPRs.

There are a number of reports of potentially commercial CT tests in the literature, but we

found only assay, a Lateral Flow Immunoassay (LFI), the SMART Cholera 0117 , which is

actually commercially available. The Cholera 01 SMART II LFI reports an LOD at 2 x 107

colony forming units (CFU) per mL17 and Spira and Fedorka-Cray found that there are

approximately 0.19 fg/CFU Cholera toxin in Vibrio cholerae 0118 . This places their detection

limit at 3.9 ng/mL of CT.

While this appears to be much lower than ours mass detection limit, we do have the

advantage of detecting small levels of CT in large volumes. Additionally, this is proof of

concept study and report that has not been optimized for number of LoBs, antibodies, or

experimental conditions.

Many research groups provide CT detection limits that fluctuate widely. This is not a

comprehensive literature review, but a few CT detection limits are: 1 nM CT on a biosensor19,

from 1 ng/mL to 0.49 ng/mL using ELISAs20,21, sandwich (indirect) assays were reported at 40

ng/mL and 1 μg/mL while direct assays were reported at 200 ng/mL22. Schofield et al. reported a

47

detection limit of 3 μg/mL using glyconanoparticles in a colorimetric assay23 making their

detection limit around 4 μg Cholera toxin.

3.4 Acknowledgements

The authors would like to thank Snowy Range Instruments for the instrumentation and

facility usage. Dr. Martoglio acknowledges the support of DePauw University for his sabbatical

leave.

3.5 References


2. Penn, S. G.; He, L.; Natan, M. J., Nanoparticles for Bioanalysis. Curr Opin Chem Biol 2003, 7 (5), 609-615.

3. Nie, S.; Emory, S. R., Probing Single Molecules and Single Nanoparticles by Surface-Enhanced Raman Scattering. Science 1997, 275 (5303), 1102-1106.

4. Carron, K.; Cox, R., Qualitative Analysis and the Answer Box: a Perspective on Portable Raman Spectroscopy. Anal Chem 2010, 82 (9), 3419-3425.

5. Schmit, V. L.; Martoglio, R.; Scott, B.; Strickland, A. D.; Carron, K., Lab-on-a-Bubble: Synthesis, Characterization, and Evaluation of Buoyant Gold Nanoparticle-Coated Silica Spheres. JACS 2011, e pub ahead of print (2011 Nov 18).

6. Kaper, J. B.; Morris, J. G.; Levine, M. M., Cholera. Clinical Microbiology Reviews 1995, 8 (1), 48-86.

7. Robert-Pillot, A.; Saron, S.; Lesne, J.; Fournier, J.-M.; Quilici, M.-L., Improved Specific Detection of Vibrio Cholerae in Environmental Water Samples by Culture on Selective Medium and Colony Hybridization Assay with an Oligonucleotide Probe. FEMS Microbiology Ecology 2002, 40, 39-46.

48

8. Goel, A. K.; Tamrakar, A. K.; Nema, V.; D.V., K.; Singh, L., Detection of Viable Toxigenic Vibrio Cholerae from Environmental Water Sources by Direct Cell Duplex PCR Assay. World J Microbiol Biotechnol 2005, 21, 973-976.

9. Wang, D.; Xu, X.; Deng, X.; Chen, C.; Li, B.; Tan, H.; Wang, H.; Tang, S.; Qiu, H.; Chen, J.; Le, B.; Ke, C.; Kan, B., Detection Of Vibrio Cholerae 01 and 0139 in Environmental Water Samples by Immunofluorescent Aggregation Assay. Applied and Environmental Microbiology 2010, 76 (16), 5520-5525.

10. FDA Bacteriological Analytical Manual (BAM). http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/BacteriologicalAnalyticalManualBAM/default.htm.

11. Lu, Y.; Yin, Y. D.; Li, Z. Y.; Xia, Y. N., Synthesis and Self-Assembly of Au@SiO2 Core-Shell Colloids. Nano Lett. 2002, 2 (7), 785-788.

12. Carron, K.; Peitersen, L.; Lewis, M., Octadecylthiol-Modified Surface-Enhanced Raman-Spectroscopy Substrates - a New Method for the Detection of Aromatic-Compounds. Environ. Sci. Technol. 1992, 26 (10), 1950-1954.

13. Frens, G., Controlled Nucleation for the Regulation of the Particle Size in Monodisperse Gold Suspensions. Nature 1973, 241 (105), 20-22.

14. Haiss, W.; Thanh, N. T. K.; Aveyard, J.; Fernig, D. G., Determination of Size and Concentration of Gold Nanoparticles from UV-Vis Spectra. Anal. Chem. 2007, 79 (11), 4215-4221.

15. Lu, Y.; Yin, Y. D.; Mayers, B. T.; Xia, Y. N., Modifying the Surface Properties of Superparamagnetic Iron Oxide Nanoparticles through a Sol-Gel Approach. Nano Lett. 2002, 2 (3), 183-186.

16. Wustholz, K. L.; Henry, A.-I.; McMahon, J. M.; R.G., F.; Valley, N.; Piotti, M. E.; Natan, M. J.; Schatz, G. C.; Van Duyne, R. P., Structure-Activity Relationships in Gold Nanoparticle Dimers and Trimers for Surface-Enhanced Raman Spectroscopy. Journal of the American Chemical Society 2010, 132.

17. Diagnostics, N. H. SMART Cholera 01 LFI. http://www.nhdiag.com/cholera_bt.shtml.

18. Spira, W. M.; Fedorka-Cray, P. J., Enterotoxin Production by Vibrio Cholerae and Vibrio Mimicus grown in Continuous Culture with Microbial Cell Recycle. Applied and Environmental Microbiology 1983, 46 (3), 704-709.

19. Singh, A. K.; Harrison, S. H.; Schoeniger, J. S., Gangliosides as Receptors for Biological Toxins: Development of Sensitive Fluoroimmunoassays Using Ganglioside-Bearing Liposomes. Analytical Chemistry 2000, 72 (24), 6019-6024.

49

http://www.nhdiag.com/cholera_bt.shtml

http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/BacteriologicalAnalyticalManualBAM/default.htm

http://www.fda.gov/Food/ScienceResearch/LaboratoryMethods/BacteriologicalAnalyticalManualBAM/default.htm

20. Uesaka, Y.; Otsuka, Y.; Kashida, M.; Oku, Y.; Horigome, K.; Nair, G. B.; Yamasaki, S.; Takeda, Y., Detection of Cholera Toxin by a Highly Sensitive Bead Enzyme Linked Immunosorbent Assay. Microbiology and Immunology 1992, 36 (1), 43-53.

21. Edwards, K. R.; March, J. C., GM1 Functionalized Liposomes in a Microtiter Plate Assay for Cholera Toxin in Vibrio Cholerae Culture Samples. Anal Biochem 2007, 368 (1), 39-48.

22. Rowe-Taitt, C.; J., C.; Patterson, C.; Golden, J.; Lingler, F., A Ganglioside Based Assay for Cholera Toxin Using an Array Biosensor. Analytical Chemistry 2000, 281 (1), 123-133.

23. Schofield, C. L.; Field, R. A.; Russell, D. A., Glyconanoparticles for the Colorimetric Detection of Cholera Toxin. Analytical Chemistry 2007, 79 (4), 1356-1361.

50

4 Dynamic SERS: Extracting SERS from Normal Raman Scattering1

4.1 Introduction

Conventional Raman spectrometers improve signal-to-noise by integration of signal in

the wells of CCD chips. With proper cooling and readout circuitry this approach leads to optical

detection that follows Poisson statistics for shot noise-limited spectra. Therefore, within a

spectrum, the variance in the signal is equal to the intensity. When individual spectra are

compared, the dominant source of variation is rms laser noise which follows a normal

distribution and is reduced through spectral averaging. However, this approach of time

indiscriminate signal collection places photons from every possible source into the spectrum.

Conventional Raman spectra contain signal contributions from the desired source in the sample

as well as fluorescence, whether intrinsic or an impurity, stray light from the optical system and

Raman interference from sample containers. Time correlation has been demonstrated as a way

to discriminate between the instantaneous scattering events and delayed fluorescence

signals2{Willis, 1990 #2}.

Colloidal nanoparticles are free floating particles that remain suspended through

Brownian motion. Surface enhanced Raman spectroscopy (SERS) from colloidal nanoparticles

was described very soon after the initial discovery of SERS at electrode surfaces3. The ease of

making colloidal gold and silver particles has made it a popular method for performing SERS

studies and analytical assays4. Additionally, the large velocity imparted on nanoparticles through

Brownian motion leads to an opportunity to discriminate between their spectroscopic signals and

the relatively rapid fluctuations of free molecular species and continuum produced by solid state

interferences.

51

We describe a statistical method for specific extraction of SERS signals from colloidal

SERS active nanoparticles. The difference in these particles’ sizes relative to the molecular

matrix creates an opportunity to statistically differentiate between their signal and the relatively

time indiscriminate fluorescence and matrix Raman signals.


4.2.1 SERS Signal Extraction

Figure 4.1 illustrates the concept of dynamic SERS (DSERS) spectroscopy. The box on

the left illustrates dynamic processes that lead to the theory of DSERS. Raman spectrometers

typically have a tightly focused laser beam to generate the Raman scattering. That small focal

volume is illustrated as the pink cylinder in figure 4.1. This volume of solvent generates a

Raman signal that is shot noise limited and has a standard deviation equivalent to the square root

of the signal. SERS signals are generated by particles moving rapidly into and out of the laser

beam. These fluctuations produce a noise level (σSERS) greater than the square root of the

average signal. The signal (SExcess) due to the excess noise contributed by the dynamic noise from

the SERS active nanoparticles can be found from the difference between the total noise in the

signal (σTotal). and total signal (STotal). The subtraction requires a factor (a) to account for the

difference between the magnitude of the standard deviation and average signals.

The spectra in Figure 4.1 (right) illustrate the results of a DSERS measurement. The top

spectrum (STotal) is from a toluene solution with approximately 8 x 105 particles/cm3 of SiO2

coated nanoparticles with a BPE coating. At this concentration, the presence of the nanoparticles

is undetectable in the average SERS spectrum which is derived from 100 spectra acquired for

100 ms. The middle spectrum (σTotal) represents the standard deviation of the 100 spectra at each

52

data point. This spectrum is still dominated by the variation produced by the laser’s rms power

fluctuations; the variation between the individual spectra is dominated by the laser fluctuations.

This signal independent noise contribution will produce a noise spectrum which has feature

intensities that have values from all sources. The important exception of the instrumental noise

sources is the nanoparticles’ SERS signal. Subtraction of the averaged spectrum (STotal) from the

total noise spectrum (σTotal) divided by the number of averages, 100 in this case, produces the

excess noise spectrum Sexcess. This is shown in the bottom spectrum of Figure 4.1 and closely

represents a SERS spectrum of BPE.

Figure 4.1: DSERS concept. Left) This schematic illustrates a colloidal nanoparticle moving through a focused laser beam. The standard deviation of the continuum, σcontinuum, will scale as the square root of the intensity while the σSERS from the nanoparticle will be larger. Right) An illustration of the signals and standard deviations for a solution of toluene with two nanoparticle events in 10 s.

Examination of the original data set shows that we observed only one major particle

event during the 10 s of data acquisitions. This is observed in Figure 4.2 (top) where an overlay

53

Sexcess = σTotal- aSTotal

σcontinuum = I1/2σSERS > I1/2σTotal = σSERS + σcontinuumWavenumbers

sTotal

σTotal

sExcess

800 1200 1600

10000 ADU

100 ADU

10 ADU

of the 100 spectra in the 1600 cm-1 region indicates that a large event occurred (red) and a

smaller event occurred (violet) during the data collection. Plotting the 1640 cm-1 data point in

time space, Figure 4.2 (bottom), shows the two events in spectrum 67 (major) and 21 (minor)

respectively.

Figure 4.2: Individual Raman spectra from Figure 1 and a plot of intensity at 1640 cm-1 vs the acquisition number.

Most significant about this aspect of the DSERS method is that it removes the interfering

spectral features. Figure 4.1 illustrated this with the observation of a single nanoparticle’s SERS

54

Time10 20 30 40 50 60 70 80 90

50 ADU

500 ADU

Wavenumbers1525 1575 1625 1675

1640 cm-1

spectrum in a neat toluene matrix. In this case, we were able to extract a SERS spectrum with a

one part per thousand relative intensity. The value of this method is its objective (autonomous)

derivation of the pure SERS spectrum in the presence of the overwhelming solvent spectrum.

Even selection of the individual spectra with the nanoparticle present requires subtraction of

toluene of a pure matrix spectrum with an unknown relative intensity to the SERS intensity.

4.2.2 Sites Selective Spectroscopy

Hotspots between nanoparticle aggregates or gaps between nanoparticle features have

been discussed as a possible mechanism for very large enhancements beyond those observed

from single particles5. Examples of experiments to prove this theory have included SEM

combined with LSPR spectroscopy6 and tilted pillar experiments which show larger signals when

pillars are collapsed to produce contact7. The difficulty of proof is the differentiation between

the SERS signal from the majority of the surface’s coverage and that of the small number of

molecules in the gap region. Even with large gap enhancements, the small area associated with

this enhancement will lead to relatively small signals that are difficult to detect in the total SERS

signal.

The challenge of site-selective nanoparticle spectroscopy is that the observed signals are

derived from an ensemble of particles in the laser beam during the integration period. Schmit et

al. recently showed the paradox between signal and fluctuation-induced noise in solution phase

nanoparticle spectroscopy8. As the number of particles decreases, the signal decreases, and the

fluctuation-produced noise, as described by Brownian induced fluctuations, increases. Increased

acquisition times only exacerbate the problem by allowing more particles to traverse the laser

beam and to enter into the observed signal. The DSERS method described herein exploits the

55

negative effect of Brownian motion-induced fluctuations and enhances the individual particle or

site selective signals.

Shelled nanoparticles have particular application as bright reporters to sandwich

paramagnetic9 or lab-on-a-bubble assays8, 10. Direct SERS assays are commonly reported for

chemical analysis and are also affected by the degree of aggregation in the sample. Knowledge

of the site of adsorption and the signal from strongly enhancing sites is valuable for assay

development. For example, if specific sites enhanced more than others and a site specific

spectroscopy existed, then the possibility of more sensitive assays could be realized. The

sensitivity of an assay can be described by the magnitude of the signal produced by an analyte

molecule relative to the noise. If a site selective chemistry can be developed specifically at the

“hotspots” of SERS active nanoparticles, the number of active sites will be dramatically

decreased. In this case, as the number of analyte molecules approaches zero, the signal from

adsorption at hotspots will be higher than it would be at adsorption to poorly enhancing spots,

even at a higher concentration of these poorly enhancing locations.

We performed a second study with unshelled nanoparticles coated with 4MP. Mullen et

al11. demonstrated that the ratio of peaks in the 1000 to 1100 cm-1 region of 4MP SERS spectra is

pH dependent; the ratio of the 1091 cm-1 peak to the ring breathing mode at ~ 1000 cm-1 is

smaller under basic (unprotonated) conditions12. These results are reproduced here and are

illustrated in Figure 4.3(left). We found that the average (SERS) spectra of 4MP-coated Au

nanoparticles exhibiting a ratio of 1091 cm-1/1000 cm-1 is 0.87 at high pH (9) and 2.12 at low pH

(5). This is illustrated in Figure 4.3 A,B. It is important to report that we observed small, but

significant, frequency shifts in the ring breathing mode upon protonation.

56

The DSERS spectra (Figure 4.3 C,D) exhibit very different results. Absent in the

DSERS spectra are the broad interfering contributions from the glass sample vial. This confirms

the ability of DSERS to remove normal Raman interferences discussed above. More

significantly, the DSERS spectra of 4MP are nearly identical in base and acid. In this case, a

drastic deviation from the SERS and DSERS spectra is observed.

Figure 4.3E illustrates the variation between two spectra in the 1000 spectra data set for

pH 9. The spectra come from acquisitions 19 (green circle) and 86 (red circle) illustrated in

Figure 4.3F. The relative intensities of the 1000 and 1091 cm-1 peaks to other features are not

distorted; the anomalous equality of the spectra in Figure 4.3 C and D does not appear to be due

to anomalous particles, rather irregular variations in the intensities of these peaks over an

ensemble of particles. While the sampling of spectra in Figure 4.3E demonstrates large

variations in the 1091 cm-1 /1010 cm-1 ratio, they indicate extremes in the variations and clearly

do not correlate to the spectrum in Figure 4.3C. None of the single acquisitions making up

Figure 4.3A correspond to Figure 4.3C. The equality of the acid and base DSERS spectral

features between 1000 and 1100 cm-1 peak must be due to a small population of identical

molecules present on particles in both the acid and base solutions. Not only are the solvent

spectral features and the glass vial’s features removed, but also the SERS features that are

common to all particles. Note: this experiment was performed with a higher concentration of

nanoparticles than the shelled BPE-coated nanoparticle study. This will lead to a reduction in the

nanoparticle peaks and will enhance the signals from variations between the particles.

57

Figure 4.3: Experimental results for 4MP on Au nanoparticles at basic and acidic pH. A) The average spectrum of 1000-100 ms acquisitions at pH 9; B) The average spectrum of 1000-100 ms acquisitions at pH 5; C) DSERS spectrum from the data set used to produce A; D) DSERS spectrum from the data set used to produce B). E) Two individual acquisitions spectra; F) Intensity vs time subset of the 1000 acquisition at 1091 cm-1.

Figure 4.4 shows an expanded view of the data in Figure 4.3. We observed the ring

breathing peak of 4MP at 1003.9, 1005.7, 1007.7, and 1010.2 at pH 5 (SERS), pH 5 (DSERS),

pH 9 (DSERS), and pH 9 (SERS), respectively. The Raman shifts indicate that the species

observed in the DSERS is inaccessible to protonation and are not located at either the acid or the

base spectral shifts. The most likely conclusion is that we are observing excess noise due to a

small population of adsorbate and, given the inaccessibility of the pyridyl nitrogen to

protonation, these species are not exposed to the solvent. This would be consistent with a model

of SERS involving super enhancements of species in the gap between aggregates or in roughness

features on particle surfaces6. In conventional spectroscopy these molecules would not be

observable due to the large population of species not in the highly enhancing gap relative to the

58

900 1100 1300 1500 1700 Wavenumbers

2.12

0.87

1.81

1.96

50 100 150 200 250 300 350 400 450 50 100 150 200 250 300 350 400 450

50 ADU

Acquisition

0.54

750 850 900 950 1000 1050 1100 800

0.78

Wavenumbers

AB

C

D

E

F

number in the gap. This experiment at relatively high nanoparticle concentration is enhancing

those spectral features which are not present on all particles at the same intensity; it represents

SERS signals that are buried in the spectrum of the ensemble of particles or the ensemble of

molecules on a single particle.

950 1000 1050 1100 1150 950 1000 1050 1100 1150

1003.9

1007.7

1010.2

Wavenumbers

pH 9pH 5

pH 9

SERS

DSERS

pH 5

1005.7

Figure 4.4: Magnified spectra from 4.3 A, B, C, D. The ring breathing mode shifts from 1003.9 cm-1 when protonated to 1010.2 cm-1 when deprotonated.

An alternative explanation might be that we are observing 4MP bound to the Au

nanoparticles through its pyridyl nitrogen. This would account for the invariance to solution pH.

However, it is unlikely that statistically significant variations in the population of 4MP bound

through the thiol or through the pyridyl nitrogen would exist between particles. The DSERS

spectrum is statistically significant and more indicative of a small population of aggregates with

59

a small population of strongly enhanced 4MP molecules in the interparticle gap. Figure 4.2

demonstrated that two particles moving into the beam were sufficient to produce a DSERS

spectrum from the overwhelming signal of neat toluene. The data in Figure 4.4 were acquired

from a large number of SERS-active particles in the beam during any individual acquisition and

the DSERS results from variations within this population. If the DSERS were a small population

of sites on every particle, we would expect it to average and not produce an excess noise signal

(Sexcess).

4.3 Conclusion

We have demonstrated two significant benefits of DSERS: removal of instrumental and

normal Raman interferences in SERS spectroscopy and site-selective spectroscopy of adsorbate

populations on SERS-active particles. Our first example of shelled nanoparticles at very low

concentrations confirmed the benefit of DSERS for removal of an overwhelmingly strong

solvent spectral interference. This benefit would be applicable to colloidal SERS studies in

solvents or mixtures that produce strong interferences that might mask observation of the desired

SERS features.

The second benefit, site selection, provides a powerful method to study small populations

of molecules adsorbed on SERS-active particles. In our example with 4MP, we were able to

observe a small population of molecules that were spectroscopically unique from the large

population of molecules on the particles. This study showed the same feature extraction benefit

as described for the shelled nanoparticles but differed in that the DSERS spectra did not match

any of the individual acquisitions or their average. The DSERS spectrum originated from excess

variance between a small population of adsorbates on the ensemble of nanoparticles.

60

4.4 Acknowledgements

The authors would like to thank Snowy Range Instruments for the instrumentation and

facility usage. Brandon Scott would like to acknowledge the NSF GK-12 grant #0948027 for

their kind support.

4.5 References


2. Willis, K. J.; Szabo, A. G.; Krajcarski, D. T., The Use of Stokes Raman-Scattering in Time Correlated Single Photon-Counting - Application to the Fluorescence Lifetime of Tyrosinate. Photochem. Photobiol. 1990, 51 (3), 375-377.

3. Jeanmaire, D. L.; Vanduyne, R. P., Surface Raman Spectroelectrochemistry. Part 1. Heterocyclic, Aromatic, and Aliphatic-Amines Adsorbed on Anodized Silver Electrode. J. Electroanal. Chem. 1977, 84 (1), 1-20.

4. Siiman, O.; Bumm, L. A.; Callaghan, R.; Blatchford, C. G.; Kerker, M., Surface-Enhanced Raman-Scattering by Citrate on Colloidal Silver. J. Phys. Chem. 1983, 87 (6), 1014-1023.

5. Chen, G.; Wang, Y.; Yang, M. X.; Xu, J.; Goh, S. J.; Pan, M.; Chen, H. Y., Measuring Ensemble-Averaged Surface-Enhanced Raman Scattering in the Hotspots of Colloidal Nanoparticle Dimers and Trimers. J. Am. Chem. Soc. 2010, 132 (11), 3644-+.


7. Ou, F. S.; Hu, M.; Naumov, I.; Kim, A.; Wu, W.; Bratkovsky, A. M.; Li, X. M.; Williams, R. S.; Li, Z. Y., Hot-Spot Engineering in Polygonal Nanofinger Assemblies for Surface Enhanced Raman Spectroscopy. Nano Lett. 2011, 11 (6), 2538-2542.


61

9. Wang, X.; Qian, X. M.; Beitler, J. J.; Chen, Z. G.; Khuri, F. R.; Lewis, M. M.; Shin, H. J. C.; Nie, S. M.; Shin, D. M., Detection of Circulating Tumor Cells in Human Peripheral Blood Using Surface-Enhanced Raman Scattering Nanoparticles. Cancer Res. 2011, 71 (5), 1526-1532.


11. Mullen, K. I.; Wang, D. X.; Crane, L. G.; Carron, K. T., Determination of pH with Surface-Enhanced Raman Fiber Optic Probes. Anal. Chem. 1992, 64 (8), 930-936.

12. Zhuang, Z. P.; Ruan, W. D.; Ji, N.; Shang, X. H.; Wang, X.; Zhao, B., Surface-enhanced Raman scattering of 4,4 '-Bipyridine on Silver by Density Functional Theory Calculations. Vib. Spectrosc. 2009, 49 (2), 118-123.

62

5 Statistical Analysis of 4-Mercaptophenol and Thiophenol on Gold Nanoparticles

5.1 Introduction

The exact mechanism of Surface Enhanced Raman spectroscopy (SERS) remains elusive

due to the many different types of nanomaterials that demonstrate the effect. Initially Raman

was observed at highly roughened silver electrodes and thought to be observable at monolayer

levels due to an increase in adsorbed molecules through greater surface area than a smooth

surface1. Since this initial observation of Raman scattering at a highly roughened surface

Jeanemaire and Van Duyne demonstrated the effect with much less roughening and even with

polished surfaces2. Kerker proved that SERS could be observed on colloidal particles in

solutions and today SERS is performed on a large variety of solid substrates, membrane

materials, colloidal particles, and even aerosols3.

Despite the large number of SERS active materials the origin(s) of the SERS effect still

remain a topic of discussion. The first widely accepted mechanism termed the electromagnetic

enhancement follows from electrostatics of an ellipsoid. This mechanism, termed the

electromagnetic effect, properly ascribed the wavelength dependence of SERS to the dielectric

functions of the SERS metals: copper, silver, and gold. It is also accepted that the formation of

electronic charge transfer states between the metal surfaces and some adsorbates produces a

resonance Raman enhancement termed the chemical effect.

Early two-dimensional gratings and island films4 were shown to produce collective

particle resonances, but were also found not to produce the large enhancements created by

electrodes, colloids, or stochastic surfaces. Experimentally colloidal nanoparticles of silver and

gold produced an interesting contradiction to the early electromagnetic theory: the electrostatic

63

theory of metal dielectric particle predicts a narrow resonance condition and for silver or gold the

condition is highly dependent on shape and is predicted to be at much shorter wavelengths that is

observed. The contradiction is that SERS is observed over a large range of wavelengths for

silver and gold, even when the particle size distribution is very narrow, and it is observed for

spherical particles at much longer wavelengths than predicted. Part of the discrepancy was

accounted for by corrections for particle size.

Dynamic Light Scattering (DLS) and electron microscopy of colloidal preparations

always indicate a minority of particles that are dimers or higher ordered aggregates. The

presence of aggregates anticipates the potential for size and shape effects to the observed SERS

signal. Eccentricity of a dimer that is freely rotating in solution will break the individual

spherical particle plasmon into two enhancements along the long and short axes of the dimer.

Aggregates also create very large fields at the gaps between the individual particles5. This can be

understood simplistically from the 1/r2 dependence of the electric field between two oppositely

charged particles and by considering the instantaneous induced dipole in the nanoparticles. At

very small distances the oscillating field will be large and will produce significant enhancements

(Figure 5.1). This “gap” effect has been elegantly described through plasmonics.

Figure 5.1: Depiction of large electric field in the gap region.

64

The challenge to understanding the SERS effect from single particles and from

aggregates is the small size of the particles relative to the typical laser beam size. Colloidal

nanoparticles remain suspended through Brownian motion and are subject to:

1¿ x2 (t )=kB T3 πrη

t

where x2(t) is the mean square position of the nanoparticle as a function of time, kB is

Boltzmann’s constant, T is temperature, r is the particles radius, η is the viscosity of the solution.

Van Duyne et al.5 examined similar particles with SEM and Raman spectroscopy and

provided theoretical calculations that indicate that dimers or higher ordered aggregates that are

encapsulated should exhibit a larger enhancement. It is possible that the dispersion of

encapsulated nanoparticles, though highly monodispersed around single particle sizes, contains a

small percentage of aggregates and these are what we observe in our SERS spectra. Equation 1

provides a method to observe different particle sizes and correlate their Raman spectra.

We have reported detection of single silica shelled nanoparticles in an organic solvent by

monitoring Raman spectra rapidly, every 150 ms with 100 ms acquisitions, by monitoring their

monition in and out of a focused 25 µm laser beam6. In a low viscosity solvent such as water or

toluene the particles remain in the beam for less than the acquisition time. We have slowed the

particles down with viscous solvents, glycerol, and observed very slow motion, 500 ms or

longer, through the laser beam (Figure 5.2). The shelled nanoparticles were monodispersed with

reported gold cores of 60 nanometers and overall size of 120 nm. Their signal was large due to

matching between the particle plasmon and the 785 nm excitation of the laser beam.

65

0 50 100 150 200 250

Time / s

Figure 5.2: BPE SERS signal intensity vs. time of shelled Raman reporters in glycerine.

In chapter 4 we examined low concentrations to observe single particles and we also

studied higher concentrations to observe unusual events in an ensemble of gold nanoparticles6.

The average spectra in these cases simply reflect the signal of the ensemble and rare SERS

signals presumably from different particles, such as dimers or higher ordered aggregates, are not

observed. We found that we could visualize the rare events in the ensemble by finding the

standard deviation of the ensemble at each wavenumber and removing a normalized average

signal. The normalization factor for the average spectra was obtained by minimizing the

variation in the subtracted spectra. This method works well to reproduce the spectrum of the rare

spectra from the ensemble average.

In this current study we continue to develop statistical methods to study the ensemble of

particles coated with 4-mercaptopyridine and we will examine two new coatings: 4-

mercaptophenol and thiophenol. Our preliminary work on this concept looked at 4-

mercaptopyridine coating since it has been shown to adsorb through its thiol group with the

potential to bind to additional nanoparticles in a “gap” environment. 4-mercaptophenol and

66

thiophenol also adsorb via thiol groups and the former also has the potential for gap binding. The

pKa values of 4-mercaptophenol and 4-mercaptopyridine are 6.8 and 3.9, respectively. It should

be noted that these are solution values. Yu et al. determined a pKa value of 5.3 ± 3 for 4-

mercaptopyridine at the surface of a self-assembled monolayer7 so it is reasonable to assume that

the pKa value of 4-mercaptophenol adsorbed to a surface is also higher than in solution. Within

the range of pH 5-10 thiophenol and 4-mercaptopyridine do not undergo acid-base reactions but

4-mercaptophenol does.

Novel data analysis methods were implemented to monitor fluctuations both within and

between vibrational modes in acidic and basic conditions for each of the three analytes. In

particular we found that 4-mercaptopyridine was invariant to pH. We studied 4-mercaptophenol

and benzenethiol as slightly acidic and neutral coatings in relation to the slightly basic 4-

mercaptopyridine examined previously to look for better control of the gap molecules. Our

ability to control aggregation and to bind specific materials into the unique environment of

nanoparticle aggregates is fundamental to our capability of manufacturing optimal SERS sensor

materials.

5.2 Materials

All chemicals were purchased from the supplier indicated: HPLC grade water (Fischer),

HAuCl4 (reagent grade, Aldrich), sodium citrate dihydrate (99.0%, EMD), 4-mercaptophenol

(99%, Acros Organics), 4-mercaptopyridine (95%, Aldrich), thiophenol (97%, Aldrich), and

sodium bicarbonate. All glassware was cleaned with aqua regia, followed by thorough rinsing

with Milli-Q water.

67

5.3 Experimental

SERS-active stock solutions were made by adding 10 μL of 1 mM thiophenol, 10 mM 4-

mercaptophenol, and 10 mM 4-mercaptopyridine to vials containing 1 mL bare gold

nanoparticles. The acidic sample solutions (pH 5) were made by adding 0.2 mL of stock solution

to 1.8 mL of 1% (w/v) sodium citrate in water. The basic sample solutions were made by adding

0.2 mL of stock solution to 1.8 mL of 1 M sodium bicarbonate in water. The pH was measured in

the final colloidal solution using pH indicator paper.

5.4 Instrumentation

Raman spectra were acquired with an IM-52 Raman microscope (Snowy Range

Instruments) using its liquid sampling feature. The IM-52 was set to 40 mW of 785 nm laser

excitation at the sample with 8 cm-1 spectral resolution. The IM-52 permits multiple spectra to

be acquired with a delay between acquisitions. An integration time of 250 ms with a delay of

250 ms were used to collect 1000 spectra for the sample solution. Particle size results were

collected by dynamic light scattering (Brookhaven Instruments) using ZetaPALS particle sizing

software.

5.5 Data Analysis

The data were analyzed using Excel to generate statistical results as a correlogram

(Figure 5.3). Prominent peaks were identified from a single spectrum and peak maxima for the

remaining spectra were identified within a ±10 cm-1 window of the reference peaks. Along the

diagonal line of the correlogram lie the marginal distributions of intensity for each peak. Each

marginal distribution is comprised of 1000 data points which correspond to the measured peak

68

intensities for a given peak. Data points of equal intensity are summed as counts for that specific

intensity and the resulting data is plotted as counts vs. intensity. The central limit theorem (CLT)

states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates

of independent random variables, each with a well-defined expected value and well-defined

variance, will be approximately normally distributed8. The marginal distributions contained

within the generated correlograms are not always normal. Asymmetric and bimodal distributions

are present. A bimodal distribution is a continuous probability distribution with two different

modes. These appear as distinct peaks (local maxima) in the marginal distribution. This is

indicative of the presence of two distinct analyte populations with different detectable signal

intensities. The top right portion of the correlogram lists the coefficients of determination (R2)

between peaks and the bottom left portion contains scatter plots of peak intensities.

815

1004

1091

1576

Peak position (cm-1) and marginal distribution plot (counts vs. intensity)

Coefficient of determination (R2)

Correlation scatter plot

(intensity @ 1091 cm-1

vs. intensity @ 815 cm-1)

Figure 5.3: Example of 4-mercaptopyridine in acid correlogram generated in Excel and descriptions of its main components.

69

5.6 Raman Modes

4-mercaptophenol, 4-mercaptopyridine, and thiophenol belong to the pseudo C2v

symmetry group. There are over 30 normal modes for each of these analytes, some of which are

Raman active. The four C2v symmetry group operators can be split into in-plane (A1 & B2) and

out-of-plane (A2 & B1) ring stretching modes. Four to five Raman modes were selected for each

of the three analytes based on relative signal intensity and variety in vibrational symmetry

(Figure 5.4). Table 5.1 includes a summary of the selected Raman modes, including comparison

to reference values and mode symmetry/descriptions. All of the Raman modes involve ring

stretching modes paired with additional functional group vibrations that are in-plane, unless

otherwise noted.

400 600 800 1000 1200 1400 1600

a bc d

e

ab

c

de

a bc

d

Wavenumber / cm-1

Figure 5.4: Average SERS spectra of 4-mercaptophenol in acid (red), thiophenol in base (green), and 4-mercaptopyridine in acid (blue) with selected peaks (a-e).

70

4-MPOH acid 4-MPOH Raman9 mode702 701 b1

818 816 a1

1006 1007 a1

1168 1172 b2 (βC-O)1589 1585 b2

PhSH base thiophenol (SERS)10 mode419 420 a1, a2

474 470 b1

695 695 a1

1073 1075 a1

1573 1575 a1

4-Mpy acid Ag colloid11 mode815 791 γ(CH)

1004 1004 Ring breathing1091 1095 Ring breathing/C-S1576 1580 ν(CC)

Table 5.1: Experimental and reference vibrational mode frequencies, descriptions and symmetries for 4-mercaptophenol (top), thiophenol (middle), and 4-mercaptopyridine (bottom).

5.7 Results

5.7.1 4-Mercaptophenol Analysis

Figure 5.5 compares correlograms of 4-mercaptophenol at pH 5 and pH 10. None of the

marginal distribution plots along the diagonal appear to be normally distributed for either

correlogram. Instead the distributions of peak intensities appear to be positively skewed. This

means that peak intensities greater than the median value were measured more frequently than

71

peak intensities less than the median value. Additionally, the marginal distributions for most of

the peaks are bimodal at pH 5. This indicates that the average SERS signal of 4-mercaptophenol

in acid is due to two distinct types of SERS enhancement.

The roughly linear correlation plots and their corresponding high determination

coefficients indicate that peak intensities are highly dependent on one another in acid and only

slightly less so in base. In other words there is little change in relative peak intensities between

the 1000 collected spectra at each pH. However, the acidic solution correlation plots contain a

single outlier point with much higher peak intensity than the other 999 collected spectra for the

peak at 1006 cm-1. This is indicative of signaling due to a single hotspot event. Although the

1006 cm-1 peak intensity is much higher in this single spectrum (Figure 5.6), the other peak

intensities are not, indicative of a hotspot spectrum that is unique in relative peak intensities,

possibly due to a unique analyte binding orientation at the hotspot interface.

702

818

1006

1168

1589

4-Mercaptophenol in Acid

702

818

1006

1166

1587

4-Mercaptophenol in Base

Figure 5.5: Correlograms of 4-mercaptophenol in AuNPs SERS spectra in acidic (left) and basic (right) solutions.

72

Figure 5.6: 4-Mercaptophenol in acid outlier spectrum (red) vs. average spectrum (blue).

In Figure 5.7 we show the DLS distributions. Most notable is that we observe

distributions in the pH 5 set of AuNP that matches uncoated nanoparticle distributions. In other

words we did not observe changes due to aggregation at this pH. Conversely at pH 10 we see a

third distribution of particles with 100 times higher concentration due to aggregation. It is also

notable that the number of dimeric aggregates is close to 1000 times less than the monomers

regardless of pH. Multimer aggregates are 100 times more concentrated at pH 10 than at pH 5.

We assume the positive skew observed in the marginal distribution plots (Figure 5.5) is

related to the particle aggregate populations observed in the DLS spectra. Because dimer and

multimer particles contain hotspot regions they likely produce larger SERS enhancements than

monomers. Although the relative population of aggregates is 1000 times less than the monomer

population they still contribute to the overall SERS signal as a result of hotspot enhancement.

73

400 600 800 1000 1200 1400 1600 400 600 800 1000 1200 1400 1600

Figure 5.7: DLS distributions of 4-mercaptophenol-functionalized AuNP aggregates in acidic (top) and basic (bottom) solutions.

The DRS spectra in Figure 5.8 indicate bands which are statistical anomalies. For

example, the 1170 cm-1 peak is unchanged between pH 5 and pH 10. However, the 1006 cm-1

and 1587 cm-1 regions exhibit minor peaks at 993 cm-1 and 1554 cm-1, respectively, in the basic

DRS spectrum. These peaks may represent unique vibrational modes due to particles in the gaps

where binding to between AuNPs occurs. Differential time-dependent signal intensity between

acidic and basic solutions shown in Figure 5.9 indicates that particle aggregation increases with

increasing pH. This observation matches with the DLS results shown in Figure 5.7. This

explains why marginal distributions of peak intensities in base exhibit more asymmetry than the

bimodality observed in the acidic solution which does not have the same degree of aggregation.

74

1000x1000000x

10000x1000x

200 400 600 800 1000 1200 1400 1600 1800

15891006

Wavenumber / cm-1

Wavenumber / cm-1200 400 600 800 1000 1200 1400 1600 1800

993 1006 1554 1587

DRSAverage

Figure 5.8: Comparison of DRS vs. average spectra of 4-mercaptophenol in AuNPs in acidic (top) and basic (bottom) solutions. Raman vibrational modes of interest are expanded.

75

0 100 200 300 400 500 600 700 800 900

Acqusition number

1006

cm-1

Inte

nsity

Figure 5.9: Plots of 1006 cm-1 SERS peak intensity vs. spectrum acquisition number for 4-mercaptophenol in acid (red) and base (blue).

The single spectrum that gave rise to the outlier peak seen in the acidic 4-mercaptophenol

correlogram at 1006 cm-1 was investigated more closely to verify that the signal was not due to

instrument error. Although the spectrum looked similar to the other collected spectra, the peak

maximum was shifted slightly to 1003 cm-1. This observation sparked interest into monitoring

how fluctuations in peak positions compared between vibrational modes and solution pH. To

investigate this behavior, histograms of peak maxima of the five vibrational modes were plotted

in Figure 5.10.

76

-4 -2 0 2 4 6 80

200

400

600

800

4-MpOH pH 5

Wavenumber Shift (cm-1)

-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10

4-MpOH pH 10

702818100611661587


Figure 5.10: Histograms of vibrational mode maxima frequency shifts for 4-mercaptophenol in AuNPs in acidic (left) and basic (right) solutions.

The results indicate that the degree of fluctuation in peak position differs between

vibrational modes with the 818 cm-1 peak undergoing the most significant shifts in peak position.

Although slight deviations in peak positions could be attributed to frequency drift the observed

fluctuations are not systematic. Additionally, the amounts of peak shifting increase for three of

the five peaks in basic solution (702 cm-1, 818 cm-1, and 1166 cm-1) while the other two peaks

remain constant. In particular, the out-of-plane ring stretching mode that gives rise to the 702 cm-

1 peak undergoes increased position fluctuations at pH 10. Although DLS data indicates that

aggregation increases in with increasing pH, significant changes in peak fluctuations between

vibrational modes and pHs suggest that pH-dependent chemical binding between 4-

mercaptophenol and AuNPs plays a role in SERS enhancement.

77

5.7.2 Thiophenol Analysis

Correlograms of thiophenol-functionalized AuNPs in acidic and basic solutions are

shown in Figure 5.11. Unlike 4-mercaptohenol the marginal distributions of thiophenol peak

intensities appear normal and invariant to pH. This is because thiophenol adsorbed to AuNPs has

no potential for pH-induced reaction chemistry. The thiophenol in base correlogram shows a

single outlier spectrum indicated by positive skewing of the otherwise normal distributions for

peaks at 419 cm-1, 474 cm-1, and 695 cm-1. This single outlier spectrum manifests as a single data

point in the upper right corner of all but one of the correlation scatter plots. Interestingly, the

peak intensities at 1073 cm-1 and 1574 cm-1 in this outlier spectrum are not significantly higher

than in the other 999 collected spectra. The determination coefficients between thiophenol peaks

vary significantly and with no clear dependence on pH. For example, 1073 cm-1 and 1574 cm-1

peak intensities exhibit a high level of dependence in base (R2 = 0.94) and are nearly independent

in acid (R2 = 0.06).

418

474

694

1073

1574

Thiophenol in Acid

419

474

695

1073

1574

Thiophenol in Base

78

Figure 5.11: Excel correlograms of thiophenol in AuNPs in acidic (left) and basic (right) solution.

The outlier spectrum was compared to the average spectrum in Figure 5.12. Although

there is an apparent shift in overall signal intensity the relative peak intensities of the average and

outlier spectra remain nearly constant. This indicates that analyte binding remains the same

between the outlier and ensemble signals and that SERS enhancement is most likely due to

thiophenol adsorbed to a hotspot region on a coalesced nanoparticle dimer.

Figure 5.12: Thiophenol in base outlier spectrum (red) vs. average spectrum (blue).

In Figure 5.13 we show the DLS distributions. Both distributions show reduced

concentrations of dimeric aggregates compared to 4-mercaptophenol coated nanoparticle

distributions. Multimer aggregates are undetectable at pH 5 and at very low concentration at pH

79

400 600 800 1000 1200 1400 1600 400 600 800 1000 1200 1400 1600

10. This indicates that thiophenol adsorption does not contribute significantly to nanoparticle

aggregation at either pH.

Figure 5.13: DLS distributions of thiophenol-functionalized AuNP aggregates in acidic (top) and basic (bottom) solutions.

Fluctuations in vibrational mode peak positions were plotted in Figure 5.14 to ensure that

the SERS enhancement of thiophenol in base was a result of coalesced AuNPs and not pH-

induced chemical bond formation. Variations in peak maxima are within ±3 cm-1 for all of the

vibrational modes regardless of pH. The largest degree of fluctuation was observed for the 474

cm-1 peak which corresponds to an out-of-plane ring breathing mode.

-4 -3 -2 -1 0 1 2 3 40

100200300400500600700800900

1000

Thiophenol pH 5


-4 -3 -2 -1 0 1 2 3 4

Thiophenol pH 10

41947469510731574


80

100000x

1000000000x

10000x

Figure 5.14: Histograms of vibrational mode maxima frequency shifts for thiophenol in AuNPs in acidic (left) and basic (right) solutions.

5.7.3 4-Mercaptopyridine Analysis

Although SERS enhancement phenomena of 4-mercaptopyridine in acid and base were

analyzed using DRS techniques described in chapter 4, correlograms and frequency shift

histograms were generated to ensure the viability of these new data analysis techniques.

Correlograms of 4-mercaptopyridine in acid and base are shown in Figure 5.15. Marginal

distributions of peak intensities exhibit mostly normal behavior with slight positive skewing in

both acid and base. Determination coefficients between peaks decrease significantly in basic

solution. Low R2 values in the first row of both correlograms indicate 815 cm-1 peak intensity is

significantly less dependendent on intensities of the other three peaks.

815

1004

1091

1576

4-Mpy in Acid

4-Mpy in Base

815

1010

1092

1576

Figure 5.15: Excel correlograms of 4-mercaptopyridine in AuNPs in acidic (left) and basic (right) solution.

81

Histograms of peak maxima of the four vibrational modes were plotted in Figure 5.16.

The vibrational modes at 1004/1010 cm-1 and 1576 cm-1 undergo significant shifts in peak

position while the vibrational modes at 815 cm-1 and 1091 cm-1 remain constant. Aside from peak

shifting from 1004 cm-1 to 1010 cm-1 between acidic and basic solutions, the two histograms are

nearly identical. These observations indicate that although the SERS enhancement is sensitive to

chemical bond formation between 4-mercaptopyridine and AuNPs it is independent of pH.

-10

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 80

100

200

300

400

500

4-Mpy pH 5


-10

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

4-Mpy pH 10

815100410911576


Figure 5.16: Histograms of vibrational mode maxima frequency shifts for 4-mercaptopyridine in AuNPs in acidic (left) and basic (right) solutions.

5.8 Summary

A summary of our results is shown in Figure 5.17 with proposed adsorption mechanisms

for the three analytes in acidic and basic solutions. Our analysis indicates that 4-mercaptophenol-

coated AuNPs undergo pH-induced aggregation resulting from O-AuNP bond formation.

82

Thiophenol-coated AuNPs functioned as a negative control because they are not affected by pH.

4-mercaptopyridine-coated AuNPs undergo aggregation resulting from N-AuNP bond formation

in both acidic and basic solutions.

O

S S

pH 5 pH 10

Figure 5.17: Summary of proposed adsorption mechanisms for 4-mercaptophenol (top), thiophenol (middle), and 4-mercaptopyridine (bottom) on AuNPs in acidic (left) and basic (right) conditions.

83

5.9 Conclusion

Correlograms and frequency shift histograms are a continuation of our previous research

efforts involving specific extraction of unique SERS signals from colloidal SERS active

nanoparticles by DSERS analysis. Outliers in marginal distributions of peak intensities expedited

the identification and extraction of highly enhanced SERS spectra. Marginal distribution profiles

indicated the absence (normal) or presence and type of hotspot formation: chemisorption

(bimodal) or physisorption (positively skewed). Scatter plots of peak intensities and their

corresponding determination coefficients identified unique relationships between specific

vibrational modes.

Frequency shift histograms indicated that certain vibrational modes, particularly out-of-

plane ring stretching modes, fluctuate more than in-plane ring breathing modes. They also

showed that fluctuations in peak intensity increased with the formation of chemisorbed

aggregates. Most importantly, results from these new statistical analysis techniques match with

results from DLS distributions and DSERS analyses.

5.10 References

1. Fleischmann, M.; Hendra, P. J.; McQuilla.Aj, Raman-Spectra of Pyridine Adsorbed at a Silver Electrode. Chem. Phys. Lett. 1974, 26 (2), 163-166.

2. Jeanmaire, D. L.; Vanduyne, R. P., Surface Raman Spectroelectrochemistry. Part 1. Heterocyclic, Aromatic, and Aliphatic-Amines Adsorbed on the Anodized Silver Electrode. J. Electroanal. Chem. 1977, 84 (1), 1-20.

3. Kerker, M.; Siiman, O.; Bumm, L. A.; Wang, D. S., Surface Enhanced Raman-Scattering (SERS) of Citrate Ion Adsorbed on Colloidal Silver. Appl. Optics 1980, 19 (19), 3253-3255.

4. Freeman, R. G.; Grabar, K. C.; Allison, K. J.; Bright, R. M.; Davis, J. A.; Guthrie, A. P.; Hommer, M. B.; Jackson, M. A.; Smith, P. C.; Walter, D. G.; Natan, M. J., Self-Assembled

84

Metal Colloid Monolayers - an Approach to SERS Substrates. Science 1995, 267 (5204), 1629-1632.



7. Yu, H.-Z. X., Nan; Liu, Zhong-Fan, SERS Titration of 4-Mercaptopyridine Self-Assembled Monolayers at Aqueous Buffer/Gold Interfaces. Anal. Chem. 1999, 71 (7), 1354-1358.

8. Siegrist, K. The Central Limit Theorem. http://www.math.uah.edu/stat/sample/CLT.html.

9. Li, R.; Ji, W.; Chen, L.; Lv, H.; Cheng, J.; Zhao, B., Vibrational Spectroscopy and Density Functional Theory Study of 4-Mercaptophenol. Spectrochimica acta. Part A, Molecular and Biomolecular Spectroscopy 2014, 122, 698-703.

10. Carron, K. T. H.; L. Gayle, Axial and Azimuthal Angle Determination with Surface-Enhanced Raman Spectroscopy: Thiophenol on Copper, Silver, and Gold Metal Surfaces The Journal of Physical Chemistry 1991, 95 (24), 9979-9984.

11. Y. Wang, H. H., S. Jing, Y. Wang, Z. Sun, B. Zhao, C. Zhao, J. R. Lombardi, Enhanced Raman Scattering as a Probe for 4-Mercaptopyridine Surface-modified Copper Oxide Nanocrystals. Analytical Sciences 2007, 23, 787-791.

85

http://www.math.uah.edu/stat/sample/CLT.html

6 Clennan Group Collaboration: Viologen-Functionalized SERS Substrates for the

Detection of Polycyclic Aromatic Hydrocarbons and Chiral Molecules

6.1 Introduction

A novel helical viologen (N, N'-Dimethyl-5,10-diaza[5]helicene) was synthesized and

characterized by the Clennan research group1. Viologens function as electron-transfer mediators,

DNA photocleaving agents, and as acceptor components of host-guest complexes. A chemical

sensor system for the detection of polycyclic aromatic hydrocarbon (PAH) pollutants using silver

nanoparticles functionalized with a viologen was demonstrated by Lopez-Tocon et al.2 and

sparked interest to develop a similar chemical sensor system using this particular viologen.

Additionally, N, N'-Dimethyl-5,10-diaza[5]helicene is chiral and undergoes racemization

in aqueous solution. Silver nanoparticles functionalized with a chiral viologen may offer

additional sensor capabilities including racemization kinetics and selective adsorption and

detection of chiral analytes. We chose cysteine as our analyte because it is a thiol that binds

readily to AgNPs and occurs as either D or L isomers. Similar research by the Balaz group

showed that CD measurements could differentiate between the chiral optical activity of D and L

cysteine functionalized quantum dots3.

6.2 Silver Nanoparticle (AgNP) Synthesis

Silver nanoparticles (AgNPs) of 30-50 nm diameter were prepared according to a well-

known Frens citrate reduction method. 100 mg of AgNO3 was added to 500 mL of hot HPLC

grade water and brought to a boil with stirring. 10 mL of 1% (w/v) sodium citrate dihydrate was

added at once and the reaction mixture was covered and left to boil with stirring for 1 hour. After

86

1 hour, the heat was turned off and the Ag colloid solution was cooled to room temperature and

transferred to a foil-covered plastic container. λmax = 425 nm of the colloidal solution was

determined by UV-vis analysis.

6.3 Instrumentation

Raman spectra were acquired with an IM-52 Raman microscope (Snowy Range

Instruments) using its solid and liquid sampling features. The IM-52 was set to 40 mW of 785

nm laser excitation at the sample with 8 cm-1 spectral resolution. Spectral data was collected for

0.5 s and 10 sequential collections were used to generate average spectra, unless otherwise noted.

Model structures and Raman spectra of phenanthrene and N, N'-Dimethyl-5,10-diaza[5]helicene

(viologen) were calculated using Gaussian 09 (B3LYP/6-311+G(2d,p)).

CD spectra were acquired using an Aviv model 430 CD spectrometer using a xenon lamp

light source. The spectrum range was set to 550-180 nm with a bandwidth of 4 nm. Spectral data

was collected for 100 ms every 0.5 nm and 5 sequential collections were used to generate an

average spectrum

6.4 Experimental

200 µL of ~20 mM viologen in acetonitrile was added to 2 mL of AgNPs in a glass

sample vial and an average spectrum was collected. 200 µL of 31.4 mM phenanthrene in

acetonitrile was added to the sample vial and an average spectrum was collected. The sample

solution was vortexed and additional spectra were collected hourly for 10 hours. An average

Raman spectrum of solid phenanthrene (i.t. 3 s) was collected.

87

6.7 mg of D-cysteine was dissolved in 2mL AgNPs in a glass sample vial and an average

spectrum was collected (i.t. 5 s, avg/5). 1 mL of 1% (w/v) sodium citrate was added to the

sample vial and indicator paper was used to determine the pH of the acidic solution (pH ~5). A

SERS spectrum was collected (i.t. 5 s, avg/5). The procedure was repeated using 10.4 mg of L-

cysteine.

Saturated aqueous solutions of pyrene, phenanthrene, napthalene, chrysene, benzene, and

anthracene were prepared by adding 10-15 mg of each PAH to 2mL water. Due to the low

solubility of these PAHs in water, most of the solid material remained undissolved, even after the

solutions were vortexed intermittently for several days. 200 µL of ~20mM viologen in

acetonitrile was added to 2mL AgNPs. 200 µL of saturated aqueous PAH solutions were added

to separate aliquots of the vNP solution and SERS spectra of the PAH-vNP solutions were

collected.

20 µL of ~20mM viologen in acetonitrile was added to 2mL AgNPs. 6-8 mg of D/L/DL

cysteine was added to separate aliquots of the vNP solution, followed by 1 mL of 1% (w/v)

sodium citrate. 0.5 mL of the cysteine-vNP solution was added to of 1.5 mL water in a quartz

cuvette and CD spectra were collected. SERS spectra of the remaining 1.5 mL of cysteine-vNP

solutions were collected at 0 and 1 hours.


Figures 6.1 and 6.2 compare experimental and model spectra of phenanthrene and

viologen. Both figures show some agreement between experimental and model spectra with

deviations in relative peak intensity and wavenumber. Although phenanthrene shows better

agreement than viologen, it should be noted that a normal Raman spectrum and SERS spectrum

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were collected for phenanthrene and viologen, respectively, and that model spectra were

calculated using a monomer in gas phase not associated with a silver nanoparticle.

400 600 800 1000 1200 1400 1600 1800 Wavenumber/ cm-1

Figure 6.1: Model Raman spectrum of phenanthrene (red), Raman spectrum of solid phenanthrene (green). Model spectrum peak at 3180 cm-1 not shown.

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400 600 800 1000 1200 1400 1600 1800 Wavenumber / cm-1

Figure 6.2: Model Raman spectrum of viologen (red), baseline corrected SERS spectrum of viologen on AgNPs (blue). Model spectrum peaks at 3070 and 3207 cm-1 not shown.

Figure 6.3 compares SERS spectra of phenanthrene in viologen-functionalized AgNPs

taken at different times over ten hours. The low solubility of PAHs in aqueous solutions and the

highly ordered structure of the self-assembled monolayer of viologen on the NP surface make

adsorption and detection of phenanthrene a slow process. Changes in the SERS spectrum are

most apparent between the 0 and 3 hours, and taper off with increasing time, most likely due to

NP aggregation. Boxes indicate regions where significant changes in the SERS spectrum occur.

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400 600 800 1000 1200 1400 1600 Wavenumber/ cm-1

Figure 6.3: Baseline-corrected SERS specta of viologen on AgNPs (blue), addition of phenanthrene taken at 0h (violet), 3h (green), and 10h (red).

Figure 6.4 contains SERS spectra of AgNPs adsorbed with D and L cysteine at neutral

and acidic pH. These results demonstrate the need to acidify the solution for adsorption and

detection of cysteine on AgNPs. At neutral pH, thiol deprotonation and dimerization via

disulfide bonding inhibits adsorption to the AgNP surface. A 1% sodium citrate buffer was used

to adjust the pH because the AgNPs are synthesized using a similar sodium citrate solution. Both

D and L cysteine SERS spectra are identical, as expected. CD spectra of these samples were

collected to further investigate the role of chirality on NP adsorption.

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400 600 800 1000 1200 1400 1600 Wavenumber / cm-1

Figure 6.4: SERS spectra of L-cysteine at pH 5 (blue) and pH 7 (violet); SERS spectra of D-cysteine at pH 5 (green) and pH 7 (red).

Figure 6.5 compares the SERS spectra of various PAHs in viologen-functionalized NP

solutions. The experimental method was varied slightly from that used in figure 3. Saturated

aqueous PAH solutions were made by adding PAHs to water in excess and mixing the solutions

for several days. This procedure was implemented to reduce the amount of time required for

PAH adsorption to the AgNPs. Differences between PAH-adsorbed spectra are subtle but are

distinct from the SERS spectrum without adsorbed PAH. It should be noted that the PAHs used

have different solubilitites in water but equal volumes of each solution were added to sample

vials. Adjusting for this will assure equal surface coverage of PAHs in solution.

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400 600 800 1000 1200 1400 1600

Wavenumber / cm-1

Figure 6.5: Baseline corrected SERS spectra of viologen on Ag NPs from top to bottom: No PAH, pyrene, phenanthrene, napthalene, chrysene, benzene, anthracene.

Figure 6.6 compares average CD spectra of D/L/DL cysteine-coated AgNPs at pH 5.

Although CD spectra of D and L cysteine are not complementary, as is expected for chiral

molecule-coated colloids, all 3 spectra show substantial and similar optical rotation. This could

be a result of chiral citrate molecules on the nanoparticle surface. However, the mechanism and

selectivity of a chiral citrate self-assembled monolayer on the nanoparticle surface has yet to be

understood.

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-7

-6

-5

-4

-3

-2

-1

180 200 220 240 260 280 300 320 340 180 200 220 240 260 280 300 320 340

Wavelength / nm

CD /

mde

g

Figure 6.6: Average CD spectra of D-cysteine (red), L-cysteine (blue) and DL cysteine (violet) on AgNPs.

Figure 6.7 compares average CD spectra of D-cysteine (red) and L-cysteine (blue) on

viologen-coated AgNPs. The two spectra are complementary to one another, as expected for

chiral molecules.

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-4

-3

-2

-1

180 200 220 240 260 280 300 320 340 360

Wavelength / nm

CD /

mde

g

Figure 6.7: Average CD spectra of D-cysteine (red) and L-cysteine (blue) on viologen-coated AgNPs.

Figure 6.8 shows the time dependence of the CD spectrum of DL cysteine on vNPs. The

five CD spectra collected for this sample showed a gradual transition from the observed CD

spectrum of viologen-functionalized AgNPs to the observed CD spectrum of DL cysteine-

functionalized AgNPs. Only the first and fifth collected CD spectra of the sample solution are

included in the figure but the other three CD spectra exhibited a similar trend.

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-8

-6

-4

-2

0

180 200 220 240 260 280 300 320 340 Wavelength / nm

CD /

mde

g

Figure 6.8: CD spectra of viologen-coated AgNPs (violet), DL cysteine on vNPs at t=0h (red), DL cysteine on vNPs at t=1h (green), DL cysteine-coated AgNPs.

Figure 6.9 compares the average SERS spectra of D/L/DL cysteine on vNPs. Although

the spectra are nearly identical, there is a distinct splitting of vibrational modes in the 660-680

cm-1 region. Thiocarboxylic acids generally exhibit a strong Raman band in the region of 500-

750 cm-1 due to the C-S stretching mode. Multiple C-S bands may be observed due to rotational

isomerism4. The DL cysteine on vNPs SERS spectrum exhibits a single broad peak in this

region, while SERS spectra of samples containing D and L cysteine isomers exhibit peak

maxima that are red and blue shifted, respectively. Although this phenomenon is subtle, it

suggests that viologen-functionalized AgNPs may serve as a chiral-sensitive SERS substrate.

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400 500 600 700 800 900 Wavenumber / cm-1

Figure 6.9: Average SERS spectra of D cysteine (red), L cysteine (blue) and DL cysteine (green) on viologen-coated Ag NPs. Peak splitting at 660-680 cm-1 appears to be indicative of D and L cysteine isomers.

6.6 References

1. Zhang, X.; Clennan, E. L.; Arulsamy, N., Photophysical and Electrochemical Characterization of a Helical Viologen, N,N'-Dimethyl-5,10-Diaza[5]Helicene. Organic letters 2014, 16 (17), 4610-3.

2. Lopez-Tocon, I.; Otero, J. C.; Arenas, J. F.; Garcia-Ramos, J. V.; Sanchez-Cortes, S., Multicomponent Direct Detection of Polycyclic Aromatic Hydrocarbons by Surface-Enhanced Raman Spectroscopy Using Silver Nanoparticles Functionalized with the Viologen Host Lucigenin. Anal Chem 2011, 83 (7), 2518-25.

3. Tohgha, U.; Varga, K.; Balaz, M., Achiral CdSe Quantum Dots Exhibit Optical Activity in the Visible Region Upon Post-Synthetic Ligand Exchange with D- Or L-Cysteine. Chem Commun (Camb) 2013, 49 (18), 1844-6.

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4. Lin-Vien, D.; Colthup, N. B.; Fateley, W. G.; Grasselli, J. G., The Handbook of Infrared and Raman Characteristic Frequencies of Organic Molecules. Academic Press: San Diego, 1991, 234.

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