Ultrasensitive Silicon Nanowire for Real-World Gas Sensing ......collected in Riga East University...

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Supporting information for:

Ultrasensitive Silicon Nanowire for Real-World Gas Sensing:

Noninvasive Diagnosis of Cancer from Breath Volatolome

Nisreen Shehada,† Gerald Brönstrup,

‡ Konrads Funka,

§,, Silke Christiansen,

‡

Marcis Leja,§,,

and Hossam Haick*,†

† The Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion –

Israel Institute of Technology, Haifa 3200003, Israel

‡ Max-Planck-Institute for the Science of Light, Gunther-Scharowsky-Strasse 1, 91058 Erlangen,

Germany.

§ Faculty of Medicine, University of Latvia, LV1006 Riga, Latvia.

Department of Research, Riga East University Hospital, LV1006 Riga, Latvia.

Digestive Diseases Centre GASTRO, Riga, Latvia.

Corresponding Author (H.H.): [email protected]

1. Methods and Materials

1.1. Growth of the Si NWs. P-type SiNWs have been grown following the vapor liquid solid

(VLS) principle using a cold wall chemical vapor deposition (CVD) reactor. During the 30

min CVD process with a total pressure of 2 mbar the flow rates of SiH2, B2H6 (100 ppm in

H2) and Ar were kept constant at 4sccm, 1sccm and 10 sccm, respectively, i.e. in a B to Si

ratio of 1:20000. The growth was catalyzed by commercially available Au colloids (British

Biocell Int.) with 30 nm diameters. The growth substrate temperature was around 500 °C. The

resulting SiNWs had an average diameter of 40±8nm an average length of 8.5±1.5 micron.1

1.2. Deposition of the Si NWs array. The as-grown Si-NWs were first etched using buffered

Hydrofluoric acid for 15 sec and then immersed in a KI/I2/H2O solution (mass ratio 4:1:40)

for 1 min to remove the gold catalyst used in the growing process, and any possible gold

contaminates remaining on the surface of the SiNW. Following the etching process, the Si

NWs were dispersed in ethanol using ultra-sonication for 5 sec. The SiNWs were later

deposited on a highly-doped (0.001 Ω∙cm resistivity) P-type Silicon (100) substrate with 300

nm thermal oxide and a Ti/Au (10/200 nm) bottom gate electrode. The deposition process

used in this work is based on a previously reported spray coating procedure. 2 The procedure

mailto:[email protected]

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started with the deposition of the substrate on a hot plate set to 75 C, the SiNW suspension

was then applied using a spray-gun (PRONA R2F) with carrier gas pressure of 40 psi, and a

tilt angle of (5º±2º) to the substrate. The spray coating process resulted in well aligned

nanowire arrays (density ~ 1 NW/ 100 μm2).

1.3. Fabrication of the SiNW FETs. The substrate coated with Si NWs was cleaned by

successive immersions in acetone, methanol and isopropanol and by 5 sec rinsing in a

buffered HF. Then, top source and drain electrodes (20 pairs of 20 nm Ti/ 160 nm Au

interdigitated electrodes, length 1300 μm, width 2 μm, spacing 2 μm) were deposited via a

photolithography (Karl Suss MA6 Mask aligner) and lift-off process on top of the SiNWs.

Surface characterization of the fabricated devices was conducted by a light microscope (dark

field) in order to determine the density of SiNWs on the device and then observed using a

scanning electron microscope (SEM) to assure proper contact between the Si NWs and the

electrodes.

1.4. Surface modification of the SiNW FETs. Following surface activation, which was

achieved by 30 sec of plasma treatment, the SiNW FET surfaces were modified with various

molecules, listed in Table 1. SiNW FETs were modified using a single-step silane

modification (SSM), a two-step silane-chloride modification (TSCM) or a two-step silane-

monomer modification (TSMM). Devices modified using the SSM approach were: (i)

immersed in a silane/chloroform solution (2 mM; 10 mL) for 45 min in room temperature,

and then (ii) successively rinsed with chloroform, acetone, ethanol, isopropanol and drying by

N2 flow. The silane molecules prepared by this SSM approach and used in this study were

trichloro(phenethyl)silane (C6H5CH2SiCl3; TPS) and trichloro(3,3,3-trifluoropropyl)silane

(CF3CH2CH2SiCl3; TTPS). SiNW FETs modified using the TSCM approach were: (i)

immersed in 3-aminopropyul-triethoxysilane (APTES) / dehydrated ethanol (10 mM, 20 mL)

for 1 h, (ii) rinsed with ethanol, acetone, isopropanol and dried by N2 flow, (iii) immersed in a

solution of acylchloride in chloroform (10 mM) with 10 µL of triethylamine for 17 h, and,

finally, (iv) successively rinsed with chloroform, acetone, ethanol and isopropanol and dried

by N2 flow. The molecular modifications prepared by the TSCM approach were heptanoyl

chloride (C7H13ClO) and decanoyl chloride (C10H19ClO). SiNW FETs modified using the

TSMM were: (i) immersed in trichloro(3,3,3-trifluoropropyl)silane (CF3CH2CH2SiCl3; TTPS)

/ chloroform (2 mM, 10 mL) for 1 h, (ii) successively rinsed with chloroform, acetone,

ethanol, isopropanol and dried by N2 flow, (iii) drop-casted with a monomer solution in

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tetrahydrofuran (THF) (1 mM, 2 µL) and kept in a vacuum oven overnight (55 ºC), and (iv)

rinsed with THF, acetone, ethanol and isopropanol and dried by N2 flow. The molecular

modifications prepared by the TSMM approach were propyl-gallate (C10H12O5) and 3-methyl-

2- phenyl valeric acid (C12H16O2). For the sake of comparison, an unmodified (bare) device

was also prepared and used.

1.5. Surface Characterization. The molecular monolayer on the surface was characterized

by high-resolution X-ray photoelectron spectroscopy (XPS) (Thermo VG Scientific, Sigma

probe, England) fitted with a monochromatized X-ray Al Kα (1486.6 eV) source.3, 4

Fig. S1

presents the XPS results as achieved by spectral analysis using the peak fitting software

(XPSPEAK version 4.1). Comparison between the modified and unmodified Si/SiO2

structures shows a substantial decrease in the oxygen level, resulting from an exchange of the

SiO2 bonds with Si-O-Si bonds. The XPS analysis also shows a significant increase in the

carbon peak of the TPS-modified Si/SiO2 structure (Fig. S1b), as expected in this phenyl-

terminated structure. In the case of TTPS molecular modification, a flour peak appeared in the

TTPS-modified substrate (Fig. S1c), as expected in this kind of molecular modifications.

Fig. S1: XPS spectra of (a) bare (unmodified) Si/SiO2 structures and Si/SiO2 structures modified with (b) TPS

and (c) TTPS molecular modifications.

1.6. Sensing experiments upon exposure to VOCs. Molecularly modified SiNW FETs were

mounted on TO5 holders by wire bonding. The TO5 holders were then mounted on a circuit

board and inserted into a stainless steel chamber. The chamber was connected to a Keithley

2636A system sourcemeter and a Keithley 3706 system switch/MultiMeter to carry out

source-drain current (Ids) vs. back gate voltage (Vgs) measurements (back sweep from +40V to

-40V; in steps of 200 mV; a constant Vds of 2V). Each of these sensors was exposed to

various concentrations VOCs that serve as biomarkers of gastric cancer in breath samples and

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other VOCs that serve as confounding VOCs (or contaminants) that appear usually in exhaled

breath.5 VOCs were produced using a commercial permeation/diffusion tube dilution (PDTD)

system (Umwelttechnik MCZ, Germany), using a purified dry N2 (99.9999%) from a

commercial N2 generator (N-30, On Site Gas Systems, USA; equipped with an N2 purifier)

as the carrier gas. A temperature controlled oven was used to mix a constant flow (100 ± 1

cm3·min

−1) of purified N2 with a constant mass flow of the vaporized VOC of interest

(Dynacal, VICI Metronics). The N2/VOC mixture exiting the PDTD system was diluted again

with purified N2 to achieve the desired concentrations (range: several ppb–several hundred

ppb). The VOC concentration was set by controlling the mass flow rate of the vaporized VOC

(by controlling the diffusion tubes’ temperature and diameter) and the total N2 volume flow

rate. The gas mixtures were pumped through ORBOTM 402 Tenax sorption tubes. The

different VOCs used and their respective concentrations is described in Table 2.5, 6

The VOCs

were delivered to the exposure chamber by means of thermal desorption. The test chamber

was cleaned prior and after exposure by vacuum. The pressure difference between the

chamber (in vacuum) and the thermal desorption chamber was the driving power in inserting

the VOC sample into the chamber.

1.7. Breath sample collection. Exhaled alveolar breath samples from 33 volunteers with

gastric cancer and from 77 control groups with dyspeptic symptoms, but without cancer, were

collected in Riga East University Hospital, Riga, Latvia. All samples were collected in the

same hospital room. Patients were asked to fast and withhold smoking and alcohol

consumption at least 2 hrs before the sampling. The sorbent tubes were stored under

refrigeration (in 4 ºC) until transport to the Laboratory of Nanomaterial Based Devices

(Technion – Israel Institute of Technology, Haifa, Israel) for analysis. The duration between

sample collection and analysis was less than 3 months (NOTE: breath VOCs can preserved in

these sorbent tubes up to 6 months without any change in their composition). The breath

samples were collected following a protocol previously described.5, 6

In order to reduce the

effect of ambient contamination, a lung washout procedure was carried out. According to the

protocol, the patient took regular, un-exerted breaths for 3 min through a mouthpiece with a

filter cartridge on the inspiratory port (Eco Medics, Duerten, Switzerland), thus greatly

reducing the concentration of ambient VOCs in the inspired air; NOTE: The subjects were

asked to breathe normally throughout the entire procedure to avoid hyperventilation.7

Following the lung washout, the subjects were asked to inhale normally once more through

the filter and exhale normally through the mouthpiece into a separate exhalation port against

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10–15 cm H2O pressure (ensuring the closure of the vellum for excluding contamination

through nasal entrainment). During exhalation, the respiratory dead space air is exhaled first

and the alveolar air from the lungs is exhaled thereafter. The dead space air was automatically

filled into a designated dead space bag at the beginning of the exhalation and was later

removed. The alveolar breath from the tidal end was filled into breath collection bags (China:

TedlarR bags, Keika Ventures, LLC; Latvia: Mylar bags, Quintron, Milwaukee, WI, USA).

The applied breath collection method is a single-step process; the volunteer does not have to

take care of exchanging the dead space bag with the collection bag. Following the collection

procedures, the breath samples were transferred to ORBOTM 402 Tenax sorption tubes

(specially treated; 35/60 mesh; 100/50 mg; from Sigma-Aldrich, USA) by pumping each

sample through a sorbent tube. We have confirmed that the VOCs that were found in the

breath of gastric cancer patients and related confounding conditions (Table 1) can be trapped

and stores in the ORBOTM 402 Tenax® TA sorption tubes for at least half year when the

samples are refrigerated and cooled properly. Amann and co-workers have offered lately a

standardization of the breath collection route that might be acknowledged and used in the

future.8

1.8. Breath analysis with the SiNW FET sensors. A manual thermal desorption (TD)

system was used to transfer the VOCs trapped in the disposable sorbent tubes. Each tube was

heated at 190 °C for 8 min under a constant N2 (99.999% purity) flow of 60 ml/min, to release

the trapped breath VOCs into the bag. Then, a pulse of the bag’s content was delivered into a

stainless steel test chamber containing the SiNW FET sensors described in Table 1. A

Keithley 2636A system sourcemeter and a Keithley 3706 system switch/MultiMeter were

used measure the Ids vs. Vgs (back sweep from +40V to -40V; in steps of 200 mV; a constant

Vds of 2V) as a function of time. For each sample, the sensors’ baseline responses were

recorded, first for 5 min in vacuum (30 mtorr), followed by 5 min exposure of the breath

sample, and 5 min during sensors’ recovery. In order to detect malfunctions and/or slight

drifts of their baseline conditions, the sensors were calibrated daily by exposure to known

concentrations of two calibration compounds, including 23.8 ppm isopropyl alcohol and 6.3

ppm Trimethylbenzene , and recording their resistance changes.

Three sensing features were read out and extracted from the time-dependent response

of each sensor that related to Vth, µh, and Ids @ Vgs=0 features. Breath patterns were obtained

from the collective response of the sensors by applying Discriminant Factor Analysis (DFA).

DFA is a linear, supervised pattern recognition method that effectively reduces the

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multidimensional experimental data, in which the classes to be discriminated are defined

before the analysis is performed. DFA was also used as a heuristic to select the sensors with

the most relevant organic functionality out of the repertoire of twenty one, by filtering out

non-contributing sensors. DFA determines the linear combinations of the input variables

(features were extracted from each sensor) so the variance in each class is minimized and the

variance between classes is maximized. The DFA output variables (i.e. canonical variables -

CV) are achieved in mutually orthogonal dimensions; the first CV is the most powerful

discriminating dimension. We have divided the dataset of each analysis as training and

validation set. 75% of each group was selected randomly for the training set and 25% of each

group left out as blind samples. Leave-one-out cross-validation was conducted in order to

calculate the classification success in terms of the number of true positive (TP), true negative

(TN), false positive (FP) and false negative (FN) predictions. Given k measurements, the

model was computed using k-1 training vectors. All possibilities of leave-one-sample-out

were considered, and the classification accuracy was estimated as the averaged performance

over the k tests. Pattern recognition and data classification were conducted using MATLAB®

(The MathWorks). Once the best features were selected, the 25% validation set samples were

plot on the model that was built and based on the estimation of these 25% we calculated the

statistical parameters.

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2. Sensing response of S2 – S7 upon exposure to VOCs

Fig. S1: Response of the selected features (Vth,norm, μh,norm, Ids,norm @ Vgs=0) extracted from S2 in

exposure to: (a-c) VOC1, (d-f) VOC2, (g-i) VOC3, (j-l) VOC4, and to (m-o) VOC5. All values

presented in this figure relate to the area-under-curve of the specific response during the exposure time

of the specific VOC(s) normalized to the value of the area-under-curve during exposure to N2.

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To compare between the responses of the features to a given concentration of VOCs, we

ploltted the responses of the features as a function of the concentration for all VOCs (Fig. S7-

Fig. S13). It is easy to see that only S1 shows a higher response for the potential biomarkers

in comparison to the confounding factors in all features, while the remaining sensors show a

similar, or superior, response to one of the confounding factors in at least part of the features

(if not all).


exposure to the five VOCs in the same concentrations.





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3. Possible scenarios for sensing the various VOCs by the SiNW FET. The shift in Vth

induced by the adsorption of VOC molecules on the surface is expressed by:

' 'cos cosMLth ML

BOX

NV

dC (1)

where d is the thickness of the molecular modification, CBOX is the capacitance of the back

gate oxide, '

ML and ML are the molecular layer's dipole moment under exposure to VOCs and

in vacuum, respectively, while ' and are the average angle of the molecular layer under

exposure to VOCs and in vacuum, respectively. Assuming the average angle of the molecular

layer remains approximately unchanged upon exposure to VOCs, the change in threshold

voltage will be given by:

' cosMLth ML

BOX

NV

dC (2)

This means that the change in Vth is due to the change in the molecular layer's dipole moment.

Since the dipole moment of the molecular layer is negative, a positive change 0thV

means a decrease in the molecular layer's dipole moment, and a negative change means an

increase in the dipole moment 0thV . As seen in Fig.3, VOC1 and VOC2 cause a positive

response in the threshold voltage of S1 (a shift towards more positive values). This

phenomenon signifies a decrease in the dipole moment of the phenyl terminated molecule

(i.e., Trichloro(phenethyl)silane of S1) according to equation (2). The decrease in the dipole

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moment is caused by the shift of electrons towards the NW, and away from the end (phenyl)

group, causing a movement of holes in the other direction.

The effect of VOCs on the different electrical features of the SiNW FET might be

explained by one or combination of the following scenarios. Firstly, VOCs that affect the µh

but not the Vth or the subthreshold current (e.g., VOC1) are governed by changes in the

conduction velocity of the charge carriers, without affecting diffusion induced current (of the

minority charge carriers) in the "off" region of the device operation. In addition, their effect

on the dipole moment is much smaller (probably due to a small change in the induced

electrical field), which causes an undetectable effect on the threshold voltage. Secondly,

VOCs that affect both the Vth and the µh (e.g., VOC2) cause a significant (electrostatic)

change in the dipole moment as well as in the conductance of charge carriers. Thirdly, VOCs

that affect the subthreshold current, but not the other features (e.g., VOC3), have a big effect

on the diffusion-induced current, caused by an increase in the number of minority charges

carriers (electrons in this case). However, the number of electrons is not comparable to the

number of majority charge carriers (holes) and so does not affect their conductance. This is

supported by the positive change in Vth observed in the case of VOC3. This change is caused

by a slight decrease in the dipole moment, implying an increase in the number of electrons on

the surface. But, the number of electrons available is too small to cause a big enough change

in Vth, to enable us to distinguish between the various concentrations. We cannot discuss any

effect of VOC4, VOC5 on S1 in these concentrations, as the sensor's response to these

compounds does not exceed the experimental error.

In order to understand the response of these devices, it is important to consider the

changes caused to the SiNW in general, and its surface states and binding sites in particular,

during the modification process. In the modification process part of the binding sites are

transferred from SiOx sites to a carbon chain with a phenyl ending group in this case. That

transfer re-distributes the charges on the surface of the NW, leading to a left-shift in Vth. Our

hypothesis is that due to the presence of the phenyl as a functional group, the electrons are

distributed evenly between the different carbons in the phenyl group, resulting in a low charge

density in the vicinity of said group. This condition is disturbed during the exposure by the

presence of the VOCs which supply additional charges. The homogeneity of the charge

distribution within the phenyl group is broken, causing the attraction of charge carriers (holes)

toward the Phenyl and away from the NW surface. But, as the concentration of these VOCs

increases (increasing the number of charge carriers), more SiOx sites on the surface of the NW

are passivated by the charges of the VOCs, causing a partial attraction of holes back toward

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the NW surface. This causes a partial increase in the dipole moment, which can explain the

decrease in the response of Vth. Because these carriers are not free, but bound to the SiOx sites,

the conductance is affected and is decreased.

Another option is that in this range, the amount of charges supplied by the VOCs is

critical. Changing the concentration from 5 ppbs to 50 ppbs results in the multiplication of the

charges contributed by the VOCs by a factor of 10, which is an enormous amount when

looking into such low concentrations. This might result in a steric change caused to the

molecular layer on the surface of the NW, decreasing the effective dipole moment of the

layer. Also, an increase of this magnitude in the number of electrons will decrease the electric

field caused by the holes on the surface of the NWs, which will cause a decrease in the

mobility.

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4. Breath sample classification achieved by various sensors

Table S1: Breath analysis using SiNW sensors modified using different molecule

Sensor Feature

Training Test

Sensitivity

(%)

Specificity

(%)

Accuracy

(%)

Sensitivity

(%)

Specificity

(%)

Accuracy

(%)

S1

Vth 42 74 67 40 63 54

µh 26 93 67 70 75 74

Ids @ Vgs=0 74 70 72 72 75 77

All 87 81 83 71 89 85

S2

Vth 50 79 72 60 75 70

µh 30 93 78 10 93 62

Ids @ Vgs=0 40 92 79 40 63 54

All 65 92 86 60 63 61

S3

Vth 63 76 73 63 73 70

µh 21 67 57 63 73 70

Ids @ Vgs=0 37 80 71 64 80 73

All 74 72 73 72 73 73

S4

Vth 95 60 70 60 56 58

µh 75 54 60 70 56 62

Ids @ Vgs=0 95 11 34 90 0 34

All 80 63 70 50 63 58

S5

Vth 100 65 73 77 57 73

µh 23 82 71 50 69 62

Ids @ Vgs=0 17 96 80 30 75 58

All 70 73 73 80 63 70

S6

Vth 55 73 69 60 75 70

µh 61 74 72 83 54 70

Ids @ Vgs=0 44 80 73 64 64 62

All 44 80 73 64 64 62

S7

Vth 94 60 68 83 50 66

µh 55 93 86 25 92 62

Ids @ Vgs=0 61 70 68 25 79 54

All 50 88 81 25 92 61

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Ultrasensitive Silicon Nanowire for Real-World Gas Sensing ......collected in Riga East University...

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Transcript of Ultrasensitive Silicon Nanowire for Real-World Gas Sensing ......collected in Riga East University...