Electrostatic Selectivity of Volatile Organic Compounds Using


Mar 6, 2018 - ... Avi Ben-Cohen‡ , Yonathan Vaknin† , Alex Henning† , Joseph Hayon† ... Biomedical Engineering, School of Electrical Engineeri...
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Electrostatic Selectivity of Volatile Organic Compounds Using Electrostatically Formed Nanowire Sensor Niharendu Mahapatra, Avi Ben-Cohen, Yonathan Vaknin, Alex Henning, Joseph Hayon, Klimentiy Anatoliy Shimanovich, Hayit Greenspan, and Yossi Rosenwaks ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.8b00044 • Publication Date (Web): 06 Mar 2018 Downloaded from http://pubs.acs.org on March 8, 2018

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Electrostatic Selectivity of Volatile Organic Compounds Using Electrostatically Formed Nanowire Sensor Niharendu Mahapatraa*, Avi Ben-Cohenb, Yonathan Vaknina, Alex Henninga,c, Joseph Hayona, Klimentiy Shimanovicha, Hayit Greenspanb, and Yossi Rosenwaksa* a

Department of Physical Electronics, School of Electrical Engineering, Tel Aviv University,

Ramat Aviv − 69978, Israel b

Department of Biomedical Engineering, School of Electrical Engineering, Tel Aviv Univer-

sity, Ramat Aviv − 69978, Israel ABSTRACT For the past several decades, there is a growing demand for the development of lowpower gas sensing technology for the selective detection of volatile organic compounds (VOCs), important for monitoring safety, pollution and healthcare. Here we report the selective detection of homologous alcohols and different functional groups containing VOCs using the electrostatically formed nanowire (EFN) sensor without any surface modification of the device. Selectivity towards specific VOC is achieved by training machine-learning based classifiers using the calculated changes in the threshold voltage and the drain-source on current, obtained from systematically controlled biasing of the surrounding gates (junction and back gates) of the field-effect transistors (FET). This work paves the way of Si complementary metal–oxide–semiconductor (CMOS)-based FET device as an electrostatically selective sensor suitable for mass production and low-power sensing technology.

KEYWORDS Electrostatic selectivity, Electrostatically formed nanowire sensor, Field-effect transistors,

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Volatile organic compounds, Selective detection, Threshold voltage, Transistor parameters, Machine learning classifiers

Recent developments in low-power sensing technologies associated with the Internet of Things (IOT) paves the way to applications such as pollution tracking, personal air-quality monitoring, explosives detection, and breath analysis for preventive health care. Mass spectromety (MS) and gas chromatography (GC)– MS are the most sensitive and selective techniques for the detection of chemical species in a complex gas mixture1-2, however, these are extremely expensive and complex techniques that cannot be used in broad deployment. Sensors based on vibrational spectroscopy, such as photoacoustic spectroscopy and Raman spectroscopy are specific, but are still not compatible with on-chip integration. Among several categories of gas sensors, resistive ceramic3 and metallic sensors4, nanoelectromechanical systems5, and field-effect transistors (FETs)6 are effective for chip-scale miniaturization and low-power operation. Implementation of metal oxide sensing technology for selective gas sensing is challenging due to its dependence on the temperature of gas-oxide interaction and also chemical changes of the sensor material following gas exposure. The FET-based sensors have emerged as a promising candidate in overcoming power consumption, size limitations, and sensitivity. FETs are typically based on semiconductors that change their surface potential following molecules adsorption; this entails a current or capacitance modulation due to interaction of surface adsorbed gas in the linear amplifying region of the transistor. The use of materials like transition metal dichalcogenides7, semiconducting nanowires6, 8-11, organic semiconductors12, graphene13, carbon nanotubes14-15 and 2D materials were demonstrated for highly sensitive detection of VOCs. Silicon nanowire field-effect transistors (SiNW FETs) have shown excellent transduction and recognition of gas6, 16-17 and (bio)chemical8-9, 18-20 species due to their large surface to

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volume ratio and low-power consumption8, 18. The challenge is the selective sensing of gaseous chemical species like volatile organic compounds (VOCs) that are associated with quality control, environmental pollution, various diseases, or explosive materials20-23. In this work ‘electrostatic-selective’ gas sensing of homologeous VOCs based on the electrostatically formed nanowire (EFN)19, 24-28 is demonstrated. As previously described19, 2426

, the EFN is a multiple gate FET fabricated in a CMOS process, where the nanowire is not

physically-shaped but is electrostatically defined post fabrication. The EFN has already been demonstrated for detection of ethanol, acetone and several aliphatic hydrocarbons25-28. Recently, the EFN has been demonstrated for electric-field-controlled sensing, a novel sensing concept29. We exploit the above sensing concept and demonstrate highly selective (~90%) detection of homologous and different functional groups containing VOCs, relying on the use of multiple parameters of the EFN sensor (threshold voltage, Vth and the drain-source on current, Ion for both junction and back gates). These sensor parameters are used as input for the training of the machine learning based classifier to make the detection of the targeted VOC. The results demonstrated here are significant for the sensing community as selectivity between homologous VOCs is extremely challenging in general and has not been reported before with a single unmodified sensing device.

EXPERIMENTAL METHODS VOCs Sensing and Electrical Measurements. The EFN fabrication and concept has been described in detail in previous work26-27. The EFN device was mounted on a chip carrier, which was placed in an air-sealed steel gas-sensing chamber connected to a gas delivery system. All the sensing experiments were performed in a controlled dry air (~ 99.9% purity) atmosphere inside the sealed gas-sensing chamber. The sensor response upto 35% relative humidity (RH) is very small and can be ignored. The target VOC saturated gas was generated

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using a bubbler system, which was then diluted with dry air at controlled flow rates using mass flow controller (Key Instruments Series FR2000 Acrylic Flowmeters) in order to achieve desired concentrations of the VOC inside the chamber. The analyte concentration was monitored by using a photoionization detector (ppbRAE 3000, RAE Systems) connected to the gas-sensing chamber. Agilent B1500A semiconductor device analyzer was used to carry out all the electrical measurements of the EFN device, before and after exposure of the analyte. All the sensing experiments were performed without any surface functionalization on the top dielectric layer of the EFN device. Six VOCs have been measured (Table 1) : four homologous alcohols (hydroxyl, -OH) with different chain lengths, one ketone (-CO-) and one carboxylic acid (-CO2H). The four homologous alcohols (hexanol, butanol, propanol and ethanol) are useful for evaluating the effect of the chain length of VOCs on the sensing selectivity. Ketone (acetone) and carboxylic acid (acetic acid) have the same chain length with propanol and ethanol, respectively and are helpful for evaluating the effect of functional (head) groups of VOCs in the sensing process. Table 1. The various VOCs used for the sensing experiments in this work. VOC

Structure

Chain length

Functional group

Hexanol

CH3CH2CH2CH2CH2CH2OH

6-carbon

Hydroxyl (-OH)

Butanol

CH3CH2CH2CH2OH

4-carbon

Hydroxyl (-OH)

Propanol

CH3CH2CH2OH

3-carbon

Hydroxyl (-OH)

Ethanol

CH3CH2OH

2-carbon

Hydroxyl (-OH)

Acetone

CH3COCH3

3-carbon

Ketone (-CO-)

Acetic acid

CH3CO2H

2-carbon

Carboxyl (-CO2H)

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Machine Learning Classifiers for Predicting Selective VOC. Linear discriminant analysis (LDA) has been employed to visualize the separability between measured change in sensor parameters for different VOCs30. Different machine-learning based classifiers, Support Vector Machines (SVM), Adaboost, Decision tree (DT), and Random forest (RF) were further employed to selectively detect targeted VOC. SVM31 tries to find a hyperplane (or a set of hyperplanes) that separate the data based on its labels in a high dimensional space. All feature measures are normalized as input to the SVM, and a linear kernel was used with the Sequential Minimal Optimization (SMO)32 algorithm. The Adaboost33 classifier is an ensemble method in machine learning that tries to generate sets of weak classifiers and collects them into a single strong classifier, while decision trees34 use a tree-like model. Each node is a decision rule; the algorithm follows the decisions from the root node down to the leaf node where the final response is located (classification). The Random forest35 is another ensemble learning method which uses multiple decision trees and outputs the class that is the mode of the classes output by individual trees.

RESULTS AND DISCUSSION In order to achieve concentration independent selectivity for the targeted VOC, we choose different concentrations of various analytes having almost identical sensor response (~ 0.95). The sensor response for a VOC is quantified by the change in drain-source current (IDS) before and after exposure of the analyte at fixed drain-source potential (VDS) = 0.1 V, junction gate potential (VJG) = -0.3 V and back gate potential (VBG) = -5 V. The sensor response is defined as ∆I/I0 = (I0 − IVOC)/I0, where ‫ܫ‬଴ and ‫ܫ‬௏ை஼ are the measured IDS in dry air environment before and after exposure to the VOC, respectively27. Thus, the selected concen-

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trations of the different VOC’s (hexanol = 200 ppm (8.8 µM), butanol = 1000 ppm (44 µM), propanol = 1700 ppm (74 µM), ethanol = 1800 ppm (79 µM), acetone = 3000 ppm (132 µM) and acetic acid = 8800 ppm (387 µM)) were used for all the experiments with the EFN device. The analyte molecules are adsorbed on the SiO2 surface, which is completely hydroxylated at room temperature36-39; this leads to reversible physisorption of organic molecules on the surface hydroxyls by forming hydrogen bondings36-40. Reversible physisorption of the VOCs on the sensor surface is in line with the observed regeneration of the EFN sensor by heating. All the VOCs measured here likely form monolayer on the SiO2 surface, as the required concentration for forming multilayers is substantially high38-39. Also, the multilayer character decreases with increasing VOC’s chain length, as would be expected from the gasphase autophobicity reported by Barto et al.41, in which multilayer adsorption of alcohol is restricted and the bulk liquid forms a nonzero contact angle. Again, it is noted that for a fixed concentration of analyte, the adsorbed amount of VOC’s on the SiO2 surface decreases in the order ethanol > propanol > butanol > hexanol38, 41. Thus, the required concentrations of VOC’s for getting the same surface coverage (i.e. similar sensor response) for different analytes increase in the order ethanol < propanol < butanol < hexanol. Measurements of IDS as a function of VBG were carried out to calculate back gate parameters, Vth (BG) and Ion (BG) of the EFN sensor at different VJG and VDS. On the other hand, measurements of IDS as a function of VJG were performed to extract the junction gate parameters, Vth (JG) and Ion (JG) of the EFN sensor at different VBG and VDS. Figures 1a and b show typical examples of IDS vs VBG measurements at different VJG; and IDS vs VJG measurements at different VBG, respectively for 0.1 V VDS in absence and presence of hexanol at a concentration of 200 ppm (8.8 µM).

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(a)

(b)

Figure 1. (a) IDS vs VBG curves at different VJG and (b) IDS vs VJG characteristic at different VBG, for 0.1 V VDS in absence (solid line) and presence (dotted line) of hexanol at a concentration of 200 ppm (8.8 µM). Relying on the IDS vs gate potential curves obtained before and after exposure to an analyte, the values of Vth and Ion for all the VOCs are calculated for both back gate (BG) and junction gate (JG), as described below and illustrated in the Supporting Information (Figure S1). Calculation of ∆Vth and ∆Ion (∆ is defined as, [parameter after exposer of analyte - parameter before exposer of analyte]) are performed for all VOCs as described in the Supporting Information (Figures S2, S3, S4 and S5). Each VOC produces different pattern of ∆Vth and ∆Ion as a function of the drain-source, back and junction gates potential (shown in Figures S2, S3, S4 and S5); this ‘finger print’ enables us to achieve selectivity between the measured VOCs. In our recent work29, we have used 3D finite element simulations (TCAD Sentaurus, Synopsys, Mountain View, USA) to compute the magnitude and distribution of the fringing electric field across the p-n-p regions of the sensor. The resulting fringing electric field strength on the top dielectric layer of the sensor is large for negative VJG, while it becomes significantly smaller for the positive VJG (Figure 2), as described in details in our previous work29. Thus the strength of the fringing electric fields strongly depends on the VJG of the

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EFN sensor. This VJG dependent fringing field has strong influence on the orientation of the analyte above the sensor surface and this in turn will affect the EFN sensor parameters in the presence of VOC as discussed later on.

Figure 2. The calculated fringing electric field at the surface of the EFN as a function of VJG. Figure 3 shows the changes in the back gate threshold voltage for all measured VOCs at a fixed VDS (0.4 V) for different VJG. For better representation of the ∆Vth (BG) for all analytes, the six VOCs are divided into three different groups based on either same functional group or same chain length. The ∆Vth (BG) of four homologous alcohols with different chain lengths are shown in Figure 3a. Figure 3b represents the ∆Vth (BG) of propanol (alcohol) and acetone (ketone) having same (three) carbon chain but with functional groups. Ethanol (alcohol) and acetic acid (carboxylic acid) have different functional groups with two carbon chain and are represented in Figure 3c. It is clearly observed that the patterns in Figure 3 strongly depend on the adsorbed molecule (analyte). The interaction between the analyte and the randomly distributed Si-OH and Si-O-Si binding sites on the top dielectric SiO2 surface (molecular gate) is affected by the strong fringing electric fields29, generated by the shallow p-n junctions of the EFN device.

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(a)

(b)

Same functional group with different chain lengths

Three carbon chain with different functional groups

(c)

Two carbon chain with different functional groups

Figure 3. Changes in Vth (BG) at different VJG of the EFN sensor at a fixed VDS = 0.4 V, on exposure to (a) four homologous alcohols (hexanol, butanol, propanol and ethanol); (b) propanol and acetone; and (c) ethanol and acetic acid. The used concentration of hexanol, butanol, propanol, ethanol, acetone and acetic acid are 200 ppm (8.8 µM), 1000 ppm (44 µM), 1700 ppm (74 µM), 1800 ppm (79 µM), 3000 ppm (132 µM) and 8800 ppm (387 µM), respectively.

The EFN sensor response is affected by two main factors: (1) the fringing electric fields at the EFN surface29 and (2) the effective channel diameter27. Our hypothesis is that the molecu-

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lar dipoles orient themselves in the direction of the electric field, generated by the biased p-n junctions of the EFN sensor, resulting in a net molecular dipole moment. For negative VJG the large fringing field (Figure 2) will orient the polar VOCs such that the negative charge of the molecule will be closer to the surface. This in turn will decrease the drain-source current (IDS) and consequently Ion will decrease and ∆Vth will be positive following exposure to the analyte, as shown in Figure 3 for all the VOCs. On the other hand, due to significantly smaller fringing field at positive VJG (Figure 2), the negatively charged surface hydroxyls will attract the positive end of the analytes which in turn will increase IDS during exposure to the VOC. Hence, Ion will increase and ∆Vth will be negative (Figure 3) following the exposure to small chain length analytes (e.g. ethanol, acetone), as these molecules are weakly hydrogen bonded with the sensor surface. An alkyl group in organic molecules has an inductive effect, which is likely to thrust its electrons to the opposite side of the molecules38-39, 42. This effect is greater in molecules with a larger number of carbon atoms: ethanol < propanol < butanol < hexanol. The greater the δ- character of oxygen atoms in alcohol molecules due to the inductive effect, the more strongly they attract electron-accepting atoms (in the present system hydrogen atoms in surface hydroxyls), resulting in the formation of strong hydrogen bondings. The H-bonding strength of the VOCs with the SiO2 surface increases in the order acetone < ethanol < acetic acid < propanol < butanol < hexanol38-39. The magnitude of the electric field at +0.2 V VJG may not be sufficient to orient the dipole of strongly hydrogen bonded VOCs with the SiO2 surface and consequently the negative charge of the molecule will be closer to the surface. Hence, IDS will decrease and consequently Ion will decrease and ∆Vth will be positive following exposure to the strongly hydrogen bonded VOCs (hexanol, butanol, propanol and acetic acid) even at positive VJG as observed from Figures 3a and c. On the other hand, the VBG affects mainly the position of the n-channel within the Si body, and has no influence on the fringing electric field. Thus, the negative charge of the molecule will remain

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closer to the sensor surface and this will decrease IDS resulting ∆Ion (JG) negative and ∆Vth (JG) positive following the exposure to all VOCs at both positive and negative VBG. This phenomenon also indicates the important role of fringing electric field on the selectivity between different VOCs. To visualize the separability between different VOCs using the measured changes in the sensor parameters we have used linear discriminant analysis (LDA)30. LDA maximizes the ratio of between-class variance to the within-class variance to guarantee maximal separability. For better visualization through LDA, the six VOCs were divided into three different groups, based on either same functional group (hexanol, butanol, propanol and ethanol) or same chain length (propanol and acetone; and ethanol and acetic acid), as described previously. The sensor parameters of each observation were projected onto the first two or three LDA axes. Figure 4a shows the first three LDA axes for homologous alcohols. Figures 4b and c show the first two LDA axes for propanol and acetone; and ethanol and acetic acid, respectively. From LDA visualization it is clear that our EFN sensor can easily discriminate between the VOCs having same chain length but with different functional groups, propanol and acetone (Figure 4b), and ethanol and acetic acid (Figure 4c). However, it seems harder to discriminate between homologous alcohols (Figure 4a) having small difference only in chain length. Thus, in order to achieve selective detection of the targeted VOC, we have employed different state of art machine-learning based techniques as described below.

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(a)

(b)

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Same functional group with different chain lengths

Three carbon chain with different functional groups

(c)

Two carbon chain with different functional groups

Figure 4. The first three LDA axes of observations taken from (a) four homologous alcohols (hexanol, butanol, propanol and ethanol). The first two LDA axes of observations taken from (b) propanol and acetone and (c) ethanol and acetic acid. Relying on the measured changes in the EFN sensor parameters, different machinelearning based techniques (as described in the experimental section) were employed to pursue the selectivity toward specific VOCs. The four sensor parameters, ∆Vth (BG), ∆Ion (BG), ∆Vth (JG), and ∆Ion (JG) are independent of each other. Dependent vectors in a sixdimensional space were used for encoding the six VOCs and these vectors serve as the output layer of the classifiers. Out of 6 elements in the VOC encoding vectors, only one element is denoted as “1” and the rest are denoted as “0”s. The position of “1” is used to identify the VOC species. For instance, hexanol and butanol are assigned by the (1, 0, 0, 0, 0, 0) and (0,

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1, 0, 0, 0, 0) vectors, respectively. The dataset obtained from the EFN includes 140 observations. To test the entire dataset, 10-fold cross validation is used, where each fold randomly samples observations with a similar portion from each VOC. Among these 10-folds data sets, training data sets are 9 folds (126 data points) and the validation (blind) data sets is 1 fold (14 data points). Four different classifiers were used to test the selectivity towards specific VOC’s. First, the training data sets were used to train and optimize these classifiers and then the validation data set was used for testing the classification system. Table 2 shows the permolecule accuracy measures as well as the average accuracy per classifier: The average accuracy was 92.8 ± 6.3 %, 87.9 ± 11.0% , 89.7 ± 5.8% , and 92.1 ± 7.4% using Adaboost, SVM, DT, and RF, respectively. The various classifiers used here, shows a small variability in the results. This variability may be explained as follows: the linear SVM is inferior to the nonlinear methods with an average accuracy of 88%. RF is an expansion of DT, and hence the improvement in the results, 92% compared to 90%. Adaboost is based on a "chain" of weak classifiers. RF is a composition of independent parallel classifiers. Both are known in the literature to give strong classification results, where RF is often more computationally intense. In the current work, the Adaboost method shows slightly better results to the RF classifier, with 93% accuracy.

Table 2. Classification accuracy [%] per-VOC and per-classifier VOC \ classifier

Adaboost

RF

DT

SVM

Hexanol

85

90

85

95

Butanol

95

85

85

75

Propanol

92

92

84

92

Ethanol

92

92

92

76

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Acetone

92

92

92

96

Acetic acid

100

100

100

92

Average

93

92

90

88

Since the highest average accuracy was obtained using the Adaboost classifier, the average prediction vectors of each VOC (probabilities ranging from 0 to 1), generated from the entire data set after testing is presented for the Adaboost classifier in Figure 5. The result shows promising recognition (or prediction) of all six VOCs with high probabilities in the correct VOC, although very similar types of VOCs (small difference either in one carbon chain length or in functional group) are used for targeted detection. In the present study we demonstrate our system classification performance. However, we believe that in a larger scope experiment, the VOCs' concentration can also be estimated using machine learning based regression methods.

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Figure 5. The average per-molecule predictions (columns) using the Adaboost classifier.

CONCLUSIONS In conclusion, we have demonstrated a combined method for selective detection of homologous alcohols and different functional groups containing VOCs that relies on the EFN sensor and machine-learning based classifiers. By using only one FET-based EFN sensor (rather than array of different sensors, as is the case in the traditional electronic nose approaches), six VOCs could be perfectly recognized without depending upon their concentration. The selective recognition of VOCs is achieved only by changing the drain-source and gate potentials of the FET, without any surface modification of the EFN sensor. Thus, we conclude that our “selective detection” technique is completely electrostatic. In spite of the small difference between each VOC, the accuracy of the selectivity presented here is impressive as observed from the LDA visualizations and prediction vectors. We believe that our EFN sensor-based “electrostatic selectivity” can be used for selective recognition of other VOCs and also can be

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used in real-world sensing applications in the gas phases, as it can be easily mass produced by standard CMOS technology.

ASSOCIATED CONTENT Supporting Information Calculation of the EFN sensor parameters and measured change in different sensor parameters for all VOCs are provided in supporting information. The Supporting Information is available free of charge on the ACS Publications website.

AUTHOR INFORMATION Corresponding Author(s) *Yossi Rosenwaks - [email protected] *Niharendu Mahapatra - [email protected]

Present Address c

Department of Materials Science and Engineering, Northwestern University, Evanston, Illi-

nois 60208, United States Author Contribution All

authors

have

given

approval

to

the

final

version

of

the

manuscript.

ACKNOWLEDGMENT This work was financially supported by the XIN centre of Tel Aviv and Tsinghua Universities and Israel Innovation Authority (Meimad).

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REFERENCES

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