Highly-Selective Optoelectronic Nose Based on Surface Plasmon

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Highly-Selective Optoelectronic Nose Based on Surface Plasmon Resonance Imaging for Sensing Volatile Organic Compounds Sophie Brenet, Aurelian John-Herpin, Francois-Xavier Gallat, Benjamin Musnier, Arnaud Buhot, Cyril Herrier, Tristan Rousselle, Thierry Livache, and Yanxia Hou Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b02036 • Publication Date (Web): 19 Jul 2018 Downloaded from http://pubs.acs.org on July 20, 2018

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Analytical Chemistry

Highly-Selective Optoelectronic Nose Based on Surface Plasmon Resonance Imaging for Sensing Volatile Organic Compounds Sophie Brenet,1 Aurelian John-Herpin,1 François-Xavier Gallat,1 Benjamin Musnier,1 Arnaud Buhot,1 Cyril Herrier,2 Tristan Rousselle,2 Thierry Livache,2 Yanxia Hou1,* 1 2

Uni. Grenoble Alpes, CEA, CNRS, INAC-SyMMES, 38000 Grenoble, France Aryballe Technologies, 38000 Grenoble, France

ABSTRACT: Monitoring volatile organic compounds (VOCs) is an important issue, but difficult to achieve on a large scale and on the field using conventional analytical methods. Electronic noses (eNs), as promising alternatives, are still compromised by their performances due to the fact that most of them rely on a very limited number of sensors and use databases devoid of kinetic information. To narrow the performance gap between human and electronic noses, we developed a novel optoelectronic nose, which features a large sensor microarray that enables multiplexed monitoring of binding events in real-time with a temporal response. For the first time, surface plasmon resonance imaging is demonstrated as a promising novel analytical tool for VOC detection in the gas phase. By combining it with cross-reactive sensor microarrays, the obtained optoelectronic nose shows a remarkably high selectivity, capable of discriminating between homologous VOCs differing by only a single carbon atom. In addition, the optoelectronic nose has good repeatability and stability. Finally, the preliminary assays using VOC binary and ternary mixtures show that it is also very efficient for the analysis of more complex samples, opening up the exciting perspective of applying it to “real-world” samples in diverse domains.

Detection of volatile organic compounds (VOCs) has become an important issue in diverse domains such as environmental monitoring for air quality control, food safety, disease diagnosis as well as in industrial manufacturing processes including quality control for beverages, food and cosmetics.1 For such applications, traditional analytical methods like gas chromatography (GC) and mass spectroscopy (MS), though accurate and reliable, require expensive equipment and are often time-consuming and laborious. On the other side, food and fragrance industries employ human sensory panels to evaluate the quality of an odor. Such sensory methods are time-consuming. Panelists are also expensive to train and employ. These approaches are thus impractical to use on a large scale or for field monitoring. To bridge the gap, an interesting alternative could be electronic noses (eNs). Inspired by the human olfactory system, the eN is a multisensor system, consisting of an array of sensors with cross-sensitivity to different samples and using advanced mathematical procedures for signal processing based on pattern recognition and/or multivariate statistics. In the last three decades, eN technologies have witnessed great progresses for providing transportable, inexpensive, reliable and rapid analyses.2,3 Nevertheless, to date, the capabilities of human olfaction far outshine the performances of existing eNs. In fact, most eNs are based on a small number of non-specific sensors each functionalized with chemical layers (such as metal oxides, polymers, etc.) having different physical adsorption properties. To improve the selectivity and increase the diversity of sensing materials for the development of eNs, different strategies have been employed, i.e. colorimetric arrays developed by Suslick’s group using various chemically responsive dyes.4 More recently, one particularly interesting tendency is the use of small organic and biological molecules as building blocks

with the involvement of surface chemistry and nanomaterials. The general idea is to use small and easily-accessible molecules as building blocks; then to make them self-assemble onto transducers via appropriate surface engineering. This gives a nanometer-scale organic or biological thin film, such as self-assembled monolayers (SAMs). The composition, structure and physicochemical properties of the monolayers can be varied and tuned rationally according to the target applications. For example, thiol monolayer capped gold nanoparticle sensor arrays5 and silane monolayer functionalized silicon nanowire field effect transistors6-8 were designed by Haick’s group using a wide variety of small organic ligands with different physicochemical properties. Campagnone and colleagues developed piezoelectric gas sensors using short peptides as sensing materials, which were screened based on molecular docking simulations.9,10 Peptides are particularly attractive as eN sensing materials thanks to their chemical robustness, diverse physicochemical properties, and their homology to natural olfactory receptors.11 Moreover, with recent advances in peptide synthesis, short peptides or polypeptides can be chemically synthesized, recombinantly produced in cells or plants with little difficulty. In the present study, monolayers of various small organic molecules and short peptides were employed as sensing materials. Regarding the transduction systems, different principles have been employed for the eN development: conductive sensors, gas sensitive field effect transistors, surface acoustic wave, quartz crystal microbalance, etc. 2,12 Each system has its own advantages and disadvantages that are specific to individual transducer type in terms of sensitivity, selectivity, response and recovery time, detection range, operating limitations. However, unlike the human nose, most eN devices typically

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employ a very few number of sensors and use an equilibrium response rather than a temporal response. In order to reduce the mismatch between the human nose and electronic noses, optical techniques offer an attractive potential for the creation of a large sensor array capable of simultaneously monitoring many binding events in real time, as demonstrated by Walt and colleagues with their bead-based optical fiber arrays for the analysis of VOCs.13,14 Another optical technique, surface plasmon resonance (SPR), can also be interesting for such applications. SPR is a label-free technique and allows observing interactions in real-time. It has been widely used for analyses of biomolecular binding events in liquid.15-19 Some preliminary assays were carried out for the detection of low-weight odorant molecules in solution using specific biomolecules such as olfactory receptors.20,21 Although in the early 1980s the feasibility of SPR for gas detection was demonstrated,22,23 since then only very few examples were reported in the literature.24-27 These systems were based on solely one or two sensitive layers, and were thereby limited in terms of sensitivity and selectivity due to a lack of surface engineering. To the best of our knowledge, we report here for the first time the development of an opto-electronic nose using surface plasmon resonance imaging (SPRI) by combining the strength of SPR with cross-reactive microarrays. Indeed, SPRI is particularly promising for eN development. First, with this technique it is possible to immobilize up to hundreds of sensing molecules on the same chip for the creation of a large sensor array. The number of sensors is only limited by the resolution of the microarray printing of the sensing molecules. It can thus be tuned accordingly without increasing the system complexity. Second, thanks to the imaging mode, the interactions between the analytes and hundreds of sensors can be simultaneously monitored using the same instrument. SPRI allows thus to record an odor in the form of an image, based on which a characteristic pattern can be generated for each sample. This information can be used for the discrimination of samples with pattern recognition and multivariate statistics. Finally, SPRI can provide temporal responses with kinetic information. Recently, our group developed an electronic tongue for the analysis of proteins and complex mixtures in liquid by SPRI using cross-reactive sensor arrays.28-31 We have shown that employing the temporal response enhances the performances of the electronic tongue as the interaction kinetics encode additional information on samples’ identity compared to a simple equilibrium response. In the present study, a homemade SPRI instrument dedicated for the analysis of VOCs in the gas phase was developed and optimized. We demonstrated the efficiency of SPRI for the analysis of diverse VOCs, the good performances of the obtained optoelectronic nose such as sensitivity, selectivity, repeatability and stability, and its great potential as a novel analytical tool for gas sensing.

EXPERIMENTAL SECTION Chemicals. 2-methylpyrazine (≥ 99%), methylcyclohexane (≥ 99%), hexane (≥ 97%), amylamine (≥ 99%), 2,3dimethylpyrazine (≥ 95%), phenol (≥ 99%), 1-propanol (≥ 99%), 1-butanol (≥ 99.4%), 1-pentanol (≥ 99%), 1-hexanol (≥ 99%), 1-heptanol (≥ 99%), 1-octanol (≥ 99%), methanoic acid (≥ 96%), ethanoic acid (≥ 99.7%), 1-propionic acid (≥ 99.5%), 1-pentanoic acid (≥ 99%), 1-hexanoic acid (≥ 98%), were purchased from Sigma-Aldrich. Isoamyl butyrate was provided by Naturamole (France). All chemicals were handled

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under a fumehood. No other significant risk was associated with this work.

Cross-reactive sensor microarray. Commercial SPRI microarrays containing seventy-two cross-reactive sensors were provided by Aryballe Technologies. They consist of a prism coated with a thin gold layer. The prism’s optimal characteristics for SPRI in the gas phase were obtained with the help of numerical simulations using a program developed in our laboratory.32 On the gold surface, sensing molecules were deposited in a microarray format using a non-contact microspotting robot (Scienion AG, Germany). Herein, eighteen biomimetic peptides and organic molecules such as thiols with diverse physico-chemical properties (hydrophobic, hydrophilic, charged, neutral, etc.) were used as sensing materials. Herein, for confidentiality concerns, the nature of these sensing molecules are not given. They were deposited on the microarray in quadruplicates and immobilized overnight via selfassembly thanks to the thiol-gold chemistry. The microarray was then thoroughly rinsed to remove any remaining molecules that were not grafted on the gold surface. In addition, an internal reference was included on the microarray, which is not sensitive to VOCs. SPRI set-up for gas sensing. The home-made SPRI system for gas sensing is based on the Kretschmann configuration (Figure 1a). A collimated beam with a wavelength of 632 nm from a LED is polarized and sent towards the functionalized gold surface through the prism to illuminate the entire microarray. When the incident light beam is totally reflected on the gold layer, an evanescent wave is created at the metalprism interface. Under certain conditions of energy, incident angle of the incoming light and refractive index of the different media, this evanescent wave resonates with the free electron plasma. Consequently, surface plasmon resonance occurs. This resonance causes a progressive variation in the reflectivity (%R), for example as a function of the light beam’s incident angle, called a plasmon curve (Figure 1b). When VOCs present in the dielectric medium in the gas phase interact with the sensing material on the microarray, there is an alteration in the local refractive index. As a result, an angular shift in the plasmon curve takes place. In practice, SPRI measurements are performed at a fixed angle, called the working angle θw. By choosing an angle of incidence at the highest slope of the plasmon curves, small changes in the resonance conditions can produce large variations in the reflectivity intensity. The plasmon curves for all the sensors on the microarray are given in Figure S-1. Upon interaction between VOCs and all the sensors on the microarray, the plasmon curves’ shift induces reflectivity changes ∆%R (Figure 1b and c). In the SPR image, the spots light up with different intensities related to interaction affinities (Figure 1d). Each spot corresponds to a sensor. In our system, this SPR image is collected by a 16-bit CCD video camera. Gas analyte generation, sampling and analysis. A fluid bench for gas sampling was built with a VOC analyte line and a reference line (Figure 1e). The analyte line consists of a 3-neck glass flask in which VOC liquid samples were evaporated and a pressure controller (Bronkhorst, Netherlands). Using mass-flow controllers (Bronkhorst, Netherlands), the analyte concentration can be adjusted by mixing the analyte line with carrier gas from the reference line. In addition, the reference line is also used for the regeneration of the microarray and the purging of the whole fluidic system after each analysis. Purified dry air serves as reference and


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carrier gas. The gas lines are connected to a SPRI analysis cell (0.25 cm3), composed of the prism, a piece of thin polytetrafluoroethylene (PTFE) gasket placed on the top of the prism and a stainless-steel cover with gas inlet and outlet. Finally, the analysis cell is connected to a photoionization detector (PID) (ppbRAE300, RAE Systems, U.S.) at the outlet for the measurement of VOC concentration. For the whole set-up, all the tubing is made of PTFE. All the connectors and valves are made of stainless steel. In practice, 50 µL of VOC was introduced into the glass flask by deposited onto a rectangular piece of fragrance blotter (1 cm2) used as a substrate. Except for phenol, which is solid at room temperature, 50 mg was weighted and placed directly into the flask. For VOC mixtures, 1 mL of solutions were prepared by mixing pure samples at the ratio of 1:1 (v/v) for binary mixtures and 1:1:1 (v/v/v) for ternary mixtures. 50 µL of such mixtures were used per analysis. Then, VOC dynamic headspace was generated by evaporating the sample under dry air at a controlled pressure (1.050 bar). Before the introduction of the VOC into the analysis cell, the headspace was evacuated via the VOC exit for 30 s to equilibrate and steady the mass flow. Afterwards, the sample was injected into the analysis cell at a constant flow rate of 100 mL.min-1 for 10 min. After each injection, the whole fluidic system was purged with dry air for 10 to 20 minutes for reuse. Prior to each series of injections, a blank injection was performed for two minutes to ensure the cleanliness of the analyte line. Before each VOC analysis, the baseline was checked to show almost no drift. All VOC analyses were performed in a random order to avoid bias due to any possible memory effect. Moreover, in this study, isoamyl butyrate was used as a reference compound to regularly check the good function of the system and to study the repeatability of the eN. For each sequence of analysis, this reference compound was systematically analyzed at the very beginning, several times in the middle and at the end of the experiment. To avoid any temperature effect, the whole analysis chamber was put in a Peltier controlled incubator at 25 °C.

Raw data process. Based on the SPRI images collected by the camera, variation of raw reflectivity (%R) was measured in real time for all the cross-reactive sensors. It was then converted into a variation of reflectivity (∆%R) by subtracting a baseline of reference taken 1 minute before the sample injection for each sensor. In this way, sensorgrams were obtained, as shown in Figure 1c, representing the interaction of the VOC with all sensors as a function of time.16,33 Equilibrium patterns were computed as the reflectivity variation (∆%R) at equilibrium for 1 minute on each sensor replicate per VOC analysis. The histograms were generated with the averaged ∆%R value for the quadruplicates of each sensor and their standard deviation indicated with the error bars. In addition, for the repeatability and statistics study, ∆%R was normalized (Rnorm) in order to avoid the VOC concentration effect. Rnorm values were calculated using the following equation (1)34

%

∑ %

. √

(1)

with N the number of cross-reactive sensors. Repeatability was evaluated using the average relative error found for the whole microarray, i.e. the average, for all sensors, of the standard deviation for each sensor divided by their Rnorm value.

Database analysis. In this study, two types of databases were generated using respectively equilibrium and kinetic response patterns. For both databases, for one VOC injection, one pattern was obtained for each of the four replicates of the 18 sensors. Several injections were also realized per VOC. This allowed taking into account the repeatability between replicates for a given injection as well as for different VOC injections. The equilibrium database was constituted of Rnorm patterns for each replicate of all sensors. As for the kinetic database, patterns were obtained using the equilibrium part as well as desorption phase of the sensorgrams. To do so, averaged signals from intervals of 18 seconds were regularly taken over a period of 3 minutes before (∆%Req) and after (∆%Rdes) the start of desorption for each quadruplicate. ∆%Req were also normalized using the equation (1) to get Rnorm values. For the desorption part, Rdesorption points were computed as the discrete first derivative of ∆%Rdes,, using the following equation (2). For each VOC analysis, four kinetics patterns were thus obtained with 10 Rnorm and 10 Rdesorption points. !"#

$%%&'( ) *$%%&'( ) + # *# +

(2)

Unsupervised multivariate statistics were performed on equilibrium and kinetic databases using R software and the FactomineR package.35 Principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) were performed on centered and reduced databases. To identify VOC groups in PCA, the name of each VOC was included in the datasets but used as supplementary information. Hence, it did not affect the obtained results. Ellipses were drawn on each PCA score plot, corresponding to the confidence ellipse of the barycenter of each group. Both databases included distinct patterns for each sensor quadruplicates to take into account the repeatability of the measurement in one analysis as well as between different analyses for the same VOC. This also allowed to increase the ratio of data points to variables to avoid erroneous classifications.36

Figure 1. Schematic illustration of the set-up of the optoelectronic nose. (a) Configuration of the home-made SPRI dedicated for the analysis of VOCs in the gas phase; (b) Plasmon curves for a given sensor before (black) and after (red) the interaction with a VOC sample; (c) Sensorgrams: a set of kinetic binding curves obtained with three sensors, plotted with the variation of reflectivity ∆%R(t) as a function of time upon the injection of a VOC sample at a fixed working angle θw; (d) SPRI differential image of the microarray recorded by the CCD camera after exposure to a VOC sample; and (e) the fluid bench constructed for gas generation, sampling and regeneration of the optoelectronic nose.


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RESULTS AND DISCUSSION Array response to individual VOCs from different families. For this study, we selected VOCs from diverse chemical families having different properties and distinct smells (alcohols, esters, carboxylic acids, ketones, hydrocarbons, aldehydes and amines) in order to evaluate the efficiency of the optoelectronic nose based on SPRI. Figure 2a-2f shows sensorgrams of six VOCs as examples, where ∆%R(t) is the baseline-corrected reflectivity change upon exposure of the sensor array to the analytes. All the sensorgrams have characteristic shapes with an association phase, equilibrium, and dissociation phase. Notably, SPR is often considered unsuitable and limited for the analysis of low weight molecules such as VOCs (molecular mass < 300 Da). Here, for the first time, we proved that SPRI is very efficient for sensing VOCs in the gas phase. In fact, when using air as an analysis medium, thanks to its low optical index, the detection noise in the gas phase remains extremely low. As a result, the binding of the small molecules generates an unexpectedly high signal/noise ratio, thereby giving a reliable signal.

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ppm); (b) phenol (34 ppm); (c) isoamyl butyrate (70 ppm); (d) 1pentanoic acid (51 ppm); (e) 1-pentanol (47 ppm); and (f) 1octanol (7.6 ppm). The chemical structure for each VOC is inserted. SPRI images and response patterns at equilibrium for (a’) 2methypyrazine; (b’) phenol; (c’) isoamyl butyrate; (d’) 1pentanoic acid; (e’) 1-pentanol, and (f’) 1-octanol.

For every VOC, clearly, all the cross-reactive sensors give responses with different affinities, with the exception of the internal reference sensors S0 that do not interact with any VOC and is thus used for a negative control. These results confirm that the eighteen organic molecules and peptides from the commercial microarray are relevant and effective. If we compare these VOCs’ sensorgrams, the set of cross-reactive sensors responds very differently to them with distinct kinetic interactions, even for the VOCs of the same family such as 1octanol and 1-pentanol. To illustrate the array’s capabilities to discriminate between VOCs, the SPRI images at equilibrium (10 min after VOC injection) are given, where the sensor array light up with different grey levels depending on affinities between the sensors and VOCs. Moreover, a pattern for each VOC was generated by plotting the overall sensor response at equilibrium (Figure 2a’-2f’). Based on SPRI images and the response profiles at equilib-rium, we can distinguish between certain VOCs, for instance between phenol and 2-methylpyrazine. For the others, more refined methods are needed, such as multivariate statistics. Finally, it is important to mention that the microarray was regenerated by clean air without increasing the carrier gas flow or heating the device. Such simple regeneration with air can also be found in eN based on polymers,37 and small organic molecules,6,38 in contrast to metal oxide based devices, which often require a high operating temperature.39

Analysis of VOCs with similar molecular structures. We further investigated the ability of the optoelectronic

Figure 2. Array responses, characteristic SPRI images and response patterns for selected VOCs. Sensorgrams depicting the sensor array response after exposure to (a) 2-methylpyrazine (290

nose to discriminate between VOCs of similar molecular structure. For this, two sets of homologous VOCs were analyzed, including six alcohols with carbon chain lengths from C3 to C8 and five carboxylic acids from C1 to C6 (Figure 3a). This allowed the discriminatory power of the device to be assessed based on not only the size of VOCs but also their chemical nature. PCA and HCPC were conducted using both the equilibrium and kinetic database for comparison. First, PCA was performed based on equilibrium patterns of all VOCs. A good separation between clusters of alcohols and those of carboxylic acids was achieved (Figure 3b). The first two PCs captured 73.18% of the total variance (eigenvalues per component given in Figure S-2). Nevertheless, there were major overlaps among clusters of VOCs from the alcohols. Even after including the third PC, the separation between these clusters was not significantly improved (Figure 3b’). We previously showed that a temporal response with kinetic information can provide additional discriminatory elements for cross-reactive arrays in contrast to an equilibrium response.28 A PCA based on kinetic patterns was thus performed. Noteworthy, the adsorption phase is VOC concentration-related while the equilibrium and desorption phase are more VOC nature-related. Therefore, we chose to leave aside the adsorption phase to avoid issues unrelated to the VOC interactions with the microarray, for instance due to dynamic headspace equilibration.40 The kinetic database was constituted of the kinetic curves including the equilibrium and desorption phases. Here, no kinetic constant was extrapolated from the data. Indeed, the desorption kinetics in Figure 2 depend


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strongly on the VOC and cannot always be exponentially

fitted

as

it

is

done

in

similar

studies.41,42

Figure 3. PCA score plots for the discrimination of structurally similar VOCs. Upper row: (a) chemical structures of the studied VOCs from the alcohol (1-6) and carboxylic acid family (I-V) with varied carbon chain lengths. In each group, different color tints are attributed as the carbon chain length increases. Middle row: PCA performed with the equilibrium database with (b) using the two first principal components, and (b’) using the three first principal components. Lower row: PCA conducted based on the kinetic database with (c) using the two first principal components, and (c’) using the three first principal components. Percentages of variability associated with each principal component are indicated along each axis.

Using multivariate statistics based on the kinetic curves themselves allowed to use the kinetic information without the need of a fitting model. Figure 3c shows PCA score plots based on kinetic patterns (eigenvalues per component given in Figure S-3). As expected, a better separation was achieved especially for the alcohols. By including the third PC, we distinguished easily all alcohols. (Figure 3c’). However, some overlaps still existed between methanoic acid and 1-hexanoic acid. In fact, PCA generates a set of orthogonal vectors based on microarray response to maximize the amount of variance into the fewest number of principal components to be used for visualization (usually two or three). Consequently, it does not fully report the extent of the discriminating capacity of our system. A more reliable classification can be obtained by including more dimensionality of the data in the statistical analysis. High dimensionality datasets can be analyzed by unsupervised hierarchical clustering methods.43 In this work, HCPC was performed using PCA coordinates retrieved for the components accounting for up to 90 % of the total variability.44 Four PCs were used to perform equilibrium patterns based HCPC and sixteen for the kinetic one. This allowed increasing the data-to-variable ratio and a more reliable classification was thus obtained.36 Figure 4 shows the two resulting dendrograms based on 64 VOC analyses, in which connectivity demonstrates the clusters most similar to each other and inter-cluster distance indicates the magnitude of dissimilarity between clusters. Figure

4a represents HCPC based on equilibrium database, and Figure 4b using the kinetic database. As expected, the former is limited in separation for VOCs from the same family. In contrast, the latter results in an almost perfect separation for all tested VOCs without any overlap among clusters. These results first showed the good repeatability of our system as the patterns are conserved in between replicates of the same sensor. The obtained classification showed no “memory effect”, since all VOC injections were performed in a random order. Furthermore, these results confirmed that the kinetics of response is closely related to the chemical function and structure of the VOC. The temporal response can provide supplementary discriminatory elements. Gratifyingly, the discriminatory power of the optoelectronic nose was substantially improved, capable of distinguishing between homologous analytes differing by a single carbon atom. Only very few existing eNs have such a high selectivity, i.e. DNAdecorated carbon nanotube-based FETs,45 microcantilever chemical sensors using specific peptides screened by phage display,46 and bioelectronic nose using human olfactory receptors.47 The last two examples employed specific sensing elements. In contrast, most eN systems using cross reactive sensors suffer from a general lack of selectivity. Of course, single carbon resolution is not our final goal, since traditional analytical techniques such as GC-MS are very efficient for it. This is only used to show the good discriminatory capacity of our system for VOCs.


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Interestingly, a further analysis of HCPC in Figure 4b reveals that the major cluster on the left corresponds to VOCs with a short carbon chain, contrary to the cluster on the right. Both of them have good separation for the two VOC families. This classification shows that the optoelectronic nose is also useful for characterizing VOCs based on their chemical characteristics (molecular weight and functional groups). Since HCPC analyses are based on temporal responses, the kinetics of interactions between VOCs and self-assembled sensing molecules are dependent on both molecular size and chemical nature. Based on these findings, it seems that the size of VOCs plays a more important role in the dissociation phase than their functional groups. This kind of information can help us to better understand the mechanism of the interaction between VOCs and sensing layers. Further investigations and characterization however need to be performed to identify a model that fits our experimental results.

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tion, only results on the internal reference and two randomly selected sensors were given as examples. As expected, no response was observed on the reference sensor, while the response was proportional to the analyte concentration for the active sensors. The maximum noise was evaluated at about 0.05 %R. Figure 5b shows the calibration curves for three randomly selected sensors and the internal reference. Overall, the variation of reflectivity is proportional and almost linear. Using their slope, the sensitivity was determined between 0.2 and 0.4 ∆%R.ppm-1 for all the sensors.

Figure 5. The sensitivity and limit of detection of the optoelectronic nose: (a) the variation of raw reflectivity in function of time upon exposure to various concentrations of 1-octanol, for the internal reference sensor (black) and two randomly selected sensors (S7 in grey and S8 in red); (b) calibration curves: averaged ∆%R at equilibrium for the four replicates of the internal reference sensor (black) and the three randomly selected sensors S7, S8 and S18 (grey, red and green, respectively) as a function of 1octanol concentration and the associated linear regression curve.

Figure 4. HCPC dendrograms based on (a) equilibrium patterns, and (b) kinetic patterns for the discrimination of structurally similar VOCs from the alcohol (1-6) and carboxylic acid family (I-V), with 64 analyses in total.

Performances of the optoelectronic nose. The performances of our system were evaluated in terms of sensitivity, repeatability, stability, and reusability. To determine the limit of detection (LOD), calibration curves were established using VOCs with different volatilities (ethanol and 1-octanol). The optoelectronic nose was successively exposed to 1-octanol at concentrations ranging from 1.43 ppm to 5.84 ppm, and then to ethanol at a higher concentration range (from 170 ppm to 1400 ppm). Figure 5a shows the real-time response of different sensors upon exposure to 1-octanol. For easier visualiza-

This value is however difficult to compare with other existing eNs. As complementary information, the resolution, namely the minimum difference in concentration that can be reliably measured (over 3x S/N ratios), was estimated between 375 to 750 ppb depending on the nature of the sensors. This parameter has the advantage to be independent of the transduction method for comparison with other eNs. Regarding the LOD of our device for 1-octanol, it is well below 1 ppm, which corresponds to the lowest concentration delivered by our fluidic set-up. Thus, LOD was extrapolated from the calibration curve as the concentration corresponding to 3 times the noise of detection, i.e. 0.15 ∆%R, for the most sensitive sensor. As a result, it was reckoned at approximately 375 ppb. Satisfyingly, these performances are comparable with other eN using similar sensitive materials. Haick’s group reported a LOD of 120 ppb and a resolution of about 400 ppb for their system based on thiol functionalized gold nanoparticles.48,49 For ethanol, the optoelectronic nose has much lower affinity. The analyses were therefore performed in a higher concentration range. The obtained calibration curves are nearly linear


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(Figure S-4). Importantly, the results showed that the optoelectronic nose is still efficient for the analysis of very low molecular weight VOCs. The repeatability and stability of the system were evaluated using isoamyl butyrate as the reference compound. It was analyzed at the very beginning, several times in the middle and at the end of each experiment. Satisfyingly, the obtained equilibrium patterns kept almost the same, indicating an excellent repeatability (Figure S-5a). An average relative error of 5 % was found in the equilibrium patterns of isoamyl butyrate between injections. A good repeatability was also obtained on different SPRI microarrays, with an average relative error of 7 %. To evaluate the stability, the drift over time was measured. It is well known that drift phenomena afflict almost all kinds of sensors. It is usually caused by complex phenomena due to sensor poisoning, aging and environmental changes.50 Figure S-5b shows the raw data of the reflectivity variation on the internal reference sensor and four randomly selected active sensors upon exposure to a series of VOCs in a period of five hours. Contrary to active sensors, the reference sensor that is not sensitive to the tested VOCs but only to environment changes has almost no drift. Therefore, the slight drift on the active sensors is most likely due to sensor poisoning and aging. Finally, to study the stability and reusability, a SPRI microarray was regularly used to analyze 75 VOCs (alcohols, acids, esters, alkanes, alkenes, and amines) including isoamyl butyrate for a period of 100 days. After each use it was stored in refrigerator at 4°C. Figure S-5c shows the overall sensor responses at equilibrium upon exposure to isoamyl butyrate on different days. Some sensors seem to be more stable than others. Nevertheless, overall, the typical response profile was conserved, demonstrating that our system is stable and reusable for at least three months. Current study is focusing on reducing further sensor poisoning and aging in order to increase the robustness of the system for complex real-world samples analyses.

Analysis of VOC mixtures. An essential feature of the human nose is its ability to discriminate between similar complex mixtures of odorants. We performed preliminary tests to evaluate the ability of the optoelectronic nose in this purpose using 1-octanol, 1-propanol, 1-propionic acid, and their binary (1:1) and ternary mixtures (1:1:1). Kinetic patterns were retrieved using information from equilibrium and desorption phases and analyzed using PCA and HCPC. A high number of PCs was found with 28 relevant components (eigenvalues per component shown in Figure S-6). Notably, the first two components already accounted for approximately 65% of the total variability. As can be seen, a very good separation was obtained between pure VOCs, their binary and ternary mixtures on both PCA score plots and HCPC dendrogram (Figure 6). Thus, our system is efficient for the analysis of mixtures and the good selectivity is not limited to pure VOCs. Additionally, in the PCA score plot, binary mixture clusters were located approximately in between the corresponding pure VOC clusters. Thus, one may expect to be able to identify a target VOC in binary and ternary VOC mixtures or even more complex mixtures after subjecting the system to a comprehensive learning phase. Interestingly, going down the HCPC dendrogram (Figure 6c), the first obtained classification showed a clear separation between 1-octanol containing samples and the others. It is likely that the response of 1-octanol is dominant in the binary

and ternary mixtures. In this study, the concentrations of each VOCs in the mixtures in gas phase were not measured. However, taking into account the vapor pressures of these three compounds (0.14 mmHg for 1-octanol, 2.4 mmHg for propionic acid, and 14.9 mmHg for 1-propanol, from SigmaAldrich). The concentration of 1-octanol in these mixtures is much lower than 1-propanol and 1-propionic acid, based on Raoult’s law. Therefore, it is most likely that this dominance is due to a stronger affinity between 1-octanol and the optoelectronic nose. These results suggest that our system is able to identify a dominant odorant in the VOC mixtures, even when interfering compounds have similar structures or similar physicochemical properties. In the near future, further development will be done to ensure that the optoelectronic nose is able to detect target analytes in a complex background with various interfering VOCs.

Figure 6. Analysis of VOC mixtures by the optoelectronic nose: (a) PCA 2D score plots and (b) 3D score plots both based on the kinetic database; (c) HCPC dendrograms. 28 VOC analyses were performed including pure 1-octanol (O), 1-propanol (P), and 1propionic acid (Pa), their 1:1 binary mixtures (OP, OPa, PaP), and 1:1:1 ternary mixture (OPaP).

CONCLUSIONS We have demonstrated that SPRI represents a new analytical tool for gas sensing. It is particularly promising for novel eN development with several advantages over existing systems, capable of simultaneously monitoring all binding events on a large sensor array in real time, giving temporal responses with kinetic information. We confirmed that such temporal responses did provide more relevant information with supplementary discriminatory elements compared to a simple equilibrium response. Based on them, the optoelectronic nose is very effective in sensing VOCs with an extremely high selectivity, able to distinguish between VOCs differing by a single carbon atom. The optoelectronic nose showed good repeatability and good stability upon repeated use and prolonged storage. Furthermore, the preliminary tests using binary and ternary VOC mixtures showed that our device was also very efficient for the analysis of mixtures, being able to identify a dominant odorant in these simplified VOC mixtures. In


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the near future, the analysis of real samples will be carried out by taking into account the effect of different parameters, such as temperature and humidity. Finally, it is important to mention that the miniaturization of such an optoelectronic nose is carried out by a local company. The design and concept of a handheld device has been validated. The dimensions of the miniaturized eN are 245 x 98 x 48 mm. The size of the prism is 10 x 10 mm. Today, functional prototypes are routinely used for odor analysis. Indeed, such a portable device could represent a promising tool for VOC monitoring in diverse application domains, such as outdoor and indoor air quality monitoring, quality control or fight against counterfeiting for the food and cosmetics industry, food safety and medical diagnosis.

ASSOCIATED CONTENT Supporting Information The supplementary figures as described in the text are available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding author *Dr. Yanxia HOU, Tel.: +33(0)438789478, Email: [email protected] Orcid Yanxia HOU: 0000-0002-2991-1030 Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS The authors thank DGA and CEA for a PhD scholarship for S. Brenet, Labex Arcane program (ANR-12-LABX-003) for their financial support for a postdoc scholarship for F.X. Gallat, and Labex LANEF program (ANR-10-LABX-51-01) for their financial support for a Predoc Scholarship for A. John-Herpin. Both Arcane and Lanef were funded by the French National Research Agency. This work was partially supported by the FUI-WISE AAP21 Minalogic project, BPI.

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Highly-Selective Optoelectronic Nose Based on Surface Plasmon

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