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Nano-optomechanical systems for gas chromatography Anandram Venkatasubramanian, Vincent T. K. Sauer, Swapan K. Roy, Mike Xia, David S. Wishart, and Wayne K Hiebert Nano Lett., Just Accepted Manuscript • DOI: 10.1021/acs.nanolett.6b03066 • Publication Date (Web): 17 Oct 2016 Downloaded from http://pubs.acs.org on October 18, 2016
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Nano Letters
Nano-optomechanical systems for gas chromatography Anandram Venkatasubramanian1, 2, Vincent T. K. Sauer1, 2, Swapan K.Roy1, 3, Mike Xia1, David S. Wishart1, 2, 4 and Wayne K. Hiebert*, 1, 3 1
National Institute for Nanotechnology, Edmonton, Alberta, Canada – T6G 2M9
2
Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada – T6G 2E9
3 4
Department of Physics, University of Alberta, Edmonton, Alberta, Canada - T6G 2E1
Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada - T6G 2E8
Supporting information Abstract: Micro-Gas Chromatography (GC) is promising for portable chemical analysis. We demonstrate a nanooptomechanical system (NOMS) as an ultrasensitive mass detector in gas chromatography. Bare, native oxide, silicon surfaces are sensitive enough to monitor volatile organic compounds at ppm levels, while simultaneously demonstrating chemical selectivity. The NOMS is able to sense GC peaks from derivatized metabolites at physiological concentrations. This is an important milestone for small-molecule quantitation assays in next generation metabolite analyses for applications such as disease diagnosis and personalized medicine. The optical micro-ring, which plays an important role in the nanomechanical signal transduction mechanism, can also be used as an analyte concentration sensor. Different adsorption kinetics regimes are realized at different temperatures allowing temporary condensation of the analyte onto the sensor surfaces. This effect amplifies the signal, resulting in a 1 ppb level limit of detection, without partition enhancement from absorbing media. This sensitivity bodes well for NOMS as universal, ultrasensitive detectors in micro-GC, breath analysis, and other chemical-sensing applications. Keywords: NOMS, Gas Chromatography, metabolite detection, gas sensing
The first use of a mass spectrometry as a detector in the gas chromatography system was in 1952 as described by James 1 and Martin . Since then, the performance of the Gas Chromatography – Mass Spectrometer (GC-MS) has improved tremendously with the development of modern computer controlled GC-MS systems leading to widespread 2, 3 4 applications in environmental monitoring , medicine , and 5 breath analysis . While progress has been made in 6-10 microscaling the GC , a commercial GC-MS system is difficult to implement in portable applications due to the need for complex instrumentation such as an ionizer and ultra-high vacuum system for the mass spectrometer. Progress made in the field of nanoelectromechanical systems (NEMS) for low cost, large scale manufacturability and 11-14 sensitivity is very promising for developing micro-GC . This is abetted by mass sensitivity in ambient conditions of -18 15 the order of attograms (10 g) . In-vacuo sensitivity of the 16 -21 order of zeptograms (10 g) for silicon resonators, and -24 yoctograms (10 g) for carbon nanotubes under ultra-low 17, 18 temperature conditions , is almost in range of resolving differences of a single Da and accessing the full power of a
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NEMS based mass spectrometry . A defining vision of our research is to explore technologies with the potential to reach single Dalton mass sensitivities under ambient conditions. This sensitivity level would give meaningful mass resolving power to a gas-phase detector and essentially turn micro-GC into micro-GC-MS without any additional instrumentation, opening a new route to portability for GCMS. One such promising technology is nano-optomechanical 23-25 systems (NOMS) . The ultrahigh displacement sensitivity 26-28 and hence potentially higher mass sensitivity of NOMS can be easily integrated over large arrays using wavelength 29 division multiplexing (WDM) . NOMS are not size-limited as the laser spot need not be focused on the resonator and hence it is possible to realize very small nanomechanical dimensions, thus improving the sensitivity. Also the NOMS method has higher responsivity (signal to input power ratio) 30 compared to the interferometric method . Therefore exploring NOMS based gas sensors is justified.
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A primary motivation to develop sensors for portable applications is to develop point of care diagnostic devices for health monitoring. As the state of our health is typically a product of the interactions between our genes and our environment, measuring these interactions is key to developing good diagnostic tools. In many cases, these interactions are manifested by changes in metabolite levels in blood or urine. Metabolites are the downstream products of numerous genomic/proteomic level interactions and are particularly useful in the study of environment-gene interactions, the identification of disease biomarkers and the 31 discovery of drugs . Among the different biofluids (blood, cerebrospinal fluid, saliva, urine and fecal water) used by researchers to monitor human health, urine is favored because it captures and concentrates many important metabolic breakdown products of diagnostic importance. It is also sterile, easy to work with and easy to obtain large volumes. In this paper, we present an initial demonstration of a NOMS integrated with conventional gas chromatography. Sensitivity is high enough that bare Si surfaces allow ppm level detection of volatile organic compounds (VOCs) and ppb level detection of derivatized (previously non-volatile) metabolites. Micro-ring photonic readout of the NOMS plays an additional role as a photonic sensor of the analyte and we demonstrate surface chemistry selectivity with both NOMS and micro-rings. We show that surface adsorption kinetics are accessible and controllable, leading to effective amplification of adsorbate signals, and demonstrate biomolecule detection at physiological levels using derivatized human urine metabolites. The nanophotonic experimental setup used for this effort is based on the experimental setup described in previous 27-30 work by our group – see Sauer et al . To this an Agilent 6890N network GC system was connected using a specially designed aluminium chamber in which the device chip was mounted. Inside the GC, a 50:50 splitter was used to partition the incoming analyte-laden carrier gas between an insulated transfer line connected to the NOMS chamber and to the Flame Ionization Detector (FID) of the GC system (as shown in Figure 1a). The position of the deactivated Fused Silica Capillary (FSC) relative to the device (as shown in the inset of Figure 1a) was controlled by a three-axis Newport 461 series manual microcontroller mounted on top of the chamber (not shown in the Figure). The sample stage was connected with a resistance heater and a temperature sensor and the temperature was maintained at 298 K. Provision was made in the chamber to connect to a vacuum system in order -4 to control the chamber pressure from below 10 Torr to atmospheric pressure. Detailed description of the 30 nanophotonic confocal system is available in Diao Z et al . A snapshot of the experimental setup is available in the supplementary section in Figure S1a. Description of the operating principle, device description and basic device characterization is given in the supplementary section under the section “Measurement System”. For this experiment a double clamped beam measuring 9.5 µm long, 160 nm wide and 220 nm deep was used (as shown in Figure 1b and c). The resonant frequency and quality factor at atmospheric pressure and 298 K were measured to be 10.78 MHz and 83 respectively. Based on a frequency fluctuation measurement (see supplementary section, Figure S1e), the noise floor was determined to be 13.7 Hz for an averaging window of 1 second
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from an Allan deviation measurement (see supplemental information, Figure S1e inset). Alternatively the standard deviation (σ) of the frequency fluctuation was measured to be 11.5 Hz and hence the adsorption peak could be resolved above 3σ or 35 Hz. The concentrations of all VOC samples used in the experiment were measured in mg/ml using hexane as the solvent. With regard to VOC testing, two separate sets of samples were made. In the first set, five samples were prepared, in which toluene and xylene were added in increasing quantities to 1 ml of hexane. In the second set, one sample was prepared, in which benzene, toluene, xylene and mesitylene were added to 1 ml of hexane. These samples were then tested in the GC-NOMS setup. Details regarding the analyte mass in each sample are given in the supplementary section in Table S1. The human urine metabolites used in this experiment were all derivatized using N,O- bis(trimethylsilyl) trifluoro acetamide for use in GC according to a standard 31 operating procedure as described in Bouatra et al . From the list of metabolites in the human metabolome database (HMDB), five metabolites namely, hydroxypropionic acid (HPA), levulinic acid (LA), ethyl malonic acid (EMA), succinic acid (SA) and 3-methyl glutaconic acid (3MGA) were derivatized in different concentrations and combinations. Details of the metabolite concentrations and the combinations used are given in the supplementary section in Table S2.
Figure 1 GC-NOMS experimental setup (a) Schematic of the GC- NOMS photonic sensor; V.C- Vacuum Chamber, F.I.D – Flame Ionization Detector, F.S.C – Deactivated Fused Silica Capillary, G.C – Gas Chromatograph, M.O – Microscope Objective, T.C – Temperature Controller, P.Z – Piezo Actuator, (b) 3D rendering of the nanophotonic double clamped beam nanomechanical sensor, (c) SEM picture of the nanomechanical sensor Gas detection tests were conducted with VOC samples containing only toluene and xylene with hexane as the solvent. Figure 2a shows a comparison between the GC’s FID response, the NOMS response and the ring resonator response for sample 3 of the two component VOC sample. The response time of both the NOMS and the ring resonator
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is slightly longer than that of the FID for VOCs (by about 5%), which could be accounted for by peak broadening in the transfer line. Following the two component VOC detection tests with multiple samples, we observed the response to be linear with a slope comparable to the GC’s FID (see supplementary section Figure S2). However, there was a noticeable difference in the peak height of the VOCs when detected by the NOMS device compared to the FID. The peak height increased, doubling from toluene to xylene for the NOMS device, whereas for the FID, the peak height was essentially unchanged. This difference is dependent on the sensing mechanism and surface chemistry of the NOMS device and identifies a feature of the bare sensors surfaces. The FID response is mainly dependent on number of carbon atoms in the analyte. Given same concentration of the VOCs, a difference of one carbon atom between the analytes may produce only a marginal difference in peak height between toluene and xylene in the FID. The NOMS response, on the other hand, is dependent on several parameters including the surface chemistry, the molar mass and the concentration of the analyte in the sample. The NOMS device was used “asfabricated” with no special surface treatment applied except the presence of native oxide on the surface. The surface chemistry between the uncoated NOMS device and the VOC analytes is governed by the functional groups on the analytes. Here the methyl groups of the VOCs are electron donating groups thus forming an induced dipole – dipole 32 interaction . Since the number of methyl groups increases from toluene to xylene (from 1 to 2), we conclude that this leads to increased relative sticking coefficient of xylene compared to toluene. On the other hand, while the weight of the molecules also increases from toluene to xylene, the difference is only about 14 amu and could only account for a 14% height increase. This idea can be explained by a rudimentary model to compare the FID response with the NOMs response. Here the relative response of xylene with respect to toluene is parameterized for the NOMS and FID response as
Adsorption ratioNOMS = (Relative sticking factor)*(Molar Mass ratio)*(Concentration ratio) Adsorption ratioFID = Concentration ratio Here the relative sticking factor for the NOMS adsorption ratio was assigned a value of 2 for xylene as it has 2 methyl groups compared to 1 methyl group in toluene. The concentration ratio was obtained from Table S1 in the supplementary section for the two component VOC samples. Comparing the peak adsorption data with the above mentioned model for both the NOMS and FID response using ANOVA analysis, the difference between the model and experiment was determined to be insignificant. To further elucidate this effect, a sample containing benzene, toluene, xylene and mesitylene (with zero, one, two, and three methyl groups, respectively) was prepared (sample concentration in Table S1 in the supplementary section) and the comparison between the FID response and the NOMS response is presented in Figure 2b. Benzene with no methyl molecule was practically undetectable on the NOMS while it was detected by the FID and the ring resonator (to a small extent) at comparable concentrations and injection conditions (1 µL injection volume and 10:1 split ratio). The other molecules with methyl groups produced responses consistent with the model, with peak heights growing closely in proportion to number of methyl groups.
On closer observation of Figure 2b, a change in slope of the mesitylene uptake peak is noticed on the NOMS signal while the shift was not perceptible in the case of FID. To delve deeper, the injection volume was changed from 1 µL to 5 µL at the split ratio of 10:1 and the response is compared in Figure S3 in the supplementary section. From the Figure, we observed changes in the adsorption peak shape as we moved from benzene to mesitylene. Clearly, surface chemistry is dominant in this case and the NOMS sensor detection is surface site limited, evident from the flattening of the NOMS mesitylene uptake curve, but not so for toluene or xylene. NOMS sensors therefore offer a route towards selectivity even without sorptive layers. Standard silicon and silicon oxide surface functionalization techniques (such as silanization etc) could be applied to tailor the selectivity, and 33 surface chemistry interactions could be studied this way . There are other benefits of having no sorptive layer on the sensor. Sensing should have faster ultimate response (no mass transport limitations). Sensor temperature can be modified over a wider range, allowing transient analysis of 34 adsorption kinetics or microcalorimetry of chemical 35 reactions . Ultimately, uncoated sensors with appropriately passivated surfaces could also be tuned to have universal 34, 36 response to most analytes . All of these benefits are realized by taking advantage of the inherent mass sensitivity of NOMS precluding the need for signal amplification via absorption and partitioning. Another advantage of NOMS detectors is that the microrings employed to transduce the mechanical displacement signals can also be applied as independent sensors of the analyte concentrations. This can be done by measuring the shift in DC voltage (ring resonator response) caused by the index change of gas near the ring. Figure 2b shows the microring response for the same benzene series analyte mixture; surprisingly, a similar signal dependence as for the NOMS on number of methyl groups is evident. While the NOMS response being affected by surface chemistry is no surprise, it was unexpected that the micro-ring response should follow suit. The micro-ring can sample the index change within the local volume (within the evanescent tail of the guided optical wave which extends a few hundred nanometers). The signal from the analyte in this volume should therefore be proportional to concentration multiplied by ∆n (where ∆n is change in refractive index of the carrier gas (helium in this case) due to the presence of analyte). This is distinct from the NOMS, which will only detect molecules that land and stick on the surface. While the ring resonator is significantly affected by the surface chemistry effects during adsorption, it still retains some of its bulk sensing attributes as evident from the detection of benzene in Figure 2b. Further, the micro-ring response to mesitylene uptake in Figure S3 changes slope like the NOMS signal although it does not plateau presumably due to some amount of bulk contribution to the signal. However the similarity of the ring signal to the NOMS signal thus implies that the ring signal is also dominated by the molecules on the surface, rather than those in the evanescent volume. This assertion is confirmed by looking at the relationship between the micro-ring and NOMS signals as the number of methyl groups on the analyte molecules changes (see Supporting Information and Figure 3 (a)). The signal ratio remains roughly the same for toluene and xylene across analyte loading (both signals growing in proportion to the
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number of methyl groups), but changes significantly for benzene where the micro-ring gives a stronger signal (see Supporting Information and Figure 3 (b)). For benzene, we can assert that the bulk signal dominates the micro-ring response. This allows us to calculate the true local concentration of benzene at the chip surface (see Supporting Information, Analyte concentration near the ring sensor) and hence estimate an average loss factor between the gas concentration exiting the FSC and the average concentration near our device.
Figure 2 Gas chromatographic separation and detection by photonic nanomechanical resonators, (a) Toluene and Xylene separation and detection by FID (GC signal), NOMS and micro-ring resonator. Sample contains 8.32 mg/ml of Toluene and 7.8 mg/ml of Xylene. Injection volume of 1 µL and a split ratio of 10:1 used, (b) Benzene, Toluene, Xylene and Mesitylene (1,3,5 Tri methyl benzene) separation and detection by FID, NOMS and micro-ring resonator. Injection volume of 1 µL and a split ratio of 10:1 used. Sample contains 6.66 mg/ml of Benzene, 7.12 mg/ml of Toluene, 6.63 mg/ml of xylene and 7.71 mg/ml of Mesitylene. The blue dashed line corresponds to zero when no adsorption takes place.
Figure 3 a) Comparison of normalized response between NOMS signal (∆F) and micro-ring signal (∆V) for Benzene, Toluene and Xylene. The signals were normalized against ∆F=124 Hz and ∆V= 1.6 mV, b) comparison of slope of normalized ∆V response over slope of normalized ∆f response for Benzene, Toluene and Xylene.
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Based on calculated benzene concentration (see Supporting Information, Table S3), the generic analyte concentration at the sensors (chip surface) is ~100X smaller than the concentration at the outlet of the capillary in our geometry. This was surprising at first glance as the chip surface is only a few hundred microns from the capillary opening and previous reports suggested limited loss in this 11 geometry . A laminar jet flow dynamics model (see Supporting Information under section: Laminar round jet flow model) suggests concentration reduction of about 6070X due to the flow dynamics alone. Further loss due to diffusion could easily account for a further reduction factor of 1-2X. The jet flow loss should be completely mitigated 12 using integrated flow paths above the chip surface . Turning to sensitivity analysis, the threshold limit of detection can be determined by calculating the analyte concentration at the exit of the FSC and applying the capillary-to-chip loss factor found above. The analyte concentration at the exit of the FSC can be determined using 11 Canalyte = C1V1VmSER/(MWanalyteF∆t) from Bargatin et al and Li 12 et al . A more detailed description of this expression is available in the supplementary section under “Analyte concentration in gas phase”. For toluene, the threshold is 3 ppm, comparable to the sensitivity stated by Scholten et al 37 (1.5 ppm) for a whispering gallery micro-ring resonator. Important to note is that the latter had a surface coating which improved absorption by a factor equivalent to the partition coefficient (of order 1000x). That the NOMS surfaces register ppm VOC concentrations, without partitioning, reinforces their exquisite level of sensitivity. Further, the mass detection threshold of the nanomechanical element (of about 9 attograms) is 6 orders of magnitude better than the reported mass threshold in Scholten et al (50 37 pg) . The NOMS and ring sensor in the present case have comparable concentration sensitivity to surface adsorbed VOCs by virtue of the larger ring surface area. Again, mass detection threshold is better in the NOMS, and can be improved by further downscaling (while our micro-rings are already at their size limit), a useful capability in stretching ultimately towards single Da-level sensitivity and gas-phase 18 mass spectrometry . Following the test with VOC’s, non-volatile human urine metabolites were derivatized and tested in different concentrations and combinations. In order to determine the capability of the GC-NOMS setup to separate and detect individual metabolites, a 5 metabolite mixture was prepared (see Supporting Information, Table S2). Derivatization of the metabolites facilitates the gas phase separation in a GC column by lowering their elution temperature. The GC column was set through a temperature ramp during which each metabolite eluted from the FSC and was detected by the NOMS sensor. Figure 4 compares the FID response (GC signal) with the NOMS response and the micro-ring response for a 5 component metabolite mixture with the transfer line temperature maintained at 150 °C ± 0.1 °C. The peak widths of NOMS and ring resonator are of comparable scale to the FID’s response (longer by about 7% depending on the metabolite, attributable to slight transfer line broadening) and elute with the same timing. The exception is the metabolite 3MGA which elutes late and exhibits a much broader adsorption peak. However, 3MGA elutes at a temperature above 150 °C; thus the transfer line temperature
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is insufficient to prevent condensation of the analyte on the transfer capillary walls.
forth between detection modes (amplified signal mode vs fast detection mode) and between hyphenated or fast sensing.
Figure 4 Human urine metabolite detection using photonic nanomechanical resonators, Comparison of FID (GC signal), NOMS and micro-ring response for a mixture of metabolites with the transfer line heated at 150 °C. NOMS and micro-ring adsorption data was smoothened using a moving average method with a window of four seconds. Blue dashed line corresponds to zero when no adsorption takes place. The black line denotes the point in time in the temperature ramp of the GC oven when the temperature crosses the transfer line temperature at 150 °C. Sample contains 2.1 mM of HPA, 666.67 µM of LA, 83.33 µM of EMA, 83.33 µM of SA and 83.33 µM of 3MGA. Injection volume is 3 µL and split ratio is 10:1
Figure 5 Human urine metabolite detection using photonic nanomechanical resonators; Comparison of (a) FID (GC signal), (b) NOMS and (c) micro-ring resonator response from Ethyl Malonic Acid (EMA) at 1mM concentration. Injection volume of 5 µL and split ratio is 10:1 used; Comparison of (d) FID (GC signal), (e) NOMS and (f) microring resonator response from Ethyl Malonic acid at 10 µM concentration. Injection volume of 5 µL and split ratio is 2:1 is used; Comparison of (g) FID (GC signal), (h) NOMS and (i) micro-ring resonator response from a mixture of 500 µM of Ethyl Malonic acid and Succinic acid with the transfer line not heated. Injection volume of 3 µL and split ratio is 2:1 is used. Blue dashed line corresponds to zero when no adsorption takes place. The black line through the NOMS and ring resonator adsorption data were obtained by smoothening the raw data using the moving average method with a window of four seconds.
To further explore the effect of unheated transfer line, a 1mM solution of pure EMA in Hexane was tested and gave a strong response (Figure 5 a, b and c). Note the very different time scales for the NOMS vs the FID signals. This is likely due to a much extended dwell time in the transfer line capillary due to condensation on the walls. More importantly, note the peak shape of the NOMS signal: it reaches a plateau and holds after the adsorption of the EMA. We hypothesize that the analyte has condensed (or quasicondensed) onto the sensor surface (whether there has been an actual phase change or not is unimportant). The product of the molecule’s sticking probability (α) and residence time (τres) has shot up dramatically due to the sensor surface being well below the analyte boiling temperature. This also has the effect of sacrificing temporal resolution to greatly amplify the signal; in other words the sensor begins integrating the arriving analyte concentration. Specifically, the most likely scenario is that the transfer line broadening accounts for lengthening of the peak leading edge while the condensation on the sensor accounts for the plateau. Importantly, this plateau after adsorption opens the door to hyphenated and orthogonal detection techniques. One can imagine applying chemical structure detection such as photothermal 38 spectroscopy to help identify the analyte. Such a development would be very useful towards the identification/quantification of unknown metabolites from mixtures without the need for mass spectrometry linked to the GC. We also note that this integrating sensing mode can be turned on and off by choosing the temperature of the sensor (as evidenced by the vastly different peak shapes from the NOMS signal between Figure 4 and Figure 5). Controlling 34 of the temperature of the chip at millisecond timescales would then be a very compelling way to switch back and
In order to take advantage of the signal amplification, we determined the limit of detection in solution phase by diluting the concentration of EMA to 10µM (which corresponds to 1 µmole/mmole creatinine in urine), which is the lowest concentration that could be detected clearly. Importantly, this is roughly at the physiological concentrations these metabolites are found in the human 31 body (Figure 5d, e and f) and shows that our system already exhibits the sensitivity needed for physiological samples without pre-concentration. From the concentration of the solution, split ratio, sample injection volume and the frequency shift of the NOMS device, the threshold of mass detection was found to be about 10 attograms for EMA. The average concentration threshold of EMA in the gas phase at the exit of the FSC was calculated to be 74 ppb using the expression Canalyte = C1V1VmSER/ (MWanalyteF∆t) from Bargatin 11 12 et al and Li et al . It is important to note that the EMA concentration at the chip surface is likely reduced by a factor of 100 from the 74 ppb present at the capillary output, giving roughly 0.8 ppb as the actual limit able to be sensed. A similar exercise to estimate the gas phase concentration limit for the heated transfer line case in Figure 4 for EMA yielded a concentration threshold of about 2.5 ppb. While the concentration threshold for the heated transfer line case is higher (~3X) than that for the unheated transfer line, this is 12 roughly near the concentration limit estimated by Li et al (0.6 ppb), but again, without the benefit of partition enhancement. Also by sacrificing the detection sensitivity
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marginally, we can match performance of the GC’s FID.
the
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The response to a two component metabolite mixture containing EMA and SA at a concentration of 500 µM with an unheated transfer line was also recorded (see Figure 5g, h and i). It is noteworthy that the peaks corresponding to these two metabolites still resolve well, in spite of the increased residence time on the sensor. The transfer line dwell time has nearly doubled the time gap between the two elutions (Figure 4 and Figure 5h). By tuning the elution speed (either via conventional GC parameters or transfer line temperature), the integrating mode of the sensors can be optimized. In this paper a working NOMS based gas sensor has been demonstrated in conjunction with a conventional gas chromatographic system. The sensitivity of the NOMS allowed surface coatings to be avoided, thereby illuminating interesting surface chemistry effects and pointing towards a universal sensing surface. On testing with common human urine metabolites such as ethyl malonic acid, the threshold of sensitivity in liquid phase was determined to be 10µM (or about 1 µmole/mmole creatinine in urine) which converts to a mass of about 10 attograms and an average head space concentration of ~1 ppb near the sensor. Testing to determine the limit of detection of volatile organic compounds, such as toluene, we found the mass detection limit to be about 9 attograms and the concentration detection limit to be 3 ppm near the sensor. Further, the option to use the heated transfer line to control the peak width and peak-to-peak time span was shown to add more flexibility to the experimental design. An additional benefit of using the room temperature NOMS device is the plateau response to biomolecules which can be used to apply chemical structure detection techniques such as photothermal spectroscopy to determine the structure of unknown metabolites. Based on the detailed analysis above, it is clear that the NOMS based sensing offers considerable promise for portable biological sensing. However there is a lot of scope for further improvement for portable applications through an integrated GC-NOMS system, improvement in selectivity through surface coatings and making use of signal amplifying absorptive layers.
Notes The authors declare there are no sources of conflicting financial interest with respect to this manuscript.
ACKNOWLEDGMENT The authors would like to thank Alberta Innovates – Health Solutions (AIHS) (through the Collaborative Research Innovation Opportunities grant), Natural Sciences and Engineering Research Council of Canada and the National Institute for Nanotechnology (NINT) for their continued support and funding for this research. The fabrication of the devices was facilitated through CMC Microsystems, and post processing was done at the University of Alberta-NanoFab.
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ASSOCIATED CONTENT Supporting information Supporting information section provided with this paper contains description of the measurement system including the operating principle, device description and basic device characterization, table listing the concentrations of the VOC samples and the human urine metabolite samples, detailed description of the procedure to calculate analyte concentration near the ring resonator, laminar round jet flow model and description of the expression to calculate analyte concentration in gas phase. This material is available free of charge via the Internet at http://pubs.acs.org.
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