Chemiresistive Electronic Nose toward Detection of Biomarkers in

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Chemiresistive electronic nose toward detection of biomarkers in exhaled breath Hi Gyu Moon, Youngmo Jung, Soo Deok Han, Young-Seok Shim, Beomju Shin, Taikjin Lee, Jin-Sang Kim, Seok Lee, Seong Chan Jun, Hyung-Ho Park, Chulki Kim, and Chong-Yun Kang ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.6b03256 • Publication Date (Web): 26 Jul 2016 Downloaded from http://pubs.acs.org on July 28, 2016

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ACS Applied Materials & Interfaces

Chemiresistive electronic nose toward detection of biomarkers in exhaled breath

Hi Gyu Moon,† ,‡, 1 Youngmo Jung, ‡ ,∥, 1 Soo Deok Han,†, § Young-Seok Shim, † Beomju Shin,⊥ Taikjin Lee,⊥ Jin-Sang Kim,† Seok Lee,⊥ Seong Chan Jun,∥ Hyung-Ho Park,*, ‡ Chulki Kim*,⊥ and Chong-Yun Kang*,†, §

†

Center for Electronic Materials, Korea Institute of Science and Technology (KIST), Seoul 136791, Republic of Korea.

‡

Department of Material Science and Engineering, Yonsei University, Seoul 120-749, Republic of Korea.

§

KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 136-701, Republic of Korea. ⊥

Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 136-791, Korea. ∥

Department of Mechanical Engineering, Yonsei University, Seoul 120-749, Republic of Korea.

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ABSTRACT: Detection of gas-phase chemicals finds a wide variety of applications including food and beverages, fragrances, environmental monitoring, chemical and biochemical processing, medical diagnostics, and transportations. One approach for these tasks is to use arrays of highly sensitive and selective sensors as an electronic nose. Here, we present a high performance chemiresistive electronic nose (CEN) based on an array of metal oxide thin films, metalcatalyzed ones and nanostructured ones. The gas sensing properties of the CEN show the enhanced sensitive detection of the H 2 S, NH 3 , and NO in 80% of relative humidity (RH) atmosphere similar to exhaled breath. The detection limits of sensor elements we fabricated are in the following ranges; 534 parts per trillion (ppt)–2.87 parts per billion (ppb) for H 2 S, 4.45 ppb– 42.29 ppb for NH 3 , and 206 ppt–2.06 ppb for NO, respectively. The enhanced sensitivity is attributed to the spillover effect by Au nanoparticles and high porosity of villi-like nanostructures providing with a large surface-to-volume ratio. The remarkable selectivity based on the collection of sensor responses manifests itself in the principal component analysis (PCA). The excellent sensing performance indicates that the CEN can detect the biomarkers of H 2 S, NH 3 , and NO in the exhaled breath and even distinguish them clearly in the PCA. Our results show high potential of the CEN as an inexpensive and non-invasive diagnostic tool for halitosis, kidney disorder, and asthma.

KEYWORDS: Nanostructural thin film metal oxides, electronic nose, chemiresistive sensor, sensor array, exhaled breath analyzer, non-invasive diagnostic tool, biomarkers.

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1. INTRODUCTION Chemiresistive electronic nose (CEN) has attracted much attention in the industrial fields of environmental monitoring, food quality inspection, cosmetics, pharmaceutics, and medical diagnostics1–6. Disease diagnosis based on exhaled breath is of substantial interest since it is a simple, inexpensive, non-invasive method to characterize abnormalities linked to medical conditions such as diabetes, lung cancer, asthma, kidney disorders, and halitosis. It is known that these diseases are reflected by biomarkers of acetone (CH 3 COCH 3 ), toluene (C 6 H 5 CH 3 ), nitrogen monoxide (NO), ammonia (NH 3 ), and hydrogen sulfide (H 2 S)7–11. The concentrations of NO and H 2 S in the exhaled breath of asthma and halitosis patients exceeded more than hundreds of parts per billion (ppb) and several parts per million (ppm) respectively, while healthy subjects were in the level of less than several tens of ppb10–15. Exhaled breath also contains ammonia at the concentration level of around 830 ppb and it was reported that its increase can be related to kidney disease, hepatic encephalopathy, and infection with helicobacter pylori caused by bacterial production in an oral cavity1, 16, 17. To date, chemiluminescence, electrochemical sensors, and gas chromatography- mass spectrometry (GC-MS) have been applied for exhaled breath analysis18–21. However, instruments for these techniques are bulky and expensive, and sample preparation (that is, breath collection and water filtering) for the usage is complex. Along this line of thought, Pan et al. reported the exhaled breath analyzer for detection of NO using the electrospray ionization mass spectrometry in real time20. However, this analyzer shows difficulties in operation due to complexities in operating procedures, and sample preparation21. Obviously, inexpensive, simple operation, and detection capability of several ppb-level in high precision are highly preferred for exhaled breath analysis. Because of the interferences between complexes of constituents in exhaled human breath, a highly sensitive and selective exhaled breath analyzer operating in a highly humid environment (RH ≥ 80%) still requires intensive 3



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studies. Recently, the use of nanomaterials (noble metals and metal oxides) has been considered as attractive elements in clinical diagnostics by breath analysis because of their large specific surface area, porosity, fast reaction, and cost-effective growth technique22–24. Especially, the large surface to volume ratio of nanostructured metal oxides (NMOs) is critical to achieve high sensitivity by the increased gas reaction sites on the surface. Catalytic nanoparticles (NPs) including Pt, Pd, Au, and Ag also functionalize the surface of NMOs leading to enhanced sensitivity and selectivity via the spillover effect25. Although the NMO based sensors have never been commercialized as a human breath analyzer, a few approaches have been reported; Xing et al. prepared Au-functionalized In 2 O 3 nanorods through a co-precipitation method for the detection of volatile organic compounds (biomarkers) at high temperature related to diabetes and lung cancer, respectively26. Also, Shin et al. synthesized Pt-functionalized SnO 2 fibers via electrospinning and applied them to diagnose diabetes and lung cancer9. However, these nanostructures formed by chemical reaction have unstable contacts at high temperature between themselves and thus, researches on them are still in an early stage studying how to integrate them with low-cost, and high reproducibility in a designed configuration27. As an alternative approach, the formation of metal oxide nanostructures by physical vapor deposition (PVD) has been considered more desirable for the CEN in terms of simplicity in synthesis, stability, reproducibility, and excellent compatibility with semiconductor fabrication processes28, 29. In this work, we propose a novel synthetic method to fabricate a 3×3 CEN. It consists of different nanostructural thin films (thin films, Au-functionalized thin films, and nanostructures) based on metal oxides including tungsten trioxide (WO 3 ), tin dioxide (SnO 2 ), and indium oxide (In 2 O 3 ). These nanostructural thin films have been achieved by using e-beam deposition in a glancing angle deposition (GAD) mode. The chemiresistive sensing properties of 9 different 4


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elements as a potential diagnostic tool for asthma, halitosis, and kidney disorders are investigated. The CEN operating at 168℃ demonstrates extremely sensitive and selective detection for NO, H 2 S, and NH 3 in 80% of relative humidity atmosphere. The calculated detection limits of the nanostructured sensors for those chemical vapors are as low as ppt-level indicating sufficient sensitivity for the diagnosis of asthma, halitosis, and kidney disease.

2. RESULTS AND DISCUSSION 2.1. Configuration of CEN. The CEN for multi-channeled and high-throughput detection on a single chip was fabricated in a mass production process with a 4-inch wafer (SiO 2 (1 µm)/Si (500 µm) substrate), comprising of 9 sensor elements (Figure 1 and S1). Figure 1A–C show the sequential processes for manufacturing the CEN. As shown in Figure 1A, each sensor element on a single chip (1 cm×1 cm) contains an active sensing layer (1 mm×1 mm) on top of Ptinterdigitated electrodes (IDEs), being individually formed into designed nanostructures. Sensing layers including thin films and Au-functionalized thin films were deposited by e-beam evaporation with an on-axis mode. The porous nanostructures were formed using a deposition technique with an off-axis mode (a glancing angle of 85°) at room temperature (Figure S2). After annealing at 500℃ for an hour, the SnO 2 , WO 3 , and In 2 O 3 film is crystallized and a 3 nm- thick Au film is agglomerated to Au NPs, resulting in thin films decorated with Au NPs on the surface. The Pt-IDE patterns with 150 nm thickness and 5 μm spacing on a SiO 2 /Si substrate were fabricated by conventional photolithography and dry etching. After PR pattering for pre-specified regions (dotted square), To provide a stable thermal energy for gas detection, a micro-heater unit is attached underneath the substrate and then placed on a designed printed circuit board (PCB) (Figure S3A). A photograph of the integrated sensor array and signal processing circuits is shown

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in Figure 1B. The surface temperature of the CEN was measured as 168℃ by an infrared sensor (SC660, FLIR) (Figure 1B). As schematically depicted in Figure 1C, the CEN consists of the thin films (1–3), Au-functionalized thin films (4–6), and nanostructures (7–9) of the SnO 2 , WO 3 , and In 2 O 3 materials.

2.2. Morphological Characteristics and Sensing Mechanism. Morphologies of thin films, Au-functionalized thin films and one-dimensional (1D) nanostructures are investigated by the field emission scanning electron microscopy (FE-SEM: SU-70, Hitachi) after annealing at 500℃ for an hour. Figure 2A–C and insets show the top-view and cross sectional FE-SEM images of thin films with around 110 nm thickness. According to the ionosorption model (receptor function), as schematically described in Figure 2D, the oxygen is adsorbed onto the surface of metal oxides in the form of negatively charged species (O 2 −, O−, O2−) at high temperature. This leads to the formation of a depletion layer30 where the number of ionized oxygens as reaction sites is proportional to the amplitude of resistance change under exposure to reducing and oxidizing gases. It is known that the ionosorption of physisorbed oxygen of O 2 (reaction 1) on the surface occurs in more than 100℃, forming O 2 − (reaction 2), this chemisorption continues up to 250℃, and the O− (reaction 3) appears in the reaction at higher temperature31. The details of reactions at different temperatures are summarized as follows.

𝑂2(𝑔𝑔𝑔) ↔ 𝑂2(𝑎𝑎𝑎)

− 𝑂2(𝑎𝑎𝑎) + e− ↔ 𝑂2(𝑎𝑎𝑎)

(1) (2) − 2− − 𝑂2(𝑎𝑎𝑎) + e− ↔ 𝑂2(𝑎𝑎𝑎) ↔ 2𝑂(𝑎𝑎𝑎) R

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(3) Based on these reactions, O 2 - is a dominant chemisorbed oxygen specie on the surface of our sensing materials at the operating temperature of 168℃. The FE-SEM images of the Aufunctionalized thin films are shown in Figure 2E–G. They were annealed at 500℃ immediately after Au films (3 nm) deposition via e-beam evaporation. Based on our previous results (Shim et al.), we have applied an optimal condition for an initial Au film with 3 nm thickness to the CEN. For 5 nm and 10 nm thicknesses, Au NPs are sparsely formed after annealing and then the catalytic effects of Au NPs are significantly weak. For 1 nm thickness, the Au NPs are so dense that the surface area for adsorption of gas molecules decreases, leading to lower the gas response. Finally, we found that 3 nm thickness of the initial Au film is the optimal condition for enhancing the gas sensing properties32. After annealing, the sizes in diameter of self-agglomerated Au NPs on the surfaces of In 2 O 3 and SnO 2 films are ~10 nm while it is ~50 nm on WO 3 . Such variation in size is attributed to the surface energy difference between Au NPs and metal oxides where smaller NPs have higher surface energies33–35. As the surface energies (γ) follow the order of γ (In 2 O 3 -2.71 J/m2) > γ (SnO 2 -1.74 J/m2) > γ (WO 3 -1.67 J/m2) at (100) surface, the above result is well agreed with the fact that the size of Au NPs-functionalized WO 3 is larger than those of other materials34–36. In general, the catalytic properties of NPs with different materials and sizes are expected to provide a high selectivity in the chemiresistive sensor37. In Figure 2E–G, the existence of large Au NPs is most likely due to the aggregation of Au NPs that act as initial seeds. Figure 2H presents a schematic illustration of the ‘‘spillover effect’’ in the surface of the Aufunctionalized metal oxides. The Au NPs have a metallic phase on the surface of the metal oxides with the reactive species such as oxygen ions. It is known that highly dispersed Au NPs exhibit a high chemical activity for gas oxidation38. Especially, the smaller Au NPs are known to be more

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reactive and contain larger interfaces39. Figure 2I–K show FE-SEM images of the villi-like nanostructures (VLNs) of WO 3 , SnO 2 , and In 2 O 3 fabricated by a GAD mode with a fixed incident angle of 85° and a fast deposition rate, which control the porosity of films and their shape. These structures are composed of porous nanostructures with the height of approximately 400 nm, the width of 30–40 nm, and porosity of 37%, as described in our previous work11. The VLNs enhance the gas sensing via the effective diffusion and adsorption of gas molecules. This can be understood by the facts that (1) the potential barriers between adjacent nanostructures enlarge the resistance variation according to the double Schottky barrier model40, 41 and (2) the so-called utility factor increases with the porosity of the nanostructure as shown in Figure 2L.42 Accordingly, nanomaterials are expected to exhibit a unique gas response with high sensitivity and selectivity providing a pattern of those responses in the end.

2.3. Interface Circuitry for Gas Sensing Measurements. A diagram of the overall measurement system is shown in Figure 3. The system contains the entire sensor-specific driving, signal conditioning circuitry with a 16-bit analog to digital convertor (ADC), and a gateway micro controller unit (MCU). This allows the measurement range of 0–3.3 V with 0.7% accuracy. This system requires a voltage supply of 3.3V and power consumption of 800 mW with the operation of a heater unit. The voltage drops across the external resistors indicated as R1 to R9 in Figure 3 are measured. They are initially tuned to be around 1.65V by connecting external resistance equal to the resistance of the as-prepared sensor such that it can respond to both reducing and oxidizing gases. To improve the accuracy and resolution of the implemented circuit, we utilized an ADC of 16-bits11.

2.4. Typical responses of CEN to biomarkers. The real-time responses of each channel 8


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in the CEN were recorded. To achieve a thermally stable condition for gas sensing, aging process for 24 hours is required before examining the CEN43. All the following experiments with metal oxide gas sensors are performed in the atmospheric condition with high relative humidity (RH, 80%). Figure 4 shows the typical response curves of the CEN to 2 ppm H 2 S, 10 ppm NH 3 , 10 ppm CH 3 COCH 3 , and 1ppm NO in 80% of RH atmosphere. The responses are quantified as(ΔV = [(V ambient / V air ) -1] ×100 (%) for oxidizing gases or [(V air / V ambient ) -1] ×100 (%)) for reducing gases. The response time is defined as the time to reach 90% of the voltage in the steady state. Since the deposited metal oxides are n-type semiconductors, when reducing molecules reacted with ionized oxygen (O 2 −), the formed depletion layer on the surface decreases, resulting in a decrease of the voltage drop across the sensor, as shown in Figure 4A–C. On the other hand, when oxidizing molecules such as NO x are adsorbed onto the surface, the depletion layer is formed, resulting in an increase of the sensor voltage, as presented in Figure 4D. These gases reduce the density of electrons near the surface as strong oxidizing agents, resulting in the increase of resistance44. Our results show that each channel of the CEN was sensitive to both reducing (H 2 S and NH 3 ) and oxidizing (NO) gases in high RH atmosphere. Here, we note that the responses of the 7, 8, and 9 channels (comprised of different materials in VLNs) show high responsivity of 50–100% to H 2 S and NH 3 , and especially, the NO is more than 110%. This result is attributed to the influence of morphology on the gas diffusion and a highly efficient modulation of potential barriers by narrow nano-necks between individual nanostructures11, 31. In addition, the response to NO at relatively low temperature (≤ 200℃) becomes higher due to the stronger oxidizing agent (NO-) than ionized oxygen species45. Since the sensing performance to the reducing gases is optimized at the temperatures of around 350–450℃, the response by the decease of oxygen active sites at 168℃ is inevitably low. Thus, the responses to other gases

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(C 2 H 5 OH, C 6 H 6 , CO) including acetone (Figure 4C) are negligible because of the insufficient thermal energy46, 47 (Figure S4). For these gases, the sensors showed responsivities of less than 10%, as shown in Figure 4E. In general, the hydrocarbon species such as acetone, benzene and ethanol are composed of strong bonds between carbons, making themselves extremely stable and leading to low response of sensors. In addition, as seen in Figure S5, The responses to 4% CO 2 show less than 0.6% in the whole range of humidity variation and they are small enough not to be considered. This is understood considering that CO 2 is chemically stable due to its centrosymmetric structure so that they are not easily detected by chemiresistive gas sensors48. Very interestingly, the Au-functionalized thin films (4, 5, and 6) show large responses and fast response time to 2 ppm H 2 S and 10 ppm NH 3 by the spillover effect which can effectively dissociate the oxygen, resulting in active sites (O 2 −, O−) at relatively low temperature9,

49, 50

.

Therefore, Au is ascribed to selective detection and improved response by chemical sensitization via the spill-over effect. The oxygen ion density on the surface of metal oxides increases due to dissociation of molecular oxygen by the Au, leading to the enhanced chemical responses to reducing gases51. Also, this is the reason why the optimal working temperature is gradually decreased by increasing the amount of the Au NPs. These results suggest that the enhanced responsivity depends on several factors including the larger number of electrons chemi-captured by

gas

molecules,

the

competition

between

NO−

and

oxygen

species

and

the

adsorption/desorption surface mechanisms at low temperatures. These effects naturally contribute to the incomplete recovery of sensor. In order to calculate the detection limits (DLs) of the CEN for H 2 S, NH 3 , and NO, the response values, ΔV, are plotted as a function of the gas concentration in a linear scale, as shown in Figure 5A–C and Figure S6. The linearity of the sensor responses for different concentrations is an important factor considering practical applications. As shown in Table S1, the theoretical 10


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DLs of the CEN (signal-to-noise ratio > 3) toward H 2 S, NH 3 , and NO are estimated to be as low as 534 ppt–2.87 ppb, 4.45 ppb–42.29 ppb, and 206 ppt–2.06 ppb, respectively52. In particular, the CEN to NO showed a DL of ppt-level, which is the lowest, to the best of our knowledge, compared to all types of the sensors based on metal oxide nanostructures. Moreover, the DLs of H 2 S, NH 3 , and NO are much lower than the abnormal medical condition such as asthma (>50 ppb NO), halitosis (>1 ppm H 2 S), and kidney disorder (> 830 ppb NH 3 )1, 10, 11. This superior sensitivity of the CEN shows that an array of sensors based on metal oxide thin films and its derivatives could provide the basis for highly sensitive detection of H 2 S, NH 3 , and NO as an inexpensive and non-invasive diagnostic tool for asthma, halitosis, and kidney disorder.

2.5. Principle component analysis (PCA). To briefly assess the responses of the CEN in differentiating target gases, the response amplitudes are mapped in colorscale (Figure 6A). It is seen that tested gases of acetone, H 2 S, NH 3 , and NO are identified by the color-coded responses of the CEN. To better examine the selectivity of the CEN, we investigate the sensing results based on the principal component analysis (PCA)53. Figure 6B–D shows principle component (PC1 and PC2) plots derived from normalized responses with the increase of the number of considered sensor elements on two-dimensional (2-D) plane. Figure 6B shows the loading plots of the responses of thin films to 8 gases (including air). With thin films only (channel 1, 2, and 3), the NH 3 and H 2 S are the most distinguished from others. However, the representation in PC space shows that, at the test concentrations used in this work, the NO area is overlapped with the one for ethanol. In Figure 5C, considering the responses of Au-functionalized thin films, NH 3 and H 2 S are well-separated in the PC space, however, the NO area is not isolated yet. Finally, when the responses of all the sensor channels are considered in the analysis, the CEN can readily distinguish targeted vapors of NO, NH 3 , and H 2 S (Figure 6D) with the benefit of Au11



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functionalized and VLNs. This shows the direct dependence of the effective analysis on the sensor elements and demonstrates that our CEN can distinguish among the classes of gases with a high degree of confidence.

3. CONCLUSION Multifunctional capability, simple and lightweight construction, low price, and high sensing performance operation are desired attributes for electronic noses that can monitor medical conditions linked to human diseases54. For these applications, nanostructural thin film metal oxides are considered to be the strongest candidate for the exhaled breath analyzer. Here, we have innovatively developed the CEN consisting of different nanostructures based on three metal oxide materials to obtain electrical responses to targeted gases. By using the GAD mode, different nanostructures have been synthesized on the SiO 2 /Si substrate with Pt-IDEs in 4-inch wafer-scale. Their gas sensing properties to H 2 S, NH 3 , and NO known as biomarkers for human diseases were carefully measured in a similar condition to exhaled breath. The results demonstrate that the CEN back-heated at 168℃ can effectively detect the H 2 S, NH 3 , and NO in 80% of RH atmosphere. Their DLs are as low as several hundreds of ppt– tens and of ppb level. The enhanced gas sensing capabilities in ppt-level was attributed to the spillover effect by Au NPs and high porosity of VLNs providing with a large surface to volume ratio for effective diffusion and adsorption of gas molecules. Moreover, high selectivity of the CEN based on the response patterns toward H 2 S, NH 3 , and NO was evaluated using PCA. The excellent sensing performance of the CEN in distinguishing chemical vapors of H 2 S, NH 3 , and NO in 80% of RH atmosphere makes itself a promising candidate for an inexpensive and non-invasive diagnostic tool for monitoring of halitosis, kidney disorder and asthma. In future studies, we have a plan to

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explore sensing performance within a complex background (a mixture of chemical vapors) at various temperatures55.

4. Materials and methods 4.1. Fabrication of the CEN. The Pt IDEs on SiO 2 /Si substrates with 4 inch wafer-scale were fabricated by using photolithography and dry etching (Oxford etcher). The 3×3 CEN consist of three different nanostructural thin films (100 nm thin films, 10–50 nm Au-functionalized thin films and 400 nm thick-VLNs) of WO 3 , SnO 2 , and In 2 O 3 , which were sequentially synthesized onto predefined regions by a photo-resistive (PR) patterning process using e-beam evaporation with a GAD mode (Figure S2). In this fabrication process, we obtained a high yield of more than 90% after annealing (at 500℃) and aging (for 24 hour) processes, and the sensor-to-sensor variation in the sensor resistance was less than 10% since the entire process was performed under high vacuum. Thin films and Au-functionalized thin films were deposited by on-axis with glancing angle of 0°, in particular, the VLNs film were carried out at a glancing angle of 85°. The base pressure and power were 2 × 10–6 Torr, 50–70 kW, respectively. For the VLNs film, the deposition rate of was 1.5–3.3 Å/s. The fabricated CEN was annealed at 500℃ in air for 60 min to crystallize the amorphous metal oxides films and treated aging process for 24 hour. After combining with CEN and PCB, consisting of the same resistances (R1–R9) corresponding to each sensors, we have the Au wire bonding process for conjunction of each other and subsequently, are coupled with the MCU and ADC of 16-bits digital output.

4.2. Characterization. The morphology of the samples was investigated by using FE-SEM (SU-70, Hitachi) with 2 nm Pt deposition on top (except for Au-functionalized thin films) under

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an accelerating voltage of 15 kV. Thermographic and optical images of the sensors were obtained using an infrared camera (SC660, FLIR) and optical microscope (BX41M-LED, Olympus Upright Microscope). To evaluate the selectivity of the CEN toward biomarkers, we applied PCA and color plots of response amplitudes. First of all, we analyzed the responses of target gases by MATLAB software-based PCA as multivariate analysis tool. The configuration of matrix is based on the normalized responses of eight gases (air, acetone, benzene, CO, ethanol, H 2 S, NH 3 and NO) and 9 channels. The entire matrixes were integrated in 32×9 formation. The covariance of each channel by the entire matrix defined as “Feature (standard for separation of gas responses)” for classifying target gas. Virtual principle component axis was determined according to descending order of the covariance and all gas response projected loading plot on 2D plane with PC1 and PC2 axis. The maximum amplitudes of the gas responses of 3×3 sensor array are visualized in a color-coded graph as well.

4.3. Sensory measurements. As shown in Figure 3 and Figure S3B, Gas sensing measurement consists of circuit with ADC of 16-bits digital output and gateway MCU with a power supply of 3.3V and power consumption of 800 mW for heater operation. Each sensor channel modulated in 1.65V was connected with 16-bits ADC and then, the ADC can assess the changes of voltage by of channels, when exposed to detecting gas. The voltage (analog value) was converted to hexadecimal digital value in ADC. The gateway MCU was controlled the ADC, data transmission and time interval for voltage readout. The fabricated CEN was examined in dry air and RH ranges of 80% using chamber with 12800 cm3 (16 cm (W) ×16 cm (H) ×50 cm (L)) volume. In this study, the sensor response is defined as ΔV = V gas /V ambient for NO (oxidizing gas) or = V ambient /V gas for H 2 S, C 2 H 5 OH, CH 3 COCH 3 , NH 3 , C 6 H 6 , and CO (reducing gases), where V gas (V g ) is the sensor voltage value in the detecting gas and V ambient (R a ) is the voltage value in 14


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80% of RH. The sensing measurement system have an injection system that can provide the humidified air with controlled RH by an auto-functionalized mass flow control (MFC) and bubbling system including heating line, as monitored by digital humidity and temperature meter (SMART SENSOR, AR837). The 80% of RH was obtained by mixing with the water vapor and dry air. All the pipe lines for gas flow were heated to maintain the constant temperature of 80℃. A highly stable humidity control was possible at the vapor pressure of 379.12 hpa (the corresponding saturated steam pressure is 0.047390 MN/m2 at 353.15 K) in 80% of RH. The total gas flow rate was fixed at 2000 cm3/min.

■ AUTHOR INFORMATION Corresponding Authors *E-mail: [email protected] *E-mail: [email protected] *E-mail: [email protected] 1

These authors contributed equally to this work.

Notes The authors declare no competing financial interest.

■ ASSOCIATED CONTENT Supporting Information Detection limits of H 2 S, NH 3 , and NO in 80% of RH; mass production process with a 4-inch wafer (SiO 2 (1 µm)/Si (500 µm)) substrate, comprising of 9 sensor elements; the sequential

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processes for manufacturing the CEN by using e-beam evaporation with a GAD mode; microback-heater for gas sensing measurement and integrated gateway with the CEN and conventional gas sensor; real-time response of each channel in the CEN to 10 ppm CO, benzene, and ethanol in 80% of RH; dynamic sensing transients of the CEN to CEN to 0.2–0.8 ppm NO, 0.6–1 ppm H 2 S, and 1–8 ppm NH 3 in 80% of RH are presented in the Supporting Information. This information is available free of charge via the Internet at http://pubs.acs.org.

■ ACKNOWLEDGEMENTS This work was partly supported by the KIST Institutional Program (Project No. 2E26370) and the Korea Ministry of Environment (GT-11-F-02-002-1), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (Project No. 2N40450).

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FIGURES

Figure 1. (A) Optical microscope images of the sequential fabrication process of CEN with a single chip (1 cmⅹ1 cm) contains an active layer (1 mmⅹ1 mm) with Pt IDEs. (B) A photograph and thermographic image of the integrated CEN and signal processing circuits. (C) Schematic illustration of the configuration of the CEN.

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Figure 2. Top-view and cross sectional (inset) FE-SEM images of (A–C) thin films, (E–G) the Au-functionalized thin films, and (I– K) VLNs of WO 3 , SnO 2 , and In 2 O 3 . (D, H, L) Their sensing mechanisms.

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Figure 3. Interface circuitry for gas sensing measurement of the 3ⅹ3 CEN.

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Figure 4. (A–D) Real-time response of each channel in the CEN to 2 ppm H 2 S, 10 ppm NH 3 , 10 ppm acetone, and 1 ppm NO in 80% of RH. (E) Response patterns of CEN to 8 gases according to ΔV = [(V ambient / V air ) -1]ⅹ100% for oxidizing gas or [(V air / V ambient ) -1]ⅹ100% for reducing gas, respectively. 25



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Figure 5. Theoretical detection limits (DL) of CEN to (A) 0.6–1 ppm H 2 S, (B) 1–8 ppm NH 3 , and (C) 0.2–0.8 ppm NO in 80% of RH.

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Figure 6. (A) Color-coded responses of the CEN to H 2 S, NH 3 , acetone, and NO. The PCA plots considering (B) thin films, (C) thin films + Au functionalized thin films, and (D) thin films + Au functionalized thin films + VLNs with PC 1 and PC 2 using responses of 8 gases as input data in 80% of RH.

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Table of Contents Graphic

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Chemiresistive Electronic Nose toward Detection of Biomarkers in

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