Hollow Pt-Functionalized SnO2

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Hollow Pt-Functionalized SnO Hemipill Network Formation Using a Bacterial Skeleton for the Non-Invasive Diagnosis of Diabetes Hi Gyu Moon, Youngmo Jung, Dukwoo Jun, Ji Hyun Park, Young Wook Chang, Hyung-Ho Park, Chong-Yun Kang, Chulki Kim, and Richard B. Kaner ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00955 • Publication Date (Web): 07 Feb 2018 Downloaded from http://pubs.acs.org on February 18, 2018

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Hollow Pt-Functionalized SnO2 Hemipill Network Formation Using a Bacterial Skeleton for the NonInvasive Diagnosis of Diabetes Hi Gyu Moon,† Youngmo Jung,‡ Dukwoo Jun,† Ji Hyun Park,§ Young Wook Chang,+ Hyung-Ho Park,+ Chong-Yun Kang,⊥ Chulki Kim,*,‡ and Richard B. Kaner*,†,∥

†

Department of Chemistry and Biochemistry, ∥ Department of Materials Science and

Engineering, and California NanoSystems Institute, University of California, Los Angeles, California 90095, United States ‡

Sensor System Research Center and ⊥ Center for Electronic Materials, Korea Institute of

Science and Technology (KIST), 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea §

Institute for Pure and Applied Mathematics, University of California, Los Angeles, California

90095, United States +

Department of Material Science and Engineering, Yonsei University, 50 Yonsei-ro,

Seodaemun-gu, Seoul 03722, Korea

*e-mail: [email protected], *e-mail: [email protected]

ACS Paragon Plus Environment

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KEYWORDS : hollow SnO2 nanostructures, bacterial skeleton, chemiresisitve sensor, exhaled breath analyzer, diabetes


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ABSTRACT

Hollow-structured nanomaterials are presented as an outstanding sensing platform because of their unique combination of high porosity in both the micro- and nanoscale, their biocompatibility, and flexible template applicability. Herein, we introduce a bacterial skeleton method allowing for cost-effective fabrication with nanoscale precision. As a proof-of-concept, we fabricated a hollow SnO2 hemipill network (HSHN) and a hollow Pt-functionalized SnO2 hemipill network (HPN). A superior detecting capability of HPN toward acetone, a diabetes biomarker, was demonstrated at low concentration (200 ppb) under high humidity (RH 80%). The detection limit reaches 3.6 ppb, a level satisfying the minimum requirement for diabetes breath diagnosis. High selectivity of the HPN sensor against C6H6, C7H8, CO, and NO vapors is demonstrated using principal component analysis (PCA), suggesting new applications of HPN for human-activity monitoring and a personal health-care tool for diagnosing diabetes. The skeleton method can be further employed to mimic nanostructures of biomaterials with unique functionality for broad applications.


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Breath analysis as a non-invasive technique for routine monitoring of metabolic disorders has attracted considerable scientific interest, since its sampling process is painless and simply performed by patients themselves.1-6 It is well known that there is a strong correlation between the concentration of chemical vapors in exhaled breath and abnormal medical conditions, such as asthma, lung cancer, and diabetes.7-9 In particular, the exhaled acetone level of diabetes patients is found to exceed 1.8 ppm, which is 2 to 6 fold times higher than that of healthy people (0.3–0.9 ppm).10,11 When ingesting foods high in carbohydrates, healthy people have a basic level of ketosis, while patients with uncontrolled diabetes have extremely elevated ketosis levels (the quantity of circulating ketone bodies) as a by-product of the fat metabolism process. While three ketone bodies (acetoacetate, β-hydroxybutyrate, and acetone) circulate in the bloodstream, acetone diffuses into the lungs and then is exhaled.12 A major interest in the field of breath analysis is to develop a way to reliably, cost-effectively, and powerfully diagnose diseases rapidly. However, despite the appearance of various detection methods, such as surface plasmon resonance (SPR), surface enhanced Raman spectroscopy (SERS), electrochemical arrays, and fluorescence methods, challenges still remain in their implementation for practical applications.13-16 For instance, these methods even with a large-scale apparatus still have many drawbacks including poor accuracy, low sensitivity, and a lack of specificity as a clinical diagnostic tool.17-19 On the other hand, metal oxide nanostructures-based chemiresistive sensors are attractive for exhaled breath analysis because of their possible miniaturization, potential high integration density, low-cost fabrication, and stable operation.20-23 In particular, the SnO2 has been regarded as a promising candidate as nanomaterials for sensing application due to its advantages such as outstanding surface adsorption properties, high chemical stability, high thermal stability, and fine adhesion to substrates.24 They play a very important role on the


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fabrication technology for improving sensor characteristics. Moreover, their sensing capability can enable high sensitivity and selectivity by tailoring with noble metals (Au, Pd, and Pt)functionalization.25-28 It is well known that the important ingredients for enhancing sensing capabilities are: a reactive surface, an efficient transducer function, and a utility factor of porous nanostructures.29,30 On the reactive surface, chemisorbed oxygen ions (O2-, O-, O2-) trap electrons in the conduction band of the metal oxide and form an electron-depletion layer on the surface.31 Since the formation of oxygen ions plays an important role in the chemical vapor reaction, metallic functionalization is one of the most effective methods to enhance the reactivity of the surface via the so-called spillover effect.32-34 Along these lines, the development of novel synthesis-based nanostructures coupled with metal-functionalization can be the best choice to improve sensing capability.35,36 Nanomaterials with outstanding properties including large specific surface area, welldefined/ordered nanostructure, scalability, and complex network features, have attracted great attention and opened up broad applications in various fields such as catalysis, adsorption and separation, drug storage and delivery, nanofabrication, etc.37-41 Amongst various architectures of nanomaterials, hollow nanostructures created by soft/hard templating strategies have received much attention due to their enhancing electron transport and providing high surface areas in chemiresistive sensors.35,36,42,43 Until recently, biological molecules such as Pseudomonas stutzeri, Magnetospirillum, Escherichia coli, Klebsiella aerogenes, and Lactobacillus strains have been used for the bacterial shaped synthesis of silver, gold, cadmium, magnetite, and titanium nanoparticles.44 Also, conventional templating method has been limited to sphere-shaped hollow structures using hard templates such as micro-sized polystyrene and silica particles, but naturally occurring biotemplates, such as DNA, proteins, and viruses were recently highlighted because


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of their abundance and structural diversity on the nanoscale.30,45 Bacteria are cultured on an industrial scale by a low-cost and environment-friendly process using microbial fermentation. Their wide varieties, such as spherical, rod-shaped, curved rod-shaped, and spiral-shaped structures can be exploited for use as templates for the synthesis and construction of threedimensional hollow structures.46 Following this line of thought, hollow nanostructures fabricated by a bacterial skeleton method can be a promising sensing platform where network formation improves the sensing capability via the effective surface modulation of double Schottky barriers (transducer function).47 Moreover, bacterial skeleton method via direct physical vapor deposition has not been extensively studied. Herein, we suggest a new way to fabricate a hollow SnO2 hemipill network (HSHN) and a hollow Pt-functionalized SnO2 hemipill network (HPN) using a bacterial skeleton method and demonstrate a proof-of-concept design for medical applications such as non-invasive diagnosis. A superior detecting capability of HPN toward acetone, a diabetes biomarker, was achieved at low concentration (200 ppb) under high humidity conditions (RH 80%). The theoretical detection limit was determined to be as low as 3.6 ppb satisfying the minimum requirement for a diabetes breath diagnosis sensor. Moreover, high selectivity of the HPN sensor against C6H6, C7H8, CO, and NO vapors was demonstrated applying principal component analysis (PCA). The utilization of the HPN can provide a new opportunity for human-activity monitoring and personal health-care diagnostics for diabetes.

RESULTS AND DISCUSSION A schematic illustration of the bacteria culturing process is shown in Figure 1a. To create a bacterial culture, E. coli O-157 cells were suspended in liquid nutrient media, such as Luria Broth and Tetracycline, in plastic tubes and then placed in a shaking incubator for 6 h for


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uniform self-growth. In this process, cell destruction (autolysis) occurs through the action of its own enzymes. After centrifugation and dehydration by a freeze-drying method for 24 h, uniformity in the bacteria size (1.2–1.6 µm) is obtained as shown in Figure 1b. The tailored pill-like bacteria network structure is clearly visible with a diameter of about 1.2 µm in a field-emission scanning electron microscope (FE-SEM) image (Figure 1c). As a sensing layer, for HSHN, a SnO2 thin film (100 nm) was deposited directly onto the bacteria network by ebeam evaporation. The bacteria template was completely removed via thermal decomposition during calcination at 550 °C for 2 h (Figure S1). The bacteria themselves work as a sacrificial mold structure for the formation of a pill-like nanostructured network with a large specific surface area. Consequently, the final product film appears as a quasi-ordered HSHN without any critical deformation of the SnO2 thin film during thermal decomposition, as shown in Figure 1d. HPN was fabricated by depositing a Pt thin film (3 nm) onto the HSHN. During calcination, the Pt film becomes agglomerated, as shown in Figure 2a. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) were performed to gain insight into the morphology and crystal structure of the HPN (Figure 2b−f). Figure 2b shows TEM images of the region indicated by the dotted box in Figure 2a. Pt nano-islands are uniformly distributed on the surface of the HSHN. We note from the high-resolution TEM images that catalytic Pt nanoparticles with a size distribution of 10−15 nm were randomly formed on the surface of the HSHN. Magnified TEM images marked by the red and white dotted boxes in Figure 2c (left) show the lattice fringes with inter-planar distances of 0.22 and 0.33 nm, which correspond to the (111) plane of Pt and the (110) plane of SnO2, respectively. Energy dispersive spectroscopy (EDS) element mapping in different colors shows the distribution of Sn (red), Pt (green), and O (blue) in Figure 2d. Figure 2e shows a selected area electron diffraction pattern (SAED) of


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HPN. All diffraction rings in the SAED image can be assigned precisely to the directions (101), (110), and (211) of the rutile SnO2 structure with a polycrystalline phase. Additionally, SAED patterns of the Pt nano-islands revealed diffraction peaks of the (111) and (200) lattice planes. These results support XRD measurements indicating typical patterns for the face-centered cubic structure of Pt (JCPDS card no. 04-0802) and the rutile structure of SnO2 (JCPDS card no. 411445) (Figure 2f). The high crystallinity of HPN allows for high stability in its sensor applications. To investigate the exhaled-breath-sensing characteristics of HSHN and HPN, we examined the sensing capabilities at temperatures ranging from 250 to 350 °C under 80% relative humidity (RH). To monitor the current-flow via the HSHN and HPN only, conventional dry etching and BOE treatment were performed between the Pt interdigitated electrode patterns (150 nm thickness and 5 µm spacing) on an SiO2 (1 µm)/Si (5 µm) substrate (Figure S2). A micro-heater unit (500 mW at 7 V) was placed underneath the substrate (Figure S3). To achieve a thermally stable condition for gas sensing, an aging process of 72 h was required. The responses of HSHN and HPN sensors as a function of NO and acetone concentrations between 0.2 and 1 ppm at 80% RH are shown in Figure 3a and b, respectively. The resistances of all the samples increased upon exposure to NO vapor (oxidizing gas) and decreased for acetone (reducing gas), which is consistent with the typical characteristics of n-type semiconducting metal oxide gas sensors. The response is defined as ∆R = Rgas/Rambient for oxidizing gases and Rambient/Rgas for reducing gases. In response to the NO and acetone vapors, the HSHN showed excellent recovery, having negligible variation in the baseline resistance over multiple cycles in an 80% RH condition. The enhanced response to NO vapor is observed due to the stronger oxidizing agent (NO−) than ionized oxygen species at relatively low temperature.[6a] And NO− vapor has a higher electron


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affinity (16 times) than acetone.48,49 Consequently, NO response curves have a spike-like shape at the beginning of the reaction, as shown in Figure 3a and b. However, as the temperature is increased, the response amplitude to NO vapor decreases due to the competitive chemisorption of ionized oxygen species (Figure 3c).50,51 From previous results,17,42,51 this outcome shows that the predominant sensing ability can be attributed not only to the enhanced surface reaction (gas adsorption sites), but also to effectively formed double Schottky barriers by narrow nano-necks between individual hemipill structures (Figure S4). For comparison, the response of a plain SnO2 sensor fabricated by e-beam evaporation was also tested (Figure S5). The HSHN sensor exhibits about 1.3 times larger response than that of the plain SnO2 film to 0.2 ppm NO at 300 ℃ under a high humidity condition (RH 80%). Interestingly, the HPN exhibited an enhanced sensing capability for acetone with a response of 4.25 at 1 ppm at 300 °C under high humidity (>80%), compared to that of HSHN. The response to acetone is approximately three times larger than that of HSHN. The enhanced sensing capability to acetone of HPN can be ascribed to the spillover process found in Pt catalysts, which effectively dissociates adsorbed oxygen molecules into ionized oxygens (O−, O2−). When acetone is near the surface of HPN, the chemisorbed oxygen ions can be desorbed by the response denoted in reaction 1.9 At high temperatures, the increase of chemisorbed oxygen ions significantly enhances the gas response (Figure 3d).

CH3COCH3 (gas) + 2O− → C+H3 + CO2 + CH3O− + 2e−

(1)

The response of HPN to NO vapor likely decreases due to its high competitive chemisorption by the previously formed oxygen species from the spillover effect. Note that the evolution of the response amplitudes to NO and acetone vapors shows the cross over behavior along the


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temperature gradient (Figure 3c and d). The 90% response time τres and 90% recovery time τrecov (90% variation of sensor resistance upon exposure to 0.2–1 ppm acetone and air) were calculated from the real-time response,52 as shown in Figure S6. The HSHN and HPN sensors showed large τres values of 68–225 s and 120–256 s at different concentrations, indicating that the reaction between acetone and negatively charged oxygen on the surface occurs slowly. It turns out that the absorption of OH− ions from water molecules impeded the formation of O− ions on the surface. Also, their τrecov value ranges from 72 to 510 s at different analyte concentrations. Long recovery time can be attributed to the slow reactions involving the adsorption, dissociation, and ionization of oxygen on the surface. In general, the higher gas response, the longer time is required for recovery because the more oxygen should be re-adsorbed. This explains the reason why the HPN sensor shows relatively longer recovery time. For other gases such as C7H8, C6H6, and CO, the response amplitudes are negligible because of their strong carbon bonds within the hydrocarbon species, making themselves extremely stable and less reactive to the sensors (Figure S7).9,17 In the case of CO, it was reported that the low response is likely due to the catalytic filtering effect of Pt nanoparticles9,53,54, which decomposes and oxidizes CO molecules before they react in the active sensing region. These processes lead to outstanding selectivity toward acetone. Assuming that the 3 dB signal-to-noise ratio is the required condition for the signal to be differentiated,55 the theoretical detection limits (DLs) of HSHN and HPN sensors fall in the following ranges; 3.6–130 ppb for acetone and 30.1–350 ppb for NO, respectively (Figure 4a and Table S1). In particular, the HPN sensor showed the highest response to 0.2 ppm acetone vapor at 300 ℃ and 80% RH among previously reported results of Pt-functionalized metal oxide sensors, as presented in Table 1.3,9,56–58 Their DLs are much lower than the corresponding concentrations found in the abnormal medical condition of diabetes (>1.8 ppm acetone), while


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DL for NO vapor is insufficient to diagnose asthma (>10–20 ppb). Such sensing ability is applicable for the selective detection of acetone for diabetes diagnosis, which needs a minimal acetone sensing performance of 100 nm/min in BOE (J. T. Baker, 6:1 volume ratio of 40% NH4F in water to 49% HF in water) was used. The fabricated samples were examined under high humidity (RH 80%) using a digital sourcemeter (Keithley 2635A) and a chamber with 12,800 cm3 (16 cm (W) ×16 cm (H) ×50 cm (L)) of volume. In this study, the sensor response is defined as ∆R = Rgas/Rambient for NO (oxidizing gas) and Rambient/Rgas for CH3COCH3, C7H8, C6H6, and CO (reducing gases), where Rgas (Rg) is the resistance value of the sensor in the detecting gas and Rambient (Ra) is the resistance value in 80% RH. The sensing measurement system has a humidified air generator with bubbling system and gas mixing chamber to dilute the gas concentration to a range of 0.2 to 1 ppm. The 80% RH was obtained by mixing water vapor with dry air. All the pipelines for gas flow were heated to maintain a constant temperature of 80 °C. The total gas flow rate was fixed at 3000 cm3/min.


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ASSOCIATED CONTENT Supporting Information. FE-SEM, a photo image of the micro-heater, detection limits, sensing properties, and XPS data. These materials are available free of charge via the Internet at “http://pubs.acs.org.”

AUTHOR INFORMATION Corresponding Author *C.K. E-mail: [email protected], Tel:+82-2-2123-2652 *R.B.K. E-mail: [email protected], Tel:+1-310-825-5346 Author Contributions H.G.M. conceived of the concept and experiments, performed data analysis and prepared the manuscript. Y.J. carried out TEM and XPS measurements. J.H.P. analyzed the data and wrote the manuscript. Y.W.C., H.-H.P., and C.-Y.K. provided advice for the research. C.K. and R.B.K. supervised the experiments and contributed to manuscript preparation. All authors discussed the results and commented on the manuscript. Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT


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This work was partly supported by the KIST Institutional Program (Project No. 2E27270), the Global Top Project funded by the Korea Ministry of Environment (GT-11-F-02-002-1).

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