Department of Electrical and Electronics Engineering
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Item SrTiO3–TiO2 Heterostructured Nanotube Arrays for Ultrafast Ethanol Sensing(ACS, 2022-10) Hazra, ArnabDiverse metal oxide semiconductors have bloomed in chemical sensing applications providing opportunities and challenges. In this work, SrTiO3–TiO2 heterostructured nanotube arrays were well designed via the electrochemical synthesis of TiO2 nanotubes, followed by a facile one-step hydrothermal route by varying the amount of Sr(OH)2·8H2O precursor (0.25–25 mM) to grow different quantities of SrTiO3 on TiO2 nanotubes. The crystalline nature, morphology, and synthesis mechanism of the nanotube array were fully elucidated. Vertically aligned SrTiO3–TiO2 heterostructured nanotube arrays were then sandwiched between the Ti bottom electrode (substrate as well) and Au top electrode to fabricate the metal–insulator–metal type sensors. SrTiO3–TiO2 nanotube sensors exhibited ethanol-selective behavior where the highly defective SrTiO3 layer acted as the main sensitive material that interacted with target species like ethanol. The SrTiO3–TiO2 heterostructured nanotube array sensor synthesized with 0.25 mM Sr(OH)2 precursor exhibited an excellent response magnitude (Ra/Rg) of ∼556 with an ultrafast response time of 0.4 s toward 50 ppm ethanol at an operating temperature of 150 °C. Moreover, the sensor exhibits excellent stability, a low detection limit of 2.94 ppb, and good selectivity. All the SrTiO3–TiO2 heterostructured nanotube array sensors showed promising humidity-tolerant behavior, and negligible change in response was obtained in the presence of 80% humid ambient air. The mechanism behind the modulated VOC sensing characteristics was explained with the comprehensive effects of the larger specific surface area, high surface defects, and modulated oxygen vacancies coming from the SrTiO3 modification in the nanotube structures.Item SrTiO3-TiO2 heterostructured nanotubes array for selective acetone sensing(IEEE, 2023) Hazra, ArnabEfficient detection of acetone is essential for medical applications. In this context, we are reporting acetone selective SrTiO 3 -TiO 2 sensor. Initially, TiO 2 nanotube array was synthesized by the electrochemical anodization and then treated with Sr(OH)2 solution through the hydrothermal reaction. The morphology and crystallinity of the SrTiO 3 -TiO 2 nanotubes were investigated. The Au/SrTiO 3 -TiO 2 /Ti sensor exhibited its natural selectivity towards acetone and showed a high response (99.5%/50 ppm) at 150°C under 80% relative humidity. The sensor exhibited a remarkable 51.2% response even for 0.5 ppm acetone. The stability and hydrophobicity of SrTiO 3 result in consistency during repeated cycle study of the sensor.Item Hybridized Graphene Field-Effect Transistors for Gas Sensing Applications(Wiley, 2023-09) Hazra, ArnabThe extraordinary properties of graphene and its derivatives from chemical, physical, electronic, and mechanical perspectives have sparked great interest in a variety of applications. The two-dimensional (2D) nature, high surface-to-volume ratio, low electronic noise, and high surface sensitivity make it a desirable channel material in the gas sensing domain. The field effect and ambipolar nature of graphene enable it for field-assisted gas sensing that provides greatly higher sensitivity and stronger selectivity toward a particular gas/volatile organic compound (VOC). This book chapter is highlighting the selective importance of graphene and its derivative-based field-effect transistors (FETs) and their application in gas/VOC sensing. This chapter begins with a brief description of the origin of graphene and its properties for a variety of applications. Then a detailed discussion involves the type, properties, and synthesis methodology of graphene FET. Finally, we introduced graphene, its derivatives, and its composites with other nanomaterials-based gas/VOC sensors in three-terminal FET configurations.Item A MoS2 quantum dot functionalized TiO2 nanotube array for selective detection of xylene at low temperature(RSC, 2024-10) Hazra, ArnabXylene is among the most complex volatile organic compounds (VOCs) and is significant in many applications. Xylene as an efficient breath marker of lung cancer raises the concern of discrimination between compounds having chemically similar nature like benzene, toluene, etc. For highly stable and selective detection of xylene, in this study, we report a 0D–1D nanocomposite, i.e. a MoS2 quantum dot (QD) functionalized TiO2 nanotube array. The nanocomposite synthesis involves a hydrothermal reaction between MoS2 QDs and the 1D TiO2 nanotube array which is synthesized by anodic oxidation of titanium foil. The Au/MoS2–TiO2 nanotube/Ti structured sandwich type sensor exhibited selective xylene detection with a high response magnitude of 188% (50 ppm xylene) which is many times higher than those of the pure MoS2 QD and TiO2 nanotube sensors at a relatively low operating temperature, i.e. 75 °C. It also displayed a fast response time (35 s) and maximum recoverability, with a lower detection limit (LOD) of 33 ppb. Notably, the highest selectivity of detection towards xylene over benzene and toluene makes the sensor potential for environmental and breath VOC monitoring. Additionally, the long-term stability of the sensor was apparent from the stable sensing behavior even after 1 month.Item Hybridized Graphene Oxide FETs with Amplified Gas Sensitivity(IEEE, 2024) Hazra, ArnabThe current study concerns a new approach to achieve amplified gas sensitivity in hybrid graphene oxide (GO) based field effect transistors (FET) sensors. Chemically synthesized TiO 2 nanoparticles, WO 3 nanoflowers and Pd nanoparticles were used to functionalize GO channel which was then implemented in back gated FET structure sensors fabricated on SiO 2 /Si substrate. Morphology of pure and hybridized GO were characterized with field emission scanning electron microscopy. IDS-VGS characteristics of all the FET sensors were measured in air and 100 ppm of ethanol/acetone ambient. Interestingly, all the sensors exhibited a peak response magnitude at a particular VGS closed to the Dirac point. Therefore, to achieve a high sensitivity, transient response was measured at VGS≈VDirac, while VDS=1V (constant). ~49%, ~55%, ~229% and ~129% response towards 100 ppm of ethanol/acetone were recorded for pure GO, p-TiO 2 -GO, WO 3 - GO and Pd-GO sensors, respectively. The recorded responses at VGS≈VDirac were. 7,11,21 and 64 times amplified than that of the VGS=0. The amplified sensitivity was achieved by modulating the carrier concentration of GO channel through optimized gate electrostatic. The functionalization of GO with TiO 2 , WO 3 and Pd further enhanced the catalytic activity, selectivity, dissociative adsorption properties of the sensing channel towards different VOCs.Item Feature Extraction From Impedance Spectrum of Au/ZrO2 Nanotube/Zr-Based MIM Sensor for Selective Discrimination of VOCs: A Potential Approach for E-Nose Application(IEEE, 2024-07) Hazra, ArnabThis report proposes a new method to extract features from the impedance spectrum of zirconium oxide (ZrO2) nanotube-based single sensor to detect and distinguish multiple volatile organic compounds (VOCs). The process of electrochemical anodization was utilized to synthesize a metal-insulator–metal (MIM) structured sensor consisting of ~8.5- μ m-long ZrO2 nanotubes (Au/ZrO2 nanotube/Zr). The impedance analysis was implemented in order to examine variations in capacitance and resistance at distinct points of contact in the presence of different VOCs. The structural interdependence was determined via the utilization of circuit modeling. The parameter values that were modeled were utilized as feature vectors in the construction of a feature matrix. The feature matrix underwent principal component analysis (PCA) and linear discriminant analysis (LDA) for processing. The combined variance of the initial two components in the PCA and LDA was 88.75% and 99.71%, correspondingly. The discrimination of acetone, benzene, formaldehyde, methanol, and toluene/xylene was successfully accomplished independent of their concentration and 96.67% of accuracy was obtained using support vector machine (SVM). Moreover, the model that has been formulated integrates a distinctive technique for extracting features with a solo and unique sensor that has the ability to differentiate various VOCs.