Nowadays, a new class of 2D material so-called MXene attracted massive attention in a variety of applications. Abundant active sites, metallic conductivity, tunable surface chemistry, and outstanding stability of MXene...

In the current study, noble metals nanoparticles functionalized MoS2 coated biodegradable low-cost paper sensors were fabricated for selective detection of low concentration volatile organic compounds (VOCs). MoS2 layer was grown...

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This research used hybrid graphene oxide (GO) field-effect transistors (FETs) based sensor array to design an electronic nose (e-nose) for identifying exhaled breath acetone to diagnose diabetes mellitus through noninvasive route. Six back gated FET sensors were fabricated with hybrid channel of GO, WO3 and noble metals (Au, Pd and Pt) nanoparticles. The experiment was carried out by using four distinct forms of synthetic breath, each with a different level of interference. Linear discriminant analysis (LDA) and artificial neural networks (ANN) were utilized to classify and analyze the sensor response vector. In contrast, partial least square (PLS) and multiple linear regression (MLR) were used to evaluate the exact acetone concentration in synthetic breath. First, LDA was used to lower the dimensionality of the response vector, which was then provided as an input to the ANN model. ANN was performed with ten perceptrons model in the hidden layer and highest accuracy of 99.1% was achieved. Additionally, by using the loading plot of PLS, three sensors (Pt/WO3/GO, Pd/WO3/GO, and WO3/GO) had the ample use to predict the concentration of breath acetone. Moreover, the MLR approach with correlation coefficient (R2) of 0.9572 and root mean square error (RMSE) of 5.63% were used for obtaining the exact concentration of acetone. Consequently, e-nose with matrix of hybrid GO-FET sensors and pattern recognition algorithms (LDA, ANN, PLS and MLR) exhibited considerable ability in selective detection of acetone in synthetic breath.

Diverse metal oxide semiconductors have bloomed in chemical sensing applications providing opportunities and challenges. In this work, SrTiO 3 −TiO 2 heterostructured nanotube arrays were well designed via the electrochemical synthesis of TiO 2 nanotubes, followed by a facile one-step hydrothermal route by varying the amount of Sr(OH) 2 •8H 2 O precursor (0.25−25 mM) to grow different quantities of SrTiO 3 on TiO 2 nanotubes. The crystalline nature, morphology, and synthesis mechanism of the nanotube array were fully elucidated. Vertically aligned SrTiO 3 − TiO 2 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. SrTiO 3 −TiO 2 nanotube sensors exhibited ethanol-selective behavior where the highly defective SrTiO 3 layer acted as the main sensitive material that interacted with target species like ethanol. The SrTiO 3 −TiO 2 heterostructured nanotube array sensor synthesized with 0.25 mM Sr(OH) 2 precursor exhibited an excellent response magnitude (R a /R g ) 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 SrTiO 3 −TiO 2 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 SrTiO 3 modification in the nanotube structures.