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Exploring the morphogical and structural properties along with gas sensing applications both pure and Ti-doped SnO₂ ultra-thin films, were meticulously crafted on micromachined silicon substrate heater devices using a combination of classical soft chemical processes and hydrothermal techniques (SCPHTP). The fabrication process involved a two-step approach: initially, a 20nm layer of tin oxide was hydrothermally deposited onto the substrates, followed by annealing in wet air at 600^∘C for 5h using a standardized temperature variation protocol. Subsequently, secondary layers with thicknesses of 20, 40 and 60nm were sequentially deposited onto the tin dioxide devices and oxidized in wet air at 550^∘C and 600^∘C for 20h each, using the same temperature modulation scheme. Throughout this process, the hydrothermal deposition temperature remained constant at 180^∘C for both the initial and secondary layers of tin dioxide deposition. Additionally, Ti layers with thicknesses of 4 and 8nm were deposited onto the 20nm + 40nm system, subjected to annealing at 550^∘C for 20h, followed by 1-min annealing in dry O₂ at 700^∘C and 800^∘C, respectively, using a Rapid Thermal Annealing (RTA) system. Characterization of the crystalline and surface structures of the devices revealed a transformation of the soft chemical tin dioxide solution into the cassiterite structure of SnO₂, resulting in uniform large surface areas for the sensor devices. Moreover, Ti metal layers of 4 and 8nm thicknesses were fully converted into TiO₂ on the surface of the devices. Subsequent testing showcased higher current values in sandwich systems of 20nm + 60nm and 20nm + 40nm compared to the 20nm + 20nm configuration. Sensitivity and stability assessments for various volatile organic compounds (VOCs) and CO gases at a constant DC temperature of 400^∘C indicated excellent performance, with sensitivity to CO gas being contingent on relative humidity (RH). Notably, RTA-annealed and Ti-8 nm-doped sensor devices exhibited superior sensitivity and reproducibility, particularly when treated at 800^∘C in dry O₂ for 1min. This heightened performance can be attributed to the occupation of chloride ions in the oxygen sites of the as-synthesized SnO₂, resulting in enhanced sensing capabilities for VOC gases.

Volatile organic compounds (VOCs) are ubiquitous in the surroundings, originating from both industrial and natural sources. VOCs directly impact the quality of both indoor and outdoor air and play a significant role in processes such as fruit ripening and the body’s metabolism. VOC monitoring has seen significant growth recently, with an emphasis on developing low-cost, portable sensors capable of both vapor discrimination and concentration measurements. VOC sensing remains challenging, mainly because these compounds are nonreactive, appear in low concentrations and share similar chemical structures that results in poor sensor selectivity. Therefore, individual gas sensors struggle to selectively detect target VOCs in the presence of interferences. Electronic noses overcome these limitations by employing machine learning for pattern recognition from arrays of gas sensors. Here, an electronic nose fabricated with four types of functionalized gold nanoparticles demonstrates rapid detection and quantification of eight types of VOCs at four concentration levels. A robust two-step machine learning pipeline is implemented for classification followed by regression analysis for concentration prediction. Random Forest and support vector machine classifiers show excellent results of 100% accuracy for VOC discrimination, independent of measured concentration levels. Each Random Forest regression analysis exhibits high R² and low RMSE with an average of 0.999 and 0.002, respectively. These results demonstrate the ability of gold nanoparticle gas sensor arrays for rapid detection and quantification.

Drought stress is one of the most significant abiotic stresses, adversely affecting the economy by tumbling or even eliminating agricultural productivity, development, and yield. Adverse effects of water scarcity can be reduced if certain precautionary actions could be taken in advance. Therefore, monitoring and early detection of the drought can be helpful for preparing a well-developed response plan. In response to the stresses, an intricate response system of the plants is involved which emits a range of Volatile Organic Compounds (VOCs) from different parts, such as flowers, leaves, roots and stems. These VOCs can be used as fingerprints for categorizing stressed and nonstressed plants. This paper addresses the optimization of an array of gas sensors used in an in-situ stress diagnosis system, and the data obtained after optimization have been used for the detection of induced stresses in plants by recording the VOCs emitted by the plants. The flow characteristics of the gas chamber were modeled using the Finite Element method before fabrication. The temperature modulation of the gas sensors used in the designed electronic nose system was accomplished. The optimization of the sensor array was performed using the radar plot and Wilks’ Lambda technique. The optimum operating temperatures for each gas sensor were selected using a radar plot. Furthermore, the number of the sensors in the sensor array was reduced by choosing the sensors having higher discriminant ability using the Wilks’ Lambda optimization technique. Twelve healthy Khasi Mandarin Orange saplings were considered for the investigation. Three different levels of water stresses are induced in the plants artificially for the experiments. The overall response and the optimized response of the developed electronic nose system are compared using Linear Discriminant Analysis (LDA) and bootstrap ensemble K-Nearest Neighbors (KNN) classifier. The Leaf Relative Water Content (LRWC) of the leaves is also measured concurrently to confirm the stress induction in the plants.

The synthesis of metallophthalocyanines (M $=$ Co, Cu, Mn) bearing four ethyl 7-oxy-4,8-dimethylcoumarin-3-propanoate moieties was performed. These novel compounds were characterized by elemental analysis, ${}^{\mathrm{\text{1}}}$H-NMR spectroscopy, FT-IR, UV-vis and mass spectral data. DC and AC electrical properties of the films of metallophthalocyanines were investigated in the temperature range of 295–523 K. AC measurements were performed in the frequency range of 40–10${}^{\mathrm{\text{5}}}$ Hz. Activation energy values of the films took place between 0.55 eV–0.93 eV. Impedance spectroscopy measurements revealed that bulk resistance decreases with increasing temperature, indicating semiconductor properties. DC conductivity results also supported this result. Their gas sensing properties were also investigated for the vapors of Volatile Organic Compounds (VOCs), $n$-butyl acetate (200–3200 ppm) and ammonia (7000–56000 ppm) between temperatures 25–100°C. Sensitivity and response times of the films for the tested vapors were reported. The results were found to be reversible and sensitive to the vapors of $n$-butyl acetate and ammonia. It was found that Mn(OAc)Pc showed better sensitivity than CoPc and CuPc for $n$-butyl acetate vapors at all measured vapor concentrations and temperatures. Mn(OAc)Pc also showed better sensitivity than CoPc and CuPc for ammonia vapors at 22°C.

Volatile organic compounds (VOCs) are ubiquitous in the surroundings, originating from both industrial and natural sources. VOCs directly impact the quality of both indoor and outdoor air and play a significant role in processes such as fruit ripening and the body’s metabolism. VOC monitoring has seen significant growth recently, with an emphasis on developing low-cost, portable sensors capable of both vapor discrimination and concentration measurements. VOC sensing remains challenging, mainly because these compounds are nonreactive, appear in low concentrations and share similar chemical structures that results in poor sensor selectivity. Therefore, individual gas sensors struggle to selectively detect target VOCs in the presence of interferences. Electronic noses overcome these limitations by employing machine learning for pattern recognition from arrays of gas sensors. Here, an electronic nose fabricated with four types of functionalized gold nanoparticles demonstrates rapid detection and quantification of eight types of VOCs at four concentration levels. A robust two-step machine learning pipeline is implemented for classification followed by regression analysis for concentration prediction. Random Forest and support vector machine classifiers show excellent results of 100% accuracy for VOC discrimination, independent of measured concentration levels. Each Random Forest regression analysis exhibits high R² and low RMSE with an average of 0.999 and 0.002, respectively. These results demonstrate the ability of gold nanoparticle gas sensor arrays for rapid detection and quantification.

Studied was the activity of the enzymes involved in the bacterial assimilation of toluene by carbon source limited Acinetobacter sp. grown in titrostat under strictly controlled conditions with specific growth rates ranking from 0.015 to 0.109 h^-1 corresponding to metabolic rates (g toluene assimilated by 1 g cells per hour) from 0.038 to 0.144 h^-1. The study confirms the supposed catabolic pathway of toluene by Acinetobacter sp. via benzoate, benzaldehyde and catechol. The activities of two enzymes of this pathway were found to increase with the rise of microbial growth rate: benzaldehyde dehydrogenase and catechol-2,3-dioxygenase (C23O). Observed was a sharp increase of the specific C23O-activity with the rise of microbial metabolism from nearly maintenance rate, 0.022 h^-1, to 0.046 h^-1 which could be explained with the generally accepted view that the meta pathway catalyzed by C23O provides considerably more energy needed for the activated constructive metabolism of the organism.