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To achieve a highly accurate and efficient analysis of mixed gases, it is crucial to develop an electronic nose system with high sensitivity of gas sensors and low data processing complexity. In this study, the metal oxide semiconductor (MOS) based micro-electromechanical system (MEMS) gas sensor array was prepared by inkjet printing sensing materials onto a micro-hotplate. The pattern recognition unit employed a one-dimensional convolutional neural network (1D-CNN) to identify 7 types of gases. While the optimal traditional machine learning algorithm achieved an 80% recognition accuracy, the 1D-CNN can achieve 99.8%. Furthermore, the impact of varying time series input lengths on the model accuracy was investigated, pinpointing an optimal sampling time of 15 s. This work showed that integrating the MEMS sensor array with the 1D-CNN algorithm might offer a promising approach for intricate gas classification and identification. [Display omitted] • Inkjet Printing MOS-based MEMS sensor array. • MEMS sensor arrays show low power consumption and miniaturization. • 1D-CNN is used to classify gas mixtures with 99.8% accuracy. • Only short time response data is needed for high accuracy classification. [ABSTRACT FROM AUTHOR]

A gas sensor array based on ZnO/SnO2 thin films has been designed and fabricated to recognize and quantify various kinds gases, such as ethanol, CO, NOx, and SO2, in combination with a backpropagation neural network (BPNN). Inkjet printing was adopted for facile preparation of the ZnO/SnO2-based thin sensing films with different metal additives due to its convenient control of composition, structure, and patterning. Precursor inks were synthesized using the sol-gel method. BPNN was applied to identify the gas species and estimate their concentration. Simulation results demonstrated that the gas sensor array could successfully identify the target gases, with overall prediction accuracy of the BPNN of 93%. This work shows that the combination of inkjet printing to prepare a thin-film gas sensor array with a BPNN is a convenient, low-cost, and efficient approach for qualitative and quantitative analysis of mixed gases. [ABSTRACT FROM AUTHOR]

Abstract: Water-dispersible Ag nanoparticles (NPs) were synthesised with polyacrylic acid as capping agents and prepared as an Ag NP-based ink. An approach to achieve coalescence and sintering of Ag NPs at room temperature was presented. After inkjet printing Ag NP-based ink on paper substrates, the Ag NPs underwent spontaneous coalescence when they came into contact with a poly(diallyldimethylammonium chloride) (PDAC) solution even without a traditional sintering process. This phenomenon rapidly decreased the sheet resistance of the films. The room-temperature sintering mechanism was then proposed based on the electrical and the morphological properties of Ag films with and without PDAC solutions. [Copyright &y& Elsevier]

An electronic nose system can enhance the selectivity of semiconductor gas-sensor systems but increase system power consumption and signal data processing complexity. Herein, we developed a gas sensor array composed of six ZnO-based micro-electro-mechanical systems (MEMS) sensors prepared by inkjet printing sensing materials on a micro-hotplate. Its power consumption decreased to 36 mW. Furthermore, the particle swarm optimization (PSO) algorithm was used to select the optimal features, decreasing the total of 72 features to 5. Finally, support vector machine and artificial neural network models based on the optimal 5 features were performed to recognize and quantify volatile organic compounds (VOCs), including formaldehyde, ethanol, toluene, and xylene. The identification accuracy and R 2 reach 97.9 % and 0.975, respectively. The result demonstrates that the MEMS sensor array, combined with PSO and pattern recognition algorithms, is a promising approach to accurately identifying and quantifying VOCs. [Display omitted] • Inkjet Printing ZnO-based MEMS sensor arrays. • The MEMS sensors show excellent consistency and low power consumption. • PSO algorithm is used to filter the optimal feature vectors. • Combining SVM and ANN produces classification analysis with 97.7 % accuracy. [ABSTRACT FROM AUTHOR]

• Enhanced flexible room temperature ammonia sensor based on FeCl 3 -PEDOT: PSS films. • Inkjet printing gas sensing films with controlled film thickness and additive level. • FeCl 3 additive plays two key role in response to NH 3. • A simple and effective method to find new sensing material with inkjet printing method. Herein we report improved ammonia (NH 3) gas sensing properties based on poly (3,4-ethylene-dioxythiophene)-poly(styrenesulfonate) (PEDOT: PSS) thin films with ferric chloride (FeCl 3) as additives, which were prepared by inkjet printing the PEDOT: PSS solution and FeCl 3 aqueous solution onto the flexible polyimide films in sequence. PEDOT: PSS based gas sensing films with different thickness and different content of FeCl 3 were formed. The morphologies and electrical properties of these films were investigated by scanning electron microscope, X-ray photoelectron spectroscopy and an instrument designed to measure the gas sensing abilities of the films. The results showed that the addition of FeCl 3 to the PEDOT: PSS (FeCl 3 -PEDOT: PSS) thin film improved the NH 3 sensing performance of the sensor significantly. The response value was increased tenfold to approximately 44%, and the response time was reduced by 30 times to 20 s than those of the pure PEDOT: PSS film for detecting 50 ppm NH 3 at 20 °C. Furthermore, the lower detectable NH 3 concentration of the FeCl 3 -PEDOT: PSS thin film sensor was 0.5 ppm at room temperature with the response value of 7.6%. Additionally, the FeCl 3 -PEDOT: PSS thin film sensor presented excellent flexible stability, showing only minor response value changes with different bending angles. The results reveal the potential application of flexible based sensor for monitoring NH 3 gas under ambient conditions and show that ink-jet printing is a simple and low-cost method to prepare flexible gas sensing films with controlled film thickness and additive level. [ABSTRACT FROM AUTHOR]

• A simple method to fabricate porous ZnO for trace-level acetone detection. • MEMS structure with small size and high sensitivity. • Non-contact inkjet printing technology for the rapid prototyping of transducers. • The sensor with 3.2 response to 10 ppb acetone at 200℃. • A good linearity relationship between response and concentration of acetone. In this study, inkjet printing tactics is employed to deposit sensitive materials, which is a nest-like array vertically assembled by petal-shaped structure composed of nanoparticles. It overcomes the disadvantages of cumbersome and unstable operation of traditional deposition technology for gas sensor sensitive materials based on MEMS. The mesoporous ZnO-sensor shows high gas-sensing response to low concentration acetone. The excellent performance of mesoporous ZnO-sensors lies in the uniform small nanoparticles, their multi-dimensional assembly structures and rich oxygen vacancy defects. [ABSTRACT FROM AUTHOR]