Quantitative prediction of ternary mixed gases based on an SnO2 sensor array and an SSA-BP neural network model.

This paper describes a tin oxide and copper doped tin oxide gas sensing material synthesized by a biological template method and simple hydrothermal reaction, which were used for the preparation of a gas sensor array. The sensor array is combined with the Sparrow Search Algorithm optimized BP neural network algorithm (SSA-BP) to predict and analyze the concentration of indoor toxic gases, including ammonia, xylene, and formaldehyde. Granular SnO2 was prepared by the biological template method and Cu/SnO2 doped with different copper ion concentrations was prepared by the hydrothermal method. The morphology of the synthesized nanomaterials was characterized by SEM, and the elemental composition and chemical state of the main elements were analyzed by XRD and XPS. The PL emission observed in the visible region is attributed to the defect level gap caused by oxygen. The optimal operating temperature, sensitivity, response/recovery time and the long-term stability of the sensor array have been studied. By combining the sensor array with the neural network algorithm in a simulated indoor environment at four humidity levels, the concentration information of the gas mixtures could be well predicted and the predicted concentration error was less than 0.84 ppm. Therefore, the sensor array prepared in this study combined with the SSA-BP algorithm achieved good results in predicting the concentrations of the three toxic mixtures.

Simulation and Optimization of a Planar-Type Micro-Hotplate with Si3N4-SiO2 Transverse Composite Dielectric Layer and Annular Heater.

Micro-hotplates (MHPs) have become widely used basic structures in many micro sensors and actuators. Based on the analysis of the general heat transfer model, we propose a new MHP design based on a transversal composite dielectric layer, consisting of different heat transfer materials. Two general proven materials with different thermal conductivity, Si3N4 and SiO2, are chosen to form the composite dielectric layer. An annular heater is designed with a plurality of concentric rings connected with each other. The relationship between MHP performance and its geometrical parameters, including temperature distribution and uniformity, thermal deformation, and power dissipation, has been fully investigated using COMSOL simulation. The results demonstrate that the new planar MHP of 2 µm thick with a Si3N4-SiO2 composite dielectric layer and annular heater can reach 300 °C at a power of 35.2 mW with a mechanical deformation of 0.132 µm, at a large heating area of about 0.5 mm2. The introduction of the composite dielectric layer effectively reduces the lateral heat conduction loss and alleviates the mechanical deformation of the planar MHP compared with a single SiO2 dielectric layer or Si3N4 dielectric layer.

Enhanced Blocking Effect: A New Strategy to Improve the NO2 Sensing Performance of Ti3C2Tx by γ-Poly(l-glutamic acid) Modification.

Titanium carbide (Ti3C2Tx) with a distinctive structure, abundant surface chemical groups, and good electrical conductivity has shown great potential in fabricating superior gas sensors, but several challenges, such as low response kinetics, poor reversibility, and serious baseline drift, still remain. In this work, γ-poly(l-glutamic acid) (γ-PGA) with a blocking effect is exploited to modify Ti3C2Tx, thereby stimulating the positive response behavior of Ti3C2Tx and improving its gas sensing performance. On account of the unique synergetic interaction between Ti3C2Tx and γ-PGA, the response of the flexible Ti3C2Tx/γ-PGA gas sensor to 50 ppm NO2has been improved to a large extent (average 1127.3%), which is 85 times that of Ti3C2Tx (only 13.2%). Moreover, the as-fabricated Ti3C2Tx/γ-PGA sensor not only exhibits a shorter response/recovery time (average 43.4/3 s) compared with the Ti3C2Tx-based sensor (∼18.5/18.3 min) but also shows good reversibility and repeatability (relative standard deviation (RSD) <1%) at room temperature within 50% relative humidity (RH). The improved gas sensing properties of the Ti3C2Tx/γ-PGA sensor can be attributed to the enhancement of effective adsorption and the blocking effect assisted by water molecules. Furthermore, the gas sensing response of the Ti3C2Tx/γ-PGA sensor is studied at different RHs, and humidity compensation of the sensor is carried out using the multiple regression method. This work demonstrates a novel strategy to enhance the gas sensing properties of Ti3C2Tx by γ-PGA modification and provides a new way to realize highly responsive gas detection at room temperature.

A Novel Sparse Representation Classification Method for Gas Identification Using Self-Adapted Temperature Modulated Gas Sensors.

A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector, the i-th class training samples are approximated as a linear combination of a few of the dictionary atoms. The optimal solutions of the synthesis dictionary and coefficient vector are found by MOD. Finally, testing samples are identified by evaluating which class causes the least reconstruction error. The proposed algorithm is evaluated on the analysis of hydrogen, methane, carbon monoxide, and benzene at self-adapted modulated operating temperature. Experimental results show that the proposed method is quite efficient and computationally inexpensive to obtain excellent identification for the target gases.

Temperature-dependent ultraviolet Raman scattering and anomalous Raman phenomenon of AlGaN/GaN heterostructure.

Temperature-dependent ultraviolet (UV) Raman scattering from AlGaN/GaN heterostructure is investigated. Compared to the visible Raman spectrum, four new peaks at 600, 700, 780, and 840 cm-1 are observed in the UV Raman spectrum. The peak at 780 cm-1 is from the AlGaN A1(LO) mode. According to the calculated dispersion relations of the interface phonon modes in the AlGaN/GaN heterostructure, the peaks at 600 and 840 cm-1 correspond to interface phonon modes. Meanwhile, the peak at 700 cm-1 is attributed to the disorder-active mode near the 2DEG interface. Due to the near-resonant enhancement effect, the intensities of the GaN A1(LO) mode, interface phonon modes, disorder active mode and the AlGaN A1(LO) mode exhibit different temperature dependence. Furthermore, the frequencies of the interface phonon modes and the disorder active mode show anomalous temperature dependence, which can be attributed to the strong built-in electric field near the 2DEG interface.

Development of a LeNet-5 Gas Identification CNN Structure for Electronic Noses.

A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH4 and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH4 and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.

DFT calculation and analysis of the gas sensing mechanism of methoxy propanol on Ag decorated SnO2 (110) surface.

Methoxy propanol has been widely used in modern industry and consumer products. Inhalation or skin exposure to methoxy propanol for a long period would bring about safety challenges on human habitat and health. Ag decorated SnO2 mesoporous material has been synthesized and shown to exhibit high sensitivity and good selectivity to methoxy propanol among other interferential VOC gases. Density Functional Theory study were conducted to yield insight into the surface-adsorbate interactions and therefore the gas sensing improvement mechanism by presenting accurate energetic and electronic properties for the Ag/SnO2 system. Firstly, an electron transfer model on Ag and SnO2 grain interface was put forward to illustrate the methoxy propanol gas sensing mechanism. Then, a three-layer adsorption model (TLAM) was proposed to investigate methoxy propanol gas sensing properties on a SnO2 (110) surface. In the TLAM method, taking SnO2 (110) surface for the basis, layer 1 illustrates the decoration of metal Ag on SnO2 (110) surface. Layer 2 represents the adsorption of molecular oxygen on metal Ag decorated SnO2 (110) surface. Layer 3 indicates the adsorption of methoxy propanol, and for comparison, three other VOC gases (namely, ethanol, isopropanol and p-xylene) on Ag decorated SnO2 (110) surface with oxygen species pre-adsorbed consecutively. All the adsorption processes were calculated by means of Density Functional Theory method; the adsorption energy, net charge transfer, DOS, PDOS and also experimental data were utilized to investigate the methoxy propanol gas sensing mechanism on Ag decorated SnO2 (110) surface with oxygen species pre-adsorbed.

Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System.

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules.