Multivariate Evaluation Method for Screening Optimum Gas-Sensitive Materials for Detecting SF6 Decomposition Products

Handheld Crop Pest Sensor Using Binary Catalyst-Loaded Nano-SnO2 Particles for Oxidative Signal Amplification

In agriculture, pest management is a major challenge. Crop releases volatiles in response to the pest; hence, sensing these volatile signals at a very early stage will ease pest management. Here, binary catalyst-loaded SnO2 nanoparticles of <5 nm were synthesized for the repeated capture and oxidation of the signature volatile and its products to amplify the chemoresistive signal to detect concentrations as low as ≈120 ppb. The sensitivity may be due to the presence of the elements in the Sn-Fe-Pt bond evidenced by extended X-ray absorption fine-structure spectroscopy (EXAFS) that captures and oxidize the volatile without escaping. This strong catalyst may oxidize nontarget volatiles and can cause false signals; hence, a molecular sieve filter has been coupled to ensure high selectivity for the detection ofTuta absolutainfestation in tomato. Finally, with the support of a mobile power bank, the optimized sensor has been assembled into a lightweight handheld device.

Low-Temperature Detection of Sulfur-Hexafluoride Decomposition Products Using Octahedral Co3O4-Modified NiSnO3 Nanofibers

Sulfur hexafluoride (SF₆) is widely used in electrical equipment because of its excellent insulating properties. The type of internal fault in the power system can be identified by detecting SF₆ decomposition products. In this manuscript, we report a novel sensing material based on octahedral Co₃O₄-modified NiSnO₃ nanofibers synthesized via a two-step process based on electrospinning followed by a hydrothermal method for detecting the SF₆ decomposition products. From the evaluation of various characterization techniques, it was determined that the Co₃O₄ octahedra adhered inflexibly to the surface of the NiSnO₃ nanofibers, which consist of smaller particles and provide a huge surface area for the adsorption of an enormous amount of gas species. Planar-type chemical gas sensors were devised, and their gas detecting performance against SF₆ decomposition products was systematically investigated. A comparison of the sensitivity properties of different amounts of charged Co₃O₄ octahedra in NiSnO₃ nanofibers shows that the S-2-based Co₃O₄@NiSnO₃ composite has a high selectivity for 100 ppm SO₂F₂ gas with a high sensing response of 22.5 at a relatively low temperature of 50 °C with a moderate response/recovery interval (∼200/∼268 s) and a low detection limit (5 ppm) over other interfering gases, such as SOF₂, SO₂, and H₂S. Interestingly, the sensing properties of the fabricated sensors based on the Co₃O₄@NiSnO₃ composites for the SO₂F₂ gas were improved in terms of lower operating temperatures, higher gas responses, and mild response/recovery intervals, which could be attributed to the unique microstructure effect, the catalytic influence of Co₃O₄ octahedra, and the creation of p/n junctions to increase the charge transfer and diffusion rate within the catalytic assembly of the sensor materials. This work highlights the importance of the heterostructure design in the construction of high-performance gas sensors for the real-time detection of SF₆ decomposition products.

Multicomponent SF6 decomposition product sensing with a gas-sensing microchip

A difficult issue restricting the development of gas sensors is multicomponent recognition. Herein, a gas-sensing (GS) microchip loaded with three gas-sensitive materials was fabricated via a micromachining technique. Then, a portable gas detection system was built to collect the signals of the chip under various decomposition products of sulfur hexafluoride (SF₆). Through a stacked denoising autoencoder (SDAE), a total of five high-level features could be extracted from the original signals. Combined with machine learning algorithms, the accurate classification of 47 simulants was realized, and 5-fold cross-validation proved the reliability. To investigate the generalization ability, 30 sets of examinations for testing unknown gases were performed. The results indicated that SDAE-based models exhibit better generalization performance than PCA-based models, regardless of the magnitude of noise. In addition, hypothesis testing was introduced to check the significant differences of various models, and the bagging-based back propagation neural network with SDAE exhibits superior performance at 95% confidence.