Preparation and Application of 2D MXene-Based Gas Sensors: A Review

Since MXene (a two-dimensional material) was discovered in 2011, it has been favored in all aspects due to its rich surface functional groups, large specific surface area, high conductivity, large porosity, rich organic bonds, and high hydrophilicity. In this paper, the preparation of MXene is introduced first. HF etching was the first etching method for MXene; however, HF is corrosive, resulting in the development of the in situ HF method (fluoride + HCl). Due to the harmful effects of fluorine terminal on the performance of MXene, a fluorine-free preparation method was developed. The increase in interlayer spacing brought about by adding an intercalator can affect MXene’s performance. The usual preparation methods render MXene inevitably agglomerate and the resulting yields are insufficient. Many new preparation methods were researched in order to solve the problems of agglomeration and yield. Secondly, the application of MXene-based materials in gas sensors was discussed. MXene is often regarded as a flexible gas sensor, and the detection of ppb-level acetone at room temperature was observed for the first time. After the formation of composite materials, the increasing interlayer spacing and the specific surface area increased the number of active sites of gas adsorption and the gas sensitivity performance improved. Moreover, this paper discusses the gas-sensing mechanism of MXene. The gas-sensing mechanism of metallic MXene is affected by the expansion of the lamellae and will be doped with H2O and oxygen during the etching process in order to become a p-type semiconductor. A p-n heterojunction and a Schottky barrier forms due to combinations with other semiconductors; thus, the gas sensitivities of composite materials are regulated and controlled by them. Although there are only several reports on the application of MXene materials to gas sensors, MXene and its composite materials are expected to become materials that can effectively detect gases at room temperature, especially for the detection of NH3 and VOC gas. Finally, the challenges and opportunities of MXene as a gas sensor are discussed

Highly Sensitive Ethanol Sensing Using NiO Hollow Spheres Synthesized via Hydrothermal Method

Excessive ethanol gas is a huge safety hazard, and people will experience extreme discomfort after inhalation, so efficient ethanol sensors are of great importance. This article reports on ethanol gas sensors that use NiO hollow spheres assembled from nanoparticles, nanoneedles, and nanosheets prepared by the hydrothermal method. All of the samples were characterized for performance evaluation. The sensors based on the NiO hollow spheres showed a good response to ethanol, and the hollow spheres assembled from nanosheets (NiO-S) obtained the best ethanol gas-sensing performance. NiO-S provided a larger response value (38.4) at 350 °C to 200 ppm ethanol, and it had good stability and reproducibility. The nanosheet structure and the fluffy surface of NiO-S obtained the largest specific surface area (55.20 m2/g), and this structure was beneficial for the sensor to adsorb more gas molecules in an ethanol atmosphere. In addition, the excellent sensing performance could ascribe to the larger Ni3+/Ni2+ of NiO-S, which achieved better electronic properties. Furthermore, in terms of commercial production, the template-free preparation of NiO-S eliminated one step, saving time and cost. Therefore, the sensors based on NiO-S will serve as candidates for ethanol sensing

Dempster–Shafer evidence theory‐based multi‐feature learning and fusion method for non‐rigid 3D model retrieval

This study introduces a novel multi‐feature‐based non‐rigid three‐dimensional (3D) model retrieval method. First, for each 3D model, compute the scale‐invariant heat kernel signature (SI‐HKS) descriptor and the wave kernel signature (WKS) descriptor of each vertex. Then, the normalised weighted bags of phrases feature is obtained and they are fed to the convolutional neural networks. The trust degree of each kind of descriptor is computed, and the total trust degree can be obtained. Finally, the fusion network is trained and the retrieval results can be obtained according to the ranking of the total trust degrees. For the training phase and the testing phase, the authors define different computation methods of the trust degrees and the total trust degrees. The Dempster–Shafer (DS) evidence‐based total trust degrees are used not only in the feature layer but also in the decision layer. The final decision results of the total trust degrees are used in the process of the network learning. So the proposed method can make full use of the complementary information of the SI‐HKS descriptor and the WKS descriptor. Extensive experiments have shown that the proposed multi‐feature fusion method has better performance than a single feature‐based method, and also outperforms other existing state‐of‐the‐art methods