Morphological analysis of Pd/C nanoparticles using SEM imaging and advanced deep learning.

In this study, we present a comprehensive approach for the morphological analysis of palladium on carbon (Pd/C) nanoparticles utilizing scanning electron microscopy (SEM) imaging and advanced deep learning techniques. A deep learning detection model based on an attention mechanism was implemented to accurately identify and delineate small nanoparticles within unlabeled SEM images. Following detection, a graph-based network was employed to analyze the structural characteristics of the nanoparticles, while density-based spatial clustering of applications with noise was utilized to cluster the detected nanoparticles, identifying meaningful patterns and distributions.

HUST bearing: a practical dataset for ball bearing fault diagnosis.

Objectives: The rapid growth of machine learning methods has led to an increase in the demand for data. For bearing fault diagnosis, the data acquisition is time-consuming with complicated processes. Existing datasets are only focused on only one type of bearing, which limits real-world applications.

Ultra-low detection limit chemoresistive NO gas sensor using single transferred MoS flake: an advanced nanofabrication.

In this work, a method of fabricating a NO nano-sensor working at room temperature with a low detectable concentration limit is proposed. A 2D-MoS flake is isolated by transferring a single MoS flake to SiO/Si substrate, followed by applying an advanced e-beam lithography (EBL) to form a metal contact with Au/Cr electrodes. The resulting chemoresistive nano-sensor using a single MoS flake was applied to detect a very low concentration of NO at the part-per-billion (ppb) level.

Using Palladium Nanocubes on ZnO Nanostructures in Hydrogen Gas Sensor for Fast Response and Recovery Time.

We developed a novel sensor structure by synthesizing Pd nanocubes (NCs) decorated on ZnO nanostructures (NSs) applied to resistive-type H₂ gas sensor with micro-length in sensing channel. The ZnO NSs were selectively grown between micro-size finger-like interdigital electrodes through microelectromechanical technology. The novel H₂ sensor structure with the sensing channel was reduced to micro-size by this proposed method to obtain a sensor with fast response/recovery time.

Fabrication of highly sensitive and selective H₂ gas sensor based on SnO₂ thin film sensitized with microsized Pd islands.

Ultrasensitive and selective hydrogen gas sensor is vital component in safe use of hydrogen that requires a detection and alarm of leakage. Herein, we fabricated a H2 sensing devices by adopting a simple design of planar-type structure sensor in which the heater, electrode, and sensing layer were patterned on the front side of a silicon wafer. The SnO2 thin film-based sensors that were sensitized with microsized Pd islands were fabricated at a wafer-scale by using a sputtering system combined with micro-electronic techniques.

Selective detection of carbon dioxide using LaOCl-functionalized SnO₂ nanowires for air-quality monitoring.

In spite of the technical important of monitoring CO(2) gas by using a semiconductor-type gas sensor, a good sensitive and selective semiconductor CO(2) sensor has been not realized due to the rather unreactive toward CO(2) of conventional semiconductor metal oxides. In this work, a novel semiconductor CO(2) sensor was developed by functionalizing SnO(2) nanowires (NWs) with LaOCl, which was obtained by heat-treating the SnO(2) NWs coating with LaCl(3) aqueous solution at a temperature range of 500-700°C. The bare SnO(2) NWs and LaOCl-SnO(2) NWs sensors were characterized with CO(2) (250-4,000 ppm) and interference gases (100 ppm CO, 100 ppm H(2), 250 ppm LPG, 10 ppm NO(2) and 20 ppm NH(3)) at different operating temperatures for comparison.