Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

Effects of impact energy on the wear resistance and work hardening mechanism of medium manganese austenitic steel

Abstract: Medium manganese austenitic steel (MMAS) fabricated through the hot rolling process has been used in the mining, military, and mechanical industries In this paper, the abrasion performance and hardening mechanism were measured under a series of impact energies The impact wear was tested at different impact energies from 05 J to 6 J using a dynamic load abrasive wear tester (MLD-10) Microstructure and surface morphologies were analyzed using scanning electron microscopy, X-Ray diffraction, and transmission electron microscopy The results suggest that MMSA has the best wear resistance at 35 J and the worst wear resistance at 15 J Furthermore, the wear mechanism and worn surface microstructure change with different impact energies There are small differences between a large amount of martensite on the worn surfaces under different impact energies and the shapes of dislocation and twins change with different impact energies

Brittle–Ductile Transition of Mudstone in Coal Measure Rock Strata under High Temperature

Abstract: In this paper, mudstone in coal measure rock strata was investigated to understand the brittle–ductile transition characteristics of mudstone under high temperature. An electrohydraulic ser...

Promoting water splitting on arrayed molybdenum carbide nanosheets with electronic modulation

Abstract: Water electrolysis, driven by earth-abundant transition-metal-based electrocatalysts, is an important reaction for sustainable energy storage. Efficient water splitting processes at electrodes are kinetically limited by the improper adsorption strengths between reaction intermediates/products and electrocatalysts. Heteroatom doping could regulate the electronic structures of transition metals, thereby allowing the optimization of adsorption strengths. In this study, we report a class of arrayed molybdenum carbide nanosheets doped with strong electronegative heteroatoms, achieving excellent water splitting performance. Spectroscopic studies, including X-ray photoelectron spectroscopy and X-ray absorption near-edge structure, verified the N-induced electronic regulation on Mo2C. In alkaline media, the arrayed N–Mo2C/NF nanosheets realize a stable overpotential of 83.9 mV and 220 mV at 10 mA cm−2 for HER and OER, respectively, comparable to the state-of-the-art precious-metal-based electrocatalysts. Theoretical calculations show that the doping of strong electronegative nitrogen dramatically reforms the electronic structure of Mo2C and thus optimizes the adsorption free energies of reaction intermediates in hydrogen and oxygen evolution reactions (HER/OER). This study provides a viable route and inspiration to fabricate highly efficient electrocatalysts with transition-metal based materials.

Moving-Object Tracking Algorithm Based on PCA-SIFT and Optimization for Underground Coal Mines

Abstract: In view of the complex and changeable environment in underground coal mines, an improved algorithm based on the principal component analysis-scale invariant feature transform (PCA-SIFT) and mean shift is proposed to address the issues for which existing tracking algorithms are not adequate; for example, when differentiating between moving targets and the background, the tracking in the case of moving objects (e.g., confusion between foreground and background) is not optimal. This results in poor resolution and the inability to deal with very dusty conditions, scale change, and rotation. The proposed feature target tracking model was developed using the scale invariance property of the PCA-SIFT feature-extraction algorithm. Finally, the mean-shift method was used to track moving objects. The experimental results showed that the optimized algorithm for tracking moving objects was significantly better and more robust than the existing algorithm.