Heilongjiang University of Science and Technology

To address the inability of traditional mechanical anemometers to satisfy the demand for high accuracy, real-time measurement, and dynamic monitoring of ventilation parameters in intelligent deep mining environments, this study focuses on the accuracy verification and analysis of a portable multi-parameter intelligent wind measurement sensor. Comparative analyses of direct-through and reflective ultrasonic measurement principles and their respective error characteristics were conducted, indicating the reflective structure’s superior accuracy. Experiments were carried out in a low-speed circular wind tunnel to investigate the impact of measurement angle deviations on instrument accuracy, resulting in the identification of an optimal measurement angle. Additionally, studies on average airflow velocity measurements were performed, leading to the generation of airflow velocity contour distributions. An average airflow velocity prediction model was developed and subsequently validated through field testing, demonstrating measurement errors within ± 0.1 m/s. This research provides both theoretical foundations and technical support for enhancing intelligent ventilation management in deep coal mining operations.

Abstract Based on the concept of resource utilization, this study successfully prepared a green and highly efficient nitrogen-doped biochar catalyst (N-BC-800) using agricultural waste cotton hulls as a raw material. This catalyst was then applied to the ozone-catalyzed degradation of N,N-diethyl-meta-toluamide (DEET), a typical insect repellent in water bodies. The apparent second-order rate constant reached 2358 M −1 s −1 , representing a 106-fold increase compared to the O 3 system alone, and a 25-fold increase compared to the O 3 /BC system. Experimental characterization and theoretical analysis indicate that C=O groups and pyridinic N structures on the material surface serve as primary catalytic active sites. These synergistically promote ozone decomposition and generate highly reactive intermediates which are further converted into reactive oxygen species (ROS), significantly enhancing DEET degradation performance. This catalyst exhibits excellent stability and applicability in real aquatic environments. It demonstrates broad-spectrum degradation effects on multiple pollutants while significantly reducing the ecotoxicity of reaction byproducts. This study provides theoretical support and practical pathways for developing highly efficient, green ozone-catalyzed materials. Graphical Abstract

Deep coal mining operations are subject to elevated geothermal gradients that fundamentally alter coal oxidation behavior, yet the coupling mechanisms between the ground temperature and coal metamorphic grade remain poorly understood. In this study, we establish a comprehensive framework integrating multiscale structural characterization, functional group reaction network analysis, and stage-resolved kinetics to elucidate geothermal-induced spontaneous combustion mechanisms. Four coals spanning a wide range of metamorphic grades (Ro,max = 0.58%-1.12%) were subjected to simulated geothermal conditions (30, 40, and 50 °C) and characterized by using TG-DTG and in situ FTIR techniques. We propose a novel "Geothermal Activation-Oxidation Acceleration" (GAOA) mechanism wherein ground temperature pretreatment activates oxygen-containing functional groups, creating reactive sites that substantially lower oxidation barriers. A functional group reaction network model was developed, revealing a hierarchical reactivity sequence: -OH > CO > C-O-C > aliphatic C-H > aromatic CC. Multimethod kinetic analysis (Coats-Redfern, FWO, KAS, and Starink) demonstrated that activation energy decreased by 15.3%-28.7% under 50 °C pretreatment, with lower-rank coals exhibiting greater sensitivity (ΔEa = 28.7 kJ/mol for long-flame coal vs 18.2 kJ/mol for coking coal). Based on these findings, we developed a Spontaneous Combustion Risk Index incorporating metamorphic grade, geothermal gradient, and functional group reactivity, providing a quantitative tool for fire hazard assessment in deep mining operations. This work advances the mechanistic understanding of geothermal effects on Coal Spontaneous Combustion and offers practical guidance for risk management in China's increasingly deeper mines.

To elucidate the wear mechanisms of diamond AFM tips during nanoscale scribing of single-crystal silicon, this study combines controlled experiments with atomistic molecular dynamics (MD) simulations. Scribing tests were conducted under systematically varied bias current, scribing speed, and scribing distance. Tip morphology evolution was quantitatively characterized. Concurrently, a three-dimensional MD model reproduced probe-silicon interactions to analyze bond breaking, atomic detachment, and structural transformation at the atomic scale. The results show that increasing current, speed, and distance significantly accelerate tip blunting. Simulations reveal a progressive transition in deformation behavior from elastic response to atomic attrition, plastic damage, brittle cracking, and catastrophic fracture as indentation depth increases, and cluster analysis establishes a quantitative correlation between process parameters and wear severity. This integrated experimental simulation framework provides mechanistic insight into diamond tip degradation and offers quantitative guidance for improving probe durability and process reliability in AFM-based nanofabrication.