Application of PTFE composite catalytic filter material in synergistic purification of multiple pollutants in waste incineration flue gas

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 25, 2025

The effective management of pollutants in waste incineration flue gas remains a critical challenge environmental protection. This study develops novel vanadium (V), molybdenum (Mo)/cerium (Ce), titanium (Ti)–polytetrafluoroethylene (VMo/CeTi-PTFE) composite catalytic filtration material to address the simultaneous removal multiple purification. A systematic investigation its performance and reaction mechanisms reveals that coating process optimizes pore structure material. While specific surface area is slightly reduced, increased volume diameter facilitate diffusion enhance efficiency. Experimental results demonstrate under high catalyst loading conditions, this exhibits outstanding denitrification, dioxin degradation, particulate removal, maintaining consistently dust efficiency over 99.97%. Additionally, binder content enhances mechanical stability, while water sulfur resistance tests confirm exceptional durability. Mechanistic analysis indicates significant synergistic effect between denitrification degradation. Specifically, − OH groups promote cleavage C–Cl bonds, enabling efficient degradation simultaneously improving nitrogen oxide (NOx) reduction suppressing formation byproduct nitrous (N2O). provides solid theoretical foundation technical support for design multifunctional purification materials underscores their broad application potential managing complex pollutants. findings have important implications enhancing benefits purification, representing step toward more cost-effective, efficient, environmentally friendly treatment solutions.

Language: Английский

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Tissue-Specific Carcinogenicity Prediction Using Multi-Task Learning on Attention-based Graph Neural Networks

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Cancer is caused by the uncontrolled growth and division of abnormal cells. In industrialized societies, chemical exposure one leading causes cancer. Indeed, since certain compounds can induce cancer damaging genes or affecting cellular metabolism, studying carcinogens essential. However, previous studies have not considered that may promote different tissue-specific carcinogenicity. Therefore, this study developed a multi-task learning framework to predict carcinogenicity in liver, lung, stomach, breast tissues. This consisted shared layer extract common features task-specific layers perform predictions. The contains graph attention network (GAT) make atom representations reflect importance neighboring atoms parallel fully connected designed for each task combination. These are then passed entire training process was conducted through stepwise learning, whereby model trained first step using partially labeled data tissues, initial weights were determined during process. second all allowing final prediction. results demonstrated proposed achieved superior performance overall. best observed stomach (AUROC: 0.825; AUPR: 0.867), outperforming single-task models 0.800; 0.840) 0.743–0.791; AUPR 0.788–0.827). We further analyzed molecules with high predicted tissue identified critical substructures prediction mechanism. research contribute predicting candidate chemicals early stages drug development, thereby reducing costs time.

Language: Английский

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Unveiling the promotional mechanisms of N-doping on the adsorption behaviors of dioxins from sintering flue gas by coconut shell-derived hierarchical porous carbon

Fuel, Journal Year: 2024, Volume and Issue: 381, P. 133640 - 133640

Published: Nov. 11, 2024

Language: Английский

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Machine learning prediction of dioxin lipophilicity and key feature Identification

Computational and Theoretical Chemistry, Journal Year: 2024, Volume and Issue: unknown, P. 115032 - 115032

Published: Dec. 1, 2024

Language: Английский

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