A5115605011- Fuel Cells and Related Materials
- Electrocatalysts for Energy Conversion
- Machine Learning in Materials Science
- Crystallization and Solubility Studies
- Hydrogels: synthesis, properties, applications
- Supramolecular Self-Assembly in Materials
- Advanced Manufacturing and Logistics Optimization
- Electrochemical Analysis and Applications
- X-ray Diffraction in Crystallography
Guizhou University of Finance and Economics
2024-2025
In the current digital economy era, takeout industry is expanding, which makes human-machine collaborative delivery management between riders and intelligent algorithms increasingly important. Based on this, this paper takes as Decision Making Units to study efficiency of algorithm from perspective input output. Firstly, a comprehensive evaluation index system for output constructed. Secondly, entropy method used obtain weights indicators at all levels index. Then, output-oriented DEA-BCC...
To avoid the step of manual feature engineering when predicting crystal properties, a graph convolutional neural network based on dual attention mechanism, named DA-CGCNN, is proposed. It fuses both channel mechanism and self-attention benefiting from capturing complex features each atom dependencies between atomic nodes better. found to have comparable or superior performance other advanced (GNN) models by five properties crystal: formation energy, total bandgap, Fermi density. In addition,...
The evolution of bifunctional catalysts for the oxygen reduction reaction (ORR) and (OER) that are highly active, stable, conductive is crucial advancing metal-air batteries fuel cells. We have here thoroughly explored OER ORR performance a category two-dimensional (2D) metal–organic frameworks (MOFs) called TM3(HADQ)2, Rh3(HADQ)2 exhibits promising OER/ORR activity, with an overpotential 0.31 V both ORR. d-band center (εd) crystal orbital Hamilton populations (COHP) utilized to study...