A5100755699- Electrocatalysts for Energy Conversion
- Advanced Photocatalysis Techniques
- Thermodynamic and Exergetic Analyses of Power and Cooling Systems
- Electrochemical Analysis and Applications
- High Entropy Alloys Studies
- Advanced Thermodynamics and Statistical Mechanics
- Solar Thermal and Photovoltaic Systems
- Advanced battery technologies research
- Fuel Cells and Related Materials
- Advanced Nanomaterials in Catalysis
- TiO2 Photocatalysis and Solar Cells
- Electrochemical sensors and biosensors
- High-Temperature Coating Behaviors
- Electronic and Structural Properties of Oxides
- Catalytic Processes in Materials Science
- Tailings Management and Properties
- Simulation and Modeling Applications
- Mining and Resource Management
- Rock Mechanics and Modeling
- Supercapacitor Materials and Fabrication
- Advanced Thermodynamic Systems and Engines
- Photonic Crystals and Applications
- Covalent Organic Framework Applications
- Mine drainage and remediation techniques
- Ammonia Synthesis and Nitrogen Reduction
University of Helsinki
2023-2025
Ningbo University
2024
Southeast University
2019-2024
Qingdao Agricultural University
2024
Ministry of Agriculture and Rural Affairs
2023-2024
Shandong Jiaotong University
2024
Zhejiang University
2024
Shandong Institute of Business and Technology
2023-2024
Liaoning Normal University
2024
China University of Mining and Technology
2024
Prussian blue analogs (PBAs) featuring large interstitial voids and rigid structures are broadly recognized as promising cathode materials for sodium-ion batteries. Nevertheless, the conventionally prepared PBAs inevitably suffer from inferior crystallinity lattice defects, leading to low specific capacity, poor rate capability, unsatisfied long-term stability. As Na+ migration within is directly dependent on periodic arrangement, it of essential significance improve hence ensure long-range...
Prussian blue analogs (PBAs) are promising catalysts for green hydrogen production. However, the rational design of high-performing PBAs is challenging, which requires an in-depth understanding catalytic mechanism. Here FeMn@CoNi core-shell employed as precursors, together with Se powders, in low-temperature pyrolysis argon atmosphere. This synthesis method enables partial dissociation inner FeMn that results hollow interiors, Ni nanoparticles (NPs) exsolution to surface, and incorporation...
FeCoNiPdWP exhibit excellent oxygen evolution and reduction reaction performance via all elements playing distinctive roles the switchable active sites in redox reactions, leading to robust zinc air batteries.
High-entropy alloys (HEAs) represent prospective applications considering their outstanding mechanical properties. The properties in HEAs can be affected by the phase structure. Artificial neural network (ANN) is a promising machine learning approach for predicting phases of HEAs. In this work, deep (DNN) structure using residual (RESNET) proposed formation prediction It shows high overall accuracy 81.9%. Compared it with models, e.g., ANN and conventional DNN, its Micro-F1 score highlights...
Prussian blue analogs (PBAs) are considered as efficient catalysts for energy-related applications due to their porous nanoscale architectures containing finely disseminated active sites. Their catalytic capability can be greatly boosted by the rational design and construction of complex PBA hybrid nanostructures. However, present-day structure engineering inevitably involves additional etchant or procedure. Herein, a facile, yet controllable one-pot self-assembly strategy is introduced...
Abstract The hydrogen evolution and nitrite reduction reactions are key to producing green ammonia. Antenna–reactor nanoparticles hold promise improve the performances of these transformations under visible‐light excitation, by combining plasmonic catalytic materials. However, current materials involve compromising either on activity or enhancement also lack control reaction selectivity. Here, we demonstrate that ultralow loadings non‐uniform surface segregation component optimize...
Abstract High-entropy alloys (HEAs) have attracted much attention for laser additive manufacturing, due to their superb mechanical properties. However, industry application is still hindered by the high entry barriers of design manufacturing and limited performance library HEAs. In most machine learning methods used predict properties HEAs, processing paths are not clearly distinguished. To overcome these issues, in this work, a novel deep neural network architecture proposed that includes...