A5000502468- Fuel Cells and Related Materials
- Electrocatalysts for Energy Conversion
- Advanced battery technologies research
- Aquaculture disease management and microbiota
- Infrastructure Maintenance and Monitoring
- Hybrid Renewable Energy Systems
- Concrete Corrosion and Durability
- Innovative concrete reinforcement materials
- Animal Virus Infections Studies
- RNA Interference and Gene Delivery
- Laser-Plasma Interactions and Diagnostics
- Advanced Photocatalysis Techniques
- Metal and Thin Film Mechanics
- Advanced Sensor and Energy Harvesting Materials
- Surface Modification and Superhydrophobicity
- Structural Health Monitoring Techniques
- Advancements in Battery Materials
- Fusion materials and technologies
- Antimicrobial Peptides and Activities
- Geotechnical Engineering and Analysis
- Nuclear Physics and Applications
- Advanced Battery Technologies Research
- Mathematical and Theoretical Epidemiology and Ecology Models
- Hydrogen Storage and Materials
- Invertebrate Immune Response Mechanisms
Huazhong University of Science and Technology
2021-2025
University of Science and Technology Beijing
2012-2024
Shaoxing University
2023-2024
Zhejiang University
2023-2024
University of British Columbia
2017-2024
North China University of Technology
2024
Hebei University
2024
Northwest A&F University
2018-2023
Guangzhou Railway Polytechnic
2022-2023
Qingdao University
2023
Porous Sb–SnO<sub>2</sub>nanowires were synthesized as a support for IrO<sub>2</sub>by an electrospinning method. The thus prepared catalyst exhibits enhanced mass activity toward OER.
The mixing ratio of the raw materials has a significant impact on concrete compressive strength. Although strength can be inferred from mix ratio, it is frequently challenging to determine how each parameter affects results. In this study, an Explainable Boosting Machine (EBM) applied predict and explain contribution factors Meanwhile, combined algorithm selection hyperparameter optimization problem in machine learning implemented by employing Bayesian technique. A dataset consisting 1030...
The precise prediction of concrete compressive strength is essential for ensuring safe and reliable infrastructure design construction. However, traditional empirical models often struggle to accurately predict due the complex nonlinear relationship between properties target strength. This study introduces an AutoML-SHAP (Automatic Machine Learning - SHapley Additive exPlanations) strategy, designed automatically provide insightful interpretations predictive outcomes. AutoML model uses...
Proton exchange membrane water electrolysis (PEMWE) is a green hydrogen production technology with great development prospects. As an important part of PEMWE, bipolar plates (BPs) play role and put forward special requirements due to the harsh environments on both anode cathode. Recently, metal-based BPs, particularly stainless steel titanium BPs have attracted much attention from researchers all over world because their advantages high corrosion resistance, low resistivity, thermal...
Proton exchange membrane steam electrolyzers suffer from insufficient catalyst activity and durability due to the slow reaction kinetics for oxygen evolution (OER) poor under harsh operating environments. Aiming at enhancement of electrode durability, composite support materials iridium oxide are synthesized via in situ phosphorization on tin doped indium possess functionalities high electronic intrinsic proton conductivity. At 130 °C a water vapor atmosphere an overall conductivity 0.72 S...
With the rapid development of plug-in hybrid electric vehicles and vehicles, high-energy layered lithium nickel-rich oxides have received much attention, but there are still many challenges due to inherent properties materials. The poor cycling performance initial capacity loss oxide associated with structural stability material Li+/Ni2+ cation disorder. Moreover, synergistic effect vacancy Li Ni in delithiation process aggravates instability oxygen, eventually resulting release oxygen. It...
Cracks can be important performance indicators for determining damage processes in new and existing concrete structures. In recent years, deep convolutional neural networks (CNNs) have shown great potential automatic crack detection segmentation. However, most of the current CNNs tend to lose high-resolution details and, therefore, lead blurry object boundaries; this results poor images with complex backgrounds engineering This study proposes a two-stream boundary-aware segmentation (BACS)...
Advanced machine learning (ML) models are utilized for accurate shear strength prediction of reinforced concrete beams (RCB), but their lack interpretability makes it unclear how make specific predictions, reducing reliability and applicability. Mainstream model-agnostic interpretation methods require numerous additional computing procedures, limiting the efficiency, model process remains unknown. This study proposes an inherently interpretable RCB with stirrups based on Explainable Boosting...