A5101430782- Electrocatalysts for Energy Conversion
- Advanced battery technologies research
- Concrete and Cement Materials Research
- CO2 Reduction Techniques and Catalysts
- Electrochemical Analysis and Applications
- Analytical Chemistry and Sensors
- Non-Destructive Testing Techniques
- Photoacoustic and Ultrasonic Imaging
- Optical Coherence Tomography Applications
- Electric and Hybrid Vehicle Technologies
- Fuel Cells and Related Materials
- Advanced Chemical Sensor Technologies
- Innovative Energy Harvesting Technologies
- Electrochemical sensors and biosensors
- Energy Harvesting in Wireless Networks
- Advanced Battery Technologies Research
- Ionic liquids properties and applications
- Topic Modeling
- Concrete Corrosion and Durability
- Gas Sensing Nanomaterials and Sensors
- Grouting, Rheology, and Soil Mechanics
- Iterative Learning Control Systems
- Ammonia Synthesis and Nitrogen Reduction
- Electric Vehicles and Infrastructure
- Infrastructure Maintenance and Monitoring
Zhejiang University
2023-2025
Hainan University
2021-2025
China West Normal University
2023-2024
University of Waterloo
2024
Fudan University
2022-2023
Zhongshan Hospital
2022-2023
Kunming University of Science and Technology
2023
Collaborative Innovation Center of Chemistry for Energy Materials
2022-2023
East China Normal University
2023
Jiaxing University
2023
Soft actuation materials are highly desirable in flexible electronics, soft robotics, etc. However, traditional bilayered actuators usually suffer from poor mechanical properties as well deteriorated performance reliability. Here, inspired by the delicate architecture of natural bamboo, we present a hierarchical gradient structured actuator via mesoscale assembly micro–nano-scaled two-dimentional MXenes and one-dimentional cellulose nanofibers with molecular-scaled strong hydrogen bonding....
Abstract Methane (CH 4 ), as the vital energy resource and industrial chemicals, is highly flammable explosive for concentrations above limit, triggering potential risks to personal production safety. Therefore, exploiting smart gas sensors real‐time monitoring of CH becomes extremely important. Herein, Pt‐Pd nanoalloy functionalized mesoporous SnO 2 microspheres (Pt‐Pd/SnO ) were synthesized, which show uniform diameter (≈500 nm), high surface area (40.9–56.5 m g −1 large mesopore size...
Highly porous sensitive materials with well-defined structures and morphologies are extremely desirable for developing high-performance chemiresistive gas sensors. Herein, inspired by the classical alkaloid precipitant reaction, a robust reliable active mesoporous nitrogen polymer sphere-directed synthesis method was demonstrated controllable construction of heteroatom-doped tungsten oxide spheres. In typical synthesis, P-doped WO3 monodisperse spheres radially oriented channels (P-mWO3-R)...
Abstract This paper proposes a method for the rapid detection of subsurface damage (SSD) SiC using atmospheric inductivity coupled plasma. As plasma etching operated at ambient pressure with no bias voltage, this does not introduce any new SSD to substrate. Plasma diagnosis and simulation are used optimize operation. Assisted by an cover, taper can be etched on substrate high material removal rate. Confocal laser scanning microscopy electron microscope analyze results, transmission (STEM) is...
Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution uncertainty techniques AI, distinguishing between aleatoric epistemic uncertainties, discusses mathematical foundations methods used to quantify these uncertainties. We provide an overview advanced techniques,...
This book begins with a detailed introduction to the fundamental principles and historical development of GANs, contrasting them traditional generative models elucidating core adversarial mechanisms through illustrative Python examples. The text systematically addresses mathematical theoretical underpinnings including probability theory, statistics, game theory providing solid framework for understanding objectives, loss functions, optimisation challenges inherent GAN training. Subsequent...
Reinforcement Learning (RL) is a distinct branch of machine learning focused on how agents should take actions in an environment to maximize cumulative rewards. Unlike supervised learning, which relies labeled datasets, RL driven by the agent's interactions with its environment, optimal behaviors through trial and error. The agent learns make decisions performing certain receiving rewards or penalties return. goal learn policy that maximizes reward over time.
Energetic Macroscopic Representation (EMR) is an energy-based graphical modelling tool to describe complex electromechanical systems. It based on the action-reaction principle organize interconnection of sub-systems according physical causality (i.e. integral causality). Moreover, inversion-based control can be systematically deduced from EMR using specific inversion rules. The aim this paper introduce basics approach and its control. An Electric Vehicle with electrical differential studied...