Miaohui Li
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Lecturer and Master's Supervisor
Faculty Researcher
Research Areas
Biomedical Subjects
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Biography and Research Information
OverviewAI-generated summary
Miaohui Li's research primarily focuses on materials science, with a particular emphasis on developing advanced materials for energy storage applications, including lithium-ion and potassium-ion batteries. Her work has involved the design and synthesis of novel cathode and anode materials, often employing strategies such as doping, surface coating, and heterostructure engineering to enhance electrochemical performance and stability. Li has investigated materials like B-doped and La4NiLiO8-coated Ni-rich cathodes, ZnS/Sn heterostructures encapsulated in nitrogen-doped carbon, and hierarchical MoSSe nanosheets modified with carbon for potassium-ion storage. She has also explored silicon-based anodes, focusing on encapsulation techniques using carbon and doping to improve lithium storage capabilities and constructing robust binder networks for silicon anodes in all-solid-state batteries.
Beyond energy storage, Li's publication record indicates engagement with other scientific domains. This includes work on plasma confinement in Tokamak devices, suggesting an interest in fusion energy research. Additionally, she has contributed to research on feature enhancement networks for small object detection, indicating an involvement in machine learning and computer vision applications. Li's scholarly output is substantial, with a reported h-index of 28, over 200 publications, and more than 2,500 citations, designating her as a highly cited researcher. She maintains an active lab website and collaborates with colleagues at the National Center for Toxicological Research.
Metrics
- h-index: 28
- Publications: 204
- Citations: 2,505
Selected Publications
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Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals (2025)
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Applications of PBPK Modeling to Estimate Drug Metabolism and Related ADME Processes in Specific Populations (2025)
Collaboration Network
Top Collaborators
- An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
- An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
- An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
- An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
- An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
- An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science
- Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals
- Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals
- Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals
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