Zhengzhi Mu 編集者 at IgMin Research
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バイオグラフィー
Dr. Zhengzhi Mu currently works as an Associate Prof. of Jilin University. He received his PhD in Bionic Science and Engineering from Jilin University (China). He had studied as Research Scholar at the University of Michigan-Ann Arbor (USA) and Visiting Scientist at RWTH Aachen University (Germany).
He has published over 30 scientific papers in many well-known journals including Adv. Mater., ACS Nano, Small, etc. He holds over 30 patents and has hosted/participated more than 10 scientific programs, including National Key R&D Program, NSFC, National Postdoctoral Program for Innovative Talents (NPPIT), etc.
He is a membership of International Society of Bionic Engineering (ISBE), American Chemical Society (ACS) and American Institute of Chemical Engineers (AIChE). He also serves as a Committee Member of International Association of Applied Science and Technology (IAAST), Topical Advisory Panel Member of International Journal of Molecular Sciences, Polymers, Biomimetics, and Guest Editors of Biomimetics and Nanomaterials.
His research interests mainly focus on bioinspired design, biomimetic fabrication, bioinspired composites and biomimetic functional surfaces, etc.
研究の興味
Biomimetic composites, bioinspired functional structures, bionic interface engineering
Editor
仕事内容
Associate Professor
Jilin University
Key Laboratory of Bionic Engineering, Ministry of Education
China
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研究論文
- The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach
- Prevalence of Diabetic Retinopathy among Self-reported Newly Diagnosed Diabetics
- Methodology of the Professional-Business Game for the Development of a Cadet Leader in Professional Training Courses (L-1B) of the Tactical Level of Military Education
- Comparing Forecasting Models for Predicting Infant Mortality: VECM vs. VAR and BVAR Specifications
- Efficient Room Temperature Ethanol Vapor Sensing by Unique Fractal Features of Tin Oxide
- Enhancing Missing Values Imputation through Transformer-Based Predictive Modeling
