Tun Naw Sut | Chemical Engineering | Best Researcher Award

Dr. Tun Naw Sut | Chemical Engineering | Best Researcher Award

Sungkyunkwan University | South Korea

Dr. Tun Naw Sut is a postdoctoral fellow specializing in nanomedicine, biomimetic membranes, and bio-sensing technologies, recognized for his interdisciplinary expertise and impactful research contributions. He holds dual doctoral training in nanomedicine and chemical engineering, supported by prior qualifications in materials science and biomedical engineering, forming a strong foundation for his work at the interface of engineering, biotechnology, and nanomaterials. His professional experience spans academic research, diagnostic platform development, electrochemical biomarker detection, phospholipid self-assembly studies, and compliance testing of medical electrical equipment, reflecting both scientific depth and industry-relevant technical capability. Dr. Sut’s research focuses on lipid-based nanomaterials, membrane biophysics, antimicrobial lipids, diagnostic sensors, and therapeutic nanoplatforms, and he has authored numerous publications in high-impact journals that advance the understanding and application of functional biomimetic systems. His leadership includes serving as guest editor and topic editor for international journals, contributing to the curation of scholarly work in biomimicry, functional materials, and membrane science. He has been recognized through competitive research grants, academic scholarships, and editorial appointments that highlight his innovation, scientific rigor, and growing influence in the field. Through his combined research excellence, interdisciplinary training, and dedication to advancing diagnostic and therapeutic technologies, Dr. Sut demonstrates exceptional potential for continued contributions to scientific innovation and research leadership.

Profiles: Scopus | ORCID

Featured Publications

1. Molla, A., Sut, T. N., Yoon, B. K., & Jackman, J. A. (2025). Headgroup-driven binding selectivity of alkylphospholipids to anionic lipid bilayers. Colloids and Surfaces B: Biointerfaces.

2. Lee, C. J., Jannah, F., Sut, T. N., Haris, M., & Jackman, J. A. (2025). Curvature-sensing peptides for virus and extracellular vesicle applications. ACS Nano.

3. Kim, D., Baek, H., Lim, S. Y., Lee, M. S., Lyu, S., Lee, J., Sut, T. N., Gonçalves, M., Kang, J. Y., Jackman, J. A., & Kim, J. W. (2025). Mechanobiologically engineered mimicry of extracellular vesicles for improved systemic biodistribution and anti-inflammatory treatment efficacy in rheumatoid arthritis. Advanced Healthcare Materials.

4. Ruano, M., Sut, T. N., Tan, S. W., Mullen, A. B., Kelemen, D., Ferro, V. A., & Jackman, J. A. (2025). Solvent-free microfluidic fabrication of antimicrobial lipid nanoparticles. ACS Applied Bio Materials.

5. Hwang, Y., Zhao, Z. J., Shin, S., Sut, T. N., Jackman, J. A., Kim, T., Moon, Y., Ju, B. K., Jeoni, J. H., Cho, N. J., & Kim, M. (2025). Nanopot plasmonic sensor platform for broad spectrum virus detection. Chemical Engineering Journal.

Dr. Tun Naw Sut’s work advances next-generation diagnostic and therapeutic technologies through innovative biomimetic membrane engineering and lipid-based nanomaterials. His research contributes to global health by enabling more effective pathogen detection, improved targeted delivery systems, and transformative strategies for sensing and treating complex diseases.

Lijuan Li | Process modeling and optimization | Best Researcher Award

Prof Dr. Lijuan Li | Process modeling and optimization | Best Researcher Award

.Deputy Director of Academic Affairs Office, Nanjing Tech University, China

Li Lijuan is a distinguished professor and doctoral supervisor at the School of Electrical Engineering and Control Science, Nanjing Tech University. She serves as the vice dean of the school and director of the Institute of Automation. Renowned for her contributions to control science, she is a candidate for the Jiangsu Province 333 High-level Talent Program and the Jiangsu Province “Six Talent Peaks” Program. Li Lijuan is an active member of various committees within the Chinese Automation Society and is a certified expert in Chinese Engineering Education. Her extensive research, numerous publications, and patents underscore her expertise and influence in the field.

Profile

Scopus

Educational Background 📚:

  • PhD in Control Science and Engineering from Zhejiang University (2005.3—2008.12)
  • Master’s in Control Theory and Control Engineering from Nanjing University of Technology (2001.9—2004.6)
  • Bachelor of Science in Production Process Automation from Nanjing University of Technology (1993.9-1997.6)

Work Experience 💼:

  • Visiting Scholar, University of Southern California, USA (2013.4-2014.4)
  • Teacher of Control Science and Engineering, Nanjing University of Technology (1997.8-Present)

Research Interests 🔬:

  • Modeling, Optimization, and Predictive Control
  • Intelligent Robots
  • Detection Technology and Embedded Systems
  • Smart Security
  • Application of Artificial Intelligence in Industrial Processes

Awards 🏆:

  • China Patent Excellence Award for Fully Automatic Bag Packaging Flexible Production Line (2020)
  • National Teaching Achievement Second Prize
  • Five provincial and ministerial teaching achievement awards

Publications Top Notes 📖:

Li, Lijuan, et al. “An optimized control strategy based on multidimensional feature operation pattern.” IEEE Transactions on Control Systems Technology, 2024. Link

Li, Lijuan, et al. “Multiresolution deep feature learning for pointer meters reading recognition.” Journal of Manufacturing Processes, 2024. Link

Li, Lijuan, et al. “YOLOv5-SFE: An algorithm fusing spatio-temporal features for detecting and recognizing workers’ operating behaviors.” Advanced Engineering Informatics, 2023. Link

Li, Lijuan, et al. “A performance assessment methodology with coupling between layers for LP-DMC systems.” International Journal of Robust Nonlinear Control, 2023. Link

Li, Lijuan, et al. “Optimization of oxygen system scheduling in hybrid action space based on deep reinforcement learning.” Computers & Chemical Engineering, 2023. Link