Yu Fang | Molecular biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Prof. Dr. Yu Fang | Molecular biotechnology | Best Researcher Award

Full Professor at Fujian Institute of Research on the Structure of Matter | China

Dr. Yu Fang is a distinguished chemist whose research lies at the interface of materials chemistry and supramolecular science, particularly in the design and synthesis of Porous Coordination Cages (PCCs) and Metal–Organic Frameworks (MOFs). His work focuses on developing advanced porous materials for applications in catalysis, gas storage, molecular recognition, and energy conversion. Dr. Fang has made significant contributions to understanding host–guest chemistry, self-assembly mechanisms, and structure–property relationships in coordination materials. His innovative research has been widely recognized in leading journals such as Nature Communications, Angewandte Chemie International Edition, Journal of the American Chemical Society, and Chemical Society Reviews. Through collaborative projects and pioneering insights, Dr. Fang’s studies have bridged molecular design and functional materials engineering, influencing both academic and industrial research in advanced materials. He has published extensively, contributing to the development of next-generation coordination architectures with tailored porosity and dynamic behavior for chemical and environmental applications. With 7132 citations and an h-index of 26 according to his Scopus profile, Dr. Fang’s scientific impact reflects both productivity and quality, positioning him as a leading figure in coordination chemistry and materials innovation.

Profile: Google Scholar | Orcid

Featured Publications

Liang, Y., Yang, X., Wang, X., Guan, Z.-J., Xing, H., & Fang, Y. (2023). Nature Communications, 14, 5223.

Su, Z., Liu, K.-K., Xu, Y.-Q., Yan, B., Wang, S., Guan, Z.-J., Zou, Y., & Fang, Y. (2024). Angewandte Chemie International Edition, 64(9), e202420945.

Peng, Y., Yuan, L., Liu, K.-K., Jin, S., & Fang, Y. (2024). Angewandte Chemie International Edition, 64(12), e202423055.

Liang, Y., Xie, G., Liu, K.-K., Jin, M., Chen, Y., Yang, X., Guan, Z.-J., Xing, H., & Fang, Y. (2024). Angewandte Chemie International Edition, 64(1), e202416884.

He, H.-H., Yuan, J.-P., Cai, P.-Y., Wang, K.-Y., Feng, L., Kirchon, A., Li, J., Zhang, L.-L., Zhou, H.-C., & Fang, Y. (2023). Journal of the American Chemical Society, 145, 17164–17175.

Fang, Y., Powell, J., Li, E., Wang, Q., Perry, Z., Kirchon, A., Yang, X., Xiao, Z., Zhu, C., Zhang, L., Huang, F., & Zhou, H.-C. (2019). Chemical Society Reviews, 48(17), 4707–4730.

Liang, Y., Li, E., Wang, K., Guan, Z.-J., He, H.-H., Zhang, L.-L., Zhou, H.-C., Huang, F., & Fang, Y. (2022). Chemical Society Reviews, 51, 8378–8405.

Ifza Shad | Industrial Biotechnology | Best Researcher Award

Published on by Biotechnology Scientist

Ms. Ifza Shad | Industrial Biotechnology | Best Researcher Award

PhD at University of Science and Technology of China | China

Ms. Ifza Shad is an emerging AI researcher specializing in computer vision, deep learning, and real-time object detection, with strong contributions to medical image analysis and intelligent automation. She completed her MS in Computer Science at Central South University, China, focusing on the development of real-time litter detection models for surface and aquatic environments, and previously earned a BS (Hons) in Computer Science from the University of Central Punjab, Pakistan, graduating as a gold medalist. Her professional experience includes serving as a Computer Vision Engineer at ITSOLERA Pvt, where she led research in medical image analysis for fracture detection and visual search systems for precision agriculture, and as a Data Analyst at Motive, USA, where she excelled in data annotation and analytics. Ifza has authored multiple research papers, including Deep Learning-Based Image Processing Framework for Efficient Surface Litter Detection (Journal of Radiation Research and Applied Sciences, 2025), Attention-Driven Sequential Feature Fusion Framework for Effective Brain Tumor Diagnosis (Significances of Bioengineering and Biosciences, 2025), and An Attention-Fused Architecture for Brain Tumor Diagnosis (Biomedical Signal Processing and Control, 2024). Her ongoing projects explore lightweight YOLO architectures for aquatic litter detection and driver distraction monitoring. With a growing Scopus profile demonstrating increasing academic visibility through 5 publications, citations, and an evolving h-index, she continues to advance AI-driven solutions that integrate sustainability, healthcare, and safety.

Profile: ORCID

Featured Publications

Shad, I. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences.

Shad, I. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering and Biosciences.

Shad, I., & Co-authors. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control.

Shad, I. (2025). ALD-Yolov9c: Lightweight architecture for aquatic litter detection in dynamic environments. IEEE. (Submitted).

Shad, I. (2024). Overcoming misinformation: Advanced detection of fake news by integration of K-fold stacked ensemble. International Journal of Software Engineering and Knowledge Engineering (IJSEKE). (Under review).