Mr. Xiangqi Dong | Nanoelectronics & Nanomaterials | Best Researcher Award

PhD Candidate | Fudan University | China

Xiangqi Dong is a researcher in microelectronics at the School of Microelectronics and the National Key Laboratory of Integrated Chips and Systems at Fudan University, specializing in two-dimensional semiconductors, integrated circuit fabrication, and device–circuit co-optimization. He is pursuing a direct doctoral degree and holds an undergraduate background in Microelectronics Science and Engineering from Northwestern Polytechnical University, with focused academic training in microelectronics and solid-state electronics. His professional experience includes optimizing wafer-scale 2D transistor processes, supervising laboratory tape-out workflows, establishing quality-control procedures, integrating advanced fabrication tools, and leading a research team working on analog circuits and DTCO-driven circuit innovation. His research contributions encompass high-performance 2D gate-stack engineering, sensing-memory-computing fusion devices, neuromorphic electronics, RF systems, and next-generation computing architectures, resulting in significant publications in leading journals, invited conference talks, and contributions to landmark achievements such as 2D microprocessors, high-linearity flash ADCs, and wafer-scale integrated RF transmitters. He has co-filed patents on transistor structures and semiconductor process optimization, and actively participates in academic outreach to promote integrated circuit education. His recognitions include multiple merit-based scholarships and academic excellence awards, reflecting strong research capability and leadership. Citations 92 by 88 documents, 17 documents, h-index 5.

Profile: Scopus

Featured Publications

Xiangqi Dong, Radiation resistant atomic layer scale radio frequency system for spaceborne communication. Nature, Under review.

Xiangqi Dong, A RISC-V 32-bit microprocessor based on two-dimensional semiconductors. Nature, Published.

Xiangqi Dong, High-linearity flash ADC achieved through design-technology co-optimization based on two-dimensional semiconductors. Science Bulletin, Online.

Xiangqi Dong, A bio-inspired neuron with intrinsic plasticity based on monolayer molybdenum disulfide. Nature Electronics, Published.

Dr. Swarup Ghosh | Energy Harvesting & Self-Powered Systems | Best Researcher Award

Assistant Professor | SR University | India

Dr. Swarup Ghosh is an Assistant Professor and Assistant Dean (Research) at the School of Computer Science and Artificial Intelligence, SR University, specializing in computational materials science, condensed matter physics, and AI-driven materials discovery. He earned his Ph.D. in Science from Jadavpur University with a focus on first-principles calculations, following an M.Sc. in Physical Sciences and a B.Sc. in Physics. Dr. Ghosh previously served as a Postdoctoral Research Associate at Jadavpur University and as a faculty member at Sammilani Mahavidyalaya, contributing to advanced computational materials research and student mentorship. His work spans density functional theory, molecular dynamics, many-body perturbation theory, electronic structure simulations, and machine-learning-enabled materials design, resulting in publications in high-impact journals and presentations at prestigious scientific forums. His research includes breakthroughs in 2D and nanomaterials, thermoelectrics, photovoltaics, spintronics, and catalytic systems, emphasizing data-centric scientific innovation. He has been honored with national research fellowships, merit-based academic distinctions, and awards for research excellence, while also serving as a reviewer for reputed international journals and participating in professional training programs and conferences. He maintains a strong scholarly impact, demonstrated by 245 citations, an h-index of 9, and an i10-index of 9, underscoring his growing influence in computational materials science and interdisciplinary research.

Profile: Google Scholar

Featured Publications

Swarup Ghosh*, Predicting photovoltaic efficiency of two-dimensional Janus materials for solar energy harvesting: A combined first-principles and machine learning study. Solar Energy Materials and Solar Cells, Accepted.

Swarup Ghosh*, First-principles study on structural, electronic, optical and photovoltaic properties of Sc₂C-based Janus MXenes for solar cell applications. Materials Today Communications, Accepted.

Swarup Ghosh, Predicting band gaps of ABN₃ perovskites: An account from machine learning and first-principles DFT studies. RSC Advances, Accepted.