Dr. Xiaojuan Pang | Technologies | Best Researcher Award

Dr. Xiaojuan Pang | Technologies | Best Researcher Award

lecturer, China University of Mining and Technology, China

Dr. Xiaojuan Pang is a dynamic Chinese computational chemist and academic serving as a Lecturer at the China University of Mining & Technology (CUMT) since 2019. With deep expertise in photochemistry, nonadiabatic dynamics, and photocatalytic hydrogen production, she bridges theoretical innovation and practical application. Her international research exposure includes a pivotal joint doctoral training at the Technical University of Munich under Prof. Wolfgang Domcke, positioning her as a global voice in computational reaction mechanism studies. 🌍

Publication Profile

ORCID

🎓 Education Background

Dr. Pang earned her Bachelor’s degree in Physics from Xinzhou Teachers University in 2013 🎓. She continued her academic journey with a Doctorate in Physics from Xi’an Jiaotong University (2013–2019), where she explored ultrafast photochemical mechanisms. Her international academic footprint includes a prestigious year (2017–2018) at the Technical University of Munich. She is currently undertaking a postdoctoral fellowship (since 2025) in a two-station program, co-hosted by CUMT and Zhejiang Changshan Textile Co., Ltd., further sharpening her cross-disciplinary skills in mining and material science. 📘🧪

👩‍🏫 Professional Experience

Dr. Pang began her academic career as a Lecturer in the Department of Physics at CUMT in 2019. She plays a vital role in teaching, curriculum reform, and scientific mentorship. Her involvement spans several cutting-edge research projects, including multiple national and provincial grants where she serves as Principal Investigator. She also collaborates with industrial partners to apply her research in real-world contexts, especially in energy materials and ultrafast dynamics. 🏫🧑‍🔬

🏅 Awards and Honors

Dr. Pang has garnered numerous accolades for her academic and teaching excellence. Highlights include the Outstanding Young Core Faculty Award (2024), Jiangsu “Double-Innovation Doctor” Talent Award (2020), and multiple teaching competition prizes. She has also been recognized as an Outstanding Communist Party Member, Outstanding Head Teacher, and earned three consecutive years of top annual performance ratings from 2020 to 2023. 🏆🎖️

🔍 Research Focus

Her core research explores the reaction mechanisms in photocatalytic water splitting, photoisomerization of molecular motors, and ultrafast nonadiabatic photochemical processes. Dr. Pang utilizes a powerful combination of computational tools—like Gaussian, Turbomole, and MNDO—to simulate and analyze excited-state dynamics. Her work significantly contributes to the development of efficient solar-to-hydrogen energy conversion technologies and light-driven molecular machines. 💡⚛️

🧩 Conclusion

With an impressive blend of academic rigor, international exposure, innovative research, and award-winning teaching, Dr. Xiaojuan Pang stands as a rising star in computational chemistry and photophysics. Her ongoing work at the intersection of theory and application is paving the way for advances in sustainable energy and smart molecular systems. 🚀

📚 Top Publications

Nonadiabatic Surface Hopping Dynamics of Photo-catalytic Water Splitting Process with Heptazine–(H2O)4 Chromophore
🔹Cited by: [Articles on MDPI and Google Scholar]

Study on the Photoinduced Isomerization Mechanism of Hydrazone Derivatives Molecular Switch
🔹Cited by: [Relevant studies in ACS database]

Effect of Load-Resisting Force on Photoisomerization Mechanism of a Single Second Generation Light-Driven Molecular Rotary Motor
🔹Cited by: [AIP citations and Scholar references]

Ultrafast Nonadiabatic Photoisomerization Dynamics Study of Molecular Motor Based on Indanylidene Frameworks
🔹Cited by: [CrossRef, ScienceDirect]

Photoinduced Electron-Driven Proton Transfer from Water to N-Heterocyclic Chromophore
🔹Cited by: 40+ citations (Google Scholar, Scopus)

Watching the Dark State in Ultrafast Nonadiabatic Photoisomerization of Light-Driven Motor
🔹Cited by: 70+ citations (ResearchGate, Google Scholar)

Juxian Zhao | Computer Science | Best Researcher Award

Dr. Juxian Zhao | Computer Science | Best Researcher Award

PhD candidate, China University of Mining and Technology School of Mechatronic Engineering, China

📚 Juxian Zhao is a PhD candidate at the China University of Mining and Technology, specializing in robotics, computer vision, and deep learning. He focuses on developing innovative technologies for intelligent firefighting equipment and autonomous operations. Currently leading R&D for a key provincial project, Juxian has made significant contributions to the field through his research and innovations.

Profile

Scopus

 

Education

🎓 Juxian Zhao is pursuing a PhD at the China University of Mining and Technology in the School of Mechatronic Engineering. His academic journey has been marked by a strong focus on robotics, computer vision, and deep learning technologies, which he integrates into his research on intelligent firefighting equipment.

Experience

💼 Juxian Zhao has extensive experience in the research and development of intelligent firefighting equipment, multi-agent collaboration, and autonomous firefighting operations. He is currently leading a key provincial-level R&D project and actively collaborating with XCMG Fire Fighting Equipment Co., Ltd., and Xuzhou XCMG Daojin Special Robot Technology Co., Ltd.

Research Interests

🔬 Juxian Zhao’s research interests include robotics, computer vision, and deep learning technologies. He is particularly focused on applying these technologies to intelligent firefighting equipment and autonomous firefighting operations, aiming to enhance efficiency and effectiveness in emergency response scenarios.

Awards

🏆 Juxian Zhao has been recognized for his contributions to the field of robotics and firefighting technology through various accolades. His work on the CG-DALNet model for autonomous firefighting has garnered attention for its innovative approach and significant performance improvements.

Publications

Accurate and Fast Fire Alignment Method Based on a Mono-binocular Vision System

Visual predictive control of fire monitor with time delay model of fire extinguishing jet

An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention