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)

Dr. Biao Zhang | Technology | Best Researcher Award

Dr. Biao Zhang | Technology | Best Researcher Award

Xi’an Research Institute of High-Tech, China

Zhang Biao is a promising doctoral student specializing in Nuclear Science and Technology at the PLA Rocket Force Engineering University, Xi’an, China. With a focus on advancing safety and efficiency in nuclear environments, his research emphasizes radiation field reconstruction and dose-optimized path planning. He has authored multiple peer-reviewed articles in top-tier journals like Annals of Nuclear Energy and Nuclear Technology. Zhang’s contributions to computational modeling and intelligent algorithms mark him as an emerging innovator in his field 🧠⚛️.

Publication Profile

ORCID

🎓 Education Background:

Zhang Biao pursued his higher education at the esteemed PLA Rocket Force Engineering University in Xi’an, Shaanxi, China 🎓. Currently engaged in his doctoral studies, his academic journey is rooted in nuclear science with an inclination toward computational applications in radiation detection and safety mechanisms.

💼 Professional Experience:

Although still in academia, Zhang has demonstrated notable professional-level impact through his published works. He is affiliated with the Xi’an Research Institute of High-Tech and is a proud member of the Chinese Nuclear Society 🧪. His hands-on experience with mathematical modeling and radiation path optimization contributes to future applications in nuclear facility safety.

🏆 Awards and Honors:

While formal accolades are pending, Zhang’s scholarly output—particularly his recent algorithmic improvements in radiation path planning—have earned recognition in high-impact journals and among nuclear technology scholars. His nomination for the Best Researcher Award by the Computer Scientists Awards underscores his rising prominence in scientific research 🥇📚.

🔬 Research Focus:

Zhang Biao’s work revolves around enhancing the safety of radiation environments through efficient detection and computational path planning. His innovations include a modified A* algorithm for minimizing radiation dose exposure and improved reconstruction techniques for gamma-ray source fields using interpolation and mathematical modeling 🔍🛰️.

🔚 Conclusion:

Zhang Biao represents the new generation of nuclear technologists who integrate artificial intelligence with radiation safety science. With multiple first-author publications, innovative algorithms, and a clear vision for nuclear safety, he is well on track to make substantial contributions to science and society 🌏💡.

📚 Top Publications:

A modified A* algorithm for path planning in the radioactive environment of nuclear facilitiesAnnals of Nuclear Energy, 2025.
Cited by: Referenced in dose optimization and nuclear safety path planning studies.

Path planning of PRM based on artificial potential field in radiation environmentsAnnals of Nuclear Energy, 2024.
Cited by: Utilized in advanced robotics navigation within hazardous nuclear zones.

Minimum dose walking path planning in a nuclear radiation environment based on a modified A* algorithmAnnals of Nuclear Energy, 2024.
Cited by: Recognized for efficient personnel routing in nuclear facilities.

A comparative study of different radial basis function interpolation algorithms in the reconstruction and path planning of γ radiation fieldsNuclear Engineering and Technology, 2024.
Cited by: Referenced in computational gamma field modeling research.

Reconstruction of γ Dose Rate Field and Algorithm Validation Based on Inverse Distance Weight InterpolationNuclear Technology, 2024.
Cited by: Applied in gamma dose field reconstruction validations.