Dongbin Qian Qian, Institute of Modern Physics, Chinese Academy of Sciences, China
Dr. Qian Dongbin is a renowned professor at the Institute of Modern Physics, Chinese Academy of Sciences, specializing in laser-induced breakdown spectroscopy (LIBS) for analyzing trace elements in loose powders. He has an extensive background in atomic and molecular physics, holding a Ph.D. from the same institute. His research interests focus on the development of LIBS technologies and their application in various fields such as material science, environmental monitoring, and energy. He has contributed significantly to both academic research and technology development. His research is marked by innovation, with collaborations across international research institutions. 🌍🔬✨
Professional Profile:
SCOPUS
🎓 Education & Experience
QIAN Dongbin obtained his Ph.D. (2007) in Atomic and Molecular Physics from the Institute of Modern Physics (IMP), CAS, after completing his Bachelor’s (2002) in Theoretical Physics at Qufu Normal University. 📘 He began his academic career as an Assistant Professor at IMP in 2007, rising to Associate Professor in 2009 and Full Professor in 2017. 👨🏫 His academic journey reflects a strong commitment to applied spectroscopy, particularly in plasma analysis for granular and soft materials. 🧬 Throughout his career, he has contributed extensively to national projects and international collaborations. 🌐
🌍 Professional Development
Prof. Qian has cultivated international expertise through repeated research visits to CNRS-ILM, University Lyon 1, between 2009–2016. ✈️ His role as a Visiting Researcher enhanced collaborations in laser-plasma interactions. He received the CAS Youth Innovation Promotion Association Fellowship (2011–2014), reinforcing his leadership among emerging scientists. 🌟 His excellence was recognized with the Young Scientists and Talents Award (2014). 🏆 Through national and international projects, Prof. Qian continues to contribute to cutting-edge LIBS technology, combining experimental physics with data-driven techniques like deep learning and AI-assisted spectroscopy. 🤖
⚗️ Research Focus
Prof. Qian’s research lies at the intersection of Applied Physics, Spectroscopy, and Materials Science. 🌡️ His work with laser-induced breakdown spectroscopy (LIBS) targets trace element detection in powders and the characterization of soft materials. He integrates machine learning models, such as transformers and CNNs, with spectroscopic data for enhanced precision. 🧠📊 His studies extend to grain size analysis, surface flatness inspection, and plasma behavior in microgranular systems, making significant strides in analytical atomic spectroscopy and AI-powered material diagnostics. 🧪 His interdisciplinary focus supports advancements in both industrial applications and fundamental plasma research. 🔬
🏅 Awards & Honors
Prof. Qian has received numerous accolades, including the Young Scientists and Talents Award (2014) from the Institute of Modern Physics. 🎖️ He was also selected for the prestigious CAS Youth Innovation Promotion Association Fellowship (2011–2014). 🧠 His international recognition is reflected in multiple Visiting Researcher appointments at CNRS-ILM, France. 🌍 He has successfully led major National Natural Science Foundation of China (NSFC) projects and CAS-funded initiatives. 📑 His leadership and innovation have solidified his reputation as a pioneer in LIBS development, machine learning integration, and atomic spectroscopy research. 🚀
Publication Top Notes:
1. Transformer-based deep learning models for quantification of La, Ce, and Nd in rare earth ores using laser-induced breakdown spectroscopy
Authors: Jiaxing Yang, Shijie Li, Zhao Zhang, Xiaoliang Liu, Zuoye Liu
Journal: Talanta, 2025
Citations: 0
Summary:
This study introduces a transformer-based deep learning model to quantify lanthanum (La), cerium (Ce), and neodymium (Nd) in rare earth ores using laser-induced breakdown spectroscopy (LIBS). The approach enhances accuracy over traditional regression methods by capturing complex spectral features and nonlinearities. The model shows promise for rapid and non-destructive elemental analysis in geological and mining applications.
2. Detection of cesium in salt-lake brine using laser-induced breakdown spectroscopy combined with a convolutional neural network
Authors: Xiangyu Shi, Shuhang Gong, Qiang Zeng, Xinwen Ma, Dongbin Qian
Journal: Journal of Analytical Atomic Spectrometry, 2025
Citations: 0
Summary:
The paper demonstrates the detection of cesium (Cs) in salt-lake brine using LIBS enhanced with convolutional neural networks (CNNs). The CNN approach effectively handles high-noise spectral data, improving detection sensitivity and accuracy. The work supports the application of AI-assisted LIBS in environmental and resource monitoring of aqueous solutions.
3. Packing thickness dependent plasma emission induced by laser ablating thin-layer microgranular materials
Authors: Kou Zhao, Qiang Zeng, Yaju Li, Lei Yang, Xinwen Ma
Journal: Journal of Analytical Atomic Spectrometry, 2024
Citations: 0
Summary:
This study explores how the thickness of microgranular material layers affects plasma emission in LIBS. It provides insights into ablation dynamics and signal variations, highlighting the importance of sample preparation in quantitative LIBS analysis. The findings contribute to standardizing LIBS for layered or coated materials.
4. Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness
Authors: Jinrui Ye, Yaju Li, Zhao Zhang, Lei Yang, Xinwen Ma
Journal: Plasma Science and Technology, 2024
Citations: 0
Summary:
This paper proposes a novel use of LIBS for assessing surface flatness at millimeter resolution. The technique exploits emission intensity variations due to laser focus offset, correlating them with surface deviations. It provides a non-contact alternative to mechanical profilometry for industrial applications.
5. Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms
Authors: Zhao Zhang, Yaju Li, Guanghui Yang, Shaofeng Zhang, Xinwen Ma
Journal: Plasma Science and Technology, 2024
Citations: 0
Summary:
The authors develop a LIBS-machine learning framework to estimate grain size in microgranular materials. By training algorithms on spectral data, they achieve high accuracy in distinguishing particle size distributions. This method offers a fast, non-invasive alternative to traditional sieving or microscopy.
Conclusion
Dr. Qian Dongbin’s blend of innovative research, global collaboration, and leadership in the scientific community makes him an ideal candidate for the Best Researcher Award. His work significantly advances both the technology of LIBS and its applications in environmental and material science, providing tangible benefits to society. His ongoing contributions to scientific excellence and research leadership clearly establish him as an exemplary figure in the field. 🌟🔬