05 Sep 2025
Recently, the National Natural Science Foundation of China (NSFC) announced the results of the 2025 project evaluations. Xi’an Jiaotong-Liverpool University (XJTLU) successfully secured funding for 24 projects, with a total direct funding amount of 8.47 million yuan. These awarded projects include one Foreign Young Scientists Fund project, six General Program projects, and seventeen Youth Science Fund (Category C) projects, covering seven major NSFC divisions such as the Division of Management Sciences, the Division of Information Sciences, and the Division of Mathematical and Physical Sciences. Among them, the School of Science at XJTLU was granted four projects. We warmly congratulate Dr. Liwen Wu, Dr. Xin Zhao Tong, Dr. Qing Mu, and Dr. Zhenghao Wu on receiving the National Natural Science Foundation grants!
Liwen Wu (Health and Environmental Sciences)
"I am grateful for this opportunity to focus my research on the critical, yet often overlooked, water exchange processes that are fundamental to the health of our river systems."
About the Project:
Spatiotemporal Dynamics of Hyporheic Zones in Arid and Semi-Arid Regions and Their Biogeochemical Reaction Potential
Think of a river's "liver"—a hidden area beneath the riverbed that filters water and supports aquatic life. This project explores this critical zone in arid western regions, where it behaves in unexpected ways. Challenging the long-held belief that it exists whenever a river flows, our research has found its dynamics are far more complex and influenced by fluctuating water pressures. By combining field surveys with advanced simulations, this study will uncover how this hidden system works in dry landscapes, providing vital science to help manage precious water resources and restore fragile ecosystems for a sustainable future.
About the Researcher
Dr. Liwen Wu hold a doctoral degree in Geography (Dr. rer. nat.) from Humboldt University of Berlin. Her research is focused on the interactions between surface water and groundwater in fresh and saline water systems. She creates numerical models, applies data analyses, and conducts field investigations to understand how flow, energy and solute transport from the surface to the subsurface regions under varying natural and anthropogenic stresses.
She is currently an assistant professor in the department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University. From 2023, she serves as an associate editor for Journal of Hydrology.
Qing Mu (Health and Environmental Sciences)
“This project marks my transition from traditional atmospheric environmental pollution research to the exploration of ecological meteorology. Pollen pollution is expected to become a hot topic in future atmospheric environment research.”
About the Project
Numerical Model Development for Atmospheric Allergenic Pollen in China
Global climate warming and increased urban green spaces have amplified atmospheric allergenic pollen pollution, presenting a significant public health risk in China. Thus, enhancing the accuracy and timeliness of pollen forecasts is crucial. Current pollen forecasting approaches in China, including pollen calendars, statistical modeling, and emerging machine learning methods, exhibit distinct limitations. Numerical pollen modeling, which integrates pollen emission modules within air quality modeling frameworks, demonstrates substantial advantages and has gained international recognition as the predominant pollen forecasting method. Nevertheless, China has yet to establish a domestically applicable numerical pollen modeling system and faces challenges such as compiling comprehensive pollen emission inventories, developing localized numerical pollen models, and performing rigorous model validation and evaluation. Addressing these gaps, this project proposes the development of a numerical pollen model tailored specifically to the Chinese environment, supported by an enhanced and comprehensive pollen emission inventory. Furthermore, the project will assess and optimize the performance of the numerical pollen model in the China domain. The outcomes of this project will address the existing gap in the development of China's numerical pollen model, facilitate operational pollen forecasting, and provide a scientific foundation for pollen management strategies under climate change scenarios.
About the Researcher
Dr. Qing Mu is currently an Assistant Professor in the Department of Health and Environmental Sciences at the School of Science, Xi’an Jiaotong-Liverpool University. She integrates atmospheric numerical modeling and artificial intelligence technologies to investigate the atmospheric processes of air pollutants, bioaerosols, and greenhouse gases. The team also analyzes the impacts of extreme climate events on the atmospheric environment and public health. Dr. Mu received her education from Nanjing University, the Institute of Atmospheric Physics of the Chinese Academy of Sciences, and the Max Planck Institute for Chemistry in Germany. Prior to joining Xi’an Jiaotong-Liverpool University, she served as a tenured Scientist at the Norwegian Meteorological Institute. She has been selected for a talent program in Jiangsu Province and serves as a committee member of the Atmospheric Chemistry Committee of the Jiangsu Meteorological Society.
Xinzhao Tong (Biosciences and Bioinformatics)
“Securing this grant significantly recognizes the critical value of conducting challenging, long-term, practical studies in a clinical setting. This research is essential for understanding how external microenvironmental pressures drive resistance development in clinical pathogens—a topic I have been dedicated to in recent years.”
About the Project
A study on the mechanism of long-term exposure to medical disinfectants on the transmission and resistance evolution of pathogenic microbes in clinical environments
Hospitals increasingly use disinfectants to control infections, but residues may induce cross-resistance in bacteria, raising infection risks. The mechanisms behind this aren't well understood. Building on a long-term practical study in a respiratory ICU that monitored pathogens in patients and the ward environment, this project will use metagenomic sequencing and microbial source-tracking to map pathogen transmission trajectories. We'll then use whole-genome sequencing and the BWA-MEM algorithm to track resistance genes in bacteria to understand how long-term disinfectant exposure drives the adaptation of pathogenic microbes. Finally, through lab experiments and transcriptomic sequencing, we'll analyze gene expression in disinfectant-tolerant bacteria to clarify the molecular mechanisms that mediate this cross-resistance. This research will inform new intervention strategies and public health policies.
About the Researcher
Dr. Xinzhao Tong is an assistant professor in the Department of Bioscience and Bioinformatics at XJTLU. Her research tackles two critical challenges at the intersection of public health and human disease. She investigates how pathogenic microbes spread and evolve resistance in clinical and public environments. Additionally, she explores the functional impact of the microbiomes and their metabolites on metabolic disorders such as Type 2 diabetes and obesity. Dr. Tong is currently recruiting PhD students with scholarships. If you have a background in bioinformatics and are interested in her research, please send your CV to Xinzhao.Tong@xjtlu.edu.cn.
Zhenghao Wu (Chemistry and Materials Science)
“All models are wrong, but some are useful.” This award will help us develop useful computational models for modeling and designing advanced polymeric materials. ”
About the Project
Transferable and Thermodynamic-Consistent Coarse-Grained Models for Crystalline Polymers via Differentiable Simulation
This study aims to construct a systematic and precise coarse-graining method specifically for semi-crystalline polymers based on the differentiable molecular simulation method in order to understand the underlying mechanisms of complex crystallization behavior of polymers at the microscopic level. Almost all systematic coarse-graining models face issues of poor transferability and thermodynamic consistency, which are more pronounced in semi-crystalline polymers. To this end, this work propose a novel method, namely, differentiable coarse-graining for semi-crystalline polymers. By introducing advanced potential functions that describe many-body effects and using automatic differentiation, the gradients of coarse-grained potential parameters are efficiently computed, enabling the unified optimization of all-atom information across multiple thermodynamic states. This approach further enhances the simulation capability of differentiable coarse-grained models for polymer crystallization processes, while improving their transferability and thermodynamic consistency. Based on these models, large-scale coarse-grained simulations can be carried out, and experimental data are compared to evaluate the accuracy and universality of the model. The effects of factors such as structures and orientations of multilamella crystals on polymer properties are explored. This project will provide a powerful theoretical and simulation tool for understanding polymer crystallization and designing high-performance polymeric materials.
About the Researcher
Zhenghao Wu is currently a faculty member in the Department of Chemistry and Materials Science. He leads a theoretical and computational research group. His group uses and develops tools such as molecular simulations, coarse-graining methods, statistical mechanical theories, and machine learning algorithms to study the behaviors of soft-matter systems such as polymers and biomolecules. The goal of his group is to understand, predict, and rationally design macromolecular materials to fight modern “Zombies” (energy, healthcare and sustainability questions).
05 Sep 2025