- Browse by Author
Browsing by Author "Liu, Xiang"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Boosting Superior Lithium Storage Performance of Alloy‐Based Anode Materials via Ultraconformal Sb Coating–Derived Favorable Solid‐Electrolyte Interphase(Wiley, 2020-01) Xiong, Bing-Qing; Zhou, Xinwei; Xu, Gui-Liang; Liu, Yuzi; Zhu, Likun; Hu, Youcheng; Shen, Shou-Yu; Hong, Yu-Hao; Wan, Si-Cheng; Liu, Xiao-Chen; Liu, Xiang; Chen, Shengli; Huang, Ling; Sun, Shi-Gang; Amine, Khalil; Ke, Fu-Sheng; Mechanical and Energy Engineering, School of Engineering and TechnologyAlloy materials such as Si and Ge are attractive as high‐capacity anodes for rechargeable batteries, but such anodes undergo severe capacity degradation during discharge–charge processes. Compared to the over‐emphasized efforts on the electrode structure design to mitigate the volume changes, understanding and engineering of the solid‐electrolyte interphase (SEI) are significantly lacking. This work demonstrates that modifying the surface of alloy‐based anode materials by building an ultraconformal layer of Sb can significantly enhance their structural and interfacial stability during cycling. Combined experimental and theoretical studies consistently reveal that the ultraconformal Sb layer is dynamically converted to Li3Sb during cycling, which can selectively adsorb and catalytically decompose electrolyte additives to form a robust, thin, and dense LiF‐dominated SEI, and simultaneously restrain the decomposition of electrolyte solvents. Hence, the Sb‐coated porous Ge electrode delivers much higher initial Coulombic efficiency of 85% and higher reversible capacity of 1046 mAh g−1 after 200 cycles at 500 mA g−1, compared to only 72% and 170 mAh g−1 for bare porous Ge. The present finding has indicated that tailoring surface structures of electrode materials is an appealing approach to construct a robust SEI and achieve long‐term cycling stability for alloy‐based anode materials.Item DEPOT: graph learning delineates the roles of cancers in the progression trajectories of chronic kidney disease using electronic medical records(medRxiv, 2023-08-16) Song, Qianqian; Liu, Xiang; Li, Zuotian; Zhang, Pengyue; Eadon, Michael; Su, Jing; Biostatistics and Health Data Science, School of MedicineChronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a real-world context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions like cancers. In this study, we use electronic health records (EHRs) to outline the CKD progression trajectory roadmap for the Wake Forest Baptist Medical Center (WFBMC) patient population. We establish an EHR cohort (n = 79,434) with patients' health status identified by 18 Essential Clinical Indices across 508,732 clinical encounters. We develop the DisEase PrOgression Trajectory (DEPOT) approach to model CKD progression trajectories and individualize clinical decision support. The DEPOT is an evidence-driven, graph-based clinical informatics approach that addresses the unique challenges in longitudinal EHR data by systematically using the graph artificial intelligence (graph-AI) model for representation learning and reverse graph embedding for trajectory reconstruction. Moreover, DEPOT includes a prediction model to assign new patients along the progression trajectory. We successfully establish the EHR-based CKD progression trajectories with DEPOT in the WFUBMC cohort. We annotate the trajectories with clinical features, including kidney function, age, and other indices, including cancer. This CKD progression trajectory roadmap reveals diverse kidney failure pathways associated with different clinical conditions. Specifically, we have identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one is associated with accelerated decline in kidney function. On this roadmap, high-risk patients are enriched in the skin and GU cancers, which differs from low-risk patients, suggesting fundamentally different disease progression mechanisms. Overall, the CKD progression trajectory roadmap reveals novel diverse renal failure pathways in type 2 diabetes mellitus and highlights disease progression patterns associated with cancer phenotypes.Item In Situ Construction of an Ultrarobust and Lithiophilic Li-Enriched Li–N Nanoshield for High-Performance Ge-Based Anode Materials(ACS, 2020-11) Xiong, Bing-Qing; Zhou, Xinwei; Xu, Gui-Liang; Liu, Xiang; Hu, Youcheng; Liu, Yuzi; Zhu, Likun; Shi, Chen-Guang; Hong, Yu-Hao; Wan, Si-Cheng; Sun, Cheng-Jun; Chen, Shengli; Huang, Ling; Sun, Shi-Gang; Amine, Khalil; Ke, Fu-Sheng; Mechanical and Energy Engineering, School of Engineering and TechnologyAlloy-based materials are promising anodes for rechargeable batteries because of their higher theoretical capacities in comparison to graphite. Unfortunately, the huge volume changes during cycling cause serious structural degradation and undesired parasitic reactions with electrolytes, resulting in fragile solid-electrolyte interphase formation and serious capacity decay. This work proposes to mitigate the volume changes and suppress the interfacial reactivity of Ge anodes without sacrificing the interfacial Li+ transport, through in situ construction of an ultrarobust and lithiophilic Li-enriched Li–N nanoshield, which demonstrated improved chemical, electrochemical, mechanical, and environmental stability. Therefore, it can serve as a versatile interlayer to facilitate Li+ transport and effectively block the attack of electrolyte solvents, thus boosting the long-term cycle stability and fast charging capability of Ge anodes. This work offers an alternative methodology to tune the interfaces of other electrode materials as well by screening for more N-containing compounds that can react with Li+ during battery operation.Item A practical phosphorus-based anode material for high-energy lithium-ion batteries(Elsevier, 2020-08) Amine, Rachid; Daali, Amine; Zhou, Xinwei; Liu, Xiang; Liu, Yuzi; Ren, Yang; Zhang, Xiaoyi; Zhu, Likun; Al-Hallaj, Said; Chen, Zonghai; Xu, Gui-Liang; Amine, Khalil; Mechanical and Energy Engineering, School of Engineering and TechnologyState-of-the-art lithium-ion batteries cannot satisfy the increasing energy demand worldwide because of the low specific capacity of the graphite anode. Silicon and phosphorus both show much higher specific capacity; however, their practical use is significantly hindered by their large volume changes during charge/discharge. Although significant efforts have been made to improve their cycle life, the initial coulombic efficiencies of the reported Si-based and P-based anodes are still unsatisfactory (<90%). Here, by using a scalable high-energy ball milling approach, we report a practical hierarchical micro/nanostructured P-based anode material for high-energy lithium-ion batteries, which possesses a high initial coulombic efficiency of 91% and high specific capacity of ~2500 mAh g−1 together with long cycle life and fast charging capability. In situ high-energy X-ray diffraction and in situ single-particle charging/discharging were used to understand its superior lithium storage performance. Moreover, proof-of-concept full-cell lithium-ion batteries using such an anode and a LiNi0.6Co0.2Mn0.2O2 cathode were assembled to show their practical use. The findings presented here can serve as a good guideline for the future design of high-performance anode materials for lithium-ion batteries.Item SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment(Oxford University Press, 2023) Tang, Ziyang; Liu, Xiang; Li, Zuotian; Zhang, Tonglin; Yang, Baijian; Su, Jing; Song, Qianqian; Biostatistics and Health Data Science, School of MedicineSpatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.