Browsing by Author "Shi, SQ"
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- ItemA customized strategy to design intercalation-type Li-free cathodes for all-solid-state batteries(Oxford University Press, 2023-01-10) Wang, D; Yu, J; Yin, X; Shao, S; Li, Q; Wang, YC; Avdeev, M; Chen, LQ; Shi, SQPairing Li-free transition-metal-based cathodes (MX) with Li-metal anodes is an emerging trend to overcome the energy-density limitation of current rechargeable Li-ion technology. However, the development of practical Li-free MX cathodes is plagued by the existing notion of low voltage due to the long-term overlooked voltage-tuning/phase-stability competition. Here, we propose a p-type alloying strategy involving three voltage/phase-evolution stages, of which each of the varying trends are quantitated by two improved ligand-field descriptors to balance the above contradiction. Following this, an intercalation-type 2H-V1.75Cr0.25S4 cathode tuned from layered MX2 family is successfully designed, which possesses an energy density of 554.3 Wh kg−1 at the electrode level accompanied by interfacial compatibility with sulfide solid-state electrolyte. The proposal of this class of materials is expected to break free from scarce or high-cost transition-metal (e.g. Co and Ni) reliance in current commercial cathodes. Our experiments further confirm the voltage and energy-density gains of 2H-V1.75Cr0.25S4. This strategy is not limited to specific Li-free cathodes and offers a solution to achieve high voltage and phase stability simultaneously. TheAuthor(s) 2023. Published byOxfordUniversity Press on behalf of China Science Publishing&Media Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License
- ItemMulti‐layer feature selection incorporating weighted score‐based expert knowledge toward modeling materials with targeted properties(Wiley, 2020-01-15) Liu, Y; Wu, JM; Avdeev, M; Shi, SQSelecting proper descriptors or features is one of the central problems in exploring structure–activity relationships of materials using machine learning models. The current feature selection algorithms usually require tedious hyperparameter tuning and do not actively consider the prior knowledge of domain experts about the features. Here, this work proposes a data‐driven multi‐layer feature selection method incorporating domain expert knowledge named DML‐FSdek, which is automated, with users entering training data without manual tuning of the hyperparameters. The domain expert knowledge is quantified by means of weighted scoring and integrated into the selection process to eliminate the risk of crucial features being removed. The test studies on ten material properties datasets demonstrate the potential of the approach to automatically search for a reduced feature set with lower root mean square errors than those for the initial feature set. Essentially, the most relevant material features, the number of which is much smaller than that in the original feature set, are automatically selected to establish a closer and more accurate structure–activity relationship for the materials of interest. As a result, the method represents the targeted properties of materials with a smaller and more interpretable set of features while ensuring equal or better prediction accuracy. © 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
- ItemThe origin of solvent deprotonation in LiI‐added aprotic electrolytes for Li‐O2 batteries(Wiley, 2023-03-27) Wang, AP; Wu, XH; Zou, Z; Qiao, Y; Wang, D; Xing, L; Chen, Y; Lin, Y; Avdeev, M; Shi, SQLiI and LiBr have been employed as soluble redox mediators (RMs) in electrolytes to address the sluggish oxygen evolution reaction kinetics during charging in aprotic Li‐O2 batteries. Compared to LiBr, LiI exhibits a redox potential closer to the theoretical one of discharge products, indicating a higher energy efficiency. However, the reason for the occurrence of solvent deprotonation in LiI‐added electrolytes remains unclear. Here, by combining ab initio calculations and experimental validation, we find that it is the nucleophile that triggers the solvent deprotonation and LiOH formation via nucleophilic attack, rather than the increased solvent acidity or the elongated C−H bond as previously suggested. As a comparison, the formation of in LiBr‐added electrolytes is found to be thermodynamically unfavorable, explaining the absence of LiOH formation. These findings provide important insight into the solvent deprotonation and pave the way for the practical application of LiI RM in aprotic Li‐O2 batteries. © 1999-2024 John Wiley & Sons, Inc