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Deng, Zhi

Publications and source records attributed to Deng, Zhi.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cation Disordered Anti-Perovskite Cathode Materials with Enhanced Lithium Diffusion and Suppressed Phase Transition

Recently, a new family of anti-perovskite Li 2 TMSO was discovered as promising cathode materials for Li-ion batteries (LIBs) with superiorities in high specific capacity, low cost, and environmental friendliness. However, the applications of these anti-perovskite materials meet severe challenges in the cyclability and rate performance. In this work, a cation-disordered anti-perovskite type solid solution Li 2 Fe 1– x Mn x SO (LFMSO, x = 0, 0.2, 0.5) with excellent electrochemical performance is reported. On the basis of comprehensive structural characterizations, the role of the cation disordering in LFMSO is clarified. In comparison with Li 2 FeSO (LFSO), the reduced Li-ion diffusion barrier and the increased Li-rich octahedral configurations in LFMSO with higher configurational entropy imply the facilitated long-range Li-ion diffusion and the suppressed phase transition, which favor the high-rate capability and cycling stability. In addition, the large lattice distortion and Coulombic interaction between the anions and cations lead to the breathing of the unit cell during charge/discharge. The variation of the unit cell volume decreases to 2.5% upon Li-ion delithiation. A superstructure is observed in LFMSO for the first time. These findings help to pave the way for the research and development of novel cathode materials for the next generation LIBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Probing how Ti- and Nb-substitution affect the stability and improve the electrochemical performance of β- and ε-LiVOPO 4

LiVOPO 4 is a promising next-generation multi-electron cathode material, boasting a theoretical capacity of 305 mA h g -1 , significantly higher than any commercially used Li-ion battery cathode material. However, the material still faces several limitations, including the difficulty in attaining the full theoretical capacity at higher rates and capacity fade over several cycles. Here, in this paper, we show that Ti- and Nb-substitution can be used to improve the thermal stability and electrochemical performance of LiVOPO 4 . We show through in situ heating with XRD and a novel gradient heating technique that both Ti- and Nb-substitution cause β-LiVOPO 4 to be stabilized relative to ε-LiVOPO 4 . This is due to transition-metal substitution, which increases the O-vacancy formation energies, pushing the β → ε transition to higher temperatures. We show that it is still possible to synthesize pure-phase ε-LiVOPO 4 through the use of high temperatures to generate these O-vacancies. We show that even 1% of Ti- or Nb-substitution can improve the initial capacity and long term cycling capability of LiVOPO 4 by improving the high-voltage capacity and reducing the capacity fade in both the high- and low-voltage regions. This is due to the overall improved Li + ion diffusion which is caused by an improved charge-transfer resistance during cycling.

25 ENERGY STORAGE↗
DOE OSTI2020

Rechargeable Alkali-Ion Battery Materials: Theory and Computation

Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a truly transformative technology, providing portable energy storage for devices ranging from small portable electronics to sizable electric vehicles. Here, we present a review of modern theoretical and computational approaches to the study and design of rechargeable alkali-ion battery materials. Additionally, starting from fundamental thermodynamics and kinetics phenomenological equations, we rigorously derive the theoretical relationships for key battery properties, such as voltage, capacity, alkali diffusivity, and other electrochemically relevant computable quantities. We then present an overview of computational techniques for the study of rechargeable alkali-ion battery materials, followed by a critical review of the literature applying these techniques to yield crucial insights into battery operation and performance. Finally, we provide perspectives on outstanding challenges and opportunities in the theory and computation of rechargeable alkali-ion battery materials.

25 ENERGY STORAGE↗
DOE OSTI2020

A Critical Review of Machine Learning of Energy Materials

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

Chen, Chi↗