Quantum Physics and Deep Learning to Reveal Multiple Dimensional Modified Regulation by Ternary Substitution of Iron, Manganese, and Cobalt on Na3V2(PO4)3 for Superior Sodium Storage

Author:

Sun Shiqi12,Liu Shubin3,Chen Yanjun12ORCID,Li Lei3,Bai Qiang45,Tian Zhen12,Huang Que2,Wang Yanzhong12,Wang Xiaomin45,Guo Li12

Affiliation:

1. School of Materials Science and Engineering North University of China Taiyuan 030051 China

2. Institute of Advanced Energy Materials and Systems North University of China Taiyuan 030051 China

3. School of Electronics and Information Engineering Sichuan University Chengdu 610065 China

4. College of Materials Science and Engineering Taiyuan University of Technology Taiyuan 030024 China

5. Shanxi Key Laboratory of New Energy Materials and Devices Taiyuan University of Technology Taiyuan 030024 China

Abstract

AbstractNa3V2(PO4)3 is regarded as a promising candidate for sodium ion batteries. Nevertheless, the poor electronic conductivity, low capacities, and unstable structure limit its further investigations. Herein, a new type of Fe/Mn/Co co‐substituted Na3V2(PO4)3 with nitrogen‐doped carbon coating (NFMC) by a facile sol‐gel route is synthesized. The introduced elements feature in both crystal bulk and carbon coating layer. Suitable heteroatom substitution activates more effective Na+ to participate in electrochemical process and reinforce the structure. An extra high voltage platform at 3.8 V resulting from the multi‐element synergy (Mn2+/Mn3+/Mn4+; Co2+/Co3+; V4+/V5+) is stably and reversibly existed in NFMC to supply added capacities, which is investigated by quantum physics calculations. The high flux paths for Na+ migration and spin quantum state distribution in NFMC are demonstrated by molar magneton calculation. Significantly, the generated polyatomic coordination environment of MNC (M = Fe/Co/Mn) in carbon layer is first proposed. The most optimized combination structures are obtained from 69 possible structures and demonstrated by X‐ray absorption spectroscopy. The superior electrochemical performance is precisely forecasted by innovative deep learning. Predicted values with high precision are obtained based on a small number of operating data, extremely short development period, and provide real‐time status references for safer use.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanxi Province

Publisher

Wiley

Subject

Electrochemistry,Condensed Matter Physics,Biomaterials,Electronic, Optical and Magnetic Materials

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