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Element Optimization in NASICON Phosphates Enhances Sodium Storage Performance.

Yuanxutong Wen1, Xiangpeng Kong2, Qiang Rong2

  • 1School of Future Technology, School of Chemistry, Engineering Research Center of Energy Storage Materials and Devices, National Innovation Platform (Center) for industry-Education integration of Energy Storage Technology, Xi'an Jiaotong University, Xi'an, 710049, China.

Small (Weinheim an Der Bergstrasse, Germany)
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PubMed
Summary
This summary is machine-generated.

This review details the evolution of NASICON materials for sodium-ion batteries, moving from simple to complex compositions. Future research focuses on AI and advanced methods to overcome current limitations and enhance performance.

Keywords:
NASICON materialselement optimizationnew‐subtype material developmentsodium‐ion batteriestrace doping

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Energy Storage

Background:

  • NASICON (Sodium Super Ionic Conductor) materials have evolved from single-transition-metal to multi-element systems for improved sodium-ion battery performance.
  • Early NASICON materials, while stable, present limitations in cost and capacity, necessitating advanced compositional strategies.

Purpose of the Study:

  • To review the iterative advancements, challenges, and future research directions for NASICON materials in sodium-ion batteries.
  • To explore novel strategies for optimizing NASICON materials, including multifunctional elemental optimization, high-entropy materials, gradient doping, and AI-driven approaches.

Main Methods:

  • Comprehensive literature review of NASICON material development and performance.
  • Analysis of challenges in elemental optimization, synthesis, and electrochemical characterization.
  • Proposal of future research pathways integrating advanced characterization, computational modeling, and artificial intelligence.

Main Results:

  • Transition from single-element to multi-element NASICON compositions enhances capacity and low-temperature performance.
  • Current challenges include synthesis inconsistencies, limited analytical techniques, and unclear doping mechanisms, hindering precise material understanding.
  • AI, machine learning, and deep learning show significant potential for accelerating NASICON material discovery and optimization.

Conclusions:

  • Continued research into multifunctional elemental optimization and high-entropy materials is crucial for synergistic effects.
  • Gradient doping offers precise control over material properties.
  • AI-driven approaches are transformative for optimizing NASICON materials and shortening development cycles for widespread sodium-ion battery applications.