2024
DOI: 10.3390/batteries10030096
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High-Entropy Materials for Lithium Batteries

Timothy G. Ritter,
Samhita Pappu,
Reza Shahbazian-Yassar

Abstract: High-entropy materials (HEMs) constitute a revolutionary class of materials that have garnered significant attention in the field of materials science, exhibiting extraordinary properties in the realm of energy storage. These equimolar multielemental compounds have demonstrated increased charge capacities, enhanced ionic conductivities, and a prolonged cycle life, attributed to their structural stability. In the anode, transitioning from the traditional graphite (372 mAh g−1) to an HEM anode can increase capac… Show more

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Cited by 7 publications
(2 citation statements)
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“…These materials, with their complex multicomponent systems, exhibit high configurational entropy that promotes the formation of single-phase solid solutions. This characteristic has been harnessed to develop new anode and cathode materials for lithium-ion batteries that display improved electrochemical performance and stability [105,106]. HEOs are particularly noted for their excellent cycling stability, safety, and efficiency, which is a critical factor for battery longevity and performance.…”
Section: Conclusion and Future Scopementioning
confidence: 99%
See 1 more Smart Citation
“…These materials, with their complex multicomponent systems, exhibit high configurational entropy that promotes the formation of single-phase solid solutions. This characteristic has been harnessed to develop new anode and cathode materials for lithium-ion batteries that display improved electrochemical performance and stability [105,106]. HEOs are particularly noted for their excellent cycling stability, safety, and efficiency, which is a critical factor for battery longevity and performance.…”
Section: Conclusion and Future Scopementioning
confidence: 99%
“…Another exciting direction is the integration of HEOs with other advanced battery technologies like solidstate batteries, where their stability and high entropy could solve common issues related to electrolyte compatibility and cycling stability. There's also potential for leveraging computational methods and machine learning to predict and optimize HEO compositions and structures, speeding up the discovery of new material combinations with enhanced properties [106].…”
Section: Conclusion and Future Scopementioning
confidence: 99%