Your browser doesn't support javascript.
loading
Highly Potent and Low-Volume Concentration Additives for Durable Aqueous Zinc Batteries: Machine Learning-Enabled Performance Rationalization.
Shang, Yuan; Kundi, Varun; Pal, Ipsita; Kim, Ha Na; Zhong, Haoyin; Kumar, Priyank; Kundu, Dipan.
Afiliación
  • Shang Y; School of Chemical Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
  • Kundi V; School of Chemical Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
  • Pal I; School of Chemical Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
  • Kim HN; Graduate School of Biomedical Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
  • Zhong H; Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore.
  • Kumar P; School of Chemical Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
  • Kundu D; School of Chemical Engineering, UNSW Sydney, Kensington, NSW, 2052, Australia.
Adv Mater ; 36(9): e2309212, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38041711
ABSTRACT
The essential virtues of aqueous zinc battery chemistry stem from the energy-dense zinc metal anode and mild aqueous electrolytes. Yet, their incompatibility - as exposed by zinc's corrosion and associated dendrite problem - poses a challenge to achieving improved cycle life under practically relevant parameters. While electrolyte additives are a scalable strategy, additives that can function at low volume concentrations remain elusive. Here, through screening alkanol and alkanediol chemistries, 1,2-butanediol and pentanediol are unveiled as highly potent additives, which operate at a practical 1 volume% concentration owing to their ability to furnish dynamic solid-electrolyte interphase through pronounced interfacial filming. This unique mechanistic action renders effective corrosion and dendrite mitigation, resulting in up to five to twenty-fold zinc cyclability enhancement with a high Coulombic efficiency (up to 99.9%) and improved full-cell performance under demanding conditions, including at elevated temperatures. A machine learning-based analysis is presented to rationalize the additive performance relative to critical physicochemical descriptors, which can pave the way for a rational approach to efficient additive discoveries.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Adv Mater Asunto de la revista: BIOFISICA / QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Australia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Adv Mater Asunto de la revista: BIOFISICA / QUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Australia