Cargando…

Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations

[Image: see text] The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations...

Descripción completa

Detalles Bibliográficos
Autores principales: Fang, Chao, Mistry, Aashutosh, Srinivasan, Venkat, Balsara, Nitash P., Wang, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975840/
https://www.ncbi.nlm.nih.gov/pubmed/36873702
http://dx.doi.org/10.1021/jacsau.2c00590
_version_ 1784898962243715072
author Fang, Chao
Mistry, Aashutosh
Srinivasan, Venkat
Balsara, Nitash P.
Wang, Rui
author_facet Fang, Chao
Mistry, Aashutosh
Srinivasan, Venkat
Balsara, Nitash P.
Wang, Rui
author_sort Fang, Chao
collection PubMed
description [Image: see text] The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations and anions. The transference number, a parameter introduced over a century ago, sheds light on the relative rates of cation and anion transport. This parameter is, not surprisingly, affected by cation–cation, anion–anion, and cation–anion correlations. In addition, it is affected by correlations between the ions and neutral solvent molecules. Computer simulations have the potential to provide insights into the nature of these correlations. We review the dominant theoretical approaches used to predict the transference number from simulations by using a model univalent lithium electrolyte. In electrolytes of low concentration, one can obtain a quantitative model by assuming that the solution is made up of discrete ion-containing clusters–neutral ion pairs, negatively and positively charged triplets, neutral quadruplets, and so on. These clusters can be identified in simulations using simple algorithms, provided their lifetimes are sufficiently long. In concentrated electrolytes, more clusters are short-lived and more rigorous approaches that account for all correlations are necessary to quantify transference. Elucidating the molecular origin of the transference number in this limit remains an unmet challenge.
format Online
Article
Text
id pubmed-9975840
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher American Chemical Society
record_format MEDLINE/PubMed
spelling pubmed-99758402023-03-02 Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations Fang, Chao Mistry, Aashutosh Srinivasan, Venkat Balsara, Nitash P. Wang, Rui JACS Au [Image: see text] The rate at which rechargeable batteries can be charged and discharged is governed by the selective transport of the working ions through the electrolyte. Conductivity, the parameter commonly used to characterize ion transport in electrolytes, reflects the mobility of both cations and anions. The transference number, a parameter introduced over a century ago, sheds light on the relative rates of cation and anion transport. This parameter is, not surprisingly, affected by cation–cation, anion–anion, and cation–anion correlations. In addition, it is affected by correlations between the ions and neutral solvent molecules. Computer simulations have the potential to provide insights into the nature of these correlations. We review the dominant theoretical approaches used to predict the transference number from simulations by using a model univalent lithium electrolyte. In electrolytes of low concentration, one can obtain a quantitative model by assuming that the solution is made up of discrete ion-containing clusters–neutral ion pairs, negatively and positively charged triplets, neutral quadruplets, and so on. These clusters can be identified in simulations using simple algorithms, provided their lifetimes are sufficiently long. In concentrated electrolytes, more clusters are short-lived and more rigorous approaches that account for all correlations are necessary to quantify transference. Elucidating the molecular origin of the transference number in this limit remains an unmet challenge. American Chemical Society 2023-02-02 /pmc/articles/PMC9975840/ /pubmed/36873702 http://dx.doi.org/10.1021/jacsau.2c00590 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Fang, Chao
Mistry, Aashutosh
Srinivasan, Venkat
Balsara, Nitash P.
Wang, Rui
Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations
title Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations
title_full Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations
title_fullStr Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations
title_full_unstemmed Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations
title_short Elucidating the Molecular Origins of the Transference Number in Battery Electrolytes Using Computer Simulations
title_sort elucidating the molecular origins of the transference number in battery electrolytes using computer simulations
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9975840/
https://www.ncbi.nlm.nih.gov/pubmed/36873702
http://dx.doi.org/10.1021/jacsau.2c00590
work_keys_str_mv AT fangchao elucidatingthemolecularoriginsofthetransferencenumberinbatteryelectrolytesusingcomputersimulations
AT mistryaashutosh elucidatingthemolecularoriginsofthetransferencenumberinbatteryelectrolytesusingcomputersimulations
AT srinivasanvenkat elucidatingthemolecularoriginsofthetransferencenumberinbatteryelectrolytesusingcomputersimulations
AT balsaranitashp elucidatingthemolecularoriginsofthetransferencenumberinbatteryelectrolytesusingcomputersimulations
AT wangrui elucidatingthemolecularoriginsofthetransferencenumberinbatteryelectrolytesusingcomputersimulations