# Unveiling Electrolyte Design Principles for Sodium-Ion Batteries Using Combinatorial Electrochemistry and Machine Learning-Assisted Analysis

https://mdr.nims.go.jp/datasets/6f69b8c5-9d9a-4839-8b4e-f9bfee409c7c

## File

- [unveiling-electrolyte-design-principles-for-sodium-ion-batteries-using-combinatorial-electrochemistry-and-machine.pdf](https://mdr.nims.go.jp/filesets/6ac4c94d-6131-4992-aa4d-e1dc1c801996/download) ([Detail](https://mdr.nims.go.jp/filesets/6ac4c94d-6131-4992-aa4d-e1dc1c801996.md))

## Id

6f69b8c5-9d9a-4839-8b4e-f9bfee409c7c

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-02-12T22:55:33.672297Z

## Updated at

2026-02-13T07:30:18.150151Z

## Published at

2026-02-13T05:13:28.020617Z

## Doi



## First published url

https://doi.org/10.1021/acsaem.5c03028

## Date published

2026-02-09

## Recorded date published

2026-2-9

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Unveiling Electrolyte Design Principles for Sodium-Ion Batteries Using Combinatorial
    Electrochemistry and Machine Learning-Assisted Analysis
  title_type: original
  lang: en

## Description

- description: To accelerate the development of high-performance electrolytes for
    sodium-ion batteries (SIBs), we systematically investigated the effects of three
    key parameters, NaFSI concentration, DMC/EMC ratio, and FEC content, on the performance
    of SIB using NaNi1/3Fe1/3Mn1/3O2 and hard carbon as the positive and negative
    electrodes. A total of 132 electrolyte formulations were prepared using automated
    liquid handling, and their electrochemical performance was evaluated using multichannel
    full-cell measurements. Data-driven analysis employing machine learning revealed
    that NaFSI concentration plays the most critical role in enabling highly reversible
    charge–discharge behavior. Long-term cycling tests and interfacial composition
    analyses were further conducted to clarify the influence of electrolyte components
    on stability. Detailed studies focusing on high-NaFSI, FEC-containing electrolytes
    showed that EMC-rich formulations outperformed DMC-rich counterparts, maintaining
    Coulombic efficiencies over 99.6% even after 300 cycles. X-ray photoelectron spectroscopy
    confirmed that these stable systems promote the formation of NaF-rich interphases
    on both electrodes. These findings provide valuable insights into electrolyte
    design strategies for durable and efficient SIBs and highlight the utility of
    high-throughput experimentation coupled with machine learning for electrolyte
    discovery.
  description_type: abstract
  lang: und

## Creator

- name: Manai Ono
  role: author
  orcid: https://orcid.org/0000-0003-4406-4113
- name: Misato Takahashi
  role: author
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
- name: Shoichi Matsuda
  role: author
  orcid: https://orcid.org/0000-0002-0640-3404

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: sodium ion battery
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: ACS Applied Energy Materials
  issn: '25740962'
  volume: '9'
  issue: '3'
  start_page: 1405
  end_page: 1411

## Conference



## Related item



## Funding

- identifier: JPMXP1121467561
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JPMJPF2016
  funder_name: Japan Science and Technology Agency
- funder_name: National Institute for Materials Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



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## Fileset

- id: 6ac4c94d-6131-4992-aa4d-e1dc1c801996
  filename: unveiling-electrolyte-design-principles-for-sodium-ion-batteries-using-combinatorial-electrochemistry-and-machine.pdf
  content_type: application/pdf
  size: 3584586
  md5: c6801181af5d7db834d29854867daae8

## Thumbnail

fileset_id: 6ac4c94d-6131-4992-aa4d-e1dc1c801996
filename: unveiling-electrolyte-design-principles-for-sodium-ion-batteries-using-combinatorial-electrochemistry-and-machine.pdf