# Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting of Multidimensional Kinetic Model with Bayesian Data-Assimilation

https://mdr.nims.go.jp/datasets/28fcd3fe-4381-4bb8-8cbf-92d759bb24e0

## File

- [wang-et-al-2024-accelerated-electrocatalyst-degradation-testing-by-accurate-and-robust-forecasting-of-multidimensional.pdf](https://mdr.nims.go.jp/filesets/340c9c29-12c5-4aa8-bcc6-be51b966b520/download) ([Detail](https://mdr.nims.go.jp/filesets/340c9c29-12c5-4aa8-bcc6-be51b966b520.md))
- [nz4c02868_si_001.pdf](https://mdr.nims.go.jp/filesets/4460c558-8814-4dce-b7e8-b7cd19b9f2bc/download) ([Detail](https://mdr.nims.go.jp/filesets/4460c558-8814-4dce-b7e8-b7cd19b9f2bc.md))

## Id

28fcd3fe-4381-4bb8-8cbf-92d759bb24e0

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-10T04:35:52.539065Z

## Updated at

2024-12-10T07:56:51.003925Z

## Published at

2025-02-17T09:32:17.246783Z

## Doi



## First published url

https://doi.org/10.1021/acsenergylett.4c02868

## Date published

2025-01-10

## Recorded date published

2025-1-10

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting
    of Multidimensional Kinetic Model with Bayesian Data-Assimilation
  title_type: original
  lang: en

## Description

- description: 'Degradation test represents a significant bottleneck in the electrochemical
    technology development, requiring occasionally tens of thousands of hours. Thus,
    a reliable degradation forecasting in short timeframe is a game-changer in accelerating
    the establishment of future electrochemical devices. Herein, we show a multidimensional
    kinetic model for electrocatalyst degradation by quantifying the relationship
    among potential, current, and time, applicable under various conditions. '
  description_type: abstract
  lang: eng

## Creator

- name: Miao Wang
  role: author
  orcid: https://orcid.org/0000-0001-9483-6877
  organization: National Institute for Materials Science
  department: Research Center for Energy and Environmental Materials (GREEN)/Hydrogen
    Technology Materials Field/Electrochemical Energy Conversion Team
- name: Akimitsu Ishii
  role: author
  orcid: https://orcid.org/0000-0002-9261-4047
  organization: National Institute for Materials Science
  department: International Center for Young Scientists
- name: Ken Sakaushi
  role: author
  orcid: https://orcid.org/0000-0003-4797-9087
  organization: National Institute for Materials Science
  department: Research Center for Energy and Environmental Materials (GREEN)/Hydrogen
    Technology Materials Field/Electrochemical Energy Conversion Team

## Contact agent



## Publisher

organization: American Chemical Society

## Managing organization



## Keyword

- subject: Bayesian Data Assimilation
  schema: not_defined
- subject: Electrocatalysts
  schema: not_defined
- subject: Kinetic Model
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: ACS Energy Letters
  issn: '23808195'
  volume: '10'
  start_page: 22
  end_page: 29

## Conference



## Related item



## Funding

- identifier: " JPMXP1122712807"
  funder_name: 文部科学省

## 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



## Software



## Custom property



## Fileset

- id: 340c9c29-12c5-4aa8-bcc6-be51b966b520
  filename: wang-et-al-2024-accelerated-electrocatalyst-degradation-testing-by-accurate-and-robust-forecasting-of-multidimensional.pdf
  content_type: application/pdf
  size: 2341127
  md5: 246a9aadf0dcec6d416cc497426a2cad
- id: 4460c558-8814-4dce-b7e8-b7cd19b9f2bc
  filename: nz4c02868_si_001.pdf
  content_type: application/pdf
  size: 1664743
  md5: 5cb1f46fb8f50f3a16d1b5865c11af81

## Thumbnail

fileset_id: 4460c558-8814-4dce-b7e8-b7cd19b9f2bc
filename: nz4c02868_si_001.pdf