Physics-Based Machine Learning Discovered Nanocircuitry for Nonlinear Ion Transport in Nanoporous Electrodes-Reference-Cited by-同舟云学术

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Physics-Based Machine Learning Discovered Nanocircuitry for Nonlinear Ion Transport in Nanoporous Electrodes

Published:2023-07-11 Issue:28 Volume:127 Page:13699-13705
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Zhan Hualin¹²^ORCID,Sandberg Richard³,Feng Fan³,Liang Qinghua¹^ORCID,Xie Ke¹,Zu Lianhai¹,Li Dan¹^ORCID,Liu Jefferson Zhe³^ORCID

Affiliation:

1. Department of Chemical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia

2. School of Engineering, The Australian National University, Canberra, ACT 2601, Australia

3. Department of Mechanical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia

Funder

Australian Research Council

Publisher

American Chemical Society (ACS)

Subject

Surfaces, Coatings and Films,Physical and Theoretical Chemistry,General Energy,Electronic, Optical and Magnetic Materials

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.3c02844

Reference39 articles.

1. Perspectives for electrochemical capacitors and related devices

2. Nanofluidics coming of age

3. Solvation‐Involved Nanoionics: New Opportunities from 2D Nanomaterial Laminar Membranes

4. Bioinspired Ionic Sensory Systems: The Successor of Electronics

5. Zhan, H. Graphene-Electrolyte Interfaces: Electronic Properties and Applications; Jenny Stanford Publishing, 2020, pp 67–204.

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1. Physics-based extraction of material parameters from perovskite experiments via Bayesian optimization;Energy & Environmental Science;2024

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