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Zhijie Xu

Government of the United States of America - Pacific Northwest National Laboratory

901 D Street

370 L'Enfant Promenade, S.W.

Washington, DC 20024-2115

United States

SCHOLARLY PAPERS

3

DOWNLOADS

278

TOTAL CITATIONS

3

Scholarly Papers (3)

1.

Computationally efficient models for aqueous organic redox flow batteries

Journal of Energy Storage, volume 134, 2025[10.1016/j.est.2025.118134]
Number of pages: 11 Posted: 23 Oct 2025
Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory and Government of the United States of America - Pacific Northwest National Laboratory
Downloads 171 (442,467)
Citation 1

Abstract:

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aqueous organic redox flow battery, comercial scale cell, analytical model, Deep Operator Networks

2.

A hybrid analytical and numerical model for cross-over and performance decay in a unit cell vanadium redox flow battery

Journal of Power Sources, volume 578, 2023[10.1016/j.jpowsour.2023.233210]
Number of pages: 47 Posted: 24 Feb 2023 Last Revised: 16 Oct 2025
Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory and Government of the United States of America - Pacific Northwest National Laboratory
Downloads 71 (867,774)

Abstract:

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redox flow battery, cross-over, performance decay, partition coefficients, migration and convection

3.

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm2 aqueous organic redox flow battery

Journal of Power Sources, volume 635, 2025[10.1016/j.jpowsour.2025.236470]
Number of pages: 37 Posted: 26 Oct 2024 Last Revised: 16 Oct 2025
Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory, Government of the United States of America - Pacific Northwest National Laboratory and Government of the United States of America - Pacific Northwest National Laboratory
Downloads 36 (1,236,159)
Citation 2

Abstract:

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Aqueous organic redox flow battery, battery performance, operation conditions, physicochemical parameters, machine learning