Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forest-Reference-Cited by-同舟云学术

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Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forest

Published:2022-09 Issue: Volume:537 Page:115826
ISSN:0011-9164
Container-title:Desalination
language:en
Short-container-title:Desalination

Author:

Park Sanghun,Angeles Anne Therese,Son Moon,Shim Jaegyu,Chon Kangmin,Cho Kyung Hwa

Funder

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Mechanical Engineering,Water Science and Technology,General Materials Science,General Chemical Engineering,General Chemistry

Reference32 articles.

1. Cobalt and nickel ferrites based capacitive deionization electrode materials for water desalination applications;Hai;Electrochim. Acta,2020

2. Free-standing Ti3C2Tx MXene film as binder-free electrode in capacitive deionization with an ultrahigh desalination capacity;Ma;Chem. Eng. J.,2020

3. Water desalination via capacitive deionization: what is it and what can we expect from it?;Suss;Energy Environ. Sci.,2015

4. Parametric study of multichannel desalination battery for low-energy electrochemical deionization of brackish water;Lee;Desalination,2021

5. Carbon-based capacitive deionization electrodes: development techniques and its influence on electrode properties;Angeles;Chem. Rec.,2021

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2. Advancement of capacitive deionization propelled by machine learning approach;Separation and Purification Technology;2025-02

3. Nitrate and phosphate removal by capacitive deionization using nanocellulose/polypyrrole electrodes;Chemical Engineering Science;2025-01

4. Machine learning modeling in flow-electrode capacitive deionization system: Prediction of ion concentrations in flow-electrode aqueous electrolytes;Journal of Water Process Engineering;2024-11

5. Progress toward adsorption mechanism exploration method for capacitive deionization: Experimental, mathematical model, computational chemistry and machine learning;Desalination;2024-10

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