Physics-informed deep learning for multi-species membrane separations-Reference-Cited by-同舟云学术

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Physics-informed deep learning for multi-species membrane separations

Published:2024-04 Issue: Volume:485 Page:149806
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Rehman Danyal,Lienhard John H.^ORCID

Funder

Massachusetts Institute of Technology Abdul Latif Jameel Water and Food Systems Lab

Massachusetts Institute of Technology Centers for Mechanical Engineering Research and Education

Publisher

Elsevier BV

Reference106 articles.

1. Prospects of metal recovery from wastewater and brine;DuChanois;Nat. Water,2023

2. Electrochemical methods for water purification, ion separations, and energy conversion;Alkhadra;Chem. Rev.,2022

3. Performance metrics for nanofiltration-based selective separation for resource extraction and recovery;Wang;Nat. Water,2023

4. Electrochemical chromium removal using nanodiamond enhanced flow-through electrosorption;Shin;Chem. Eng. J.,2023

5. Novel positively charged metal-coordinated nanofiltration membrane for lithium recovery;Wang;ACS Appl. Mater. Interfaces,2021

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1. Metal incorporated into magnetic hybrid covalent organic framework for high selective uptake of scandium ion;Separation and Purification Technology;2025-03

2. Stepwise tuning carbon slits at sub-angstrom scale for dynamical separation of hydrogen isotope;Separation and Purification Technology;2025-02

3. Efficiently selective adsorption Rb(I) based on ion-imprinted membrane chromatography: Batch adsorption and dynamic filtration;Separation and Purification Technology;2025-02

4. An intelligent approach: Integrating ChatGPT for experiment planning in biochar immobilization of soil cadmium;Separation and Purification Technology;2025-01

5. Evaluating the Fe-doping effect on intermediate adsorption modulation for Co-based vanadates toward separation of hydrogen from water electrolysis;Separation and Purification Technology;2024-12

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