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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks
Jafar Abdi, Golshan Mazloom
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks
Jingqi Wang, Jiapeng Liu, Hongshuai Wang, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 28
Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery
Chunhua Li, Luqian Bao, Yixin Ji, et al.
Coordination Chemistry Reviews (2024) Vol. 514, pp. 215888-215888
Closed Access | Times Cited: 15
Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning
Ting Xiong, Jiawen Cui, Zemin Hou, et al.
Journal of Environmental Management (2023) Vol. 347, pp. 119065-119065
Closed Access | Times Cited: 30
Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment
Iman Salahshoori, Majid Namayandeh Jorabchi, Alireza Baghban, et al.
Chemosphere (2023) Vol. 350, pp. 141010-141010
Closed Access | Times Cited: 21
(Zn/Co)-ZIFs@TiO2 composite catalysts for oxidative desulfurization: Impacts of Zn2+/Co2+ on TiO2 interactions
Zhaleh Jafari, Golshan Mazloom, Azam Akbari
Journal of environmental chemical engineering (2024) Vol. 12, Iss. 1, pp. 111874-111874
Closed Access | Times Cited: 11
Prospects of layered double hydroxide (LDH)-based adsorbents for the remediation of environmental inorganic pollutants from wastewater: a critical review
Deepmoni Brahma, Manash Pratim Barman, Dipanwita Basak, et al.
Environmental Science Water Research & Technology (2025)
Closed Access
Computational methods for adsorption study in wastewater treatment
Alhadji Malloum, Kayode Adesina Adegoke, Joshua O. Ighalo, et al.
Journal of Molecular Liquids (2023) Vol. 390, pp. 123008-123008
Closed Access | Times Cited: 14
Data Driven Discovery of MOFs for Hydrogen Gas Adsorption
Samrendra Singh, Abhishek T. Sose, Fangxi Wang, et al.
Journal of Chemical Theory and Computation (2023) Vol. 19, Iss. 19, pp. 6686-6703
Closed Access | Times Cited: 14
Machine Learning for Heavy Metal Removal from Water: Recent Advances and Challenges
Xiangzhou Yuan, Jie Li, Juin Yau Lim, et al.
ACS ES&T Water (2023) Vol. 4, Iss. 3, pp. 820-836
Closed Access | Times Cited: 14
Assessing industrial wastewater effluent toxicity using boosting algorithms in machine learning: A case study on ecotoxicity prediction and control strategy development
Nguyen Duc Viet, Jihae Park, Hojun Lee, et al.
Environmental Pollution (2023) Vol. 341, pp. 123017-123017
Closed Access | Times Cited: 11
Artificial Intelligence for Predicting the Performance of Adsorption Processes in Wastewater Treatment: A Critical Review
Mohammad Mansour, M. Bassyouni, Rehab F. Abdel-Kader, et al.
Earth and environmental sciences library (2024), pp. 153-173
Closed Access | Times Cited: 3
Machine learning vs. statistical model for prediction modeling and experimental validation: Application in groundwater permeable reactive barrier width design
Fengshi Guo, Yangmin Ren, Yongyue Zhou, et al.
Journal of Hazardous Materials (2024) Vol. 469, pp. 133825-133825
Closed Access | Times Cited: 3
Machine Learning to Assess and Support Safe Drinking Water Supply: A Systematic Review
Feng Feng, Yuanxun Zhang, Zhenru Chen, et al.
Journal of environmental chemical engineering (2024), pp. 114481-114481
Closed Access | Times Cited: 3
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
Feng Feng, Yuanxun Zhang, Zhenru Chen, et al.
(2024)
Open Access | Times Cited: 2
Aqueous arsenic (III) removal using a novel solid waste based porous filter media block: Traditional and machine learning (ML) approaches
Nazmul Hassan Mirza, Takeshi Fujino
Desalination and Water Treatment (2024) Vol. 319, pp. 100536-100536
Open Access | Times Cited: 2
Chemical modification of zeolites for the recovery of rare earth elements evaluated by machine learning algorithms
Óscar Barros, Píer Parpot, Isabel C. Neves, et al.
Colloids and Surfaces A Physicochemical and Engineering Aspects (2023) Vol. 683, pp. 132985-132985
Open Access | Times Cited: 6
Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach
Niki Pouyanfar, Mahnaz Ahmadi, Seyed Mohammad Ayyoubzadeh, et al.
Materials Today Communications (2023) Vol. 38, pp. 107938-107938
Closed Access | Times Cited: 6
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
Feng Feng, Zhenru Chen, Jianyuan Ni, et al.
(2024)
Open Access | Times Cited: 1
Machine-learning-driven discovery of metal–organic framework adsorbents for hexavalent chromium removal from aqueous environments
Mingxing Jiang, Wei‐Wei Fu, Ying Wang, et al.
Journal of Colloid and Interface Science (2024) Vol. 662, pp. 836-845
Closed Access | Times Cited: 1
Machine Learning Application to Optimize Spent Mushroom Compost (SMC) Biochar as Filter Media for Heavy Metal Adsorption in Abandoned Mine Water
Zafira Madzin, Izni Zahidi, Amin Talei, et al.
Advanced Sustainable Systems (2024) Vol. 8, Iss. 8
Open Access | Times Cited: 1
Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China
Danying Du, Baozhong He, Xuefeng Luo, et al.
Sustainability (2024) Vol. 16, Iss. 7, pp. 2706-2706
Open Access | Times Cited: 1
Machine-Learning-Assisted Material Discovery of Pyridine-Based Polymers for Efficient Removal of ReO4–
Ling Yuan, Haolin Guo, Qinyang Li, et al.
Environmental Science & Technology (2024)
Closed Access | Times Cited: 1
Arsenic Removal from Water using Marble Powder Waste: A Comprehensive Study on Adsorption Dynamics and Machine Learning Predictions
Pooja Devi, Sania Kanwal, Zubair Ahmed, et al.
Desalination and Water Treatment (2024), pp. 100912-100912
Open Access | Times Cited: 1
Development of the design and synthesis of metal–organic frameworks (MOFs) – from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions
Zongsu Han, Yihao Yang, Joshua Rushlow, et al.
Chemical Society Reviews (2024)
Open Access | Times Cited: 1
Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges
V. Godvin Sharmila, M. Dinesh Kumar, K. Tamilarasan
Separation and Purification Reviews (2024), pp. 1-21
Closed Access | Times Cited: 1
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