Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 Modeling of Environmentally Relevant Chemical Reactions for Organic Compounds
Kai Zhang, Huichun Zhang
ACS ES&T Water (2022) Vol. 4, Iss. 3, pp. 773-783
Closed Access | Times Cited: 23

Showing 23 citing articles:

Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach
Jinqi Jiang, Xiang Xiang, Qinhao Zhou, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 29, pp. 12989-12999
Closed Access | Times Cited: 12
Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment.
François Nkinahamira, Anqi Feng, Lijie Zhang, et al.
TrAC Trends in Analytical Chemistry (2024), pp. 117980-117980
Closed Access | Times Cited: 8
Machine learning framework for predicting cytotoxicity and identifying toxicity drivers of disinfection byproducts
Rabbi Sikder, Huichun Zhang, Peng Gao, et al.
Journal of Hazardous Materials (2024) Vol. 469, pp. 133989-133989
Closed Access | Times Cited: 7
Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system
Ying Yu, Md. Mahjib Hossain, Rabbi Sikder, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 175573-175573
Closed Access | Times Cited: 6
Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds
Ying He, Guohong Liu, Song Hu, et al.
Journal of Hazardous Materials (2023) Vol. 458, pp. 131942-131942
Closed Access | Times Cited: 13
Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment
Md Hasan-Ur Rahman, Rabbi Sikder, Tanvir Ahamed Tonmoy, et al.
Journal of Water Process Engineering (2025) Vol. 70, pp. 106941-106941
Closed Access
An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening
An Su, Yingying Cheng, Chengwei Zhang, et al.
The Science of The Total Environment (2024) Vol. 921, pp. 171229-171229
Closed Access | Times Cited: 4
Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning
Yu Zhang, Min Xia, Hongwei Song, et al.
The Journal of Physical Chemistry A (2024) Vol. 128, Iss. 12, pp. 2383-2392
Closed Access | Times Cited: 4
Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge
Yu‐Shu Cheng, Kai Zhang, Kuan Huang, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 29, pp. 12976-12988
Closed Access | Times Cited: 4
Leveraging big data to elucidate the impact of heavy metal nanoparticles on anammox processes in wastewater treatment
Yanbin Hong, Zhenguo Chen, Zehua Huang, et al.
Journal of Environmental Management (2025) Vol. 382, pp. 125243-125243
Closed Access
How machine learning boosts the understanding of organic pollutant adsorption on carbonaceous materials: A comprehensive review with statistical insights
Zichu Wang, Qi Wang, Fan Yang, et al.
Separation and Purification Technology (2024) Vol. 350, pp. 127790-127790
Closed Access | Times Cited: 3
Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures
Piotr Cysewski, Maciej Przybyłek, Tomasz Jeliński
Materials (2023) Vol. 16, Iss. 18, pp. 6336-6336
Open Access | Times Cited: 7
Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron
Nurul Alvia Istiqomah, Donghwi Jung, Jeehyeong Khim
Journal of Water Process Engineering (2023) Vol. 56, pp. 104303-104303
Closed Access | Times Cited: 7
Advances in the analysis of disinfection by-products with mass spectrometry: Sample preparation and target/non-target screening
Jianfa Gao, Foon Yin Lai, Cristina Postigo, et al.
TrAC Trends in Analytical Chemistry (2024) Vol. 173, pp. 117621-117621
Closed Access | Times Cited: 2
Advanced Oxidation Processes for Activated Carbon Regeneration: From Fundamental to Application
Jian Liu, Shaohua Chen, Xin Ye, et al.
ACS ES&T Water (2024) Vol. 4, Iss. 8, pp. 3119-3130
Closed Access | Times Cited: 2
Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals
Yu Zhang, Jinhui Yu, Hongwei Song, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 16, pp. 5097-5106
Closed Access | Times Cited: 5
Advancing the Comprehension of Iron Ions with Different Valences in Anammox-Based Processes: Insight into the Response and Mechanism Based on Prediction and Classification Machine Learning Models
Zhicheng Jiang, Xinxin Xu, Yuhang He, et al.
ACS ES&T Water (2023) Vol. 3, Iss. 11, pp. 3716-3727
Closed Access | Times Cited: 5
Advancements in electrocoagulation for oily wastewater treatment: Mechanisms, efficiency, and applications
Hansa Muvel, Manoj Kumar Jindal, P.K. Tewari, et al.
Journal of Water Process Engineering (2024) Vol. 68, pp. 106291-106291
Closed Access | Times Cited: 1
Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals
Yu Zhang, Jinhui Yu, Hongwei Song, et al.
(2023)
Open Access | Times Cited: 2
Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments
Huichun Zhang, Carla A. Ng
ACS ES&T Water (2024) Vol. 4, Iss. 3, pp. 761-763
Open Access
Predicting reaction rate constants of organic compounds with oxidants in the atmospheric aqueous-phase through multi-task learning
Linghao Gu, Zhongming Chen
Atmospheric Environment (2024) Vol. 337, pp. 120775-120775
Closed Access
Machine Vision and Image Processing for Drones
Kwok Tai Chui, Varsha Arya, Akshat Gaurav, et al.
Advances in computational intelligence and robotics book series (2024), pp. 137-158
Closed Access
Tree-Based Ensemble Machine Learning Model For Predictive and Interpretability Analysis of Nitrate Reduction by Zero-Valent Iron Rate Constant and Product Conversion
Donghwi Jung, Nurul Alvia Istiqomah
(2023)
Closed Access
Machine Learning Application In Modeling Organic Pollutant Adsorption on Carbonaceous Materials: A Comprehensive Review with Statistical Insights
Zichu Wang, Jianwei Yu, Qi Wang, et al.
(2023)
Closed Access
Page 1

Scroll to top