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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.

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Showing 1-25 of 117 citing articles:

Nanotechnology’s frontier in combatting infectious and inflammatory diseases: prevention and treatment
Yujing Huang, Xiaohan Guo, Yi Wu, et al.
Signal Transduction and Targeted Therapy (2024) Vol. 9, Iss. 1
Open Access | Times Cited: 125
Artificial intelligence to bring nanomedicine to life
Nikita Serov, Vladimir V. Vinogradov
Advanced Drug Delivery Reviews (2022) Vol. 184, pp. 114194-114194
Closed Access | Times Cited: 88
Environmental behavior, human health effect, and pollution control of heavy metal(loid)s toward full life cycle processes
Haoyu Deng, Yuling Tu, Han Wang, et al.
Eco-Environment & Health (2022) Vol. 1, Iss. 4, pp. 229-243
Open Access | Times Cited: 75
Fertilizer management for global ammonia emission reduction
Peng Xu, Geng Li, Yi Zheng, et al.
Nature (2024) Vol. 626, Iss. 8000, pp. 792-798
Closed Access | Times Cited: 51
Advancing Computational Toxicology by Interpretable Machine Learning
Xuelian Jia, Tong Wang, Hao Zhu
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17690-17706
Open Access | Times Cited: 47
Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms
Yunchi Zhou, Ying Wang, Willie J.G.M. Peijnenburg, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17786-17795
Open Access | Times Cited: 42
Survey on Explainable AI: Techniques, challenges and open issues
Adel Abusitta, Miles Q. Li, Benjamin C. M. Fung
Expert Systems with Applications (2024) Vol. 255, pp. 124710-124710
Closed Access | Times Cited: 19
Machine Learning Boosts the Design and Discovery of Nanomaterials
Yuying Jia, Xuan Hou, Zhongwei Wang, et al.
ACS Sustainable Chemistry & Engineering (2021) Vol. 9, Iss. 18, pp. 6130-6147
Closed Access | Times Cited: 80
Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches
Zhoumeng Lin, Wei-Chun Chou, Yi‐Hsien Cheng, et al.
International Journal of Nanomedicine (2022) Vol. Volume 17, pp. 1365-1379
Open Access | Times Cited: 67
Merging data curation and machine learning to improve nanomedicines
Chen Chen, Zvi Yaari, Elana Apfelbaum, et al.
Advanced Drug Delivery Reviews (2022) Vol. 183, pp. 114172-114172
Open Access | Times Cited: 57
Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences
Anja Leist, Matthias Klee, Jung Hyun Kim, et al.
Science Advances (2022) Vol. 8, Iss. 42
Open Access | Times Cited: 56
Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning
Xueye Chen, Honglin Lv
NPG Asia Materials (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 50
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
Fubo Yu, Xiangang Hu
Journal of Hazardous Materials (2022) Vol. 432, pp. 128730-128730
Closed Access | Times Cited: 43
Combinatorial Polycation Synthesis and Causal Machine Learning Reveal Divergent Polymer Design Rules for Effective pDNA and Ribonucleoprotein Delivery
Ramya Kumar, Ngoc Le, Felipe Oviedo, et al.
JACS Au (2022) Vol. 2, Iss. 2, pp. 428-442
Open Access | Times Cited: 42
Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives
Xiaotong Wu, Qixing Zhou, Mu Li, et al.
Journal of Hazardous Materials (2022) Vol. 438, pp. 129487-129487
Closed Access | Times Cited: 40
Intelligent control of nanoparticle synthesis through machine learning
Honglin Lv, Xueye Chen
Nanoscale (2022) Vol. 14, Iss. 18, pp. 6688-6708
Closed Access | Times Cited: 38
Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification
Ye Sun, Zhiyuan Zhao, Hailong Tong, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17990-18000
Closed Access | Times Cited: 32
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Xiliang Yan, Tongtao Yue, David A. Winkler, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8575-8637
Closed Access | Times Cited: 32
Harnessing Hafnium‐Based Nanomaterials for Cancer Diagnosis and Therapy
Shuaishuai Ding, Lei Chen, Jing Liao, et al.
Small (2023) Vol. 19, Iss. 32
Closed Access | Times Cited: 26
Development potential of nanoenabled agriculture projected using machine learning
Peng Deng, Yiming Gao, Mu Li, et al.
Proceedings of the National Academy of Sciences (2023) Vol. 120, Iss. 25
Open Access | Times Cited: 24
Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach
Ravi Maharjan, Shavron Hada, Ji‐Eun Lee, et al.
International Journal of Pharmaceutics (2023) Vol. 640, pp. 123012-123012
Closed Access | Times Cited: 23
Current applications and future impact of machine learning in emerging contaminants: A review
Lang Lei, Ruirui Pang, Zhibang Han, et al.
Critical Reviews in Environmental Science and Technology (2023) Vol. 53, Iss. 20, pp. 1817-1835
Closed Access | Times Cited: 22
Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS
Junqin Liu, Jiang Zhao, Jiapan Du, et al.
Journal of Hazardous Materials (2024) Vol. 468, pp. 133797-133797
Closed Access | Times Cited: 9
Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions
Lingxuan Meng, Beihai Zhou, Haijun Liu, et al.
The Science of The Total Environment (2024) Vol. 946, pp. 174201-174201
Closed Access | Times Cited: 9
Machine learning predicts heavy metal adsorption on iron (oxyhydr)oxides: A combined insight into the adsorption efficiency and binding configuration
Junqin Liu, Jiang Zhao, Jiapan Du, et al.
The Science of The Total Environment (2024) Vol. 950, pp. 175370-175370
Closed Access | Times Cited: 9
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