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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:

Integrating machine learning interpretation methods for investigating nanoparticle uptake during seed priming and its biological effects
Hengjie Yu, Zhilin Zhao, Da Liu, et al.
Nanoscale (2022) Vol. 14, Iss. 41, pp. 15305-15315
Closed Access | Times Cited: 10

Showing 10 citing articles:

Next generation chemical priming: with a little help from our nanocarrier friends
Gholamreza Gohari, Meng Jiang, George A. Manganaris, et al.
Trends in Plant Science (2024) Vol. 29, Iss. 2, pp. 150-166
Open Access | Times Cited: 26
The drug loading capacity prediction and cytotoxicity analysis of metal–organic frameworks using stacking algorithms of machine learning
Yang Wang, Liqiang He, Meijing Wang, et al.
International Journal of Pharmaceutics (2024) Vol. 656, pp. 124128-124128
Closed Access | Times Cited: 5
Deciphering silver nanoparticles perturbation effects and risks for soil enzymes worldwide: Insights from machine learning and soil property integration
Zhenjun Zhang, Jiajiang Lin, Gary Owens, et al.
Journal of Hazardous Materials (2024) Vol. 469, pp. 134052-134052
Closed Access | Times Cited: 4
Nanoparticles-based biopriming for enhanced biotic stress mitigation
Babita Choudhary, Avinash Mishra
Elsevier eBooks (2025), pp. 67-80
Closed Access
Machine learning-assisted adsorption capacity prediction of ion exchange or chelate resin for heavy metals in aqueous solutions: External validation via multi-factor experiments
Mujian Xu, Lingxing Zhang, Ling Yuan, et al.
Separation and Purification Technology (2025), pp. 133019-133019
Closed Access
Averaging Strategy for Interpretable Machine Learning on Small Datasets to Understand Element Uptake after Seed Nanotreatment
Hengjie Yu, Shiyu Tang, Sam Fong Yau Li, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 34, pp. 12760-12770
Closed Access | Times Cited: 6
Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation
Hengjie Yu, Dan Luo, Sam Fong Yau Li, et al.
Nanoscale (2023) Vol. 15, Iss. 32, pp. 13437-13449
Open Access | Times Cited: 4
What influence farmers’ relative poverty in China: A global analysis based on statistical and interpretable machine learning methods
Wei Huang, Yinke Liu, Peiqi Hu, et al.
Heliyon (2023) Vol. 9, Iss. 9, pp. e19525-e19525
Open Access | Times Cited: 3
Interpretable machine learning for investigating complex nanomaterial–plant–soil interactions
Hengjie Yu, Zhilin Zhao, Dan Luo, et al.
Environmental Science Nano (2022) Vol. 9, Iss. 11, pp. 4305-4316
Closed Access | Times Cited: 3
Interpretable machine learning-accelerated seed treatment by nanomaterials for environmental stress alleviation
Hengjie Yu, Dan Luo, Sam Fong Yau Li, et al.
arXiv (Cornell University) (2023)
Open Access
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