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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:

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning
Wentao Zhang, Ronghua Chen, Jie Li, et al.
Biochar (2023) Vol. 5, Iss. 1
Open Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
Van Giao Nguyen, Prabhakar Sharma, Ümit Ağbulut, et al.
Biofuels Bioproducts and Biorefining (2024) Vol. 18, Iss. 2, pp. 567-593
Closed Access | Times Cited: 23
Machine learning insights in predicting heavy metals interaction with biochar
Xin Wei, Yang Liu, Lin Shen, et al.
Biochar (2024) Vol. 6, Iss. 1
Open Access | Times Cited: 21
Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives
Weilin Fu, Menghan Feng, Changbin Guo, et al.
Bioresource Technology (2024) Vol. 403, pp. 130861-130861
Closed Access | Times Cited: 17
Machine learning applications for biochar studies: A mini-review
Wei Wang, Jo‐Shu Chang, Duu‐Jong Lee
Bioresource Technology (2024) Vol. 394, pp. 130291-130291
Closed Access | Times Cited: 16
Precise Prediction of Biochar Yield and Proximate Analysis by Modern Machine Learning and SHapley Additive exPlanations
Lê Anh Tuấn, Ashok Pandey, Ranjan Sirohi, et al.
Energy & Fuels (2023) Vol. 37, Iss. 22, pp. 17310-17327
Closed Access | Times Cited: 31
Eco-friendly synthesis of biochar supported with zinc oxide as a heterogeneous catalyst for photocatalytic decontamination of Rhodamine B under sunlight illumination
Mahmood A. Albo Hay Allah, Hanadi K. Ibrahim, Hassan Abbas Alshamsi, et al.
Journal of Photochemistry and Photobiology A Chemistry (2023) Vol. 449, pp. 115413-115413
Closed Access | Times Cited: 30
Machine learning and computational chemistry to improve biochar fertilizers: a review
Ahmed I. Osman, Yubin Zhang, Zhi Ying Lai, et al.
Environmental Chemistry Letters (2023) Vol. 21, Iss. 6, pp. 3159-3244
Open Access | Times Cited: 28
Coordination environment manipulation of single atom catalysts: Regulation strategies, characterization techniques and applications
Wentao Zhang, Yue Zhao, Wenguang Huang, et al.
Coordination Chemistry Reviews (2024) Vol. 515, pp. 215952-215952
Closed Access | Times Cited: 14
Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning
Zhaolin Du, Xuan Sun, Shunan Zheng, et al.
Journal of Hazardous Materials (2024) Vol. 476, pp. 135065-135065
Closed Access | Times Cited: 14
Machine learning prediction of biochar physicochemical properties based on biomass characteristics and pyrolysis conditions
Yuanbo Song, Zipeng Huang, Mengyu Jin, et al.
Journal of Analytical and Applied Pyrolysis (2024) Vol. 181, pp. 106596-106596
Closed Access | Times Cited: 13
Ecological risk of per- and polyfluorinated alkyl substances in the phytoremediation process: a case study for ecologically keystone species across two generations
Dezhan Liang, Caibin Li, Hanbo Chen, et al.
The Science of The Total Environment (2024) Vol. 951, pp. 174962-174962
Closed Access | Times Cited: 11
Biochar as an eco-friendly adsorbent for ibuprofen removal via adsorption: A review
Harez Rashid Ahmed, Kawan F. Kayani, Anu Mary Ealias, et al.
Inorganic Chemistry Communications (2024) Vol. 170, pp. 113397-113397
Closed Access | Times Cited: 11
Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
Van Giao Nguyen, Prabhakar Sharma, Ümit Ağbulut, et al.
International Journal of Green Energy (2024) Vol. 21, Iss. 12, pp. 2771-2798
Closed Access | Times Cited: 8
Machine learning (ML): An emerging tool to access the production and application of biochar in the treatment of contaminated water and wastewater
Sheetal Kumari, Jyoti Chowdhry, Manish Kumar, et al.
Groundwater for Sustainable Development (2024) Vol. 26, pp. 101243-101243
Closed Access | Times Cited: 8
Agricultural Lignocellulose Biochar Material in Wastewater Treatment: A Critical Review and Sustainability Assessment
Aqib Zahoor, Xiao Liu, Yuxin Liu, et al.
Environmental Functional Materials (2025)
Open Access | Times Cited: 1
Machine learning-driven prediction of nitrate-N adsorption efficiency by Fe-modified biochar: Refined model tuning and identification of crucial features
Chen Li, Xie Guixian, Jing Li, et al.
Journal of Water Process Engineering (2025) Vol. 70, pp. 107026-107026
Closed Access | Times Cited: 1
Hydrogen bonds between the oxygen-containing functional groups of biochar and organic contaminants significantly enhance sorption affinity
Dong Wei, Jing Xing, Quan Chen, et al.
Chemical Engineering Journal (2024) Vol. 499, pp. 156654-156654
Closed Access | Times Cited: 7
Biochar-based polymeric film as sustainable and efficient sorptive phase for preconcentration of steroid hormones in environmental waters and wastewaters
Francesca Merlo, Enriqueta Anticò, Rachele Merli, et al.
Analytica Chimica Acta (2024) Vol. 1308, pp. 342658-342658
Open Access | Times Cited: 6
Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar
Xiaorui Liu, Haiping Yang, Tang Yuanjun, et al.
Bioresource Technology (2024) Vol. 403, pp. 130865-130865
Closed Access | Times Cited: 5
Revolutionizing biochar synthesis for enhanced heavy metal adsorption: Harnessing machine learning and Bayesian optimization
Hongwei Yang, Xiangrong Liu, Yingliang Liu, et al.
Journal of environmental chemical engineering (2023) Vol. 11, Iss. 5, pp. 110593-110593
Closed Access | Times Cited: 12
Simulation and optimization of co-pyrolysis biochar using data enhanced interpretable machine learning and particle swarm algorithm
Chao Chen, Rui Liang, Junxia Wang, et al.
Biomass and Bioenergy (2024) Vol. 182, pp. 107111-107111
Closed Access | Times Cited: 4
Machine learning–assisted prediction of engineered carbon systems’ capacity to treat textile dyeing wastewater via adsorption technology
Om Kulkarni, Priya Dongare, Bhavana Shanmughan, et al.
Environmental Monitoring and Assessment (2025) Vol. 197, Iss. 2
Closed Access
Prediction of perfluorooctanoic acid adsorption properties of porous carbon materials based on machine learning
Yongcheng Jiang, Bo Wei, Yanan Shang, et al.
Separation and Purification Technology (2025), pp. 132089-132089
Closed Access
Predicting the Adsorption Capacity of Geopolymers for Heavy Metals in Solution Based on Machine Learning
Yongming Han, Wenting Dai, Lu Zhou, et al.
Journal of environmental chemical engineering (2025), pp. 115978-115978
Closed Access
Orientational dipole interaction mediated by crystallites and defects in biomass derived carbon materials of heterogeneous catalytic ozonation process
Benjie Zhu, Jialiang Liu, Lingyu Liu, et al.
Chemical Engineering Journal (2025), pp. 161464-161464
Closed Access
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