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Tetracycline adsorption research (2015–2025): A bibliometric analysis of trends, challenges, and future directions

四环素 文献计量学 区域科学 数据科学 地理 计算机科学 图书馆学 生物 微生物学 抗生素
作者
Ramesh Vinayagam,Thivaharan Varadavenkatesan,Raja Selvaraj
出处
期刊:Results in engineering [Elsevier BV]
卷期号:27: 106383-106383 被引量:12
标识
DOI:10.1016/j.rineng.2025.106383
摘要

• Tetracycline adsorption research shows rapid growth in the past decade (2014–2025). • VOSviewer and Bibliometrix visualized the Bibliometric data (567 articles). • China, USA, India are top contributors. • Metal organic frameworks and magnetic biochar are trending adsorbents. • DFT, AI/ML and continuous studies are major research gaps. Tetracycline is a commonly used antibiotic that has emerged as a significant environmental contaminant due to its widespread use and persistence in aquatic ecosystems. Its presence in water bodies poses ecological risks and contributes to the growing concern over antibiotic resistance. Among the various treatment technologies, adsorption has gained prominence as a cost-effective and efficient approach for removing tetracycline from contaminated water. This bibliometric review analyzes 567 articles published between 2015 and 2025 retrieved from the Web of Science Core Collection to uncover the scientific trends, influential contributors, research hotspots, and emerging gaps in the field of tetracycline adsorption. Using VOSviewer and Bibliometrix, the study presents a comprehensive visual mapping of publication growth trends, leading countries, institutions, authors, and journals. China, the USA, and India dominate the research output, with significant institutional contributions from Hunan University and Anhui Agricultural University. “Desalination and Water Treatment” and “Bioresource Technology” are identified as prominent journals, with the latter having 2236 citations. Keyword co-occurrence and thematic evolution analyses highlight trending materials like magnetic biochar and metal-organic frameworks. Additionally, it highlighted underexplored areas, including computational modeling, Artificial Intelligence/Machine Learning integration, and continuous adsorption. The findings offer a holistic understanding of the current research landscape and provide strategic directions for advancing future studies in tetracycline adsorption for environmental sustainability.
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