ArticleScientific reports2024
Enhanced removal of tetracycline by vitamin C-modified cow manure biochar in water.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Adsorption of methylene blue and tetracycline on nitrogen-doped cotton stalk-based biochar and mechanism research.BMC chemistry · 2026Article
- Adsorptive performance of SPE via modified POM biochar for Pb(II) and tetracycline with concurrent antimicrobial action.Scientific reports · 2026Article
- Analysis of conservation practices for black soil based on organic matter and nitrogen contents in the black soil region of Northeast China.Scientific reports · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Vitamin C (VC), due to its chemical properties, can provide more oxygen-containing functional groups such as hydroxyl groups for biochar (BC), which promotes the adsorption of tetracycline on biochar. Therefore, in this study, cow dung biochar (CDBC) was modified with VC and VC-modified CDBC (CDBC-VC) was synthesized. The modified biochar was characterized and related factors, adsorption kinetics, isotherms and adsorption mechanisms were investigated. Adsorption kinetics indicate a fast rate of adsorption. The adsorption isotherms showed that the maximum adsorption capacity was 31.72 mg/g (CDBC) and 50.90 mg/g (CDBC-VC), respectively, and the adsorption process was inhomogeneous with multiple molecular layers and the adsorbent has a higher affinity. Mechanistic studies showed that hydrogen bonding interactions, π-π electron donor-acceptor interactions, hydrophobic interactions, and electrostatic interactions were the key to the adsorption process. The analysis of adsorbent regeneration showed that CDBC-VC had good adsorption performance. CDBC and CDBC-VC showed the best performance in simulated industrial wastewater with removal rates of 78.81% and 93.69%. The adsorption mechanism was comprehensively analyzed using six machine learning models. The extreme gradient boosting model gave the best fit. Analysis of the weights of the input variables for predicting adsorption efficiency showed that the ratio of initial TC concentration to BC dosage (29.8%), specific surface area (23%), isoelectric point (8.8%), and ash content (7.7%) had a significant effect on the predicted results.
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