Remediation of tetracycline contaminated soil and water phases by straw-derived biochar: Adsorption performance and bacterial community evolution
Bibliographic record
Abstract
Tetracycline (TC), as a typical recalcitrant pollutant, posed environmental risks due to its long-term persistence. This study investigated the efficacy of straw-derived biochar (KBC800) in mitigating TC pollution in aquatic and terrestrial environments. In aqueous medium, TC was adsorbed by KBC800 primarily through film diffusion and intraparticle diffusion, dominated by physical adsorption. The adsorption capacity of TC by biochar was affected by soil constituents, yet KBC800 still exhibited a high adsorption capacity of 355 mg·g −1 . The machine learning-based extreme gradient boosting (XGB) model accurately identified total carbon (TC), humic acid (HA), and sodium chloride (NaCl) concentrations as the most critical factors influencing adsorption efficiency. Furthermore, soil amendment with 10 g·kg −1 KBC800 improved soil properties, enhanced microbial diversity, stimulated plant growth, and alleviated phytotoxicity. Microbial community analysis revealed the positive effects of KBC800 on microbial community structure, with Conexibacter and Paucibacter emerging as dominant genera. Notably, KBC800 addition reduced the stimulatory impact of TC on soil microorganisms. These findings demonstrated the potential of biochar for effective remediation of antibiotic-contaminated environments and provided insights for straw biomass valorization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".