Development of Vitamin B6-Mediated Biochar with Nano Zero-Valent Iron Coating for Oxytetracycline Removal through Adsorption and Degradation under Harsh Acidic Conditions
Bibliographic record
Abstract
Oxytetracycline-containing wastewater, particularly produced by pharmaceutical industries, is too acidic to treat with iron-assisted materials. In order to tackle this issue, vitamin B6-mediated biochar with nano zero-valent iron coating (nZVI/B6@BC) was developed. Oxytetracycline (OTC) removal performance of biochar (BC), vitamin B6-coated biochar (B6@BC), nZVI-coated biochar (nZVI@BC), and vitamin B6-mediated biochar with nano zero-valent iron coating (nZVI/B6@BC) were investigated to analyze contributions and mechanisms of adsorption and degradation. Through modification, the adsorption capacity of B6@BC was slightly increased from 81.38 mg/g of BC to 85.64 mg/g. In the removal test, the 5-min OTC removal efficiencies with nZVI@BC and nZVI/B6@BC were 52.25% and 59.05%, yet the BC and B6@BC were limited to 5.61% and 8.54%. The distinct difference may be attributed to the existence of nZVI on biochar strongly improving the reactivity from adsorption to chemical reaction. Moreover, 98.28% of OTC was removed within 60 min in the nZVI/B6@BC suspension. The adsorption of OTC on BC fitted the Freundlich isotherm, Temkin isotherm, and intramolecular diffusion model, whereas that on B6@BC fitted Langmuir isotherm and pseudo-second-order better. Based on HPLC-MS analyses, there were three pathways proposed for OTC degradation in nZVI/B6@BC suspension. nZVI provided active sites on biochar for OTC degradation through oxidization, de-hydroxylation, ring-opening, reduction, addition, demethylation, and alkylation reactions. B6 as a mediate helped improve the stabilization and distribution of nZVI on biochar, which facilitates the capability of nZVI/B6@BC for OTC removal through adsorption and degradation under acidic conditions. The OTC can not only be captured on biochar but also be metabolized to achieve complete removal from aquatic systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".