Investigating the different transformations of tetracycline using birnessite under different reaction conditions and various humic acids
Bibliographic record
Abstract
Prior studies have successfully used manganese oxides to facilitate the transformation of tetracycline in aqueous solution. To further understand the kinetic and the transformation pathway of tetracycline via birnessite (δ-MnO 2 ) under different conditions, experiments were conducted at pH levels of 3, 6, and 9 in the presence or absence of Aldrich humic acid (ADHA). Tetracycline removal followed the pseudo-second-order reaction model in all investigated cases, and the removal efficiency (g mg −1 h −1 ) followed the following trend: pH 3 (0.45/0.27) > pH 6 (0.036/0.087) > pH 9 (0.036/0.103) in the absence/presence of ADHA. Liquid chromatography–mass spectrometry/mass spectrometry results identified five main transformation products at m/z 495, 477, 493, 459, and 415, produced by the transformation reactions, including hydration, oxidation , desaturation , and oxy reduction. Notably, in the presence of ADHA at pH 3, products with higher toxicity secondary (m/z 477 and 495) were reduced, while less toxicity products (m/z 459 and 415) were enhanced. The experiments utilizing tetracycline and δ-MnO 2 with varied humic acids (HA) revealed that HA with high polar organic carbon groups, such as O-alkyl, exhibited higher removal efficiency at pH 6. This research offers the first comprehensive insights into the pathway transformations of tetracycline via δ-MnO 2 under different pH conditions and HA types. For further understanding, future work should investigate the binding of HA, TTC, and/or Mn 2+ and the oxidation capacity of MnO 2 after the reaction to clarify Mn 2+ elution mechanisms.
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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".