Enzymatic Removal of Diclofenac and Aceclofenac from Water by Soybean Peroxidase
Bibliographic record
Abstract
Pharmaceuticals are a class of emerging contaminants that have been widely detected in wastewater treatment facilities' influent and effluent. They threaten the environment and non-target life. Thus, a promising treatment method, soybean peroxidase (SBP; EC 1.11.1.7), which catalyzes the oxidation of phenolic and anilino donors in the presence of hydrogen peroxide, was investigated as a treatment method. The aim was to remove two non-steroidal anti-inflammatory drugs, diclofenac (DCF) and aceclofenac (ACF), from synthetic wastewater via enzymatic oxidation, oligomerization, and precipitation. SBP can be extracted from soybean hulls, a byproduct of the soybean industry. DCF (0.10 mM) and ACF (0.10 mM) were amenable to SBP-catalyzed removal under the optimal operational parameters of pH 5 and 4; hydrogen peroxide: 0.40 and 0.45 mM; and minimum effective enzyme concentration: 0.15 and 0.60 U/mL, respectively. The initial first-order rate constant and half-life of each substrate were also determined under the established optimum conditions. Under these optimum conditions, the half-lives for DCF and ACF were 1.43 ± 0.01 and 0.84 ± 0.05 min, respectively. The results demonstrated that SBP is a robust enzyme that can achieve more than 95% removal for both compounds. Mass spectrometric analysis of the enzymatic treatment products of DCF revealed the formation of an oxidative tetramer. The SBP-catalyzed reaction is a highly effective method for removing DCF and ACF from synthetic wastewater, highlighting its potential for environmental cleanup of pharmaceutical contaminants.
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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".