Enzymatic Removal of Phenol and Chlorophenols Using Soybean Seed Hulls
Bibliographic record
Abstract
Abstract Soybean peroxidase (SBP), (EC 1.11.1.7) can be readily extracted from soybean seed hulls. This study reports on the direct use of soybean seed‐hull extracts for the bioremediation of phenolic wastes. The crude SBP extract from the hulls, like pure soybean peroxidase, is catalytically active in a broad range of pH and temperatures. As SBP is gradually released into the aqueous solution from seed hulls, the direct use of soybean seed hulls can reduce SBP inactivation by H2O2 and enhance the utilization efficiency of SBP through the slow release of the enzyme from the seed hulls. However, large doses of soybean seed hulls were found to be ineffective in phenol removal. Gradual additions of H2O2 in combination with the SBP released from the hulls were applied to optimize the bioremediation. Since the crude extract contains a mixture of multiple soybean proteins, soybean seed hull slurry required a higher concentration of H2O2 to remove the phenolic substrates than did the purified enzyme. Under the experimental conditions, 80 % of phenol (10.6 mM), 96 % of 2‐chlorophenol (3.9 mM), 95 % of 2,4‐dichlorophenol (3.1 mM), and 94 % of mixed phenol and chlorophenols were removed using soybean seed hulls in a single batch reactor. These results demonstrate that soybean seed hulls, compared to purified SBP, may be a more cost‐effective alternative in the enzymatic removal of phenolic compounds through polymerization reactions.
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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) | 0.001 | 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".