Bibliographic record
Abstract
In the presence of hydrogen peroxide (H2O2), soybean peroxidase (SBP) extracted from the soybean seed coat catalyzes the polymerization of various chlorinated phenols from wastewater. The resulting polymer precipitates from solution and can be removed through a simple sedimentation or filtration process. To date, the majority of the research has focused on horseradish peroxidase (HRP) as the enzyme source. Recently, researchers have discovered that SBP is thermally more stable and can work in a different pH range than HRP. Since the soybean seedcoat is a waste product of the soybean industry, developing a value-added process to utilize the waste would be economical. Several limitations need to be resolved prior to this enzymatic process becoming a legitimate treatment. For instance, peroxidase enzyme (PE) inactivation is a primary concern in trying to make the process more viable. Researchers have postulated that the PE is inactivated by entrapment into the forming polymers. To prevent such occurrences, high molecular weight substance such as gelatin and polyethylene glycol (PEG) have been added to solution. PEG 8000 provides a significant increase in protection compared to PEG 3350, a standard additive used for HRP. To date, researchers studying SBP as an alternative to HRP have focused mostly on the treatment of phenol. The effectiveness of SBP to treat chlorinated phenols is presented in this research. Some of the parameters studied include the effects and interactions of temperature, reaction time, pH, SBP concentration, PEG molecular weight, PEG concentration, type of substrate, substrate concentration, mode of substrate addition, mode of SBP addition and mode of H2O 2 addition. SBP was found to be a suitable alternative to HRP offer similar results and in some cases, improved the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".