Soybean peroxidase for industrial wastewater treatment: a mini review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The potential of oxidoreductases, such as laccases and peroxidases, to remove organic pollutants from industrial wastewater and process water is addressed in this short review, with an emphasis on the peroxidase work completed or in progress in the authors’ laboratory. The major drawback to this treatment is the cost of the enzyme. However, with new sources and recent advances in the biotechnology industry, it is becoming a feasible alternative. A niche where enzymatic treatment may be first applied is not as a primary treatment but as a secondary treatment (pretreatment or polishing) coupled to existing physico-chemical or biological processes to increase their overall efficiency and economy. Soybean seed coat peroxidase is well suited because of its stability, ease of extraction, widespread availability and potential for adding to the soy value chain. Since crude enzyme often works better than purified enzyme, the only additional cost may be in concentrating the extract. This review briefly covers aspects of the enzymatic treatment such as cost, use of additives for increased enzyme economy, enzyme recycling and studies already completed on industrial wastewaters.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it