Two-Phase Partitioning Bioreactors Operating with Polymers Applied to the Removal of Substituted Phenols
Bibliographic record
Abstract
Significant improvement in biodegradation performance has been demonstrated arising from the reduction of cytotoxicity provided by the sequestering of 4-nitrophenol (4NP) within Hytrel polymer beads added to a two-phase partitioning bioreactor (TPPB) operating in sequencing batch reactor (SBR) mode. This reduced toxicity is particularly apparent as the feed substrate concentration is increased; in fact it was shown that at a feed of 1000 mg/L 4NP, the inhibitory effect of the substrate completely prevents degradation from occurring in a single-phase system, whereas at only a 5% polymer loading, rapid and compete biodegradation is achieved. Different polymer/aqueous phase ratios were used to detoxify varying feed concentrations, and degradation rates were enhanced through the use of increased polymer loadings. As demonstrated in oxygen uptake experiments, the addition of polymers also reduces the maximum demand for oxygen, relative to single-phase operation, and smoothes the demand for oxygen throughout the degradation process. Polymer regeneration has also been further characterized by quantifying the number of methanol washes required to achieve satisfactory 4NP residuals, and the addition of a small amount of cosolvent has been shown to dramatically increase the rate of bioregeneration to produce beads ready for reuse.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".