Foam control in biopesticide production from sewage sludge
Bibliographic record
Abstract
Several antifoam agents were evaluated for the ability to control foam in the production of Bacillus thuringiensis-based biopesticides using sewage sludge as a raw material. Experiments were conducted in shake flasks as well as in 15 l fermentors with controlled parameters. Polypropylene glycol (PPG), the most commonly used antifoam agent in B. thuringiensis fermentation, inhibited cell growth, sporulation and decreased the entomotoxicity yield even at a concentration of 0.1% (v/v) in sewage sludge medium. About 40% reduction in entomotoxicity was observed when PPG was used at 0.3% (v/v). The impact of PPG on sporulation and toxin synthesis in tryptic soy yeast broth (TSYB) medium was also studied. The inhibitory effects were less severe in TSYB than in sludge medium. Another silicone-based antifoam agent, “Antifoam A”, showed less severe effect on growth and stendotoxin production. The problem of the inhibitory effect of chemical antifoam agents on growth and endotoxin production was minimised substantially with the use of vegetable oils such as canola, olive, and peanut oils. Canola and peanut oil stimulated both sporulation and δ-endotoxin synthesis. The stimulus effect varies with the monounsaturated fat contents of oils. Journal of Industrial Microbiology & Biotechnology (2000) 25, 86–92.
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) | 0.001 | 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 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".