Screening of Different Adjuvants for Wastewater/Wastewater Sludge-Based <I>Bacillus thuringiensis</I> Formulations
Bibliographic record
Abstract
Screening of different adjuvants, namely, suspending agents, phagostimulants, stickers, antimicrobial agents, and UV screens to develop aqueous biopesticidal suspensions of Bacillus thuringiensis (Bt) variety kurstaki HD-1 fermented broths, specifically, nonhydrolyzed sludge, hydrolyzed sludge, starch industry wastewater, and soya (commercial medium), were investigated. The selected suspending agents [20% (wt:vol)] included sorbitol, sodium monophosphate, and sodium metabisulfite with corresponding suspendibility of 74-92, 69-85, and 71-82%, respectively. Molasses [0.2% (wt:vol)] increased adherence by 84-90% for all fermented broths. The optimal phagostimulants [0.5% (wt:vol)], namely, soya and molasses, caused entomotoxicity increase of 3-13 and 7-13%, respectively. Sorbic and propionic acids showed high antimicrobial action [0.5% (wt:vol)], irrespective of fermentation medium. Sodium lignosulfonate, molasses, and Congo red, when used as UV screens [0.2% (wt:vol)], showed percent corresponding entomotoxicity losses of 3-5, 0.5-5 and 2-16, respectively. The Bt formulations, when exposed to UV radiation, showed higher half-lives (with and without UV screens) than the fermented broths or semisynthetic soya medium and commercial Bt formulation. UV screen-amended nonhydrolyzed, hydrolyzed, and starch industry wastewater formulations showed 1.3-1.5-fold higher half-lives than commercial Bt formulation. Thus, the recommended formulation comprises sorbitol, sodium monophosphate, sodium metabisulfite (suspending agents); molasses, soya flour (phagostimulants); molasses and skimmed milk powder (rainfasteners); sorbic and propionic acids (antimicrobial agents) and sodium lignosulfate; and molasses and Congo red (UV screens). These waste-based Bt formulations offer better UV resistance in comparison with commercial formulation.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".