Antimicrobial, Preservative, and Hazard Assessments from Eight Chemical Classes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Preservatives, such as isothiazolinones and formaldehyde-releasing compounds, provide safety and stability in consumer products by preventing microbial contamination. Yet these ingredients present human and environmental hazards, including allergic contact dermatitis and aquatic toxicity. The development of safer alternatives has been stymied by trade-offs between safety and efficacy. To enable the identification of safer preservatives, substances from eight functional classes were assessed for antimicrobial efficacy and human and environmental hazards. First, 130 substances were evaluated for microbial inhibitory activity against two relevant model microorganisms, Aspergillus brasiliensis (filamentous fungi) and Pseudomonas aeruginosa (Gram-negative bacteria). High-performing compounds within each class were assessed for hazards across a broad suite of human and environmental health end points. Four promising compounds were selected for further testing based on microbial inhibition, hazard profiles, and commercial availability. These ingredients were tested for biocidal activity in model home care formulations using methods adapted from industrial preservative challenge guidelines (USP-51). Two substances were identified, caprylhydroxamic acid and caprylyl glycol, that provided adequate preservation and improved toxicity profiles compared to isothiazolinone and formaldehyde-releasing preservatives. This study highlights trade-offs between antimicrobial activity and hazards across a broad spectrum of chemical classes relevant to safer preservative development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".