Partition Controlled Delivery of Hydrophobic Substances in Toxicity Tests Using Poly(dimethylsiloxane) (PDMS) Films
Bibliographic record
Abstract
Interpretation of toxicity test results may be hampered when doubt exists about the actual exposure concentration. Processes that are responsible for differences between the nominal and the actual concentration in aqueous test systems may include sorption, precipitation, volatilization, chemical and biological degradation, and uptake into biological or test tissue. In this study, the use of a poly(dimethylsiloxane) (PDMS) film containing the test compound is introduced as a versatile technique for partition controlled delivery of hydrophobic compounds to aqueous toxicity tests. Two methods developed produced preloaded films, having toxicant added to the PDMS prepolymer solution before film deposition and curing, and postloaded films, which are created by the addition of toxicant in a solvent to an already-polymerized PDMS film. Preloaded films were generally more easily prepared, may better accommodate larger molecules, and have a higher capacity than postloaded films. Postloaded films provided film-solution partition coefficients with higher precision and allowed for the use of films from stock and thus for a more portable technique. Chemical analysis showed that equilibrium between films and the aqueous solution was established within 7-10 min and was maintained for a suite of aromatic compounds (log Kow ranging from 2.8 to 6.1). The reliability of the film technique was demonstrated by application to the Microtox bacterial toxicity tests of solutions of polycyclic aromatic hydrocarbons (PAHs).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".