Direct measurements of polyurethane foam (PUF) ‒ air partitioning coefficients for chemicals of emerging concern capable of equilibrating in PUF disk samplers
Bibliographic record
Abstract
Organophosphate esters (OPEs), novel flame retardants (NFRs) and organochlorine pesticides (OCPs) are volatile to semi-volatile chemicals and therefore susceptible to approach equilibrium during typical deployments of polyurethane foam (PUF) disk passive air samplers. A generator column approach was used to measure the PUF-air partitioning coefficient (KPUF-air) for these targeted chemicals. KPUF-air values are required for these chemicals to estimate sampled equivalent air volumes, which vary substantially with temperature. Log KPUF-air measurements were made at temperatures ranging from +6 to +35 °C and resulting values ranged from 5.14 to 7.77. Enthalpies of phase change for PUF to air (ΔHPUF-air, kJ/mol) ranged from 51.3 to 98.9. Two relationships of log KPUF-air versus log Koctanol-air (KOA) were derived, grouping OPEs and NFRs separately. The relationship for NFRs was in fair agreement (within about 0.6 log units) to a long-standing relationship by Shoeib and Harner (since 2002) for polychlorinated biphenyls (PCBs). However, the estimated values using Shoeib-Harner relationship substantially underestimates KPUF-air for the OPEs than directly measured values (by about an order of magnitude). These findings highlight the importance of the direct measurements of KPUF-air for emerging classes of chemicals whose concentrations are at risk of equilibrating in PUF disk samplers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".