Development of a method for quantification of toluene diisocyanate and methylenediphenyl diisocyanate migration from polyurethane foam sample surface to artificial sweat by HPLC-UV-MS
Bibliographic record
Abstract
The US Environmental protection agency (EPA) has published guidance that includes test procedures for evaluating indoor exposure to chemicals from products. One of the test procedures represents the migration test for evaluating potential dermal exposure from home furniture. Such an evaluation involves the chemical measurement of the sweat which is currently unavailable in the literature. The objective of this project was to develop and validate an analytical method for quantification of migration of 4,4'-methylenediphenyl diisocyanate (MDI), 2,6-toluene diisocyanate (2,6-TDI) and 2,4-toluene diisocyanate (2,4-TDI) from a polyurethane (PU) flexible foam to artificial sweat that meets the recommendations of the EPA test protocol. Following the EPA protocol, six synthetic sweat solutions were prepared and used in evaluation of isocyanate recovery performance. The migration tests were conducted using five foam types that were chosen and supplied by PU foam manufacturers to represent the types most commonly found in commercial products, and with formulations anticipated to have the highest potential residual TDI or MDI. Migration tests were conducted using glass fiber filters (GFF) coated with 1-(2-methoxyphenyl)piperazine (1,2-MP) and analyzed using HPLC equipped with a UV detector for quantification and a MS detector to qualify peaks. The detection limits of the method were 0.002 µg/mL for 2,6-TDI, 0.011 µg/mL for 2,4-TDI, and 0.003 µg/mL for MDI. Quantification limits were 0.006 µg/mL, 0.037 µg/mL, and 0.010 µg/mL, respectively. The recovery tests on a Teflon surface for 5 of the 6 EPA-recommended synthetic sweat solutions indicate the recovery percentage was approximately 80% for diisocyanates. Recovery for the sixth sweat solution was low, approximately 30%. TDI and MDI migration was not observed when testing was conducted on foam samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".