Determination of certain VOCs in paints and architectural coatings by dynamic headspace gas chromatography-mass spectrometry
Bibliographic record
Abstract
A quantitative method for the determination of the following VOCs: acetone, dichloromethane, dimethyl carbonate, methyl acetate, tertiary butyl acetate, chlorobenzotrifluoride (4-CBTF) and propylene carbonate in paints was developed in support of Environment and Climate Change Canada's Automotive Refinishing Product and Architectural Coatings VOC Concentration Limits regulations. These compounds are excluded from the VOC definition by Canadian Environmental Protection Act (CEPA) regulations, and their content do not contribute to the overall VOC content in products for regulatory purposes. The method is based on Dynamic Headspace GC-MS. It was determined that activated carbon is the best trapping medium for these compounds. The technique has been compared to a currently used direct injection technique, with comparable results. Contrary to the direct injection method which requires complex sample handling prior to injection in the gas chromatograph, the dynamic headspace method practically eliminates the need for sample handling allowing for much shorter sample turnover and reducing the possibility of sampling handling errors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".