Reducing VOC emissions in the furniture and cabinets industries: An opportunity for hybrid coating systems
Bibliographic record
Abstract
Three hybrid systems were compared to a solvent-based low-VOC system. The goal of the project was to determine if the hybrid systems met the requirements of the kitchen cabinet and furniture industries. Adhesion of each system to the substrate was first evaluated, and significant differences were recorded. Subsequently, resistance to heat and moisture was evaluated using hot box and hot and cold check tests. These tests indicated that the experimental systems generally performed relatively well in terms of thermal resistance, but the appearance of those prepared from solvent-borne sealers and lacquers were less stable. The chemical resistance of these systems was also studied. The ability of the finish to withstand the effects of substances typically found in a kitchen was tested, as was their resistance to detergents and water. All the systems performed very well in vertical testing. Volatile organic compound emissions were finally measured after conditioning times of 3 and 21 days. These tests showed that the different systems behaved quite differently. The systems prepared with solvent-borne sealers and lacquers produced a high level of VOCs at the beginning of the test, with a rapid decrease thereafter, while the opposite was observed in systems based on water-borne sealers and lacquers.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".