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Record W1546446492 · doi:10.15376/biores.5.2.770-779

Reducing VOC emissions in the furniture and cabinets industries: An opportunity for hybrid coating systems

2010· article· en· W1546446492 on OpenAlexafffund
Véronic Landry, Pierre Blanchet, Éric Vaillancourt

Bibliographic record

VenueBioResources · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsFPInnovations
FundersFPInnovations
KeywordsCoatingMaterials scienceSolventMoisturePulp and paper industryEnvironmental scienceChemical resistanceComposite materialWaste managementChemical engineeringChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.259
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2010
Admission routes2
Has abstractyes

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