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Record W4311049236 · doi:10.3390/coatings12121867

Effect of a Thermal Catalyst on Organosilanes Treatment to Improve Durability and Stability of Canadian Wood

2022· article· en· W4311049236 on OpenAlexaffabout
Diane Schorr, Gabrielle Boivin, Rod Stirling

Bibliographic record

VenueCoatings · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
Fundersnot available
KeywordsDurabilityThermal stabilityLeaching (pedology)AluminiumMaterials scienceCatalysisThermal treatmentCuring (chemistry)SwellingSorptionComposite materialChemical engineeringChemistryOrganic chemistryEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

The demand for exterior wood siding is stagnating in North America due in part to perceptions of low durability and the need for frequent maintenance. One way to address these concerns is to modify the wood to improve its physical properties, while maintaining its appearance. In this study, white spruce was treated with organosilanes and a combination of aluminum treatments followed by a thermal treatment to improve the dimensional stability and the wood durability. Anti-swelling efficiency (ASE), leaching and decay tests were performed on the treated Canadian wood species. The quantity of hydroxyls available after treatment was evaluated by water vapor sorption. The results showed that the treatment improved the dimensional stability up to 50%. Available hydroxyls decreased by as much as 37%. The organosilanes treatment was resistant to leaching, while the aluminum was observed to leach. Organosilanes in combination with aluminum showed brown rot resistance. The addition of aluminum to the organosilanes treatment did not have effect on dimensional stability but it had a great impact on the brown rot resistance.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.190
Teacher spread0.181 · 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 designBench or experimental
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

Citations4
Published2022
Admission routes2
Has abstractyes

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