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Record W3007695948 · doi:10.3390/ma13040973

Improvement of White Spruce Wood Dimensional Stability by Organosilanes Sol-Gel Impregnation and Heat Treatment

2020· article· en· W3007695948 on OpenAlexafffundabout
Diane Schorr, Pierre Blanchet

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversité LavalFPInnovationsNatural Sciences and Engineering Research Council of Canada
FundersMitacsFPInnovations
KeywordsMaterials scienceComposite materialChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Wood is a living material with a dimensional stability problem. White spruce wood is a Canadian non-permeable wood that is used for siding applications. To improve this property, white spruce wood was treated with organosilanes sol-gel treatment with different moisture content (oven dried, air dried, and green wood). No major morphological changes were observed after treatment. However, organosilanes were impregnated into the cell wall without densifying the wood and without modifying the wood structure. Si-O-C chemical bonds between organosilanes and wood and Si-O-Si bonds were confirmed by FTIR and NMR, showing the condensation of organosilanes. The green wood (41% moisture content) showed only 26% dimensional stability due to the presence of too much water for organosilanes treatment. With a moisture content of 14%-18% (oven dried or air dried wood), the treatment was adapted to obtain the best improvement in dimensional stability of 35% and a 25% reduction of water vapor sorption. Finally, impregnation with organosilanes combined with the appropriate heat treatment improved the dimensional stability of white spruce wood by up to 35%. This treated Canadian wood could be an interesting option to validate for siding application in Canada.

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 score0.001
Threshold uncertainty score0.391

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations18
Published2020
Admission routes3
Has abstractyes

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