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Record W2038190607 · doi:10.1515/hf-2012-0103

Modification of <i>Pinus sylvestris</i> L. wood with quat- and amino-silicones of different chain lengths

2012· article· en· W2038190607 on OpenAlexaff
Shyamal C. Ghosh, Holger Militz, Carsten Mai

Bibliographic record

VenueHolzforschung · 2012
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsScots pineSiliconePinus <genus>Materials scienceScanning electron microscopeSide chainLeaching (pedology)ChemistryPolymer chemistryChemical engineeringComposite materialBotanyPolymerBiologySoil water

Abstract

fetched live from OpenAlex

Abstract The water-related properties of Scots pine ( Pinus sylvestris L.) sapwood have been improved by treatment with quat- and amino-silicones of different chain lengths. Standard leaching test and hot water Soxhlet extraction (6 h) showed that the amino-silicones are better fixed in wood than the quat-silicones. A water dipping test (24 h) revealed that both quat- and amino-silicones made wood hydrophobic; however, amino-silicones were more effective in reducing water uptake. The long-chained silicones of both types resulted in higher water repellent effectiveness compared with the respective short-chained silicones. As demonstrated by assessing cell wall bulking, scanning electron microscopy (SEM), and SEM energy-dispersive X-ray analysis, silicones with short chains penetrated the cell wall better than those with long chains regardless of the silicone type. The maximum antishrink efficiency of approximately 60% was attained with short-chained amino-silicone at approximately 39% weight percent gain. The short-chained silicones show more potential to enhance the water-related properties of solid wood and for the development of silicone-based industrial wood modification processes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.342

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 head0.016
GPT teacher head0.201
Teacher spread0.184 · 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 designObservational
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

Citations10
Published2012
Admission routes1
Has abstractyes

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