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Record W4392956775 · doi:10.1515/hf-2024-0006

Photodegradation stability of huminated European pine (<i>Pinus sylvestris</i> L.) microveneers

2024· article· en· W4392956775 on OpenAlexaff
Amir Ghavidel, Reza Hosseinpourpia

Bibliographic record

VenueHolzforschung · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPinus <genus>PhotodegradationIndustrial chemistryScots pineChemistryForestryBotanyEnvironmental scienceBiologyGeographyOrganic chemistryPhotocatalysisEngineeringBiochemical engineering

Abstract

fetched live from OpenAlex

Abstract Humins are heterogeneous and polydisperse furanic macromolecules derived from sugar biorefinery. Improving wood properties by humination has become of interest recently. This study examined the photodegradation stability of European pine sapwood microveneers modified with humins at different concentrations of citric acid (CA) and succinic acid (SA) as reaction catalysts, e.g., 1.5 %, 3 %, and 4.5 % wt.%. The photostability of huminated wood was assessed after 48 h, 96 h, and 144 h of exposure to the accelerated weathering test by means of mass loss and finite-span tensile strength. The results were compared with unmodified and also unweathered samples. The FT-IR spectroscopy showed apparent changes in the chemical structure of wood by humination modifications. The weight percentage gains of the samples increased with increasing the concentration of the catalyst. While no differences were observed between the samples after two weeks of the water leaching. The strength losses of weathered microveneers were, however, mostly reduced by humin-based formulas containing catalysts, where the microveneers modified with 1.5 % and 3 % CA showed respectively 32 % and 41 % lower strength loss values than the unmodified samples after 144 h of weathering. Overall, the results showed a high potential for humins to protect wood against photodegradation.

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 score0.171
Threshold uncertainty score0.179

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 head0.021
GPT teacher head0.200
Teacher spread0.178 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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