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Record W3187645616 · doi:10.3390/coatings11080916

Surface Protection of Wood with Metal Acetylacetonates

2021· article· en· W3187645616 on OpenAlexaffabout
Yuner Zhu, Philip D. Evans

Bibliographic record

VenueCoatings · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTitaniumManganeseMetalWeatheringMaterials scienceMetal ions in aqueous solutionAcetylacetoneChemistryInorganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Metal acetylacetonates are coordination complexes of metal ions and the acetylacetonate anion with diverse uses including catalysts, cross-linking agents and adhesion promotors. Some metal acetylacetonates can photostabilize polymers whereas others are photocatalysts. We hypothesize that the ability of metal acetylacetonates to photostabilize wood will vary depending on the metal in the coordination complex. We test this hypothesis by treating yellow cedar veneers with different acetylacetonates (Co, Cr, Fe, Mn, Ni, and Ti), exposing veneers to natural weathering in Australia, and measuring changes in properties of treated veneers. The most effective treatments were also tested on yellow cedar panels exposed to the weather in Vancouver, Canada. Nickel, manganese, and titanium acetylacetonates were able to restrict weight and tensile strength losses and delignification of wood veneers during natural weathering. Titanium acetylacetonate was as effective as a reactive UV absorber at reducing the greying of panels exposed to 6 months of natural weathering, and both titanium and manganese acetylacetonates reduced the photo-discoloration of panels finished with a polyurethane coating. We conclude that the effectiveness of metal acetylacetonates at photostabilizing wood varies depending on the metal in the coordination complex, and titanium and manganese acetylacetonate show promise as photoprotective primers for wood.

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 categoriesInsufficient payload (model declined to judge)
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 score0.031
Threshold uncertainty score0.998

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)0.0030.000

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 head0.037
GPT teacher head0.213
Teacher spread0.176 · 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.

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

Citations10
Published2021
Admission routes2
Has abstractyes

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