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Record W2075506412 · doi:10.1002/app.24161

Impact of melt impregnation on the color of wood–plastic composites

2006· article· en· W2075506412 on OpenAlexaff
Yaolin Zhang, S. Y. Zhang, Ying Hei Chui

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsIntertek (Canada)University of New Brunswick
Fundersnot available
KeywordsHueLightnessComposite materialMaterials scienceSaturation (graph theory)Wood flourPolyethyleneColor differenceMathematicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The aesthetics of wood–plastic composites (WPCs) can affect the acceptance of the products by consumers. This study was aimed at providing a better understanding of how impregnation variables affect color changes, thereby allowing for the development of an optimal process for WPCs. The effects of impregnation parameters and impregnants on the WPC color were investigated in this study via a screening design. Sixteen runs of resolution IV design for seven factors at two levels were conducted. The seven factors were the ratio of maleated polyethylene in the formulations, the ratio of polyethylenes with different molecular weights, four process factors (vacuum, pressure, time, and temperature), and wood species (red maple and aspen). The studied color parameters included the lightness change, chroma change, hue angle change, saturation change, and total color change. All treatments darkened the wood and increased the chroma values and the saturation. Even though all treatments had an impact on the hue angle, the changes were very small. The wood species, impregnants, impregnation time, and temperature played significant roles in the color change and chroma coordinates. However, no parameter dominated the hue angle change and saturation. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 2149–2157, 2006

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 score0.012
Threshold uncertainty score0.162

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

Explore more

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