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Weathering performance of dyed bamboo fiber composites

2020· article· en· W3097510444 on OpenAlexaff
Yuan Hu, Sheng He, Mei He, Kate Semple, Mei‐Ling Chen, Chunping Dai

Bibliographic record

VenueBioResources · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBamboo properties and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBambooMaterials scienceComposite materialDyeingPhyllostachysLigninWeatheringFiberPulp and paper industryChemistry

Abstract

fetched live from OpenAlex

Flattened and crushed fiber-bundles of moso bamboo (Phyllostachys pubescens) were dyed with a water or alcohol-based dye at 20 °C, 75 °C or 90 °C for 72 h, followed by drying and coating with PF resin. Hot-pressed bamboo fiber composites (BFC) were manufactured, and the weathering performance of the composites made from dyed and natural bamboo material were tested over 90 days of the Autumn/Winter season in Beijing. Dye was able to penetrate the bamboo tissue to create a rich red color that degraded in color faster than BFC made from un-dyed bamboo. BFC made from un-dyed tissue remained a lighter but more consistent color over the weathering period. Dying the bamboo in ethanol-based dye was effective at pigmenting the product, but made it more susceptible to color degradation as well as greater mass and thickness loss due to the chemical degradation of the lignin caused by ethanol. Higher temperature of water dying treatment (90 °C) gave the greatest resistance to mass and thickness loss during outdoor exposure, and the bamboo may have benefitted from the longer period of ‘heat treatment’ during dyeing. Further work is needed to quantify dye leaching from bamboo tissue and to develop more water-tight and color-fast, weather resistant dyes for bamboo products.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.466

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 head0.025
GPT teacher head0.175
Teacher spread0.150 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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