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Record W4286251218 · doi:10.1016/j.jobab.2022.02.001

Preparation and properties of hydrophobic and transparent wood

2022· article· en· W4286251218 on OpenAlexvenueno aff
Linhu Ding, Xiaoshuai Han, Lian Chen, Shaohua Jiang

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsMaterials sciencePolymer scienceChemistry

Abstract

fetched live from OpenAlex

Natural wood (NW) was treated with sodium chlorite to obtain delignified wood (DW) in this study, then epoxy was impregnated to get transparent wood (TW), and finally the TW was coated with perfluorodecyltriethoxysilane (FAS) to acquire hydrophobic and transparent wood (HTW). The hydroxyl group generated by the hydrolysis of the FAS and the hydroxyl group of the epoxy underwent a dehydration condensation reaction to generate a Si–O–C bond, while the FAS molecules were also dehydrated and condensed to form a Si–O–Si bond according to Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). Therefore, the mechanical property and thermal stability of the HTW were better than the TW based on their tensile tests and thermogravimetric analysis (TGA). Due to the large reduction of hydroxyl in epoxy, the hydrophobicity of the HTW was greatly improved compared with the TW, and their contact angles were 113° and 77°, respectively. The results of scanning electron microscopy (SEM) showed that epoxy was filled in the voids of wood. In addition, the coating of the FAS did not obviously reduce the transmittance, and the transmittance of the TW and HTW was 69% and 67% at 750 nm. All in all, the HTW has potential for application in transparent decoration.

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 score0.025
Threshold uncertainty score0.199

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 head0.031
GPT teacher head0.268
Teacher spread0.238 · 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

Citations45
Published2022
Admission routes1
Has abstractyes

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