MétaCan
Menu
Back to cohort

Effect of Chlorinated Paraffin Nanoemulsion on the Microstructure and Water Repellency of Ultra-Low Density Fiberboard

2016· article· en· W2337663418 on OpenAlexaff
Lili Cai, Qilan Fu, Min Niu, Zhenzeng Wu, Yongqun Xie

Bibliographic record

VenueBioResources · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité Laval
FundersNational Natural Science Foundation of China
KeywordsContact angleMedium density fiberboardMaterials scienceFourier transform infrared spectroscopyAbsorbanceMicrostructureChlorineFiberboardX-ray photoelectron spectroscopyScanning electron microscopeComposite materialSpectroscopyChemical engineeringChemistryChromatography

Abstract

fetched live from OpenAlex

This work describes a water repellent ultra-low density fiberboard (ULDF) prepared by chlorinated paraffin nanoemulsion (CPNE). Compared with the untreated ULDF, the contact angle of ULDF treated with 150 mL of CPNE increased from 40° to 134°, while its apparent surface free energy decreased from 24.19 mN/m to 10.06 mN/m. Moreover, the water absorbance of ULDF treated with CPNE decreased from 88.2% to 24% in the first hour. The improved hydrophobicity and hygroscopicity of ULDF was supported by the presence of a film on the surface of fibers, as observed by environmental scanning electron microscopy. The occurrence of chlorine and the chemical structure changes in ULDF before and after CPNE treatment were also confirmed by X-ray photoelectron spectroscopy analysis and Fourier transform infrared spectroscopy respectively. This ULDF with enhanced water repellency is a promising insulation material.

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 score0.008
Threshold uncertainty score0.282

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 spread0.202 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBioResourcesSame topicSurface Modification and SuperhydrophobicityFrench-language works237,207