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Record W1584316623 · doi:10.15376/biores.7.3.4132-4149

Hydrophobization of bleached softwood kraft fibers via adsorption of organo-nanoclay

2012· article· en· W1584316623 on OpenAlexaff
Ning Yan

Bibliographic record

VenueBioResources · 2012
Typearticle
Languageen
FieldChemistry
TopicAdsorption, diffusion, and thermodynamic properties of materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceThermogravimetric analysisComposite materialThermal stabilityInverse gas chromatographyAdsorptionSoftwoodScanning electron microscopeFourier transform infrared spectroscopyKraft paperSurface modificationFiberPulp (tooth)Kraft processContact angleSurface energyChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Montmorillonite clay particles that had been prepared with an alklyl-ammonium surfactant were used to modify the moisture-sensitivity of bleached softwood kraft fibers through solvent exchange and adsorption methods. Moisture absorption and water uptake of the wood pulp fibers were significantly lower after the organo-nanoclay treatment. Thermal stability, surface energy, and surface morphology of the treated fibers were characterized using Thermogravimetric Analysis (TGA), Inverse Gas Chromatography (IGC), Scanning Electron Microscopy-Energy Dispersive X-ray Analysis (SEM-EDX), and Transmission Electron Microscopy (TEM) imaging. The Fourier Transform Infrared (FT-IR) spectral characteristics of the treated fibers were obtained to better understand the modified surface functional groups of the treated fibers. The treated bio-fibers had nano-scale surface roughness and a much reduced surface energy. The contact angle of water on the treated fiber mat was found to be higher than 160º. The thermal stability of the treated fibers was not affected by the modification.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2012
Admission routes1
Has abstractyes

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