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Record W2118846430 · doi:10.5539/jmsr.v2n1p23

Evaluation of Inter-fiber Bonding in Wood Pulp Fibers by Chemical Force Microscopy

2012· article· en· W2118846430 on OpenAlexaffvenue
Dongbo Yan, Kecheng Li

Bibliographic record

VenueJournal of Materials Science Research · 2012
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of New BrunswickFPInnovations
Fundersnot available
KeywordsMaterials sciencePulp (tooth)Composite materialvan der Waals forceFiberAtomic force microscopyCellulose fiberBond strengthAdhesionMoleculeNanotechnologyAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

Atomic force microscopy with chemically modified tips was used to evaluate the inter fiber bonding properties of typical wood pulp fibers. Using –OH functionalized AFM tips as a model of cellulosic pulp fiber surfaces, pull-off forces and work of adhesion were measured in aqueous media. Three distinct tip-surface interactions were identified from force-displacement curves, representing three typical surface conditions of wet pulp fiber surfaces: solid, swollen and micro-fibrillated. The work of adhesion calculated shows that van der Waals forces are the major contributing factor on non-swollen solid regions of fiber surfaces. The difference in inter-molecular bond strength of different pulp fibers is relatively small. The inter-fiber bonding properties of pulp fibers are mainly controlled by the surface deformability, which determines the area of molecular contact at fiber-fiber physical interaction points.

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 score0.004

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 head0.074
GPT teacher head0.412
Teacher spread0.338 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

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Same venueJournal of Materials Science ResearchSame topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207