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Record W2316763949 · doi:10.1021/bm400465p

Preparation and Characterization of Kraft Lignin-Based Moisture-Responsive Films with Reversible Shape-Change Capability

2013· article· en· W2316763949 on OpenAlexaff
Ian Dallmeyer, Sudip Chowdhury, John F. Kadla

Bibliographic record

VenueBiomacromolecules · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLigninGel permeation chromatographyCharacterization (materials science)Kraft paperChemical engineeringFiberMaterials scienceMoisturePolymerElectrospinningChemical structureScanning electron microscopeRheologyChemistryComposite materialOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

Preparation of moisture-responsive Kraft lignin-based materials by electrospinning blends of Kraft lignin fractions with different physical properties is presented. The differences in thermal mobility between lignin fractions are shown to influence the degree of interfiber fusion occurring during oxidative thermostabilization of electrospun nonwoven fabrics, resulting in different material morphologies including submicrometer fibers, bonded nonwovens, porous films, and smooth films. The relative amount of different lignin fractions and degree of fiber flow and fiber fusion is shown to influence the tendency for the electrospun materials to be transformed into moisture-responsive materials capable of reversible changes in shape. Material characterization by scanning electron microscopy and atomic force microscopy as well characterization of the chemical and physical properties of Kraft lignin fractions by dynamic rheology, 1H and 13C NMR, and gel permeation chromatography combined with multiangle laser light scattering are presented. A proposed mechanism underlying moisture-responsiveness, shape change, and shape recovery is discussed based on the differences in chemical structure and physical properties of Kraft lignin fractions.

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 score0.029
Threshold uncertainty score0.429

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 head0.013
GPT teacher head0.238
Teacher spread0.226 · 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

Citations76
Published2013
Admission routes1
Has abstractyes

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