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Record W1991807881 · doi:10.1063/1.3050096

Characterization of anisotropic poly(vinyl alcohol) hydrogel by small- and ultra-small-angle neutron scattering

2009· article· en· W1991807881 on OpenAlexafffund
Stephen D. Hudson, Jeffrey L. Hutter, Mu‐Ping Nieh, Jeremy Pencer, Leonardo E. Millon, Wankei Wan

Bibliographic record

VenueThe Journal of Chemical Physics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsNational Research Council CanadaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsVinyl alcoholAnisotropyMaterials scienceNeutron scatteringSmall-angle neutron scatteringSelf-healing hydrogelsScatteringCharacterization (materials science)Small-angle scatteringNeutronComposite materialChemical physicsChemical engineeringNanotechnologyOpticsPolymerPolymer chemistryChemistryPhysics

Abstract

fetched live from OpenAlex

Poly(vinyl alcohol) (PVA) hydrogels are formed from PVA solution when physical cross-links form during freeze/thaw cycling. By applying a stress during the freeze/thaw process, PVA hydrogels with anisotropic mechanical properties are produced. We have used small- and ultra-small-angle neutron scattering to study the structure at length scales of 2 nm to 10 mum. By supplementing the neutron data with data from atomic force microscopy, we have probed a large range of length scales within which structural changes responsible for bulk anisotropy occur. We model the gel as interconnected PVA blobs of size 20-50 nm arranged in fractal aggregates extending to micrometers or tens of micrometers. Bulk mechanical anisotropy appears to be due to the alignment of blobs and connections between blobs. This information is essential for tailoring mechanical properties for applications where anisotropy is desirable such as to match the properties of natural tissue in coronary grafts and to control diffusive properties in active wound dressings.

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 score0.005
Threshold uncertainty score0.396

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations38
Published2009
Admission routes2
Has abstractyes

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