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Record W2514825595 · doi:10.1002/adfm.201601476

Modulation of Mechanical Interactions by Local Piezoelectric Effects

2016· article· en· W2514825595 on OpenAlexaff
Cameron Brown, Jennifer L. Boyd, Antony Palmer, Michael A. Phillips, Charles‐André Couture, Maxime Rivard, P A Hulley, Andrew Price, Andreas Ruëdiger, François Légaré, Andrew Carr

Bibliographic record

VenueAdvanced Functional Materials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsHydro-QuébecInstitut National de la Recherche Scientifique
FundersArthritis Research UKNational Institute for Health and Care Research
KeywordsPiezoelectricityMaterials scienceDissipationFibrilStiffnessModulation (music)NanotechnologySuperstructureNanometreComposite materialBiophysicsAcousticsStructural engineering

Abstract

fetched live from OpenAlex

Piezoelectricity is a well‐established property of biological materials, yet its functional role has remained unclear. Here, a mechanical effect of piezoelectric domains resulting from collagen fibril organisation is demonstrated, and its role in tissue function and application to material design is described. Using a combination of scanning probe and nonlinear optical microscopy, a hierarchical structuring of piezoelectric domains in collagen‐rich tissues is observed, and their mechanical effects are explored in silico. Local electrostatic attraction and repulsion due to shear piezoelectricity in these domains modulate fibril interactions from the tens of nanometre (single fibril interactions) to the tens of micron (fibre interactions) level, analogous to modulated friction effects. The manipulation of domain size and organisation thus provides a capacity to tune energy storage, dissipation, stiffness, and damage resistance.

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 score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.230
Teacher spread0.223 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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