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Multistep deformation of helical fiber electrospun scaffold toward cardiac patches development

2023· preprint· en· W4315710004 on OpenAlexaff
Aleksander Czekanski, Ahmed AlAttar, Elli Gkouti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsYork University
Fundersnot available
KeywordsViscoelasticityScaffoldMaterials scienceSofteningStress relaxationDeformation (meteorology)Composite materialTension (geology)Stress (linguistics)FiberBiomedical engineeringUltimate tensile strengthCreepEngineering

Abstract

fetched live from OpenAlex

Acquiring the mechanical behaviour of a helical fibered cardiac scaffold propels the fabrication of a scaffold exhibiting similar properties with the native cardiac tissue. Selecting fiber-based scaffolds will contribute to mimicking the complex function performed during the muscle's expansion and contraction. In our study, we exposed fabricated electrospun samples to repeated multistep tension by applying and removing deformation in order to mimic the mechanical behaviour of helical fibered cardiac scaffolds. Since the fiber-based specimens exhibit viscoelastic behaviour, the transient responses to constant deformation caused stress relaxation and stress recovery. Nevertheless, stress softening and repeated buckling phenomena, which are viscoelastic characteristics, had an unexpected impact on the samples' behaviour, leading to inconsistent material characterization. Eliminating techniques are also explored to ensure representative results about the fabricated scaffolds' behaviour.

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 score0.002

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 head0.032
GPT teacher head0.279
Teacher spread0.247 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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