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Record W4389547254 · doi:10.3390/molecules28248025

Recent Progress in Advanced Polyester Elastomers for Tissue Engineering and Bioelectronics

2023· article· en· W4389547254 on OpenAlexafffund
Yawei Zhao, Wen Zhong

Bibliographic record

VenueMolecules · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioelectronicsPolyesterElastomerBiocompatibilityMaterials scienceTissue engineeringNanotechnologyNeural tissue engineeringBiocompatible materialPolymer scienceBiomedical engineeringComposite materialEngineeringBiosensor

Abstract

fetched live from OpenAlex

Polyester elastomers are highly flexible and elastic materials that have demonstrated considerable potential in various biomedical applications including cardiac, vascular, neural, and bone tissue engineering and bioelectronics. Polyesters are desirable candidates for future commercial implants due to their biocompatibility, biodegradability, tunable mechanical properties, and facile synthesis and fabrication methods. The incorporation of bioactive components further improves the therapeutic effects of polyester elastomers in biomedical applications. In this review, novel structural modification methods that contribute to outstanding mechanical behaviors of polyester elastomers are discussed. Recent advances in the application of polyester elastomers in tissue engineering and bioelectronics are outlined and analyzed. A prospective of the future research and development on polyester elastomers is also provided.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.268
Teacher spread0.261 · 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
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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