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Record W2412038818 · doi:10.1002/bip.22888

Phase separation and mechanical properties of an elastomeric biomaterial from spider wrapping silk and elastin block copolymers

2016· article· en· W2412038818 on OpenAlexaff
Lisa D. Muiznieks, Fred W. Keeley

Bibliographic record

VenueBiopolymers · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsSILKElastinElastomerToughnessPolymer sciencePolymerCopolymerChemistrySpider silkUltimate tensile strengthBiomaterialMaterials scienceComposite materialPolymer chemistryNanotechnology

Abstract

fetched live from OpenAlex

Elastin and silk spidroins are fibrous, structural proteins with elastomeric properties of extension and recoil. While elastin is highly extensible and has excellent recovery of elastic energy, silks are particularly strong and tough. This study describes the biophysical characterization of recombinant polypeptides designed by combining spider wrapping silk and elastin-like sequences as a strategy to rationally increase the strength of elastin-based materials while maintaining extensibility. We demonstrate a thermo-responsive phase separation and spontaneous colloid-like droplet formation from silk-elastin block copolymers, and from a 34 residue disordered region of Argiope trifasciata wrapping silk alone, and measure a comprehensive suite of tensile mechanical properties from cross-linked materials. Silk-elastin materials exhibited significantly increased strength, toughness, and stiffness compared to an elastin-only material, while retaining high failure strains and low energy loss upon recoil. These data demonstrate the mechanical tunability of protein polymer biomaterials through modular, chimeric recombination, and provide structural insights into mechanical design. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 693-703, 2016.

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 score0.003
Threshold uncertainty score0.553

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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