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Record W3003905740 · doi:10.1021/acs.biomac.9b01615

Ultrastructures and Mechanics of Annealed <i>Nephila clavipes</i> Major Ampullate Silk

2020· article· en· W3003905740 on OpenAlexaff
Thomas W. Dugger, Sourangsu Sarkar, Sandra M. Correa-Garhwal, Mikhail Zhernenkov, Yugang Zhang, Gitanjali Kolhatkar, Ramya Mohan, Luz Cruz, Aura D. Lubio, Andreas Ruëdiger, Cheryl Y. Hayashi, Kathryn E. Uhrich, David Kisailus

Bibliographic record

VenueBiomacromolecules · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersArmy Research OfficeAir Force Office of Scientific ResearchDivision of Graduate Education
KeywordsSILKPolyacrylonitrileComposite materialMaterials scienceSpider silkCrystallinityUltimate tensile strengthNatural fiberAnnealing (glass)GraphitePolymer scienceFiberSynthetic fiberPolymer

Abstract

fetched live from OpenAlex

The semicrystalline protein structure and impressive mechanical properties of major ampullate (MA) spider silk make it a promising natural alternative to polyacrylonitrile (PAN) fibers for carbon fiber manufacture. However, when annealed using a similar procedure to carbon fiber production, the tensile strength and Young's modulus of MA silk decrease. Despite this, MA silk fibers annealed at 600 °C remain stronger and tougher than similarly annealed PAN but have a lower Young's modulus. Although MA silk and PAN graphitize to similar extents, annealing disrupts the hydrogen bonding that controls crystal alignment within MA silk. Consequently, unaligned graphite crystals form in annealed MA silk, causing it to weaken, while graphite crystals in PAN maintain alignment along the fiber axis, strengthening the fibers. These shortcomings of spider silk when annealed provide insights into the selection and design of future alternative carbon fiber precursors.

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 score0.624

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 head0.011
GPT teacher head0.234
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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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