MétaCan
Menu
Back to cohort
Record W1980966667 · doi:10.1021/bm900103n

Water Permeability of Spider Dragline Silk

2009· article· en· W1980966667 on OpenAlexaff
Xiang Li, Philip T. Eles, Carl A. Michal

Bibliographic record

VenueBiomacromolecules · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSILKSpider silkChemistryHydrogen–deuterium exchangeDeuteriumAmideCrystalliteHydrogenChemical engineeringCrystallographyMaterials scienceOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The water permeability of spider dragline silk was studied by measuring changes in amide deuteration of D(2)O-soaked silk with solid-state NMR. (13)C-D rotational-echo double-resonance (REDOR) NMR experiments showed that chemical exchange of amide hydrogen occurs in a large fraction of amino acids, including over 50% of alanine residues, which are known to exist predominantly in beta-sheet crystallites. This suggests that a substantial fraction of the crystalline regions are permeable to water, at least on the time scale of hours, implying that they are more dynamic, and therefore susceptible to chemical exchange with water, than previously thought. Wideline deuterium NMR spectra of dried D(2)O-soaked silk showed a combination of quadrupolar broadened and motionally averaged isotropic components whose intensities change on the time scale of hours. These results are interpreted in terms of chemical exchange between deuterium on the protein backbone, residual water within the silk, and water vapor in the ambient atmosphere. A simple compartmental model fits the results well and yields rate constants for the exchange processes. The model requires the inclusion of a compartment that does not undergo exchange. This compartment, likely related to the crystalline region, is interesting because it is accessible to water in wet silk, but impervious to any remaining free water when the silk is dried.

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 categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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.

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

Citations13
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueBiomacromoleculesSame topicSilk-based biomaterials and applicationsFrench-language works237,207