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Record W2011187736 · doi:10.1021/bm900379g

Correlating Mechanical Properties with Aggregation Processes in Electrochemically Fabricated Collagen Membranes

2009· article· en· W2011187736 on OpenAlexaff
Manoj Kumar, Erika F. Merschrod S., Kristin M. Poduska

Bibliographic record

VenueBiomacromolecules · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMembraneChemistryChemical engineeringBiophysicsMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

We show that mechanical stiffness is a useful metric for characterizing complex collagen assemblies, providing insight about aggregation products and pathways in collagen-based materials. This study focuses on mechanically robust collagenous membranes produced by an electrochemical synthesis process. Changing the duration of the applied electric field, or adjusting the electrolyte composition (by adding Ca(2+), K(+), or Na(+) or by changing pH), produces membranes with a range of Young's moduli as determined from force-displacement measurements with an atomic force microscope. The structural organization, characterized by UV-visible spectroscopy, Raman spectroscopy, optical microscopy, and atomic force microscopy, correlates with the mechanical stiffness. These data provide insights into the relative importance of different aggregation pathways enabled by our multiparameter electrochemically induced collagen assembly process.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.216
Teacher spread0.205 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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