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Record W4306841596 · doi:10.1021/acs.langmuir.2c02372

Mussel-Inspired Reversible Molecular Adhesion for Fabricating Self-Healing Materials

2022· review· en· W4306841596 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLangmuir · 2022
Typereview
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsByssusMusselSelf-healingNanotechnologyCatecholAdhesionBiomimeticsChemistryBiochemical engineeringMaterials scienceEngineeringEcologyBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Nature offers inspiration for the development of high-performance synthetic materials. Extensive studies on the universal adhesion and self-healing behavior of mussel byssus reveal that a series of reversible molecular interactions occurring in byssal plaques and threads play an essential role, and the mussel-inspired chemistry can serve as a versatile platform for the design of self-healing materials. In this Perspective, we provide an overview of the recent progress in the detection, quantification, and utilization of mussel-inspired reversible molecular interactions, which includes the elucidation of their binding mechanisms via force-measuring techniques and the development of self-healing materials based on these dynamic interactions. Both conventional catechol-medicated interactions and newly discovered chemistry beyond the catechol groups are discussed, providing insights into the design strategies of advanced self-healing materials via mussel-inspired chemistry.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.074
GPT teacher head0.355
Teacher spread0.281 · 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