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Record W1860854841 · doi:10.1039/c5cc04531b

Recent strategies to develop self-healable crosslinked polymeric networks

2015· review· en· W1860854841 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

VenueChemical Communications · 2015
Typereview
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsConcordia University
FundersKorea Research Institute of Chemical TechnologyCentre québécois sur les matériaux fonctionnels
KeywordsSelf-healingMaterials scienceSelf-healing materialMechanical strengthNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Autonomous self-healable crosslinked materials designed with built-in ability to repair physical damage and cracks can prevent catastrophic failure and thus extend the lifetime of materials. They also retain their dimensional stability, mechanical strength, thermal stability, and solvent resistance. These features promote the development of effective self-healing materials for various applications. This review summarizes recent advances in the development of novel self-healable polymeric materials, both through extrinsic methods involving the encapsulation of extra healing agents in microcapsules and through intrinsic methods utilizing the formation of reversible chemical or physical crosslinks. Further, the outlook is briefly discussed on the important aspects for the current and future development of self-healable materials.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.103
GPT teacher head0.376
Teacher spread0.272 · 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