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Record W3193585383 · doi:10.1002/marc.202100391

Dynamic Covalent Polyurethane Network Materials: Synthesis and Self‐Healability

2021· review· en· W3193585383 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.

Bibliographic record

VenueMacromolecular Rapid Communications · 2021
Typereview
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsConcordia University
Fundersnot available
KeywordsCovalent bondDynamic covalent chemistryPolyurethaneMaterials sciencePolymerMaleimideImineIsocyanateNanotechnologyPolymer sciencePolymer chemistryChemistryOrganic chemistryComposite materialMolecule

Abstract

fetched live from OpenAlex

Polyurethane (PU) has not only been widely used in the daily lives, but also extensively explored as an important class of the essential polymers for various applications. In recent years, significant efforts have been made on the development of self-healable PU materials that possess high performance, extended lifetime, great reliability, and recyclability. A promising approach is the incorporation of covalent dynamic bonds into the design of PU covalently crosslinked polymers and thermoplastic elastomers that can dissociate and reform indefinitely in response to external stimuli or autonomously. This review summarizes various strategies to synthesize self-healable, reprocessable, and recyclable PU materials integrated with dynamic (reversible) Diels-Alder cycloadduct, disulfide, diselenide, imine, boronic ester, and hindered urea bond. Furthermore, various approaches utilizing the combination of dynamic covalent chemistries with nanofiller surface chemistries are described for the fabrication of dynamic heterogeneous PU composites.

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 score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.002
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 head0.030
GPT teacher head0.314
Teacher spread0.284 · 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