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Record W4310206565 · doi:10.3390/polym14235181

Highly Self-Healable Polymeric Coating Materials with Enhanced Mechanical Properties Based on the Charge Transfer Complex

2022· article· en· W4310206565 on OpenAlexaff
Chanjae Ahn, Pyong Hwa Hong, Juhen Lee, Jinsil Kim, Gyeongmin Moon, Sungkoo Lee, In Park, Haksoo Han, Sung Woo Hong

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité de Montréal
FundersNational Research Foundation of Korea
KeywordsMaterials scienceSelf-healingPeekCoatingComposite materialPolysulfoneThermal stabilityPolymerChemical engineering

Abstract

fetched live from OpenAlex

Polymeric coating materials (PCMs) are promising candidates for developing next-generation flexible displays. However, PCMs are frequently subjected to external stimuli, making them highly susceptible to repeated damage. Therefore, in this study, a highly self-healing PCM based on a charge transfer complex (CTC) was developed, and its thermal, self-healing, and mechanical properties were examined. The self-healing material demonstrated improved thermal stability, fast self-healing kinetics (1 min), and a high self-healing efficiency (98.1%) via CTC-induced multiple interactions between the polymeric chains. In addition, it eliminated the trade-off between the mechanical strength and self-healing capability that is experienced by typical self-healing materials. The developed PCM achieved excellent self-healing and superior bulk (in-plane) and surface (out-of-plane) mechanical strengths compared to those of conventional engineering plastics such as polyether ether ketone (PEEK), polysulfone (PSU), and polyethersulfone (PES). These remarkable properties are attributed to the unique intermolecular structure resulting from strong CTC interactions. A mechanism for the improved self-healing and mechanical properties was also proposed by comparing the CTC-based self-healing PCMs with a non-CTC-based PCM.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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