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Record W4389056829 · doi:10.3390/polym15234544

Highly Self-Healable Polymeric Coating Materials Based on Charge Transfer Complex Interactions with Outstanding Weatherability

2023· article· en· W4389056829 on OpenAlexaff
Pyong Hwa Hong, Gyeongmin Moon, Jinsil Kim, Kiwon Choi, Min Jae Ko, Ho Gyu Yoon, Sung Woo Hong

Bibliographic record

VenuePolymers · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité de Montréal
FundersMinistry of Trade, Industry and Energy
KeywordsMaterials scienceCoatingSelf-healingThermal stabilityComposite materialKineticsChemical engineering

Abstract

fetched live from OpenAlex

In this study, we prepare highly self-healable polymeric coating materials using charge transfer complex (CTC) interactions. The resulting coating materials demonstrate outstanding thermal stability (1 wt% loss thermal decomposition temperature at 420 °C), rapid self-healing kinetics (in 5 min), and high self-healing efficiency (over 99%), which is facilitated by CTC-induced multiple interactions between the polymeric chains. In addition, these materials exhibit excellent optical properties, including transmittance over 91% and yellow index (YI) below 2, and show enhanced weatherability with a ΔYI value below 0.5 after exposure to UV light for 72 h. Furthermore, the self-healable coating materials developed in this study show outstanding mechanical properties by overcoming the limitations of conventional self-healing 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.

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 categoriesMeta-epidemiology (narrow), 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 score0.014
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.266
Teacher spread0.237 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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