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Record W2294109719 · doi:10.1016/j.proeng.2015.08.1109

Characterization of the Self-Healing Mechanism of VHB 4910

2016· article· en· W2294109719 on OpenAlexaff
Fan Fan, Jerzy A. Szpunar

Bibliographic record

VenueProcedia Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Saskatchewan
FundersDivision of Materials ResearchChina Scholarship Council
KeywordsSelf-healingMaterials scienceRaman spectroscopyAmorphous solidSelf-healing materialUltimate tensile strengthSelf-healing hydrogelsComposite materialNanotechnologyCrystallographyPolymer chemistryOpticsChemistryMedicine

Abstract

fetched live from OpenAlex

Self-healing materials have been heavily studied because of its ability to extend the service life of materials. However, current self-healing materials with strong mechanical property requires energy input to trigger the healing process while the materials with autonomous self-healing ability are not tough enough for practical applications. Surprisingly, we found a commercial material, VHB 4910, compromising a strong mechanical property as elastomers and rapid self-healing ability as hydrogels. We confirmed the self-healing ability of VHB 4910 with tensile tests. Raman and infrared spectra illustrate the bonding structure of this material. X-ray diffraction pattern shows an amorphous inner structure of this material. We confirmed that both hydrogen bonding and chain diffusion process contributed to the self-healing ability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.181
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2016
Admission routes1
Has abstractyes

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