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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 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

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
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