A Self-Healing Material Based on Microcapsules of Poly(Urea-Formaldehyde)/Bis-Propargyl-Succinate Containing in Polyurethane Matrix
Bibliographic record
Abstract
With the development of current technology, several concepts of self-healing materials (SHMs) have recently been proposed, and capsule-based SHMs are explored. In our study, a terminal alkyne compound (bis-propargyl-succinate, BPS) is concerned as a healing agent to be used as a core material, and poly(urea-formaldehyde) (PUF) is employed as a wall shell. Besides, the chemical, morphological and thermal properties of the microcapsules (MCs) are also determined by Fourier-transform infrared spectroscopy (FTIR), gas chromatography (GC), thermogravimetric analysis (TGA), and optical microscopy (OM). Additionally, the MCs have better thermal stability up to 257 °C with the rough outer surface. The MCs have successfully encapsulated 75.0% of BPS with a size range of 63 – 125 μm and PUF shell thickness range of 5.72 – 11.35 μm; moreover, the stability of MCs is well maintained within 50 days at room temperature basing on the solvent extraction method. Concomitantly, self-healing ability is activated by the breakup of the MCs as cracks, then the healing agent (BPS) is released into the cracked regions to react with azide groups of the polymeric matrix. The BPS in the MCs is moved to cracked regions, which involves MCs diameter and weight fraction of PUF capsules. Moreover, the self-healing ability can reach high when BPS amounts (i.e., SHMs containing 5% and 10% of MCs) are available sufficiently to be outrightly filled into the cracked regions. Thereby, MCs' size and weight fraction can be reasonably selected to result in an optimal healing capacity for a pre-established size of cracks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".