Overall Mechanical Properties of Self-Healing Composites: Effects of Microcapsules Shape, Volume Concentration, Shell Thickness, and Material Properties
Bibliographic record
Abstract
Abstract The present work investigates the influence of embedding spherical and spheroidal capsules containing the healing agent within the base polymers to create a self-healing effect on the overall mechanical properties of the entire medium, using numerical and analytical homogenization approaches. The effects of geometrical and mechanical parameters, including capsule shape, the healing agent volume concentration, the capsules shell thickness, and mechanical properties on the overall responses of the self-healing composites are studied. A square-array configuration is assumed for the distribution of the capsules. This idealization enables computing the behavior of such composites computationally via FE analysis and variationally using Hashin-Shtrikman upper bound by taking a unit cell composed of a cube of the matrix with a perfectly bonded capsule at its center. The boundary conditions on the unit cell are applied in a way that the deformation of the unit cell captivates the overall mechanical responses of the entire medium. Our results show a general decrease in the overall properties of the composites, which can be alleviated by increasing the thickness of the capsules shell or using a stiffer shell. Additionally, we show that the geometry of the capsule shell plays a role in reducing the drop in the overall properties of the composite.
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 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 | 0.001 |
| 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) | 0.001 | 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".