Recovery of Mechanical Properties in an Epoxy Vitrimer: Molecular Dynamics Simulations and Experimental Measurements
Bibliographic record
Abstract
This study investigates the self-healing mechanism in vitrimer materials by integrating molecular dynamics (MD) simulations with experimental methods.We focus on elucidating the self-healing properties of epoxy vitrimers containing disulfide bonds and understanding their underlying mechanisms, aiming to contribute to the design of high-performance materials.MD simulations reproduce the molecular structure of epoxy vitrimers synthesized from tetraglycidyl diaminodiphenylmethane (TGDDM) and 4-aminophenyl disulfide (AFD).Tensile simulations show bond cleavage under external forces, and subsequent simulations demonstrate the recombination of cleaved disulfide bonds, restoring the material's mechanical properties."Compression and tensile tests conducted after the repair confirmed the recovery of the material's ability to withstand stress.These results highlight the critical role of disulfide bonds in the self-healing process.Double cantilever beam (DCB) tests are experimentally performed to quantitatively evaluate self-healing.The results show a marked recovery of fracture toughness after heating, confirming the self-healing process.These experimental findings align with MD simulation predictions, reinforcing the importance of disulfide bonds in the self-healing behavior.
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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 | 0.001 |
| 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 | 0.001 | 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".