Insights into interphase thickness characterization for graphene/epoxy nanocomposites: a molecular dynamics simulation
Bibliographic record
Abstract
This work presents a molecular dynamics simulation study on the interfacial characterization of graphene/epoxy nanocomposites. In polymeric nanocomposites, the thermo-mechanical properties of a system strongly depend on the characteristics of the interphase region between the matrix and the inclusions. The first step in the characterization of this interphase is to distinguish its border limit (i.e., the interphase thickness). Here, we present a methodology to systematically quantify the interphase thickness based on analyzing the variation of the local mass density profile. To this end, three functions (average accumulated mass density, accumulated standard deviation (ASD) and its first derivative) are successively applied on the local mass density profile. Using this procedure, the interphase limit can be easily detected regardless of the oscillatory nature of the local mass density. The effect of the epoxy crosslinking density and number of graphene layers on the interphase thickness is then investigated, and the results are analyzed by studying the interaction energies, polymer dynamics and distribution quality of reacted and unreacted components, as well as conformational changes of the polymer chains in the interphase region. The results reveal that the crosslinking density is the most influential parameter on the interphase thickness: the higher the crosslinking degree, the thicker the interphase region. To a lower extent, the interaction energy has also an effect on the interphase thickness since there is an inverse relationship between the interaction energy and the crosslinking density in our case study. Overall, the reported findings highlight useful insights into the detection and properties of the interphase region in thermoset composites.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".