Mechanical properties of carbon nanotube reinforced polyurethane matrix using computational method: a molecular dynamics study
Bibliographic record
Abstract
Abstract The reinforcing nanostructures can be made up of nanoparticles, nanosheets or nanofibres such as carbon nanotubes (CNTs) and graphene nanosheets. To investigate the reinforce mechanism, the changes in mechanical behavior of CNT reinforced Polyurethane (PU) matrix with various chirality was studied using molecular dynamics (MD) method in current work. We used the DREIDING and Tersoff force-fields for simulation of the PU and CNT samples, respectively. To report the mechanical properties of pristine PU matrix and reinforced PU/CNT structure, some physical parameters such as interaction energy between polymer chains and nanotube atoms, ultimate strength, and Young’s modulus are calculated. MD outputs indicated inserting CNT with zigzag edge into pristine matrix enlarged the Young’s modulus by 17.10% and the ultimate strength by 25.69%. These results indicated the promising effect of CNT-based nanostructures on the mechanical properties of PU matrix.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".