Boron Nitride Nanotubes: Force Field Parameterization, Epoxy Interactions, and Comparison with Carbon Nanotubes for High-Performance Composite Materials
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Boron nitride nanotubes (BNNTs) are a very promising reinforcement for future high-performance composites because of their excellent thermo-mechanical properties. To take full advantage of BNNTs in composite materials, it is necessary to have a comprehensive understanding of the wetting characteristics of various high-performance resins. Molecular dynamics (MD) simulations provide an accurate and efficient approach to establish the contact angle values of engineering polymers on reinforcement surfaces, which offers a measure for the interaction between the polymer and reinforcement. In this research, MD simulations and experiments are used to determine the wettability of various epoxy systems on BNNT surfaces. The reactive interface force field (IFF-R) is parameterized and utilized in the simulations to accurately describe the interaction of the epoxy monomers with the BNNT surface. The effect of the epoxy monomer type, hardener type, local atomic charges, and temperature on the contact angle is established. The results show that contact angles decrease with increases in temperature for all the epoxy/hardener systems. The bisphenol-A-based epoxy system demonstrates better wettability with the BNNT surface than the bisphenol-F based epoxy system. Furthermore, the MD predictions demonstrate that these observations are validated with experimental results, wherein the same contact angle trends are observed for macroscopic epoxy drops on nonwoven nanotube papers. As wetting properties drive the resin infusion in the reinforcement materials, these results are important for the future manufacturing of high-quality BNNT/epoxy nanocomposites for high-performance applications such as aerospace and aeronautical vehicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 teacher head, 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".