Development and modeling of an ultra-robust TPU-MWCNT foam with high flexibility and compressibility
Bibliographic record
Abstract
Abstract Developing a cost-effective industrially scalable manufacturing method that can improve the mechanical properties of nanocomposite foams with higher flexibility, compressibility, and, at the same time, mechanically robustness is of significant interest. In this study, porous thermoplastic polyurethane (TPU)/multiwalled carbon nanotube (MWCNT) was fabricated with the chemical blowing agent (CBA) by a combination of compounding-compression molding methods. The effects of CBA and MWCNT contents on the foam morphology, porosity, foam cell size, Young’s modulus, and compressibility of fabricated samples were investigated. Through conducting cyclic compressive tests, it was observed that nanocomposite foams exhibited consistent mechanical responses across multiple compressive cycles and demonstrated notable characteristics, including high compressibility (up to 76.4% compressive strain) and high elastic modulus (up to 8.8 ± 2.6 MPa). Moreover, theoretical approaches were employed to predict the elastic modulus of solid and foam TPU/MWCNT. For solid MWCNT/TPU, a specific micromechanical model based on different modifications of the Halpin-Tsai (HT) approach was used, which showed a good agreement with experimental data at different MWCNT contents. Furthermore, the constant parameters of Gibson and Ashby’s method were found to successfully predict the elastic modulus of foam TPU/MWCNT at different MWCNT and CBA percentages.
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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 | 0.000 |
| 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) | 0.001 | 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".