Fabrication of High Content Carbon Nanotube–Polyurethane Sheets with Tailorable Properties
Bibliographic record
Abstract
We have fabricated carbon nanotube (CNT)-polyurethane (TPU) sheets via a one-step filtration method that uses a TPU solvent/nonsolvent combination. This solution method allows for control of the composition and processing conditions, significantly reducing both the filtration time and the need for large volumes of solvent to debundle the CNTs. Through an appropriate selection of the solvents and tuning the solvent/nonsolvent ratio, it is possible to enhance the interaction between the CNTs and the polymer chains in solution and improve the CNT exfoliation in the nanocomposites. The composition of the nanocomposites, which defines the characteristics of the material and its mechanical properties, can be precisely controlled. The highest improvements in tensile properties were achieved at a CNT:TPU weight ratio around 35:65 with a Young's modulus of 1270 MPa, stress at 50% strain of 35 MPa, and strength of 41 MPa, corresponding to ∼10-fold improvement in modulus and ∼7-fold improvement in stress at 50% strain, while maintaining a high failure strain. At the same composition, CNTs with higher aspect ratio produce nanocomposites with greater improvements (e.g., strength of 99 MPa). Electrical conductivity also shows a maximum near the same composition, where it can exceed the values achieved for the pristine nanotube buckypaper. The trend in mechanical and electrical properties was understood in terms of the CNT-TPU interfacial interactions and morphological changes occurring in the nanocomposite sheets as a function of increasing the TPU content. The availability of such a simple method and the understanding of the structure-property relationships are expected to be broadly applicable in the nanocomposites field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".