Carbon Nanotube–Polyurethane Composite Sheets for Flexible Thermoelectric Materials
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Integration of single-wall carbon nanotubes (SWCNTs) in the form of fabriclike sheets or other preformed assemblies (films, fibers, etc.) simplifies their handling and allows for composites with higher nanotube contents, which is needed to better exploit their outstanding properties and achieve multifunctional materials with improved performance. Here, we show the development of p-type SWCNT–thermoplastic polyurethane (TPU) fabric materials with a wide range of SWCNT contents (from 5 to 90 wt %) by employing a one-step filtration method using a suspension of SWCNTs in a TPU solvent/nonsolvent mixture. The mechanical and thermoelectric (TE) properties of these SWCNT–TPU nanocomposites were tailored by varying the SWCNT/TPU wt % ratio, achieving significant advantages relative to the pristine SWCNT buckypaper (BP) sheets in terms of strength and stretchability. In particular, the SWCNT–TPU nanocomposite with a 50/50 wt % ratio composition (equivalent to 15 vol % of SWCNTs) shows a power factor (PF) of 57 μW m –1 K –2, slightly higher compared to the PF of the SWCNT BP prepared under the same conditions (54 μW m –1 K –2 ), while its mechanical properties significantly increased (e.g., ∼7-, 25-, and 250-fold improvements in stiffness, strength, and tensile toughness, respectively). These results represent a significant step toward the development of easy-to-process self-supporting and stretchable materials with robust mechanical properties for flexible thermoelectric devices.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".