Free‐Standing, Multifunctional Thermoelectric and Acoustic Absorbing Nanocomposite Foams
Bibliographic record
Abstract
Abstract Thermoelectric materials are potential energy harvesting technologies that enable direct, clean conversion between thermal and electrical energy. The efficacy of thermoelectric energy conversion is influenced by the electrical conductivity, thermal conductivity, and Seebeck coefficient. Flexibility, manufacturability, and cost‐effectiveness are also important factors. Polymeric nanocomposites offer advantages in these respects. However, the development of conductive‐polymer thermoelectric materials is limited to an in‐plane architecture, which does not resemble common real‐world scenarios. Moreover, existing works have low thermoelectric properties or rely on additives for performance improvement. In this work, a free‐standing thermoelectric nanocomposite foam is fabricated via the integration of thermally activated microspheres. Due to the microstructure, a thermal conductivity as low as 0.03 W m−1 K−1 is achieved, which is lower than reported for aerogels fabricated via freeze‐drying methods. Additionally, the nanocomposite foam can reach a maximum electrical conductivity of 1.13 S cm−1, power factor of 0.12 µW m−1 K−2, and thermoelectric figure of merit of 3.0 × 10−4. The study also evaluated the compressive stiffness and demonstrated the potential for sound absorption. With the unique combination of the thermoelectric, sound absorption, and mechanical behavior, these nanocomposite foams would offer versatile solutions to address the next generation energy harvesting and acoustic absorption applications.
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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) | 0.001 | 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".