Thermoelectric Nanocomposite Foams Using Non-Conducting Polymers with Hybrid 1D and 2D Nanofillers
Bibliographic record
Abstract
A facile processing strategy to fabricate thermoelectric (TE) polymer nanocomposite foams with non-conducting polymers is reported in this study. Multilayered networks of graphene nanoplatelets (GnPs) and multi-walled carbon nanotubes (MWCNTs) are deposited on macroporous polyvinylidene fluoride (PVDF) foam templates using a layer-by-layer (LBL) assembly technique. The open cellular structures of foam templates provide a platform to form segregated 3D networks consisting of one-dimensional (1D) and/or two-dimensional (2D) carbon nanoparticles. Hybrid nanostructures of GnP and MWCNT networks synergistically enhance the material system’s electrical conductivity. Furthermore, the polymer foam substrates possess high porosity to provide ultra-low thermal conductivity without compromising the electrical conductivity of the TE nanocomposites. With an extremely low GnP loading (i.e., ~1.5 vol.%), the macroporous PVDF nanocomposites exhibit a thermoelectric figure-of-merit of ~10−3. To the best of our knowledge, this ZT value is the highest value reported for organic TE materials using non-conducting polymers and MWCNT/GnP nanofillers. The proposed technique represents an industrially viable approach to fabricate organic TE materials with enhanced energy conversion efficiencies. The current study demonstrates the potential to develop light-weight, low-cost, and flexible TE materials for green energy generation.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".