2D and 3D nanostructuring strategies for thermoelectric materials
Bibliographic record
Abstract
Thermoelectric materials have attracted increased research attention as the implementation of various nanostructures has potential to improve both their performance and applicability. A traditional limitation of thermoelectric performance in bulk materials is the interconnected nature of the individual parameters (for example, it is difficult to decrease thermal conductivity while maintaining electrical conductivity), but through the rational design of nanoscale structures, it is possible to decouple these relationships and greatly enhance the performance. For 2D strategies, newly investigated materials such as graphene, transition metal dichalcogenides, black phosphorus, etc. are attractive thanks to not only their unique thermoelectric properties, but also potential advantages in ease of processing, flexibility, and lack of rare or toxic constituent elements. For 3D strategies, the use of induced porosity, assembly of various nanostructures, and nanoscale lithography all offer specific advantages over bulk materials of the same chemical composition, most notably decreased thermal conductivity due to phonon scattering and enhanced Seebeck coefficient due to energy filtering. In this review, a general summary of the popular techniques and strategies for 2D and 3D thermoelectric materials will be provided, along with suggestions for future research directions based on the observed trends.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".