Improving Thermoelectric Figure of Merit through Materials Engineering: MinimizingThermal Conductivity via Lone Pairs and Introducing Resonant Levels to Increase PowerFactor
Bibliographic record
Abstract
Thermoelectric devices offer a lot of value to an ever growing demand on the energy market.These devices are able to provide scalable, steady state heating and cooling when provided with power, or they can be used to recover waste heat to convert to electrical power.The efficiency of the device to perform these functions is primarily limited by the thermoelectric material properties, which are ultimately summarized by the thermoelectric figure of merit, zT.In this work, two approaches are taken to optimize zT: the use of lone pair electrons to minimize lattice thermal conductivity, and resonant levels to increase the power factor.In the first approach, we establish low thermal conductivities, in the range of 0.4-1 W/mK, for a variety of I-V-VI2 compounds including a newly established extension to alkali based compounds.In a collaboration between experiment and theory, we determined the root effect of lone pairs on this class of compounds.The knowledge gained from this particular study can then be extended to other classes of compounds to determine which materials can be expected to have low thermal conductivity.In the second approach, we explore several promising systems to seek an effective resonant level, that is, one which increases the density of states in such a way as to increase the Seebeck coefficient above the Pisarenko relation.In the process, we discover a resonant effect in PbTe:Ti that allows for robust production methods.We also discover an effective resonant level in CoSb3:Al that results in a two-fold increase in Seebeck coefficient over literature values at relatively high carrier concentration.Additionally, we were able to provide some insight into a material system, PbTe:Cr, that had previously been misconstrued as an effective resonant level.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".