Direct Measurement of Absolute Seebeck Coefficient Using Graphene As a Zero Coefficient Reference
Bibliographic record
Abstract
With increasing interest in thermoelectrics for energy applications, determining the thermoelectric power (also known as the Seebeck coefficient) is critical for the characterization and optimization of thermoelectric materials. Obtaining the absolute Seebeck coefficient, however, is difficult due to induced response in the probes contributing to the observed Seebeck effect. Current methods for obtaining the absolute Seebeck coefficient require using a reference material as probes and: estimating it via the Thomson effect, or measuring it directly using a superconductor as a zero coefficient reference. These methods are either cumbersome or are limited to low temperatures, respectively. Graphene, like superconductors, has a zero absolute Seebeck coefficient at the dirac point; additionally, it offers many advantages over superconductors as a reference material as it is stable over a wider range of temperatures and conditions. In this work, we use graphene as a zero coefficient reference to obtain direct measurements of the absolute Seebeck coefficient of five different materials (Au, W, Mo, chromel and constantan) from 250 K to 390 K, and compare to known results with good agreement. Here, we show that graphene’s unique characteristics, including its stability, insensitivity to impurities and ease of tunable electrical properties allowing for in-situ calibration, make it an excellent candidate as a reference standard for direct measurements of the absolute Seebeck coefficient. This work minds an important 75 years pending gap in the field of thermoelectricity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".