Self-contained calibration samples and measurements of the thermoelectric figure of merit: A method to improve accuracy
Bibliographic record
Abstract
Despite more than seven decades of active research and development in thermoelectricity, the accurate measurement of the thermoelectric (TE) properties of bulk materials has remained a challenge, mainly because of the strong interrelation between thermal and electrical phenomena. This work highlights practical advancements in methods and instrumentation dedicated to the simultaneous measurements of TE properties such as the Seebeck coefficient (S), the thermal (κ), and electrical (σ) conductivities and the dimensionless TE figure of merit ZT = S2σT/κ. The accuracy of a Harman based approach, as implemented by the ZT-Scanner (TEMTE Inc.), applicable to the simultaneous measurement of the above TE properties, has been made possible by a self-contained calibration procedure, which is based on the availability of two samples of the same homogeneous material having different shape factors. It is of practical importance that this approach provides a simple procedure to obtain the calibration for the figure of merit ZT and the thermal conductivity in the temperature interval from 300 to 720 K. In addition, we show that a simplified Harman setup with no thermocouples attached to the sample can also be used for self-contained calibrated ZT measurements. It is concluded that the implemented steady-state approach decreases the relative error down to 1%–2% for ZT measurements and can be recommended for most applications not involving dynamical behavior. In particular, it is proposed that self-generated calibration samples can critically increase the quality and ease of comparison of TE measurements if they are adopted by the TE community.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".