Thermoelectric Properties of Ge-Rich GeSn Films Grown on Insulators
Bibliographic record
Abstract
Thermoelectric generator has attracted attention because of their ability to recover waste heat with converting it to electricity. The energy conversion efficiency is related to the thermoelectric figure of merit Z, expressed as Z = S2σ/κ, where σ and κ is electrical and thermal conductivities, respectively, and S is the Seebeck coefficient. Consequently, in order to achieve a high Z value, the material must possess a unique combination of electrical and thermal properties, i.e., metal-like high σ and glass-like low κ. In our previous study on electrical and thermal properties of a Ga-doped Ge0.929Sn0.071 thin film grown on Ge(001), it was revealed that simultaneous realization of a high σ (6.5×102 S/cm) and a low κ (2.0 Wm-1K-1) by the incorporations of the Sn and Ga atoms into Ge matrix [1]. Very recently, Uchida et al. [2] has reported heat transport property of polycrystalline GeSn (poly-GeSn) films on SiO2, together with the carrier transport property, to assess self-heating effect of the poly-GeSn channel thin-film transistors. Here, they showed a relatively lower κ of 5–9 Wm-1K-1 for poly-Ge1-xSnx (x<0.14) compared with that for bulk Ge (60 Wm-1K-1) [3]. These results are quite beneficial in the field of Ge-based thermoelectric generator. However, the thermoelectric properties of GeSn films have not been entirely understood. It is therefore, in the present study, we aim to reveal the thermoelectric properties of the Ge-rich poly-Ge1-xSnx films grown by solid phase crystallization. Doping effects in poly-Ge1-xSnxon the thermoelectric properties will be discussed. Acknowledgments The authors would like to thank Drs. Hiroshi Onoda and Yoshiki Nakashima of Nissin Ion Equipment Co., Ltd. for providing the opportunity to use ion implanters. This work was partially supported by a Grant-in-Aid for Scientific Research (S) (Grant No. 26220605) of the JSPS and PRESTO from JST. References [1] M. Kurosawa, M. Fukuda, K. Takahashi, M. Sakashita, O. Nakatsuka, and S. Zaima, “Thermophysical characterizations of Ge1-xSnx epitaxial layers aiming for thermoelectric device,” 9th International Conference on Silicon Epitaxy and Heterostructures (ICSI-9), Montreal, Canada, 4.4.1, May 21, 2015. [2] N. Uchida, T. Maeda, R. R. Lieten, S. Okajima, Y. Ohishi, R. Takase, M. Ishimaru, and J.-P. Locquet, Appl. Phys. Lett. 107, 232105 (2015). [3] C. J. Glassbrenner and G. A. Slack, Phys. Rev. 134, A1058 (1964).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".