Nano- and Microstructure Engineering: An Effective Method for Creating High Efficiency Magnesium Silicide Based Thermoelectrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Considering the effect of CO 2 emission together with the depletion of fossil fuel resources on future generations, industries in particular the transportation sector are in deep need of a viable solution to follow the environmental regulation to limit the CO 2 emission. Thermoelectrics may be a practical choice for recovering the waste heat, provided their conversion energy can be improved. Here, the high temperature thermoelectric properties of high purity Bi doped Mg 2 (Si,Sn) are presented. The samples Mg 2 Si 1–x–y Sn x Bi y with x(Sn) ≥ 0.6 and y(Bi) ≥ 0.03 exhibited electrical conductivities and Seebeck coefficients of approximately 1000 Ω –1 cm –1 and −200 μV K –1 at 773 K, respectively, attributable to a combination of band convergence and microstructure engineering through ball mill processing. In addition to the high electrical conductivity and Seebeck coefficient, the thermal conductivity of the solid solutions reached values below 2.5 W m –1 K –1 due to highly efficient phonon scattering from mass fluctuation and grain boundary effects. These properties combined for zT values of 1.4 at 773 K with an average zT of 0.9 between 400 and 773 K. The transport properties were both highly reproducible across several measurement systems and were stable with thermal cycling.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it