Solid State Synthesis of Ternary Thermoelectric Magnesium Alloy, Mg<SUB>2</SUB>Si<SUB>1&minus;<I>x</I></SUB>Sn<I><SUB>x</SUB></I>
Bibliographic record
Abstract
Profound understanding and survey of magnesium base intermetallic compounds is hindered by various difficulties in their processing and fabrication. Solid-state synthesis via the bulk mechanical alloying (BMA) is free from contaminations and segregation through high reactivity of elemental constituents against crucibles or vials. Magnesium–tin system is employed to demonstrate the solid-state reactivity to Mg2Sn from the elemental powder mixture. This process is characterized by the gradual solid-state reaction to Mg2Sn with processing time. Since the blended mixture of magnesium and tin with the initial molar ratio of Mg66.7%Sn33.3%, is repeatedly strained via BMA in the controlled conditions, the solid-state reaction advances monotonically with refinement of interparticle distance between magnesium and tin. Ternary semi-conductive compounds, Mg2Si1−xSnx for 0≤x≤1, are also synthesized by this process. Thermoelectric properties of this ternary alloy are investigated to discuss the effect of tin content on the band-gap, the thermal conductivity, the Seebeck coefficient and the figure-of-merit. In addition, these data are compared to the previously reported results by using melt and solidified samples in order to describe the common features in the solid-solution type thermoelectric compounds. Furthermore, p–n transition behavior is also reported in this ternary alloy system.
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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 | 0.001 |
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".