Obtaining and characterisation of thermoelectric Mg2Si compound via wet and dry mechanical alloying and spark plasma sintering
Bibliographic record
Abstract
Mg2Si thermoelectric compound was obtained by dry and wet (isohexane and benzene) mechanical alloying route. The Mg2Si compound was characterised by X-ray diffraction, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), laser particle size analysis and thermoelectric measurements. After 14 h of milling, the complete reaction of the elements is achieved by both routes. The powders particle size distribution obtained after 14 hours of dry or wet milling (using benzene) reveals a bimodal curve. The wet milling moves the particle size distribution towards smaller particle sizes. The SEM analysis confirms the results of particles size analysis. DSC analyses performed for samples milled up to 14 hours present stress relief and recrystallisation thermal events. For the benzene wet-milled sample, the DSC curve shows an additional thermal event at 350 °C, associated with benzene removal. Thermoelectric properties were determined on spark plasma sintered compacts. The milling process with benzene leads to a higher value of Seebeck coefficient (≈580 μV/K). The electrical conductivity is low at room temperature and increases exponentially with temperature.
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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 | 0.001 | 0.001 |
| 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".