Self-propagating high-temperature synthesis for compound thermoelectrics and new criterion for combustion processing
Bibliographic record
Abstract
The existing methods of synthesis of thermoelectric (TE) materials remain constrained to multi-step processes that are time and energy intensive. Here we demonstrate that essentially all compound thermoelectrics can be synthesized in a single-phase form at a minimal cost and on the timescale of seconds using a combustion process called self-propagating high-temperature synthesis. We illustrate this method on Cu2Se and summarize key reaction parameters for other materials. We propose a new empirically based criterion for sustainability of the combustion reaction, where the adiabatic temperature that represents the maximum temperature to which the reacting compact is raised as the combustion wave passes through, must be high enough to melt the lower melting point component. Our work opens a new avenue for ultra-fast, low-cost, large-scale production of TE materials, and provides new insights into combustion process, which greatly broaden the scope of materials that can be successfully synthesized by this technique. The existing methods to synthesize thermoelectric materials remain constrained to multi-step processes that are usually time and energy consuming. Here, Su et al.use a fast, one-step combustion approach to synthesize various compounds, which holds promise for scalable industrial processing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".