Achieving zT > 1 in Inexpensive Zintl Phase Ca<sub>9</sub>Zn<sub>4+</sub><i><sub>x</sub></i>Sb<sub>9</sub> by Phase Boundary Mapping
Bibliographic record
Abstract
Complex multinary compounds (ternary, quaternary, and higher) offer countless opportunities for discovering new semiconductors for applications such as photovoltaics and thermoelectrics. However, controlling doping has been a major challenge in complex semiconductors as there are many possibilities for charged intrinsic defects (e.g., vacancies, interstitials, antisite defects) whose energy depends on competing impurity phases. Even in compounds with no apparent deviation from a stoichiometric nominal composition, such defects commonly lead to free carrier concentrations in excess of 10 20 cm −3 . Nevertheless, by slightly altering the nominal composition, these defect concentrations can be tuned with small variation of the chemical potentials (composition) of each element. While the variation of chemical composition is undetectable, it is shown that the changes can be inferred by mapping (in nominal composition space) the boundaries where different competing impurity phases form. In the inexpensive Zintl compound Ca 9 Zn 4+ x Sb 9 , the carrier concentrations can be finely tuned within three different three‐phase regions by altering the nominal composition ( x = 0.2–0.8), enabling the doubling of thermoelectric performance (zT). Because of the low thermal conductivity, the zT can reach as high as 1.1 at 875 K, which is one of the highest among the earth abundant p‐type thermoelectrics with no ion conducting.
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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) | 0.002 | 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".