Synthesis and Transport Properties of ZnSnP2-yAsy Chalcopyrite Solid Solutions
Bibliographic record
Abstract
This work focuses on the synthesis and properties of quaternary ZnSnP2-yAsy chalcopyrite solid solutions. Full miscibility of the solid solution is achieved using ball milling followed by hot press sintering. The measured electrical conductivity increases substantially with As substitution from 0.03 S cm−1 for ZnSnP2 to 10.3 S cm−1 for ZnSnAs2 at 715 K. Band gaps calculated from the activation energies show a steady decrease with increasing As concentration from 1.4 eV for ZnSnP2 to 0.7 eV for ZnSnAs2. The Seebeck coefficient decreases significantly with As substitution from nearly 1000 μV K−1 for ZnSnP2 to −100 μV K−1 for ZnSnAs2 at 650 K. Thermal conductivity is decreased for the solid solutions due to alloy phonon scattering, compared to the end members with y = 0 and y = 2, with the y = 0.5 and y = 1.0 samples exhibiting the lowest values of 1.4 W m−1 K−1 at 825 K. Figure of merit values are increased for the undoped solid solutions at lower temperatures when compared to the end members due to the decreased thermal conductivity, with the y = 0.5 sample reaching zT = 1.6 × 10−3 and y = 1 reaching 2.1 × 10−3 at 700 K. The largest values of the figure of merit zT for the undoped series was found for y = 2 with zT = 2.8 × 10−3 at 700 K due to the increasing n-type Seebeck coefficient. Boltztrap calculations reveal that p-doping could yield zT values above unity at 800 K in case of ZnSnAs2, comparable with ZnSnP2.
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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 | 0.000 |
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".