Tuning Novel NaLaS<sub>2(1–<i>x</i>)</sub>(Se or Te)<sub>2<i>x</i></sub> Alloys as Light-Absorbing Materials by Dopant-Induced Crystallographic Phase and Electronic Structure Transitions
Bibliographic record
Abstract
The electronic and optical properties of a NaLaS 2 compound, doped with different concentrations of Se and Te atoms, are explored using periodic density functional theory calculations. The primary objective is to identify light-absorbing materials for use in photovoltaic applications. It is found that the dopant concentration can induce a crystalline structure phase transition from cubic to trigonal, which is accompanied with drastic changes in both the nature (direct/indirect) and extent of the energy band gap. As the proportion of selenium atoms in the NaLaS 2(1– x ) Se 2 x alloys increases from 0 to 100%, the gap decreases from 2.93 to 2.41 eV, respectively. For the NaLaS 2(1– x ) Te 2 x alloys, the gap undergoes a (sharp) decrease reaching as low as 1.75 eV as the proportion of tellurium increases to 75%. The transport properties of the alloys reveal a significant drop in the effective mass and exciton binding energies, which is particularly marked for the NaLaS 2(1– x ) Te 2 x alloys with a Te concentration range of 25–100%. The exciton binding energy for NaLaS 0.5 Te 1.5 is 4.37 meV, much less than the thermal energy at room temperature (25 meV). Therefore, the NaLaS 0.5 Te 1.5 alloy shows potential as a light-absorbing material for photovoltaic applications that is worthy of further investigations.
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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".