Composition dependent band offsets of ZnO and its ternary alloys
Bibliographic record
Abstract
Abstract We report the calculated fundamental band gaps ofwurtziteternary alloys Zn1−xMxO (M = Mg, Cd) and the band offsets of the ZnO/Zn1−xMxO heterojunctions, these II-VI materials are important for electronics and optoelectronics. Our calculation is based on density functional theory within the linear muffin-tin orbital (LMTO) approach where the modified Becke-Johnson (MBJ) semi-local exchange is used to accurately produce the band gaps, and the coherent potential approximation (CPA) is applied to deal with configurational average for the ternary alloys. The combined LMTO-MBJ-CPA approach allows one to simultaneously determine both the conduction band and valence band offsets of the heterojunctions. The calculated band gap data of the ZnO alloys scale asEg = 3.35 + 2.33xandEg = 3.36 − 2.33x + 1.77x2for Zn1−xMgxO and Zn1−xCdxO, respectively, wherexbeing the impurity concentration. These scaling as well as the composition dependent band offsets are quantitatively compared to the available experimental data. The capability of predicting the band parameters and band alignments of ZnO and its ternary alloys with the LMTO-CPA-MBJ approach indicate the promising application of this method in the design of emerging electronics and optoelectronics.
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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".