Energy band gaps of InN containing oxygen and of the InxAl1−xN interface layer formed during InN film growth
Bibliographic record
Abstract
The effect of known growth artifacts on the absorption and photoluminescence properties of InN films is determined using linear combination of atomic orbitals electron band structure calculations. InxAl1−xN interfacial layers are examined for various atomic fractions of Al, since these layers are observed to be relatively thick (up to 100 nm) for thin films of InN deposited on AlN or sapphire. It is found that for penetration of Al atoms in InN, forming In-rich InxAl1−xN, a decrease of the energy band gap of InN occurs, despite AlN having a much larger band gap than InN. Γc13↔Γν154 exciton emissions for InxAl1−xN are found to have an energy of 0.765–0.778 eV and may explain recent photoluminescence data for InN. Optical absorption for this alloy is dominated by a 1.58–1.62 eV transition. The second artifact investigated here is high concentration oxygen impurity atoms in wurtzite InN. Segregated oxygen species are not considered, only alloyed species with oxygen substituting on the nitrogen site. For this arrangement a new ternary semiconductor InOyN1−y with y∼0.1 is identified. A model of the tetrahedral cell In–O is made and the energy band gap of InOyN1−y is calculated. It is found that the presence of O atoms in InN can decrease the energy band gap. Optical absorption as low as 1.19 eV can be evident. The exciton emissions Γc12↔Γν151 in InOyN1−y were found to vary in energy over the range 0.84–1.01 eV.
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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 | 0.000 |
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".