Investigation of LPE grown dilute nitride InGaAs(Sb)N layers for photovoltaic applications
Bibliographic record
Abstract
We report on LPE growth and characterization of dilute nitride InGaAs(Sb)N layers nearly lattice matched to GaAs. In order to obtain high quality epitaxial layers without phase separation low-temperature variant of LPE method has been used. The composition and crystalline quality of the grown InGaAs(Sb)N layers have been determined by energy dispersive X-ray microanalysis and X-ray diffraction methods. SEM and AFM measurements on grown samples revealed flat interfaces and surface roughness in the range 0.2 - 0.3 nm. In order to identify the N-bonding mechanism in the alloys and the nature of nitrogen related clusters IR absorption and Raman scattering spectroscopy have been applied. The optical band gap of the samples is studied by photoluminescence (PL) spectroscopy at low and room temperatures and by surface photovoltage (SPV) spectroscopy at room temperature. The SPV and PL spectra reveal a red shift of the absorption edge and PL peak position as compared to GaAs, as well as localized states near the conduction band minimum. However, the optical band gap bowing of the samples appears smaller with respect to the random alloy, which is explained by the short-range ordering favored by LPE growth at near-equilibrium conditions. Variable angle ellipsometry is applied to determine the spectral behavior of the complex refractive index and estimate the band gap energy of the samples.
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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) | 0.000 | 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".