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Record W2928817831 · doi:10.1063/1.5097977

Electronic band structure of nitrogen diluted Ga(PAsN): Formation of the intermediate band, direct and indirect optical transitions, and localization of states

2019· article· en· W2928817831 on OpenAlexafffund
Maciej P. Polak, R. Kudrawiec, Oleg Rubel

Bibliographic record

VenueJournal of Applied Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNarodowe Centrum Nauki
KeywordsOptoelectronicsNitrogenElectronic band structureDirect and indirect band gapsMaterials scienceChemical physicsChemistryBand gapCondensed matter physicsPhysics

Abstract

fetched live from OpenAlex

The electronic band structure of Ga(PAsN) with a few percent of nitrogen is calculated in the whole composition range of Ga(PAs) host using density functional methods including the modified Becke-Johnson functional to correctly reproduce the bandgap and unfolding of the supercell band structure to reveal the character of the bands. Relatively small amounts of nitrogen introduced to Ga(PAs) lead to the formation of an intermediate band below the conduction band, which is consistent with the band anticrossing model, widely used to describe the electronic band structure of dilute nitrides. However, in this study, calculations are performed in the whole Brillouin zone and they reveal the significance of the correct description of the band structure near the edges of the Brillouin zone, especially for the indirect bandgap P-rich host alloy, which may not be properly captured with simpler models. The influence of nitrogen on the band structure is discussed in terms of the application of Ga(PAsN) in optoelectronic devices such as intermediate band solar cells, light emitters, as well as two color emitters. Additionally, the effect of nitrogen incorporation on the carrier localization is studied and discussed. The theoretical results are compared with experimental studies, confirming their reliability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.195
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2019
Admission routes2
Has abstractyes

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