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Record W4408776524 · doi:10.1063/5.0253677

Effect of substrate orientation on the optical properties of InGaAsN(P) quantum wells

2025· article· en· W4408776524 on OpenAlexaff
M. Gładysiewicz, Marek S. Wartak

Bibliographic record

VenueAPL Quantum · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsOrientation (vector space)Substrate (aquarium)Quantum wellMaterials scienceOptoelectronicsOpticsPhysicsMathematicsGeometryGeology

Abstract

fetched live from OpenAlex

This study examines the effect of substrate orientation on the optical properties of InGaAsN(P) quantum wells using an eight-band k · p Hamiltonian extended to account for strain effects in arbitrary orientations. Numerical simulations were performed for key substrate orientations, including (001), (110), (111), and (112), with particular focus on the impact of strain-induced piezoelectric fields on band alignment and wavefunction localization. The results reveal substantial differences in the band structure, wavefunctions, absorption coefficients, and material gain across the studied orientations. The (111) orientation exhibits the strongest piezoelectric fields, leading to enhanced confinement and the formation of additional bound states in the conduction band. Band structure analysis indicates significant strain-induced variations in the bandgap and energy levels, particularly in non-(001) orientations. In addition, the absorption coefficient strongly depends on the substrate orientation. The (111) and (001) orientations exhibit the highest absorption coefficient values, making them strong candidates for solar cell applications. Other orientations may be more suitable for laser applications due to their high material gain values.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.399

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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