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Record W4411086134 · doi:10.1002/adts.202500282

Ultrafast Radiative Recombination Engineering in InGaN/GaN Quantum Wells through Temperature, Alloy Fraction and Layer's Width Tuning for Photonics

2025· article· en· W4411086134 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Theory and Simulations · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsOptoelectronicsPhotonicsMaterials scienceUltrashort pulseAlloyQuantum wellLayer (electronics)RecombinationOpticsNanotechnologyPhysicsComposite materialLaserChemistry

Abstract

fetched live from OpenAlex

Abstract Radiative lifetime (RT) on the picosecond to femtosecond scale plays a key role in enabling ultrafast optoelectronic technologies. This study investigates the RT in InGaN/GaN quantum wells (QWs), focusing on the effects of well thickness, temperature, and indium (In) composition using the finite element method. The results show a significant dependence of RT on these parameters for both subband (ISB) and band‐to‐band (BTB) recombination. Specifically, radiative lifetime for ISB transitions extend to nanosecond timescales, with values reaching up to 6 ns, reflecting reduced wave function overlap and lower recombination probabilities at higher temperatures. In contrast, BTB recombination exhibits much faster dynamics in the fs range, with lifetimes as short as 10 fs, which is critical for high‐speed applications. This research highlights the importance of precisely controlling QWs parameters, as well as internal and external factors, to optimize device performance in emerging InGaN‐based ultrafast technologies.

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.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.483

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 head0.009
GPT teacher head0.272
Teacher spread0.264 · 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