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Record W4408599179 · doi:10.1117/12.3045580

Ultraviolet-C band AlGaN heterostructures grown on nanopatterned sapphire substrates for lighting and photodetection applications

2025· article· en· W4408599179 on OpenAlexaff
Sharif Md. Sadaf, Nirmal Anand, Christy Giji Jenson, Md Zunaid Baten, Dipon Kumar Ghosh, Md. Afjalur Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhotodetectionSapphireOptoelectronicsMaterials scienceHeterojunctionUltravioletWide-bandgap semiconductorPhotodetectorOpticsLaserPhysics

Abstract

fetched live from OpenAlex

AlGaN-semiconductor based ultraviolet-C (UV-C) heterostructures are poised to revolutionize the UV lamp market by replacing bulky and toxic low-pressure mercury lamps. This is due to their compact size, long lifespan, lower direct current (DC) operating voltages, and the absence of toxic materials. However, the external quantum efficiency (EQE) of UV-C LEDs remains significantly lower than that of visible InGaN emitters. In this study, we have demonstrated that using nanopatterned sapphire substrates (NPSS) for UV-C epitaxial heterostructure growth can address these challenges. This approach reduces threading dislocations and improves TM-polarized light extraction through effective scattering, enhancing EQE and overall performance of UV-C LEDs. Furthermore, we explore the size-dependent optoelectronic properties of planar AlGaN UV-C <i>&mu;</i>LEDs and compare them with ultra-dense nanowire <i>&mu;</i>LEDs fabricated via a top-down approach. We also study the AlGaN heterostructure-based device performance under UV light irradiation for photodetection at reverse bias, highlighting the advantage of NPSS, and nanostructure fabrication for enhanced light absorption in the active layer and charge collection, thus presenting effective strategies for multifunctional integrated photonic devices.

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 score0.006
Threshold uncertainty score0.469

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 head0.008
GPT teacher head0.251
Teacher spread0.243 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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