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Record W4327576559 · doi:10.21203/rs.3.rs-2673748/v1

Ultralow Threshold Surface Emitting Ultraviolet Lasers with Semiconductor Nanowires

2023· preprint· en· W4327576559 on OpenAlexafffund
Mohammad Fazel Vafadar, Songrui Zhao

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsLasing thresholdOptoelectronicsMaterials scienceLaserUltravioletNanowireSemiconductorSemiconductor laser theoryWavelengthPhotonicsOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Surface-emitting semiconductor lasers have changed our everyday life in various ways such as communication and sensing. Expanding the operation wavelength of surface-emitting semiconductor lasers to shorter ultraviolet (UV) wavelength range further broadens the applications to disinfection, medical diagnostics, phototherapy, and so on. Nonetheless, the UV surface-emitting lasers demonstrated so far are all using conventional vertical cavities, all with large lasing thresholds in the range of several hundred kW/cm^2 to MW/cm^2. Here, we report ultralow threshold surface-emitting lasing in the UV range using novel epitaxial nanowire photonic crystal structures. Lasing at 367 nm is measured, with a threshold of only 7 kW/cm^2, a factor of 100× reduction compared to the previously reported surface-emitting UV lasers at similar wavelengths. Further given the excellent electrical doping that has already been demonstrated in nanowires, this work offers a viable path for the development of the long-sought-after surface-emitting semiconductor UV lasers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.075
GPT teacher head0.378
Teacher spread0.303 · 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.

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
Published2023
Admission routes2
Has abstractyes

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