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Record W4384925747 · doi:10.1038/s41467-023-40140-0

Short-wave infrared cavity resonances in a single GeSn nanowire

2023· article· en· W4384925747 on OpenAlexafffund
Youngmin Kim, Simone Assali, Hyo‐Jun Joo, Sebastian Koelling, Melvina Chen, Lu Luo, Xuncheng Shi, Daniel Burt, Z. Ikonić, Donguk Nam, Oussama Moutanabbir

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
FundersArmy Research OfficeHORIZON EUROPE Framework ProgrammeMitacsCanada Research ChairsAgence Nationale de la RechercheNational Research FoundationMinistry of Education - SingaporeNational Research Foundation SingaporeNatural Sciences and Engineering Research Council of CanadaMinistry of Education, IndiaEuropean Commission
KeywordsNanowireInfraredOptoelectronicsMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Nanowires are promising platforms for realizing ultra-compact light sources for photonic integrated circuits. In contrast to impressive progress on light confinement and stimulated emission in III-V and II-VI semiconductor nanowires, there has been no experimental demonstration showing the potential to achieve strong cavity effects in a bottom-up grown single group-IV nanowire, which is a prerequisite for realizing silicon-compatible infrared nanolasers. Herein, we address this limitation and present an experimental observation of cavity-enhanced strong photoluminescence from a single Ge/GeSn core/shell nanowire. A sufficiently large Sn content ( ~ 10 at%) in the GeSn shell leads to a direct bandgap gain medium, allowing a strong reduction in material loss upon optical pumping. Efficient optical confinement in a single nanowire enables many round trips of emitted photons between two facets of a nanowire, achieving a narrow width of 3.3 nm. Our demonstration opens new possibilities for ultrasmall on-chip light sources towards realizing photonic-integrated circuits in the underexplored range of short-wave infrared (SWIR).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.281
Teacher spread0.242 · 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 designNot applicable
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

Citations21
Published2023
Admission routes2
Has abstractyes

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