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Record W4394742525 · doi:10.1021/acs.nanolett.4c00759

Mid-infrared Imaging Using Strain-Relaxed Ge<sub>1–<i>x</i></sub>Sn<sub><i>x</i></sub> Alloys Grown on 20 nm Ge Nanowires

2024· article· en· W4394742525 on OpenAlexafffund
Lu Luo, Mahmoud R. M. Atalla, Simone Assali, Sebastian Koelling, Gérard Daligou, Oussama Moutanabbir

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationHorizon 2020 Framework ProgrammeAir Force Office of Scientific ResearchMitacsCanada Research ChairsInnovation for Defence Excellence and Security
KeywordsInfraredMaterials scienceNanowireStrain (injury)GermaniumX-ray crystallographyCrystallographyDiffractionNanotechnologyOptoelectronicsOpticsChemistryPhysicsSilicon

Abstract

fetched live from OpenAlex

Germanium–tin (Ge 1– x Sn x ) semiconductors are a front-runner platform for compact mid-infrared devices due to their tunable narrow bandgap and compatibility with silicon processing. However, their large lattice parameter has been a major hurdle, limiting the quality of epitaxial layers grown on silicon or germanium substrates. Herein, we demonstrate that 20 nm Ge nanowires (NWs) act as effective compliant substrates to grow extended defect-free Ge 1– x Sn x alloys with a composition uniformity over several micrometers along the NW growth axis without significant buildup of the compressive strain. Ge/Ge 1– x Sn x core/shell NWs with Sn content spanning the 6–18 at. % range are achieved and processed into photoconductors exhibiting a high signal-to-noise ratio at room temperature with a cutoff wavelength in the 2.0–3.9 μm range. The processed NW devices are integrated in an uncooled imaging setup enabling the acquisition of high-quality images under both broadband and laser illuminations at 1550 and 2330 nm without the lock-in amplifier technique.

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 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 score0.033
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.210
Teacher spread0.200 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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