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Record W2924942378 · doi:10.1088/1361-6528/ab3209

Axial GaAs/Ga(As, Bi) nanowire heterostructures

2019· preprint· en· W2924942378 on OpenAlexaff
Miriam Oliva, Guanhui Gao, E. Luna, Lutz Geelhaar, Ryan B. Lewis

Bibliographic record

VenueNanotechnology · 2019
Typepreprint
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
FundersAlexander von Humboldt-Stiftung
KeywordsNanowireHeterojunctionMetastabilityMolecular beam epitaxyMaterials scienceSemiconductorNanotechnologyPlanarCrystal growthEpitaxyCrystal (programming language)CrystallographyOptoelectronicsCondensed matter physicsChemistryPhysicsLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Bi-containing III-V semiconductors constitute an exciting class of metastable compounds with wide-ranging potential optoelectronic and electronic applications. However, the growth of III-V-Bi alloys requires group-III-rich growth conditions, which pose severe challenges for planar growth. In this work, we exploit the naturally-Ga-rich environment present inside the metallic droplet of a self-catalyzed GaAs nanowire (NW) to synthesize metastable GaAs/GaAs1−x Bi x axial NW heterostructures with high Bi contents. The axial GaAs1−x Bi x segments are realized with molecular beam epitaxy by first enriching only the vapor–liquid–solid (VLS) Ga droplets with Bi, followed by exposing the resulting Ga-Bi droplets to As2 at temperatures ranging from 270 °C to 380 °C to precipitate GaAs1−x Bi x only under the NW droplets. Microstructural and elemental characterization reveals the presence of single crystal zincblende GaAs1−x Bi x axial NW segments with Bi contents up to (10 ± 2)%. This work illustrates how the unique local growth environment present during the VLS NW growth can be exploited to synthesize heterostructures with metastable compounds.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.223
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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