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Record W4400310144 · doi:10.1088/1361-6463/ad5f38

Nucleation of InP on Si under micro-crucibles at ultra-high vacuum using a two-step VLS process

2024· article· en· W4400310144 on OpenAlexafffund
Galih R. Suwito, S. Haffouz, Dan Dalacu, Philip J. Poole, Nathaniel J. Quitoriano

Bibliographic record

VenueJournal of Physics D Applied Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsNational Research Council CanadaMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNucleationCrucible (geodemography)CatalysisEpitaxyMaterials scienceUltra-high vacuumMetalMolecular beam epitaxyMetallurgyChemistryChemical engineeringNanotechnologyComputational chemistry

Abstract

fetched live from OpenAlex

Abstract We reported nucleation mechanisms of InP directly on Si (8% lattice mismatch) under confined structures, called micro-crucibles, at ultra-high vacuum (UHV) by chemical beam epitaxy. These micro-crucibles are used to induce lateral growth in the presence of a micro-scale Au catalyst. It is found that at this UHV condition, the kinetics is dictated predominantly by adatom surface diffusion. Using a two-step growth process ((1) In-only exposure, then, (2) simultaneous In and P exposures), InP islands have been successfully nucleated on Si substrates under micro-crucible structures. The nucleation of these InP islands strongly depends on the metal catalyst location relative to the micro-crucible opening with metal catalysts residing closer to the opening having a higher chance to get incorporated with In and P atoms. Importantly, we found that using smaller micro-crucibles with double openings can increase the possibility of having metal catalysts reside near either opening and nucleate InP under micro-crucibles.

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 score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.290
Teacher spread0.268 · 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
Published2024
Admission routes2
Has abstractyes

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