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Selective-area vapour–liquid–solid growth of InP nanowires

2009· article· en· W2025479156 on OpenAlexaff
Dan Dalacu, A. Kam, D. G. Austing, Xiaohua Wu, J. Lapointe, G. C. Aers, Philip J. Poole

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsInstitute for Microstructural Sciences
Fundersnot available
KeywordsNanowireMaterials scienceIndiumSubstrate (aquarium)NanotechnologyVapor–liquid–solid methodEpitaxyFabricationOptoelectronicsLayer (electronics)

Abstract

fetched live from OpenAlex

A comparison is made between the conventional non-selective vapour-liquid-solid growth of InP nanowires and a novel selective-area growth process where the Au-seeded InP nanowires grow exclusively in the openings of a SiO(2) mask on an InP substrate. This new process allows the precise positioning and diameter control of the nanowires required for future advanced device fabrication. The growth temperature range is found to be extended for the selective-area growth technique due to removal of the competition between material incorporation at the Au/nanowire interface and the substrate. A model describing the growth mechanism is presented which successfully accounts for the nanoparticle size-dependent and time-dependent growth rate. The dominant indium collection process is found to be the scattering of the group III source material from the SiO(2) mask and subsequent capture by the nanowire, a process that had previously been ignored for selective-area growth by chemical beam epitaxy.

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)0.0000.000

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations130
Published2009
Admission routes1
Has abstractyes

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