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Record W2090946478 · doi:10.1063/1.4751988

Controlled axial and radial Te-doping of GaAs nanowires

2012· article· en· W2090946478 on OpenAlexaff
O. Salehzadeh, K. L. Kavanagh, S. P. Watkins

Bibliographic record

VenueJournal of Applied Physics · 2012
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceNanowireEpitaxyDopingMetalorganic vapour phase epitaxyAnalytical Chemistry (journal)Scanning electron microscopeTelluriumSubstrate (aquarium)GalliumOptoelectronicsNanotechnologyChemistryMetallurgy

Abstract

fetched live from OpenAlex

Tellurium (Te)-doping of Au-catalyzed GaAs nanowires (NWs) grown by metalorganic vapor phase epitaxy (MOVPE) via the vapor-liquid-solid (VLS) mechanism is presented. Electrical measurements were performed inside a scanning electron microscope by contacting a tungsten nanoprobe to the Au end of individual NWs grown on a heavily n-type GaAs substrate. Rectifying current-voltage (I-V) characteristics are observed due to the formation of a junction at the Au nanoparticle (NP)/NW interface. The electron concentration ne and contact barrier heights, φ0b, were determined from the analyses of these characteristics. As expected, φ0b increased (from 0.63 ± 0.03 eV to 0.71 ± 0.02 eV) with decreasing Te-precursor flow rate, corresponding to a decrease in ne from (9 ± 1) × 1017 cm−3 to (1.5 ± 0.5) × 1017 cm−3. Meanwhile, undoped NWs had space-charge-limited characteristics. There was a large influence of the residual gallium (Ga) in the NP, on barrier properties, controlled by the group V precursor flow (on or off) during the cooling of the NW sample at the end of the growth process. With the group V flow off during cooling, a decrease in φ0b from 0.79 ± 0.04 eV to 0.63 ± 0.03 eV is observed consistent with a higher Ga alloy concentration in the NP, confirmed by energy dispersive spectroscopy measurements. We also demonstrate the fabrication of core/shell, undoped/Te-doped, GaAs NWs with very high Te doping (∼1019 cm−3).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.213
Teacher spread0.203 · 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 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

Citations12
Published2012
Admission routes1
Has abstractyes

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