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Record W1997292204 · doi:10.1063/1.3122596

Quantitative compositional analysis and strain study of InAs quantum wires with InGaAlAs barrier layers

2009· article· en· W1997292204 on OpenAlexafffund
Kai Cui, M. Robertson, B. J. Robinson, Carmen M. Andrei, Dylan Thompson, Gianluigi A. Botton

Bibliographic record

VenueJournal of Applied Physics · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsBrockhouse Institute for Materials ResearchAcadia UniversityMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsOntario Centres of Excellence
KeywordsIndiumTransmission electron microscopyMaterials scienceQuantum dotBarrier layerElectron diffractionScanning transmission electron microscopyRutherford backscattering spectrometryDiffractionOptoelectronicsLayer (electronics)Condensed matter physicsNanotechnologyThin filmOpticsPhysics

Abstract

fetched live from OpenAlex

Quantitative compositional analysis of InAs quantum wires deposited between In0.53Ga0.37Al0.1As barrier layers grown on InP substrates was performed by electron energy loss spectrometry and energy dispersive x-ray spectrometry. An indium-rich region in the center of the wire, with decreasing indium concentration toward the interface with the barrier layers, was observed from indium concentration maps for individual quantum wires. “Stripelike” contrast modulation was observed in diffraction contrast transmission electron microscope images of the In0.53Ga0.37Al0.1As barrier layer immediately above the quantum wires. The contrast originated from indium compositional modulations in the upper barrier layer as confirmed by electron energy loss spectrometry and the modulation is attributed to the presence of an inhomogeneous elastic strain field generated by the buried quantum wires. These results suggest that quantitative analysis of the composition and strain distributions at very high spatial resolution provides insights necessary to further model the physical properties and to understand the growth of these nanostructures.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score0.494

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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 designObservational
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

Citations7
Published2009
Admission routes2
Has abstractyes

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