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Record W2315585140 · doi:10.1063/1.4858382

Strain-induced vertical self-organization of semiconductor quantum dots: A computational study

2013· article· en· W2315585140 on OpenAlexaff
N. Shtinkov

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsQuantum dotCondensed matter physicsMaterials scienceAnisotropyLattice (music)SemiconductorValence (chemistry)Substrate (aquarium)NanotechnologyOpticsPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Atomistic strain simulations based on the valence force field method are employed to study the vertical arrangements of semiconductor quantum dot (QD) multilayers. The effects of the QD shape, dimensions, and materials parameters are systematically investigated, varying independently the following parameters: spacer width H, QD lateral spacing D, base b, and height h, slope of the side facets, elastic properties of the dot and the substrate materials, and lattice mismatch between the dot and the substrate. The transition between vertically aligned and anti-aligned structures is found to be determined mainly by the ratios H/D and b/D, as well as by the strain anisotropy of the substrate and to a lesser extent of the QD. The dependence on the QD height h is significant only for steep side facets and large aspect ratios h/b, and the effects of the lattice mismatch strain and the bulk elastic moduli are found to be negligible. The comparison with experimental data shows an excellent agreement with the results from the simulations, demonstrating that the presented analysis results in precise theoretical predictions for the vertical self-organization regime in a wide range of QD materials systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.620

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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