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Coupled Effects in Quantum Dot Nanostructures with Nonlinear Strain and\n Bridging Modelling Scales

2007· preprint· en· W4298338620 on OpenAlexfundno aff
Roderick Melnik, D. Roy Mahapatra

Bibliographic record

VenuearXiv (Cornell University) · 2007
Typepreprint
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPiezoelectricityNonlinear systemContext (archaeology)Statistical physicsBridging (networking)QuantumQuantum dotStrain engineeringEnergy minimizationPhysicsComputer scienceCondensed matter physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We demonstrate that the conventional application of linear models to the\nanalysis of optoelectromechanical properties of nanostructures in bandstructure\nengineering could be inadequate. The focus of the present paper is on a model\nbased on the coupled Schrodinger-Poisson system where we account consistently\nfor the piezoelectric effect and analyze the influence of different nonlinear\nterms in strain components. The examples given in this paper show that the\npiezoelectric effect contributions are essential and have to be accounted for\nwith fully coupled models. While in structural applications of piezoelectric\nmaterials at larger scales, the minimization of the full electromechanical\nenergy is now a routine in many engineering applications, in bandstructure\nengineering conventional approaches are still based on linear models with\nminimization of uncoupled, purely elastic energy functionals with respect to\ndisplacements. Generalizations of the existing models for bandstructure\ncalculations are presented in this paper in the context of coupled effects.\n

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.171
Teacher spread0.145 · 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 designSimulation or modeling
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".

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Citations0
Published2007
Admission routes1
Has abstractyes

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