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Record W2164346250 · doi:10.1109/isot.2010.5687365

Quantum-wire conductance manipulating by asymmetric quantum dot-molecules

2010· article· en· W2164346250 on OpenAlexaff
Ali Rostami, H. Rasooli, Ashkan Ghanbari, S. Zabihi, Farrokh Janabi‐Sharifi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsQuantum dotConductanceQuantum wireQuantum tunnellingConductance quantumCondensed matter physicsHamiltonian (control theory)QuantumQuantum point contactNanostructurePhysicsMoleculeResonance (particle physics)Quantum mechanicsQuantum well

Abstract

fetched live from OpenAlex

The electronic conductance at zero temperature through a quantum wire with side-attached asymmetric quantum dot-molecules (as a scatter system) is theoretically studied using the non-interacting Anderson tunneling Hamiltonian method. We show that the asymmetric configuration of QD-scatter system strongly impresses the amplitude and spectrum of quantum wire nanostructure transmission characteristics. It is shown that whenever the balanced number of chains-quantum dots in one molecule is substituted by unbalanced scheme, the number of forbidden mini-bands in quantum wire conductance increases to the sum of the number of quantum dots in two chains and thus the QW-nanostructure electronic conductance contains rich spectral properties due to appearance of the new anti-resonance and resonance points in spectrum. Considering the suitable inner gap between QD-chains in one molecule or outer gap between QD-molecules, can strengthen the amplitude of new resonant peaks in QW conductance spectrum. The proposed asymmetric-QD scatter system idea in this paper opens a new insight on designing quantum wire nanostructures for given electronic conductance.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.000

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)0.0010.000

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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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