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Record W2908671388 · doi:10.1103/physrevb.99.035423

Coulomb drag between quantum wires: A nonequilibrium many-body approach

2019· article· en· W2908671388 on OpenAlexafffund
Chenyi Zhou, Hong Guo

Bibliographic record

VenuePhysical review. B./Physical review. B · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsPhysicsCoulombNon-equilibrium thermodynamicsDragQuantum mechanicsFormalism (music)Classical mechanicsElectronMechanics

Abstract

fetched live from OpenAlex

We present a real-space theoretical formalism and its numerical implementation for investigating nonequilibrium quantum Coulomb drag between parallel two-terminal transport structures in quasi-one-dimension. In addition to the Coulomb interaction and the finite external potential bias, our formalism takes into account the effects of impurity disorder. The theory is formulated in the nonequilibrium Green's function formalism, with the long-range Coulomb interaction treated at the many-body GW approximation level and the disorder-average carried out at the coherent potential approximation (CPA) level. The coupled GW and CPA equations are solved self-consistently so that the fundamental conservation laws are ensured. The effects related to the electron-hole symmetry in the Coulomb drag physics have been generalized to the nonlinear transport regime. A set of symmetry-induced relations linking physical quantities with the particle distribution were established on generic footing and, remarkably, they are found robust against uniformly distributed impurities. As an application, the theoretical formalism is employed to analyze the Coulomb drag transport physics in quasi-one-dimensional systems. The dependencies of the drag current on external bias, chemical potential, temperature, and the system sizes are predicted.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.320
Teacher spread0.308 · 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.

Study designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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