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Record W2962771361 · doi:10.1103/physrevb.100.045413

Altshuler-Aronov effects in nonequilibrium disordered nanostructures

2019· article· en· W2962771361 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
KeywordsMesoscopic physicsNon-equilibrium thermodynamicsPhysicsNanodeviceFeynman diagramQuantumCondensed matter physicsQuantum mechanicsQuantum dotNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

The Altshuler-Aronov (AA) effect is one of the most basic quantum many-body effects in the mesoscopic regime. It originates from the coexistence of disorder and electron-electron interaction. In this paper, we reformulate the Feynman diagrammatic theory of the AA effect in a real-space nonequilibrium Green's function framework, in which an effective medium technique (via coherent potential) is employed to evaluate disorder-induced vertices in the diagrams. As such the developed real-space formalism is compatible with the prevailing nanodevice simulation paradigm, leading to an effective numerical approach to calculating the AA effects on electronic structures and quantum transport properties of nanostructures. As an application, we analyze the $I\text{\ensuremath{-}}V$ characteristics and the full local density of states (DOS) profile of an Anderson-Hubbard lattice sandwiched between biased electrodes. We show how the DOS anomaly due to the AA mechanism is reshaped by the geometrical confinement and the nonequilibrium effects in a nanosystem. Our numerical findings are well understood by the analytical results we provide in this paper.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.298
Teacher spread0.294 · 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 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
Published2019
Admission routes2
Has abstractyes

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