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Record W4391401592 · doi:10.1021/acs.nanolett.3c04445

Delta-T Flicker Noise Demonstrated with Molecular Junctions

2024· article· en· W4391401592 on OpenAlexafffund
Ofir Shein-Lumbroso, Matthew Gerry, Abhay Shastry, Ayelet Vilan, Dvira Segal, Oren Tal

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Toronto
FundersH2020 European Research CouncilNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsFlicker noiseFlickerNoise (video)Electrical conductorShot noiseConductorNanoscopic scaleOptoelectronicsPhysicsCondensed matter physicsNoise spectral densityMaterials scienceJohnson–Nyquist noiseVoltageNanotechnologyElectrical engineeringOpticsNoise figureComputer scienceEngineeringQuantum mechanics

Abstract

fetched live from OpenAlex

Electronic flicker noise is recognized as the most abundant noise in electronic conductors, either as an unwanted contribution or as a source of information on electron transport mechanisms and material properties. This noise is typically observed when a voltage difference is applied across a conductor or current is flowing through it. Here, we identify an unknown type of electronic flicker noise that is found when a temperature difference is applied across a nanoscale conductor in the absence of a net charge current or voltage bias. The revealed delta-T flicker noise is demonstrated in molecular junctions and characterized using quantum transport theory. This noise is expected to arise in nanoscale electronic conductors subjected to unintentional temperature gradients, where it can be a performance-limiting factor. On the positive side, delta-T flicker noise can detect temperature differences across a large variety of nanoscale conductors, down to atomic-scale junctions with no special setup requirements.

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 score0.574
Threshold uncertainty score0.535

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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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