Characterization and control of charge transfer in a tunnel junction
Bibliographic record
Abstract
Abstract Charge transfer in a tunnel junction is studied under dc and ac voltage bias using quantum shot noise. Under dc voltage bias V, spectral density of noise measured within a very large bandwidth enables to deduce the current–current correlator in the time domain by Fourier transform. This correlator exhibits regular oscillations proving that electrons try to cross the junction regularly, every . Using harmonic and bi‐harmonic ac voltage bias, we then show that quasiparticles excitations can be transferred through the junction in a controlled way. By measuring the reduction of the excess shot noise, we are able to determine the number of electron–hole pairs surrounding the injected electrons and demonstrate that bi‐harmonic voltage pulses realize an on‐demand electron source with a very small admixture of electron–hole pairs. A time‐dependent voltage drive applied to a contact with transmission generates an incoming excitation giving rise to transmitted and reflected quasiparticles. The excess noise given by the difference between the noise measured with and without the ac excitation is measured by an ammeter with a bandwidth . It gives the number of electron–hole pairs surrounding the transmitted electrons: where is the repetition frequency of .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".