Andreev bound states in a few-electron quantum dot coupled to superconductors
Bibliographic record
Abstract
A direct measurement of the density of Andreev bound states (ABSs) is experimentally investigated in a superconductor--quantum dot--superconductor hybrid nanowire system. A hard proximity-induced superconducting gap is observed, arising from superconducting correlations, in the transport spectrum of the hybrid system. Conductance peaks observed inside the superconducting gap reveal that the subgap states participate in the transport of the hybrid junction. We explore the evolution of low-energy Andreev bound states in a few-electron quantum dot (QD) coupled to superconductors, by probing the magnetic field dependence of exquisite detailed conductance spectra with a small bias voltage applied on the superconducting lead. In the presence of low magnetic fields, the resonance current is enhanced and broadened, attributed to the transport through Andreev bound states in QD, as the energy of ABSs reaches the threshold set by the applied bias voltage with the increase of the magnetic field. The simulated transport spectrum matches the experimentally observed evolution patterns of conductance, further implying the superconducting correlation nature of the observed electron transport. At high magnetic fields, the conductance maxes as the Fermi level reaches the degeneration point of Landau levels, leading to conductance peaks shown in the alternating narrow and wide patterns of Coulomb blockade oscillations.
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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 | 0.000 |
| 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".