Electronic excitations and tunneling spectra of metallic nanograins
Bibliographic record
Abstract
Tunneling-induced electronic excitations in a nonmagnetic metallic nanograin are classified in terms of generations: subspaces of excitations containing a specific number of electron-hole pairs. This yields a hierarchy of populated excited states of the nanograin which strongly depends on (a) the available electronic energy levels and (b) the ratio between the electronic relaxation rate within the nanograin and the bottleneck rate for tunneling transitions. To study the response of the electronic-energy-level structure of the nanograin to the excitations, and its signature in the tunneling spectrum, we propose a microscopic mean-field theory. Two main features emerge when considering an Al nanograin coated with Al oxide. First, the electronic energy response fluctuates strongly in the presence of disorder, from level to level and excitation to excitation. Such fluctuations produce a dramatic sample dependence of the tunneling spectra. On the other hand, for excitations that are energetically accessible at low applied bias voltages, the magnitude of the response, reflected in the renormalization of the single-electron energy levels, is smaller than the average spacing between energy levels. Second, if the tunneling and electronic relaxation time scales are such as to admit a significant nonequilibrium population of the excited nanoparticle states, it should be possible to realize much higher spectral densities of resonances than have been observed to date in such devices. These resonances arise from tunneling into ground-state and excited electronic energy levels, as well as from charge fluctuations present during tunneling.
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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".