COLLECTIVE ELECTRONIC EXCITATIONS IN SYSTEMS EXHIBITING QUANTUM WELL STATES
Bibliographic record
Abstract
We present high-resolution electron energy loss spectroscopy (HREELS) measurements on surface plasmon (SP) dispersion in systems exhibiting quantum well states (QWS), i.e. Na / Cu (111), Ag / Cu (111), and Ag / Ni (111). Our results demonstrate that the dominant coefficient of SP dispersion for thin and layer-by-layer Ag films presenting QWS is quadratic even at small q || , in contrast with previous measurements on Ag semi-infinite media and Ag thin films deposited on Si (111). We suggest that this behavior is due to screening effects enhanced by the presence of QWS shifting the position of the centroid of the induced charge less inside the geometrical surface compared with Ag surfaces and Ag / Si (111). For ultrathin Ag films, i.e. two layers, the dispersion was found to be not positive, as theoretically predicted. Annealing of the Ag film caused an enhancement of the free-electron character of the QWS, thus inducing a negative linear term of the dispersion curve of the SP. Moreover, we report the first experimental evidence of chemical interface damping in thin films for K / Ag / Ni (111). As regards Na / Cu (111), we found a different dispersion curve compared with thick Na films, thus confirming the enhanced screening by Na QWS. Results reported here should shed light on the influence of QWS on dynamical screening phenomena in thin films.
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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".