Plasmon excitation in metal slab by fast point charge: The role of additional boundary conditions in quantum hydrodynamic model
Bibliographic record
Abstract
We study the wake effect in the induced potential and the stopping power due to plasmon excitation in a metal slab by a point charge moving inside the slab. Nonlocal effects in the response of the electron gas in the metal are described by a quantum hydrodynamic model, where the equation of electronic motion contains both a quantum pressure term and a gradient correction from the Bohm quantum potential, resulting in a fourth-order differential equation for the perturbed electron density. Thus, besides using the condition that the normal component of the electron velocity should vanish at the impenetrable boundary of the metal, a consistent inclusion of the gradient correction is shown to introduce two possibilities for an additional boundary condition for the perturbed electron density. We show that using two different sets of boundary conditions only gives rise to differences in the wake potential at large distances behind the charged particle. On the other hand, the gradient correction in the quantum hydrodynamic model is seen to cause a reduction in the depth of the potential well closest to the particle, and a reduction of its stopping power. Even for a particle moving in the center of the slab, we observe nonlocal effects in the induced potential and the stopping power due to reduction of the slab thickness, which arise from the gradient correction in the quantum hydrodynamic model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".