Efficiency of Hot-Electron Generation in Plasmonic Nanocrystals with Complex Shapes: Surface-Induced Scattering, Hot Spots, and Interband Transitions
Bibliographic record
Abstract
The generation of hot electrons is an intrinsic property of all plasmonic nanocrystals under illumination. However, the number of such excited electrons will strongly depend on the shape, material, and excitation wavelength. In this paper, we develop a practical self-consistent formalism to describe the generation of energetic electrons in a plasmonic nanocrystal with an arbitrary shape. We apply our formalism to gold nanospheres, nanorods, and nanostars. Among the investigated shapes, the nanostar geometry demonstrates the best performance, with an internal energy efficiency of ∼25%. This superior capability of hot-electron generation in the nanostars comes from the following factors: strong hot spots in the red spectral region, isotropic optical response, and the absence of interband transitions at the plasmonic resonance. Spherical gold nanocrystals show strong interband absorption at the plasmon resonance, and the related efficiency of the generation of hot holes in the d band can reach a level of 70%. By analyzing the energy performance of nanocrystals under CW illumination, we show that the most relevant parameter to consider is the rate of hot-electron generation, whereas the steady-state numbers of thermalized and nonthermalized electrons play secondary roles. The physical principles formulated in this study can be used to design a variety of plasmonic nanomaterials for applications in photocatalysis and photodetection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".