Mapping Bright and Dark Modes in Gold Nanoparticle Chains using Electron Energy Loss Spectroscopy.
Bibliographic record
Abstract
We present a scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) investigation of gold nanosphere chains with lengths varying from 1 to 5 particles. We show localized EELS signals from the chains and identify energy-loss peaks arising due to l = 1, 2, 3, 4, and 5 plasmon modes through the use of EELS mapping. We also show the evolution of the energy of these modes as the length of a given chain increases, and we find that a chain containing N particles can accommodate at least N experimentally observable modes, in addition to the transverse mode. As the chain length is increased by the addition of one more gold particle to the chain, the new N + 1 mode becomes the highest energy mode, while the existing modes lower their energy and eventually asymptote as they delocalize along the chain. We also show that modes become increasingly difficult to detect with the EELS technique as l approaches N. The data are compared to numerical simulations.
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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".