High bunch charge low-energy electron streak diffraction
Bibliographic record
Abstract
For time-resolved diffraction studies of irreversible structural dynamics upon photoexcitation, there are constraints on the number of perturbation cycles due to thermal effects and accumulated strain, which impact the degree of crystal order and spatial resolution. This problem is exasperated for surface studies that are more prone to disordering and defect formation. Ultrafast electron diffraction studies of these systems, with the conventional stroboscopic pump-probe protocol, require repetitive measurements on well-prepared diffraction samples to acquire and average signals above background in the dynamic range of interest from few tens to hundreds of picoseconds. Here, we present ultrafast streaked low-energy electron diffraction (LEED) that demands, in principle, only a single excitation per nominal data acquisition timeframe. By exploiting the space-time correlation characteristics of the streaking method and high-charge 2 keV electron bunches in the transmission geometry, we demonstrate about one order of magnitude reduction in the accumulated number of the excitation cycles and total electron dose, and 48% decrease in the root mean square error of the model fit residual compared to the conventional time-scanning measurement. We believe that our results demonstrate a viable alternative method with higher sensitivity to that of nanotip-based ultrafast LEED studies relying on a few electrons per a single excitation, to access to all classes of structural dynamics to provide an atomic level view of surface processes.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".