Influence of wavelength and pulse duration on single-shot x-ray diffraction patterns from nonspherical nanoparticles
Bibliographic record
Abstract
We introduce a complex scaling discrete dipole approximation (CSDDA) method and study single-shot x-ray diffraction patterns from non-spherical, absorbing nanotargets in the limit of linear response. The convergence of the employed Born series-based iterative solution of the discrete dipole approximation problem via optimal complex mixing turns out to be substantially faster than the original approach with real-valued mixing coefficients, without additional numerical effort per iteration. The CSDDA method is employed to calculate soft x-ray diffraction patterns from large icosahedral silver nanoparticles with diameters up to about . Our analysis confirms the requirement of relatively long wavelengths to map truly 3D structure information to the experimentally accessible regions of 2D scattering images. On the other hand, we show that short wavelengths are preferable to retain visibility of fine structures such as interference fringes in the scattering patterns when using ultrashort x-ray pulses in the attosecond domain. A simple model is presented to estimate the minimal pulse duration below which the fringe contrast vanishes. Knowledge of the impact of the bandwidth of short pulses on the diffraction images is important to extract information on ultrafast dynamical processes from time-resolved x-ray diffractive imaging experiments on free nanoparticles, in particular at long wavelengths.
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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) | 0.000 | 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".