Comparison of soft X-ray spectro-ptychography and scanning transmission X-ray microscopy
Bibliographic record
Abstract
Over the past decade advances in instrumentation and software have enabled development of spectro-ptychography (SP) as a higher spatial resolution extension of scanning transmission X-ray microscopy (STXM). Direct comparisons are made of same-area chemical state imaging of Cu nanoparticles using STXM and SP in order to compare and contrast the two approaches. We show that SP gives very similar chemical state information as STXM with significantly better spatial resolution and much higher quality images and chemical maps, on account of finer pixels in the reconstructed images. When defocused spot sizes are used (i.e., 1–3 μm, as opposed to full-focus 30–50 nm) SP data acquisition is faster and the radiation dose delivered to the sample is smaller than the corresponding STXM measurement. The limitations of SP are primarily related to the time and complexity of the ptychographic reconstruction. We argue that these documented advantages mean that SP rather than STXM should be used for more complex studies such as tomography and in situ studies, especially when radiation damage is a concern. The main point of this manuscript is to illustrate, with scientifically relevant samples, the significant advantages of SP relative to conventional STXM, with the goal of encouraging greater use of SP. • Quantitative comparisons of STM and spectro-ptychography (SP) of copper nanoparticles. • SP provides similar results with better spatial resolution, statistical precision than STXM. • SP using defocused spot sizes delivers less radiation dose thus less damage than the corresponding STXM.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".