A bidirectional scanning method for scanning transmission X-ray microscopy
Bibliographic record
Abstract
Scanning mode is a key factor for the comprehensive performance, including imaging efficiency, of scanning transmission X-ray microscopy (STXM). Herein is presented a bidirectional scanning method designed for STXM with an S-shaped moving track. In this method, artificially designed ramp waves are generated by a piezo-stage controller to control the two-dimensional scanning of the sample. The sample position information is measured using laser interferometric sensors and sent to a field-programmable gate array (FPGA) board which also acquires the X-ray signals simultaneously from the detector. Since the data recorded by the FPGA contain the real position of each scanned point, the influence of the backlash caused by the back-turning movement on the STXM image can be eliminated. By employing an adapted post-processing program, a re-meshed high-resolution STXM image can be obtained. This S-track bidirectional scanning method in fly-scan mode has been implemented on the STXM endstation at the Shanghai Synchrotron Radiation Facility (SSRF), and successfully resolved the ∼30 nm interval between the innermost strips of a Siemens star. This work removes the limitation on bidirectional scanning caused by motor backlash and vibration, and significantly improves the efficiency of STXM experiments.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".