High-energy resolution monochromated STEM-EELS mapping across large areas
Bibliographic record
Abstract
Scanning transmission electron microscopy (STEM) allows for high spatial-resolution analysis of materials and, when coupled with electron energy loss spectroscopy (EELS), becomes capable of providing substantial insight into both chemical and optical properties. In recent years, focus has moved towards understanding material properties at the atomic level using EELS. However, there are still significant barriers when attempting to perform high-energy resolution monochromated STEM-EELS analysis on large structures. Off-axis distortions cause additional aberrations to couple into the spectrometer when scanning across large regions. This often limits STEM-EELS mapping to small areas to maintain the energy resolution or requires sacrificing this resolution to spectrum maps spanning multiple microns. We propose here a methodology enabling low-loss STEM-EELS spectrum mapping to be performed over tens to hundreds of microns while maintaining high energy-resolution through modification of the EELS collection conditions. This is accomplished not only through careful alignment of the scan/descan coils, but, more importantly, through implementation of elongated camera lengths that effectively magnify the object over the EELS entrance aperture, cutting out higher order aberrations and reducing shifts on the spectrometer.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".