High-Resolution STEM and EELS as Tools to Study Structure and Bonding of Oxides
Bibliographic record
Abstract
Due to the developments of aberration correctors, bright electron sources, stable microscopes and electron monochromators, electron microscopy has dramatically evolved in the recent years. The current microscopes provide structural, chemical and spectroscopic information with sub-angstrom resolution and with synchrotron-quality spectroscopic performance ranging from the mid-infrared to the hard X-ray regime. Using a combination of scanning transmission electron microscopy (STEM) and electron energy loss spectroscopy (EELS) with better than 0.1eV energy resolution (down to 10meV), we provide here a review of recent studies where EELS and STEM have allowed us to probe the structure, the local chemistry and the nature of the local electronic structure of a range of complex oxides. These studies show that it is possible to determine the location of particular atomic species used as dopants in a crystal [1], the local coordination and valence of atoms in crystals and at surfaces [2,3], and also the nature of the hybridization and valence in perovskites [4] and superconductors [5,6]. These applications show that EELS and STEM can be used to resolve ambiguities in structure refinements of oxides by deducing the site preference of transition metal atoms and their coordination. We also show that it is possible to extract valence information and localization of electron charge in a range of materials, thus providing essential information on termination at interfaces [7]. With these techniques, we explore defects in materials and the nature of the electronic structure at interfaces.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".