Insight Into the Properties of Functional Materials with Energy Filtered and Analytical TEM
Bibliographic record
Abstract
Abstract Energy filtered (EF) imaging and energy loss fine structure analysis have now become routine tools to characterize very complex microstructures at high spatial resolution. When combined with energy dispersive x-ray spectroscopy microanalysis in a field emission gun transmission electron microscope (TEM) and with samples prepared by a large array of techniques such as FIB or microtoming, these analytical techniques can tackle real-life and more fundamental problems in materials science. This paper presents some examples of this work that highlight the complementarity of information obtained either from EF imaging, near-edge structures and EDS microanalysis. These examples are taken from work in the development of battery materials for automotive applications and in the study of ultrathin magnetic layers. Other examples of high spatial resolution energy loss fine structure at interfaces of high-K dielectric-Si in interface are shown in elsewhere in these proceedings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".