Electron Channeling Contrast Imaging (ECCI) of Ion Battery Cathode Materials
Bibliographic record
Abstract
The microstructural characterization of ion battery cathode materials is often studied by transmission electron microscope (TEM) due to its high spatial and angular resolution which enables precise analysis of the battery materials.However, TEM studies present several challenges that might hinder their suitability for numerous potential applications.The conventional processes of preparing thin foils for TEM analysis, such as twin jet electropolishing, are time consuming and expensive.Moreover, when high-energy electron beams interact with the specimen in TEM, the studied sample may experience damage through various mechanisms, including knock-on displacement, radiolysis, and heating, leading to changes in the microstructure and properties of materials, particularly those sensitive to beams [1,2].Here, another interesting approach for bulk specimens is presented that offers both a high spatial resolution and a large field of view at relatively low accelerating voltages: the use of ECCI in a field emission scanning electron microscope (FESEM).ECCI is an imaging method that relies on the change of backscattered electron (BSE) intensity caused by differences in the angle between the incoming electron beam and the crystallographic orientation of the lattice planes in crystalline samples.When the incoming electrons are parallel (or very close to parallel) to the lattice planes, low BSE intensity and hence a darker area can be anticipated, while with the increase in the angle, higher intensity and a brighter area are expected.ECCI technique allows us to figure out how the specimen's crystallographic orientation changes, along with identifying features like grain boundaries and cracks, as well as individual lattice defects such as dislocations [3,4].In this study, the microstructural evolution of two different layered Li-ion based (composed of secondary particles) and Na-ion based (composed of primary particles) cathode materials in their pristine state was investigated.High resolution secondary electron (SE) and ECC images were acquired with the use of Hitachi SU8000 dedicated FESEM at a relatively low accelerating voltage of 4 kV.For this purpose, cross-sectional samples were prepared using a Hitachi IM4000 ion milling machine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".