Low-Voltage STEM-Eels Quantification for Lithium Ion Battery Material Characterization
Bibliographic record
Abstract
In material sciences, in order to develop new compounds with different electrical, mechanical or optical properties we need to understand the link between these properties and the microstructure of the material. This apply also to understand the decrease of efficiency of a battery after multiple charge-discharge cycle where the structural changes need to be identify in order to prevent it. Scanning transmission electron microscopy (STEM) has been proven to be a powerful tool for the study of microstructure and morphology with a subnanometer resolution. Furthermore, a STEM paired with electron energy loss spectroscopy (EELS) can provide useful information on the specimen elemental composition. However, these technique are normally used with a high electron beam voltage (80-200 keV) and the high energy of the electron beam induce beam damage into low Z materials such as lithium compound. This becomes problematic when the microstructure of a LIB is observed since the change into the material cause by the beam damage is close to the changes cause by the use of batteries during cycling. To prevent beam damage such as knock-on damage, the electron beam voltage need to be reduce to relatively low kV. However, no work on low voltage EELS quantification has been reported. This study will present the acquisition of EELS spectrum using a state-of-the-art dedicated transmission scanning electron microscope (Hitachi-SU9000EA) at 30 keV. Low-voltage EELS quantification on standard will be presented using the integration ratio method. Interesting results will be presented on the inelastic cross-section measurement at low-voltage with the combination of convergent beam electron diffraction(CBED) and EELS. Theses values will be compared with the cross-section obtain using computational calculation that are normally used for high voltage EELS quantification.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".