Corelative Imaging of Battery Heterogeneities: Ionic Transport, Electronic Structure and Chemistry
Bibliographic record
Abstract
Deeper understanding of the heterogeneities, an intrinsic characteristic in batteries, including ionic transport, and chemical and electronic structures at interface and bulk of active materials and its dependence on other components in a battery is crucial for developing better performance batteries, especially for electric transportation purposes. Element specific X-ray absorption near-edge structures (XANES) spectroscopy, explores detailed information on the local chemistry and electronic states of the absorbing atom which makes it an excellent tool to understand chemical bonding, electronic structure, and surface and bulk chemistry difference in batteries. XANES with nano-focused X-ray beam (STXM) and surface sensitive field-imaging X-PEEM can collect chemical imaging of high chemical sensitivity with good spatial resolutions. In this talk I will show nanoscale chemical imaging studies of battery materials by STXM and X-PEEM at CLS. The emphasis is imaging inhomogeneity under “normal” or abusive conditions in a commercial like electrode with preserved interface structure. The studies show that the battery heterogeneity is an interplay of local environment, morphology and facet orientation of electrode active materials. Such novel characterization tools shall lead new insights on battery engineering toward a better battery with high performance, long service life and high safety.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".