In-Depth Understanding of Battery Interfaces by Nanoscale Chemical Imaging
Bibliographic record
Abstract
Deeper understanding of the interface (chemical and electronic structure) between components within hybrid active electrode materials and the interface between active electrode materials and the electrolyte is crucial for developing better performance batteries. 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 for battery interface investigation to understand chemical bonding, electronic structure, and surface and bulk chemistry difference. XANES with nano-focused X-ray beam (STXM) and surface sensitive field-imaging X-PEEM can collect chemical imaging of high chemical sensitivity and spatial resolution. In this talk I will show nanoscale chemical imaging studies of battery materials by STXM and X-PEEM at CLS SM beamline. The emphasize is novel STXM with yield signal detection (XRF and TEY) and exploratory X-PEEM for surface imaging. Correlative chemical imaging of interface in a commercial like electrode without sample preparation was obtained by X-PEEM. This approach maintains the nature of the interface which is an interplay of local environment, morphology and facet orientation of electrode active materials. That information will lead new insights on battery material surface engineering in and/or out of battery cell toward a battery with high performance and long serve life.
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 | 0.000 | 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 | 0.001 |
| 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".