Synchrotron‐Based X‐ray Absorption Fine Structures, X‐ray Diffraction, and X‐ray Microscopy Techniques Applied in the Study of Lithium Secondary Batteries
Bibliographic record
Abstract
Abstract Owing to the recent advance of third‐generation synchrotron radiation (SR) sources, SR‐based X‐ray techniques have been widely applied to study lithium‐ion batteries, lithium–sulfur batteries, and lithium–oxygen batteries to solve material challenges. SR‐based techniques provide high chemical and physical sensitivity and a comprehensive picture of material structure and reaction mechanisms. An in‐depth understanding of batteries is imperative for the development of future energy storage devices with enhanced electrochemical performance to meet societies' growing need for devices with high energy density. Here, recent progress in the application of SR techniques for lithium secondary batteries with a focus on several techniques, including X‐ray absorption fine structure, synchrotron X‐ray diffraction, and synchrotron X‐ray microscopy techniques is reviewed. The working principle for all characterization techniques is introduced to provide context for how the technique is used in the field of energy storage. Through discussing the utilization of SR techniques in different directions of batteries, including electrodes, electrolytes, and interfaces, the practical application strategies of techniques in batteries are clarified. By summarizing and discussing the application of SR techniques in batteries, the aim is to highlight the crucial role of SR characterization in the development of advanced energy materials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".