Resonant inelastic X-ray scattering for studying materials for renewable energy conversion and storage
Bibliographic record
Abstract
Renewable energy conversion and storage technologies, including batteries, fuel cells, and electrolyzers, have garnered global attention. Electrode materials are crucial in determining the performance and lifespan of the corresponding devices. Transition metals and light elements are key components in electrode materials, enabling efficient energy conversion by directly providing active sites or indirectly optimizing material electronic structures. To understand the relationship between electronic structures and device performance, advanced techniques like X-ray absorption spectroscopy (XAS) have been developed. Recently, resonant inelastic X-ray scattering (RIXS) features coupled with X-ray emission spectra (XES) have emerged as a complementary tool, providing additional insights into material electronic structures. This review focuses on recent advances in using XES, particularly RIXS, for studying energy conversion and storage materials, highlighting the unique features and potential of RIXS for electronic structure characterization and quantitative analysis. This work aims to stimulate interests in utilizing RIXS features in the field of energy conversion and storage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".