High energy resolution fluorescence detected X-ray absorption spectroscopy (HERFD-XAS) for studies of metals and metalloids in biology: current innovations and future perspectives
Bibliographic record
Abstract
X-ray absorption spectroscopy (XAS) is a technique which is frequently used in metallomics research, providing a valuable tool for the elucidation of element-specific electronic and geometric structural information. Recent decades have seen the development of related synchrotron-based X-ray techniques with enhanced analytical capabilities, including X-ray emission spectroscopy (XES), resonant inelastic X-ray scattering (RIXS), and high energy resolution fluorescence detected X-ray absorption spectroscopy (HERFD-XAS). With appropriate experimental configuration, HERFD-XAS can generate spectra with significantly improved spectroscopic resolution and background rejection compared to conventional XAS, providing a substantial advantage in the analysis of dilute analytes in biological samples. These improvements arise from the capability to interrogate selected fluorescence lines with the use of multiple crystal analyzers, minimizing the effects of core-hole lifetime broadening. Herein, we review a range of existing and emerging applications of HERFD-XAS for the study of metals and metalloids in biology and medicine. Direct comparisons of conventional XAS and HERFD-XAS spectra highlight the substantial improvements in resolution, and greater potential for the interpretation of metal speciation in complex and dilute biological samples. We also discuss current challenges with the design of HERFD-XAS experiments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".