Developing Laboratory-Based X-ray Spectroscopies for Energy and Materials Research spectroscopy
Bibliographic record
Abstract
Advanced x-ray spectroscopies interrogate a material's electronic structure in an elementspecific manner.Traditionally, X-ray absorption fine structure (XAFS) and X-ray emission spectroscopy (XES) studies are performed at synchrotron X-ray light sources.These facilities serve to push the forefront of science and, thus, operate under an access model which necessarily excludes projects requiring routine analytical characterization, rapid feedback for prototyping, or regular access.In response to this deficit, my dissertation presents a laboratory -based XAFS and XES spectrometer of high energy resolution, reproducibility, and efficiency, along with other improvements in instrumentation, especially as pertains to the utilized crystal analyzer.A range of basic and applied materials problems were addressed with this and similar instrumentation.Select applied research studies include operando XAFS analysis of a prototype lithium-ion battery's state-of-charge and state-of-health and an XES-based method for the quantification of hexavalent chromium in manufactured plastics that is being develop ed into a standard test method.Basic research spanned a study of photoexcitation dynamics in Ni metal and timedependent density functional theory interpretations of valence-to-core XES spectra collected from a series of vanadium oxide and vanadyl phosphate energy storage materials candidates.This thesis provides strong evidence that laboratory -based X-ray spectroscopy instrumentation can serve as a powerful tool for increasing productivity and understanding in the fields of chemistry and materials science.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".