Experimental methods in chemical engineering: X‐ray fluorescence— <scp>XRF</scp>
Bibliographic record
Abstract
Abstract X‐ray fluorescence (XRF) is a non‐destructive spectrometric technique to detect elements with an atomic number from 11 (sodium) and beyond 92 (uranium). When X‐rays or gamma rays eject tightly bound inner core electrons, an electron from an outer shell will fill the empty orbital and fluoresce. Every element has a characteristic fluorescence, which depends on the element and the electrons in the orbitals that are ejected and those that fill the orbital. With the characteristic energy of the fluorescence, we determine elemental composition and concentration when adequately calibrated. Typical run times range from a second to a few minutes with an sensitivity to as low as (ppm). XRF guns are portable devices that produce qualitative data while libraries loaded to laboratory instruments are capable of producing quantitative data. A broad range of scientists and engineers apply XRF in research—140 of the 250 scientific categories in the Web of Science (WoS) cite XRF analyses. Of the 10,000 articles indexed in WoS since 2018, chemical engineering ranks fifth with the most articles. The focus of the research in this category includes adsorption and waste water, combustion and pyrolysis, catalysis and zeolites, and nanoparticles and oxidation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".