Experimental methods in chemical engineering: Electron probe micro‐analysis— <scp>EPMA</scp>
Bibliographic record
Abstract
ABSTRACT Electron probe microanalysis (EPMA) is a non‐destructive spectroscopic technique to map the chemical composition—quantitative elemental distribution and layer thickness—with a micro‐scale resolution. An electron beam illuminates the surface of a sample and produces backscattered electrons (BSE), secondary electrons (SE), characteristic X‐rays, and light known as cathodoluminescence (CL). Combining energy dispersive spectrometry with wavelength‐dispersive spectrometry improves trace analysis and differentiates overlapping X‐ray lines, but the detection threshold is not much better than 100 ppm (parts per million). Implementing Monte Carlo simulation with better electronics and software are ongoing research areas to improve the method's precision, sensitivity, and spatial resolution. To detect Li, Be, and B ( 11) requires wavelength‐dispersive X‐ray spectroscopy (WDS), or soft X‐ray emission spectroscopy (SXES), or a combination of BSE/EDS method. Researchers in metallurgy & metallurgical engineering, mineralogy, geochemistry & geophysics, and mining & mineral processing apply the method most. Chemical engineering is ranked 21st among the 250 scientific categories that use the technique. It is also applied to reconstitute works of art and the antiquities. A bibliometric map identified four clusters of research and for each cluster the major nodes were: (1) geochemistry, (2) mechanical properties, microstructure, and alloys, (3) Fe, Cu, and Cr, and (4) phase equilibria.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.017 |
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".