X‐ray microanalysis of porous materials using Monte Carlo simulations
Bibliographic record
Abstract
Quantitative X-ray microanalysis models, such as ZAF or φ(ρz) methods, are normally based on solid, flat-polished specimens. This limits their use in various domains where porous materials are studied, such as powder metallurgy, catalysts, foams, etc. Previous experimental studies have shown that an increase in porosity leads to a deficit in X-ray emission for various materials, such as graphite, Cr(2) O(3) , CuO, ZnS (Ichinokawa et al., '69), Al(2) O(3) , and Ag (Lakis et al., '92). However, the mechanisms responsible for this decrease are unclear. The porosity by itself does not explain the loss in intensity, other mechanisms have therefore been proposed, such as extra energy loss by the diffusion of electrons by surface plasmons generated at the pores-solid interfaces, surface roughness, extra charging at the pores-solid interface, or carbon diffusion in the pores. However, the exact mechanism is still unclear. In order to better understand the effects of porosity on quantitative microanalysis, a new approach using Monte Carlo simulations was developed by Gauvin (2005) using a constant pore size. In this new study, the X-ray emissions model was modified to include a random log normal distribution of pores size in the simulated materials. This article presents, after a literature review of the previous works performed about X-ray microanalysis of porous materials, some of the results obtained with Gauvin's modified model. They are then compared with experimental results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".