Optimization of microprobe analysis of cementitious materials incorporating glass powder under electron beam to avoid alkali migration
Bibliographic record
Abstract
The growing use of alkali-rich glass powder (GP) as a supplementary cementitious material (SCM) in concrete has led to a rising number of studies focused on the microstructure of cementitious matrices incorporating GP. Electron probe microanalyzer (EPMA) is commonly used to characterize cementitious materials. However, alkali migration induced by electron irradiation - a well-known phenomenon in inorganic materials - remains underexplored in this context. This migration often leads to underestimation of Na and K and overestimation of Si and Ca, thus compromising the analysis of key elements in cementitious hydrates, such as C-S-H. Due to the lack of a tailored protocol for EPMA analysis of alkali-rich SCMs, this study established analytical conditions to minimize errors in quantifying pozzolanic GP. Mixed glass culets and GP particles embedded in 7-year-old ternary concrete made with GP and silica fume were analyzed using ten different current densities by varying beam size, current, and the sub-counting method. The results show that alkali migration is highly sensitive to material composition and irradiation conditions. Na losses exceeded 70% as Ca and Si overestimation reached approximately 13% at current densities above 0.354 nA/μm². Literature-reported densities often surpass this threshold. At those conditions, the implementation of a sub-counting method effectively reduces the Na loss to 3%. However, it introduced a tendency for Na overestimation at lower current densities . Among all conditions, a beam diameter of 6 µm and a current of 10 nA, was the most accurate, reducing losses to under 2% and closely matching the reference glass analysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".