Laboratory and synchrotron validation of µ-XRF for sulfur mapping in CTMP paper samples
Bibliographic record
Abstract
Abstract The transition toward renewable, fiber-based packaging requires an improved understanding of chemical modifications in high-yield pulps such as chemithermomechanical pulp (CTMP). Sulfonation uniformity is essential for the energy-efficient production of high-strength CTMP pulp. However, laboratory methods only measure total sulfur and cannot illustrate its distribution at the fiber level, which can be visualized using µ-XRF. In this work, we present a laboratory µ-XRF system developed at Mid Sweden University and assess its capability to detect light elements in CTMP paper handsheets. A 32×32 point grid scan (1.6 × 1.6 mm 2 field of view, 50 μm step, 300 s/point) successfully resolved sulfur K α (2.31 keV) and calcium K α (3.69 keV) fluorescence without helium flushing. Comparative measurements at the Elettra synchrotron confirmed consistency of sulfur peak position and spatial distribution, with higher spectral resolution and signal-to-noise ratio. Histogram analysis using Wasserstein distance metrics demonstrated close agreement between datasets despite differing acquisition conditions. These results demonstrate that laboratory XRF can reproducibly detect and map sulfur in CTMP fibers under ambient conditions, providing a practical tool to complement synchrotron studies and supporting the development of energy-efficient, fiber-based packaging materials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".