New method for characterizing paper coating structures using argon ion beam milling and field emission scanning electron microscopy
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Bibliographic record
Abstract
We have developed a new method for characterizing microstructures of paper coating using argon ion beam milling technique and field emission scanning electron microscopy. The combination of these two techniques produces extremely high-quality images with very few artefacts, which are particularly suited for quantitative analyses of coating structures. A new evaluation method has been developed by using marker-controlled watershed segmentation technique of the secondary electron images. The high-quality secondary electron images with well-defined pores makes it possible to use this semi-automatic segmentation method. One advantage of using secondary electron images instead of backscattered electron images is being able to avoid possible overestimation of the porosity because of the signal depth. A comparison was made between the new method and the conventional method using greyscale histogram thresholding of backscattered electron images. The results showed that the conventional method overestimated the pore area by 20% and detected around 5% more pores than the new method. As examples of the application of the new method, we have investigated the distributions of coating binders, and the relationship between local coating porosity and base sheet structures. The technique revealed, for the first time with direct evidence, the long-suspected coating non-uniformity, i.e. binder migration, and the correlation between coating porosity versus base sheet mass density, in a straightforward way.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it