Visualizing pulp fibers using X-ray tomography: Enhancing the contrast by labeling with iron oxide nanoparticles and the use of immersion oil
Bibliographic record
Abstract
In this study, we present a protocol to visualize the architecture of tracer fibers in paper using X-ray tomography. We prepared tracer fibers by depositing iron oxide nanoparticles on the surface of select papermaking fibers, through a multicycle labeling technique that achieved 14 wt% of iron. Labeled and unlabeled fibers on their own, as well as laboratory-formed paper containing a small fraction of the tracer fibers, were imaged in air and after immersion in a non-polar oil. We found that labeled fibers could be segmented from the background through simple binarization when in the immersed state whereas segmentation failed when the samples were imaged in air. We propose that the oil served as a mask, created through compositional and density matching of the unlabeled fibers to the saturated void volume. This new labeling and immersion protocol opens avenues to enhance the contrast of tracers for improved characterization of cellulosic materials via X-ray tomographic imaging in an approach that does not require advanced image processing methods for segmentation.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".