Enhanced analysis of cellulose nanofibril film structure
Bibliographic record
Abstract
Structural analysis of cellulose nanofibril (CNF) films remains challenging due to the stochastic assembly of fibres and presence of nanopores. This limits our ability to link the film structures to their properties, restricting their commercial use. While X-ray micro-computed tomography (μCT) is a powerful technique to map the structure of traditional and commercial paper, there has been limited success in its application for CNF films due to the poor contrast between pores and material, making the reconstructed images difficult to segment and analyse. Mercury intrusion porosimetry (MIP) can be used to measure film pore size distributions down to the nanoscale, but provides limited information on the pore morphology. Here, we demonstrate the power of combining both methods. Samples are first subjected to MIP. Then, μCT measurements at 0.7 μm voxel size are performed on the mercury intruded samples. The contrast between mercury-filled pores and fibres is drastically enhanced, allowing for direct binarisation of the 3D volume with conventional segmentation, which is not possible without mercury. Detailed information on the pore morphology and connectivity could be determined with μCT, while MIP provided complementary information about the nanoscale pores.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".