On the development of a continuous methodology to fractionate microfibriallated cellulose
Bibliographic record
Abstract
Abstract The focus of this study is the development of a methodology to mechanically separate or fractionate micro-fibrillated fibre suspensions (MFC) into different size classes. We extend the principle outlined by Madani et al. (2010) and create a continuous separation in an annular gap undergoing spiral Poiseuille flow (solid body rotation superimposed on pressure driven flow). Achieving hydrodynamic stability of this flow was the main scientific challenge for scale-up. This work is presented in two different studies. In the first study, we perform a series of batch-wise centrifugation tests to develop the criteria for motion of the individual classes of particles which compose a Eucalyptus MFC suspension. Here, we suspend the MFC in a weak gel and demonstrate a linear reduction in average particle size with increasing centrifugal force; motion is initiated in heavier particles before the lighter ones. In the second study, we use this batch-wise data to design a continuous prototype and we successfully demonstrate a continuous separation with performance similar to that achieved in the batch-wise tests.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".