Isolation of cellulose microfibrils - An enzymatic approach
Bibliographic record
Abstract
Isolation methods and applications of cellulose microfibrils are expanding rapidly due to environmental benefits and specific strength properties, especially in bio-composite science. In this research, we have success-fully developed and explored a novel bio-pretreatment for wood fibre that can substantially improve the microfibril yield, in comparison to current techniques used to isolate cellulose microfibrils. Microfibrils currently are isolated in the laboratory through a combination of high shear refining and cryocrushing. A high energy requirement of these procedures is hampering momentum in the direction of microfibril isolation on a sufficiently large scale to suit potential applications. Any attempt to loosen up the microfibrils by either complete or partial destruction of the hydrogen bonds before the mechanical process would be a step forward in the quest for economical isolation of cellulose microfibrils. Bleached kraft pulp was treated with OS1, a fungus isolated from Dutch Elm trees infected with Dutch elm disease, under different treatment conditions. The percentage yield of cellulose microfibrils, based on their diameter, showed a significant shift towards a lower diameter range after the high shear refining, compared to the yield of cellulose microfibrils from untreated fibres. The overall yield of cellulose microfibrils from the treated fibres did not show any sizeable decrease.
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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 | 0.001 |
| 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".