Effects of sequential ball milling and loosenin-like protein treatment on pulp fibers
Bibliographic record
Abstract
Loosenins are non-hydrolytic proteins that enhance cellulose accessibility by disrupting hydrogen bonding. This study investigates the effect of a novel fungal loosenin-like protein, PcaLOOL12, on never-dried hardwood kraft pulp fibers pre-treated by mechanical ball milling. Ball milling at varying energy levels (50–150 kWh/t) was used to open the fiber structure and improve protein accessibility. A treatment energy of 50 kWh/t was selected for subsequent experiments. Fiber morphology, water retention, and dissolution behavior were analyzed as a function of PcaLOOL12 dosage and incubation time. Mechanical treatment increased fiber width, fibrillation, and water retention, with minimal impact on fiber length or nanoscale porosity. PcaLOOL12 further enhanced fibrillation and water retention, particularly at higher dosages (5–10 wt%) and longer treatment times (24–72 h), suggesting surface-level delamination. No significant increase in nanoscale porosity was observed, indicating surface-specific action. CED-solubility decreased following protein treatment, possibly due to fibril aggregation or altered fiber–solvent interactions. These results demonstrate that combining mechanical and protein treatments can modulate fiber morphology and accessibility, supporting their use in bio-based product development. • Mild mechanical and protein treatment offers eco-friendly pre-treatment for cellulosic pulp fibers. • Protein enhances fiber surface fibrillation and water retention, improving material performance. • New approach enables cellulose modification for sustainable manufacturing and value-added applications.
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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) | 0.001 | 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".