The production of high-bulk mechanical pulp sheets by fractionation and the addition of microfibrillated lignocellulose
Bibliographic record
Abstract
There has been a growing interest in broadening the application of mechanical pulp, particularly in the development of high-bulk sheets with a wide range of physical and optical properties. This study explores incorporating microfibrillated lignocellulose (MFLC) into fractionated and unfractionated bleached chemi-thermomechanical pulp (BCTMP) of spruce-pine-fir (SPF) to develop high-bulk, strengthened composite. MFLC was generated from BCTMP-aspen through low-consistency (LC) refining, and BCTMP-SPF fibers were fractionated using a Bauer-McNett fiber classifier. The properties of the resulting composite sheets were compared with those sheets prepared from SPF-LC refined pulp at different specific refining energies. The resulting handsheets and Dynamic Sheet Former sheets were analyzed for freeness, fiber length, fine percentage, curl index, bulk, tensile index, tensile energy absorption, stretch percentage and tear index. Additionally, the morphology of the sheets was assessed using scanning electron microscopy. The findings showed that adding MFLC to BCTMP-SPF whole pulp and long fiber fractions presents a promising energy saving and green approach to increase sheets stretch, tensile and tear strength while preserving sheet bulk, potentially extending their application for packaging.
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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".