Low consistency refined ligno-cellulose microfibre: an MFC alternative for high bulk, tear and tensile mechanical pulp papers
Bibliographic record
Abstract
Low consistency (LC) refining of (chemi-)thermomechanical pulp (TMP) provides an energy efficient alternative to high consistency refining for pulp property development. However, the benefit of LC refining is often limited by excessive fibre shortening, lower tear strength and a reduction of bulk caused by the refining process. In this study, microfibres produced by LC refining of TMP and kraft pulp fibres were investigated for their reinforcement potential in high freeness mechanical pulp. Primary pulp at 645 mL Canadian Standard Freeness was LC refined to different energy targets as a baseline for mechanical and optical property development. In contrast, the same primary pulp was reinforced with different microfibre types in ratios that yielded the same specific energies of the baseline LC refined pulp. The study revealed that at equivalent energies, the addition of TMP microfibres to the high freeness primary pulp displayed tensile development identical to the LC refined pulp, with significantly improved tear and bulk. The addition of kraft microfibre to primary pulp produced the highest tensile and tear strength but compromised light scattering. Additionally, all microfibre composites showed improved elongation, as opposed to no notable change in elongation with conventional LC refining. This investigation proposes an alternative, cost-effective approach for developing high bulk, high strength mechanical pulp by limiting the extent of second stage refining and using LC refined microfibres for pulp reinforcement. The high tear-high bulk open construction of the composite paper is likely to benefit boxboard and packaging 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 | 0.001 | 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".