Chemo-enzymatic treatment of mechanical pulping: An efficient method for energy reduction and pulp strengthening
Bibliographic record
Abstract
Conventional mechanical pulping faces critical challenges of high energy intensity (1200–2000 kWh/t) and compromised fiber quality, driving demand for innovative solutions to balance energy efficiency with performance. This study established a chemo-enzyme synergistic pretreatment system and elucidated its effects on refining energy consumption and pulp properties in eucalyptus mechanical pulping through multiscale characterization (SEM, XRD, XPS, HSQC NMR). By optimizing the synergistic pretreatment with NaOH (6 %) and cellulase (10 FPU/g), refining energy consumption was significantly reduced to 765.11 kWh/t, achieving energy savings of 40.99 % and 33.99 % compared to conventional mechanical pulp (MP) and chemical-mechanical pulp (C-MP), respectively. Alkaline pretreatment dissolved 20.33 % of hemicellulose and 7.02 % of lignin, cleaving ester bonds in lignin-carbohydrate complexes (LCC) and breaking β-O-4 lignin linkages (9.7 % reduction quantified by NMR), while increasing the syringyl/guaiacyl (S/G) ratio from 2.02 to 3.05, thereby enhancing cellulose accessibility for enzyme pretreatment. Subsequent enzyme pretreatment hydrolyzed 25.59 % of cellulose and 22.07 % of hemicellulose, promoting S1-S2 layer delamination of cellulose fiber wall during refining. Enzyme pretreatment shortened fiber length and increased fiber fines content, but it results in significant increases in tensile strength and burst index for the bio-chemical-mechanical pulp (B-MP) compared to MP, respectively. The synergistic "softening-enzymolysis" mechanism enabled the targeted deconstruction of dense fiber structures, establishing a theoretical foundation for low-energy dissociation of lignocellulosic feedstocks. • Alkali pretreatment enhances the accessibility of cellulase. • Enzymes liberate cellulose and hemicellulose, separating the wood S1-S2 layers. • Alkali-enzyme synergy shows major energy savings. • Multi-scale characterization reveals alkali-enzyme synergy.
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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".