Enzyme mediated nanofibrillation of cellulose by the synergistic actions of an endoglucanase, lytic polysaccharide monooxygenase (LPMO) and xylanase
Bibliographic record
Abstract
Abstract Physiochemical methods have generally been used to “open-up” biomass substrates/pulps and have been the main method used to fibrillate cellulose. However, recent work has shown that canonical cellulase enzymes such as endoglucanases, in combination with “amorphogenesis inducing” proteins such as lytic polysaccharide monooxygenases (LPMO), swollenin and hemicellulases, are able to increase cellulose accessibility. In the work reported here different combinations of endoglucanase, LPMO and xylanase were applied to Kraft pulps to assess their potential to induce fibrillation at low enzyme loading over a short time period. Although gross fiber properties (fiber length, width and morphology) were relatively unchanged, over a short period of time, the intrinsic physicochemical characteristics of the pulp fibers (e.g. cellulose accessibility/DP/crystallinity/charge) were positively enhanced by the synergistic cooperation of the enzymes. LPMO addition resulted in the oxidative cleavage of the pulps, increasing the negative charge (~100 mmol kg −1 ) on the cellulose fibers. This improved cellulose nanofibrilliation while stabilizing the nanofibril suspension (zeta potential ζ = ~60 mV), without sacrificing nanocellulose thermostability. The combination of endoglucanase, LPMO and xylanases was shown to facilitate nanofibrillation, potentially reducing the need for mechanical refining while resulting in a pulp with a more uniform nanofibril composition.
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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) | 0.000 | 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".