Enzyme mediated nanofibrillation of cellulose by the synergistic actions of an endoglucanase, lytic polysaccharide monooxygenase (LPMO) and xylanase
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it