Enzyme-Mediated Lignocellulose Liquefaction Is Highly Substrate-Specific and Influenced by the Substrate Concentration or Rheological Regime
Bibliographic record
Abstract
The high viscosities/yield stresses of lignocellulose slurries makes their industrial processing a significant challenge. However, little is known regarding the degree to which liquefaction and its enzymatic requirements are specific to a substrate’s physicochemical and rheological properties. In the work reported here, the substrate- and rheological regime-specificities of liquefaction of various substrates were assessed using real-time in-rheometer viscometry and offline oscillatory rheometry when hydrolysed by combinations of cellobiohydrolase (Trichoderma reesei Cel7A), endoglucanase (Humicola insolens Cel45A), glycoside hydrolase (GH) family 10 xylanase, and GH family 11 xylanase. In contrast to previous work that has suggested that endoglucanase activity dominates enzymatic liquefaction, all of the enzymes were shown to have at least some liquefaction capacity depending on the substrate and reaction conditions. The contribution of individual enzymes was found to be influenced by the rheological regime; in the concentrated regime, the cellobiohydrolase outperformed the endoglucanase, achieving 2.4-fold higher yield stress reduction over the same timeframe, whereas the endoglucanase performed best in the semi-dilute regime. It was apparent that the significant differences in rheology and liquefaction mechanisms made it difficult to predict the liquefaction capacity of an enzyme or enzyme cocktail at different substrate concentrations.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".