The influence of enzymatic liquefaction and slurry rheology on high-solids cellulose hydrolysis
Bibliographic record
Abstract
Enzyme-mediated hydrolysis of lignocellulosic materials for their conversion into bioproducts would benefit significantly if high solids concentrations (low water-to-biomass ratios) could be processed effectively. However, the fibrous nature of lignocellulosic biomass makes effective reaction mixing difficult, resulting in mass transfer limitations that reduce process yields. Overcoming these rheological challenges will require a better understanding of the substrate properties that influence slurry rheology, and in particular, how the action of carbohydrate-active enzymes can better facilitate slurry viscosity reduction, or ‘enzymatic liquefaction’. To this end, the work described in this thesis assessed: the underlying causes of the rheological challenge of high-solids bioconversion; the possible mechanisms of enzymatic liquefaction; how liquefaction is influenced by the nature of the substrate; and the roles the various enzymes play in liquefaction. It was apparent that the relationship between substrate properties, slurry rheology and high-solids hydrolysis kinetics is complex and multifaceted. Substrate–water interactions were shown to be a key determinant that influenced the mass transfer boundary and the scaling of yield stress with solids loading. The yield stress profiles of the various pretreated substrates varied extensively, indicating that ‘high solids’ is a relative, substrate-dependent quality. Substrate rheological characteristics, especially slurry yield stress, were shown to be directly linked to liquefaction efficiency and to reductions in hydrolysis yield with increasing solids loading. It appeared that enzyme-mediated liquefaction of biomass was achieved through a combination of material dilution, particle fragmentation and alteration of interparticle interactions. Cellobiohydrolases and endoglucanases were shown to be the key enzymes involved in these mechanisms. However, the effectiveness of the various enzymes was strongly influenced by the substrate’s physicochemical properties and concentration. Notably, an enzyme’s low-solids slurry viscosity-reducing capacity did not necessarily reflect its capacity to catalyze liquefaction at high solids loadings. Furthermore, reaction efficiencies tested at low solids loadings did not reliably predict efficiency at high solids due to substrate-specific rheological differences. In summary, this work provided rheological and enzymological insights into the highly disparate reaction kinetics and rheological challenge prevailing at commercially relevant 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".