Enhanced High-Solids Fed-Batch Enzymatic Hydrolysis of Sugar Cane Bagasse with Accessory Enzymes and Additives at Low Cellulase Loading
Bibliographic record
Abstract
High cellulase loading is still a major impediment in the production of fermentative sugars from high-solids enzymatic hydrolysis of lignocellulosic substrates in the enzyme-based “biorefinery” industry. This study attempted a high-solids (20%) enzymatic hydrolysis of lignocellulosic substrate at a very low cellulase loading with mixed use of additives and accessory enzymes by fed-batch mode. To avoid the high initial biomass viscosity, the high-solids enzymatic hydrolysis of lignocellulosic substrates was initiated with a solids content of 8%. Thereafter, 4% of the additional substrates were consecutively fed into the hydrolysis system after 6, 12, and 18 h to reach a final solids content of 20%. Some additive mixtures (40 mg/g substrateTween 80 + 10 mg/g substrate tea saponin +20 mg/g substrate BSA) were observed to enable this fed-batch hydrolysis to increase 30% of the glucose yield after the 48 h. The combination of these additives and accessory enzymes (2.4 mg/g substrate xylanase and 1 mg/g substrate AA9) in the high-solids hydrolysis system further boosted the sugar release. This allowed us to achieve an industrially relevant sugar yield (83% cellulose and 90% xylan hydrolysis) and fermentable sugar titer (∼160 g/L) after 72 h, with a low cellulase enzyme loading (3 FPU/g substrate). Our results indicate that the fed-batch substrate addition process is a favorable model for high-solids enzymatic hydrolysis of lignocellulosic substrates. Moreover, the synergism between the additives and accessory enzymes can greatly boost the high-solids enzymatic hydrolysis of lignocellulosic substrates.
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.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".