Evaluating the Distribution of Cellulases and the Recycling of Free Cellulases during the Hydrolysis of Lignocellulosic Substrates
Bibliographic record
Abstract
The recycling of cellulase enzymes is one potential strategy for reducing the cost of the enzymatic hydrolysis step during the bioconversion of lignocellulosics to ethanol. To determine the influence of lignin on the post-hydrolysis distribution of cellulase enzymes between the liquid and solid phases, the hydrolysis of Avicel was compared to an organosolv-pretreated Douglas fir substrate with a lignin content of 3.0%. After a 12 h hydrolysis reaction on Avicel, 90% of the added cellulases (including beta-glucosidases) remained "free" in the liquid phase compared to only 65% in the case of the hydrolysis of the organosolv-pretreated Douglas fir substrate. The readsorption of free cellulases by supplementing the hydrolysis reaction with fresh substrate was explored as a potential means of recovering the free cellulases that remain in the liquid phase after hydrolysis. The Langmuir adsorption isotherm was used to develop a model predicting that 82% of the free cellulases could be recovered via readsorption onto fresh substrates during the hydrolysis of an ethanol-pretreated mixed softwood substrate with a lignin content of 6%. Recoverable free cellulase values of 85% and 88% based on cellulase activity and protein content, respectively, were obtained after experimental verification of the model. The readsorption of free cellulases onto fresh lignocellulosic substrates was shown to be an effective method for free enzyme recovery.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".