Exploring the use of supercritical carbon dioxide in enzymatic hydrolysis of cellulosic substrate
Bibliographic record
Abstract
Enzymes are critical components for production of fermentable sugars in lignocellulosic biorefineries. Their activity could be influenced by various reaction parameters. Herein, a formulated cellulolytic enzyme cocktail was immobilized into mesoporous aerogel material and used under high pressure environment of supercritical CO 2 (scCO 2 ). The cocktail was immobilized at different enzyme loads (36.0 to 144.0 mg enzyme/g aerogel) using sol-gel entrapment method and scCO 2 drying. The immobilized cocktail at 144.0 mg/g aerogel enzyme load retained a residual yield of >50.0 % after fourth cycle of reuse compared to first cycle under atmospheric pressure. Sugar yield by the cocktail aerogel was below 10 wt% in second cycle under scCO 2 due to enzyme leaching. Additionally, surface area and porous volume of reused aerogels was decreased over the reuse cycles. The study suggests that scCO 2 could be employed for aerogel preparation after optimizing the immobilization process for pure enzymes rather than crude enzymes. • Cellulolytic enzyme cocktail formulation was optimized for enhanced activity. • Enzyme immobilized into silica oxide aerogel by entrapment method for enzyme reuse • Enzymatic hydrolysis of water-soluble/insoluble cellulose conducted under scCO 2 • Surface area and porous volumes of aerogel reduced over reuse cycles • >50 % of the activity of immobilized enzyme was retained after four cycles.
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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 | 0.000 |
| 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 | 0.000 | 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 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".