Reusability of Immobilized Cellulases with Highly Retained Enzyme Activity and their Application for the Hydrolysis of Model Substrates and Lignocellulosic Biomass
Bibliographic record
Abstract
Enzyme immobilization is a promising approach to reduce enzyme cost in lignocellulose-based biorefining. This paper describes the reusability of immobilized cellulases and examines hydrolysis of various components of lignocellulose and industrial lignocellulosic biomass when using immobilized cellulases. Two different commercial cellulases, previously denoted as Cellulases 1 (C1) and Cellulases 2 (C2), were separately immobilized on nonporous (S1) and porous (S2) silica. Enzyme immobilization was achieved using a simple, cheap, and safe absorption method that maintains high hydrolysis yields by creating a cellulosome-like environment. Here, we show that all immobilized cellulases could be reused for at least 4 cycles while maintaining ≥ 50% of their activity. In fact, systems containing immobilized C1 displayed >40% activity in the 6th cycle, regardless of the silica used. We obtained relatively high-retained enzyme activities when our immobilized cellulases were employed to hydrolyze cellophane paper (60%–78%), phosphoric acid swollen cellulose (72%–79%), a common component of hemicellulose (xylan; 62%– 84%), steam-exploded poplar (41%–62%), and waste office automation paper (34%–48%). Thus, the immobilized cellulase systems used in this study may be industrially feasible as they can be reused while maintaining relatively high levels of enzyme activities. Importantly, we also show that our immobilized cellulase systems can be applied to not only model substrates, but also to industrially produced lignocellulosic biomass.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".