Recycling Cellulases during the Hydrolysis of Steam Exploded and Ethanol Pretreated Lodgepole Pine
Bibliographic record
Abstract
Recycling of cellulases is one way of reducing the high cost of enzymes during the bioconversion process. The effects of surfactant addition on enzymatic hydrolysis and the potential recycling of cellulases were studied during the hydrolysis of steam exploded Lodgepole pine (SELP) and ethanol pretreated Lodgepole pine (EPLP). Three cellulase preparations (Celluclast, Spezyme CP, and MSUBC) were evaluated to determine their hydrolysis efficiencies over multiple rounds of recycling. The surfactant, Tween 80, significantly increased the yield from 63% to 86% during the hydrolysis of the SELP substrate. The addition of surfactant to the hydrolysis of the EPLP substrate increased the free enzymes in the supernatant from 71% of the initial protein to 96%. Based on the Langmuir adsorption constants, cellulases (Celluclast and Spezyme CP) from Trichoderma reesei showed a higher affinity (3.48 mL/mg and 3.17 mL/mg) for the EPLP substrate than did the Penicillium enzyme (0.62 mg/mg). The Trichoderma reesei enzyme was used in four successive rounds of enzyme recycling using surfactant addition and readsorption onto fresh substrates during the hydrolysis of EPLP. In contrast, the Penicillium-derived enzyme preparation (MSUBC) could only be recycled once. When the same recycling strategy was carried out using the SELP substrate, the hydrolysis yield declined during each enzyme recycling round. These results suggested that the higher lignin content of the SELP substrate, and the low affinity of cellulases for the SELP substrate limited enzyme recycling by readsorption onto fresh substrates.
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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) | 0.001 | 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 | 0.000 |
| 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".