Simultaneous Enzymatic Cellulose Hydrolysis and Product Separation in a Radial-Flow Membrane Bioreactor
Bibliographic record
Abstract
Hydrolysis is the heart of the lignocellulose-to-bioethanol conversion process. Using enzymes to catalyze the hydrolysis represents a more environmentally friendly pathway compared to other techniques. However, for the process to be economically feasible, solving the product inhibition problem and enhancing enzyme reusability are essential. Prior research demonstrated that a flat-sheet membrane bioreactor (MBR), using an inverted dead-end filtration system, could achieve 86.7% glucose yield from purified cellulose in 6 h. In this study, the effectiveness of flat-sheet versus radial-flow MBR designs was assessed using real, complex lignocellulose biomass, namely date seeds (DSs). The tubular radial-flow MBR used here had more than a 10-fold higher membrane surface area than the flat-sheet MBR design. With simultaneous product separation using the flat-sheet inverted dead-end filtration MBR, a glucose yield of 10.8% from pretreated DSs was achieved within 8 h of reaction, which was three times higher than the yield without product separation, which was only 3.5% within the same time and under the same conditions. The superiority of the tubular radial-flow MBR to hydrolyze pretreated DSs was confirmed with a glucose yield of 60% within 8 h. The promising results obtained by the novel tubular MBR could pave the way for an economic lignocellulose-to-bioethanol process.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".