Valorizing Every Carbon Atom: A Cascade Bioprocess for Advanced Biofuels from Corn-Stover-Derived Lignocellulose
Bibliographic record
Abstract
Conventional biorefining of lignocellulosic biomass, such as corn stover (CS), is hampered by poor carbon efficiency, as nearly half of the substrate carbon is lost as CO 2 during ethanol fermentation. This study presents a holistically integrated cascade process designed to capture and valorize all major carbon streams. The system synergistically couples three stages: (1) high-productivity ethanol fermentation (1.68 g/L/h) using the engineered yeast Saccharomyces cerevisiae CE10; (2) anaerobic digestion of the resulting stillage, which efficiently converted residual organics into methane (171 L/kg COD) with ca. 80% COD removal; and (3) cultivation of the cyanobacterium Desertifilum tharense BERC03 using the nutrient-rich digestate and captured fermentation CO 2 . This integrated approach boosted the carbon utilization from a baseline of 48% to 62%. A comprehensive techno-economic analysis of an industrial-scale (2000 t/d) facility projected a Minimum Ethanol Selling Price (MESP) of $2.44 per gallon, a value approaching current market competitiveness. The analysis identified the feedstock (30%) and cellulase (17%) as the primary cost drivers. These findings demonstrate a validated biorefinery model that significantly enhances carbon recovery and outlines a viable pathway for the coproduction of multiple biofuels from lignocellulosic resources.
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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 | 0.000 | 0.001 |
| 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".