Optimization of direct bioconversion of cellulose into biofuels: medium improvement, scale-up and use of alternative nutrients
Bibliographic record
Abstract
Despite the long-term economic and environmental benefits of cellulosic biofuel production, low rates of cellulose utilization and products syntheses are major techno-economical barriers to the commercialization. Optimized medium composition and low-cost nutrient source could greatly enhance the feasibility of large-scale biofuels synthesis by direct cellulose fermentation using a consolidated bioprocessing (CBP) approach. This study developed an improved growth medium for Clostridium thermocellum, an excellent cadidate for CBP that utilizes cellulose to produce ethanol, hydrogen, and other value-added biochemicals. An experimental design to determine the importance of nutrient components and concentrations on H2 and ethanol production from cellulose by C. thermocellum initially considered seven growth nutrients. Three most significant components - α-cellulose, yeast extract, and magnesium chloride were investigated in detail for their influence on rates and yields of H2 and ethanol production during cellulose fermentation by C. thermocellum. To explore individual and interactive effects of these nutrients on ethanol and hydrogen (H2) production, a central composite face-centered design and the response surface methodology was applied to predict optimum nutrient compositions for H2 and ethanol production. Experimental verification of predicted optima produced about 3-fold and 4-fold more H2 and ethanol respectively compared with the reference medium. These small-scale results were successfully verified in large-volume (7L), atmospheric cultures. Irrespective of culture conditions, relative improvement in rates and productivities of H2 and ethanol in optimized medium compared with reference medium were consistent with small-volume cultures. Various ethanol distillery co-products were tested for their potentials to replace expensive medium ingredients. Medium prepared with these co-products show excellent ability to suppport cell-growth and production of ethanol and H2 at concentrations equivalent to those generated from the reagent grade medium. Utilization of these low-cost nutrient sources to replace expensive reagent ingredients may potentially contribute to the viability of both grain-based ethanol and cellulosic biofuels. With medium optimization, scale-up and use of low-cost nutrient sources, this study represents one of the very few systematic research approaches to improve direct bioconversion of cellulosic biomass into biofuels.
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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 | 0.001 |
| 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".