Butanol and ethanol production from lignocellulosic feedstock: biomass pretreatment and bioconversion
Bibliographic record
Abstract
Abstract Lignocellulosic feedstock has tremendous potential to sustain the renewable production of biofuels such as ethanol and butanol. Although lignocellulosic biomass is a storehouse of energy in the form of cellulose and hemicellulose, yet lignin acts as a barrier against their hydrolysis. A dilute acid pretreatment disintegrates the biomass complex and allows cellulolytic enzymes to hydrolyze cellulose and hemicelluloses in releasing fermentable sugars. The current study investigates the effect of different H2SO4 doses (0–2.5%) on the three lignocellulosic feedstock material, especially pinewood, timothy grass, and wheat straw at 121°C for 1 h. Furthermore, the pretreated feedstock was subjected to enzymatic hydrolysis using cellulase, β‐glucosidase, and xylanase at 45°C for 72 h. The biomass hydrolysates containing monomeric sugars (glucose and xylose) were fermented using Saccharomyces cerevisiae and Clostridium beijerinckii for ethanol and butanol production, respectively. A comparative evaluation for the concentrations of ethanol and butanol, residual sugars as well as byproducts such as acetone, acetate, and butyrate from biomass hydrolysates was performed. Pinewood hydrolysate revealed high ethanol (24.1 g/L) and butanol (11.6 g/L) concentrations due to greater sugar content. In contrast to ethanol fermentation by S. cerevisiae, butanol fermentation by C. beijerinckii demonstrated low butanol levels in the hydrolysates due to butanol toxicity toward clostridia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".