Conversion of Lignocellulosic Biomass to Reducing Sugars in High Pressure and Supercritical Fluids: Greener Alternative for Biorefining of Renewables
Bibliographic record
Abstract
Abstract Supercritical fluids offer great potential to be employed in lignocellulosic biomass (LCB) fractionation in biorefinery. Supercritical carbon dioxide and water are greener alternatives compared with conventional reagents and have been investigated for the pretreatment and hydrolysis of lignocellulosic biomass. This review is focused on examining the fundamentals that govern the function of supercritical fluids in the pretreatment stage, as well as in the main hydrolysis reaction. Sub/supercritical carbon dioxide is used in pretreatment and sub/supercritical water has been the solvent of choice in hydrolysis of LCB. Significant research has gone into understanding the effect of process parameters such as temperature, pressure, cosolvent, and use of external catalyst on the sugar yield in biorefining of the LCB in supercritical fluids. It is shown that processes with reduced environmental impact and energy consumption can significantly enhance biorefining of LCB at commercial scale. Enzymatic hydrolysis of LCB in supercritical carbon dioxide is a promising approach that can accommodate mild reaction conditions. Developing an understanding of the performance of enzymes in high pressure systems and designing carriers for enzyme immobilization and further recycling is expected to enable one pot pretreatment and hydrolysis and is an important milestone in processing renewable resources for deriving biofuel and value‐added chemicals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".