Agricultural byproducts-based biosorbents for purification of bioalcohols: a review
Bibliographic record
Abstract
For the purification of alcohols derived from microbial fermentations, extensive processing is required. Adsorption is described as one of the most cost-effective and efficient techniques for the separation of water and purification of alcohols. Biobased sorbents (called biosorbents) are advantageous for dehydration of alcohols as they can be developed from cost-effective feedstocks such as waste agricultural biomass or byproducts, have adsorption capacities at par with chemical adsorbents, and can be safely disposed. Alternatively, the spent adsorbents can be reused for fuel or energy production. Agricultural byproducts are low cost and abundantly available materials containing cellulose, hemicellulose, proteins, and lignin as their constituents. Biosorbents have the capability to adsorb water by the polar interaction of their hydroxyl, carboxyl, carbonyl, and amine groups with water molecules. The pore size distribution and thermal stability of biosorbents are also industrially relevant features. They are a promising option to be used in industries for dehydration of alcohols. This paper reviews adsorptive purification of bioalcohols with a focus on using biosorbents, and describes their structure, global availability, water adsorption mechanism, and the use of biosorbents in liquid phase and vapor phase adsorption systems for the purification of ethanol, butanol, and other higher alcohols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".