Microalgal biomass pretreatment for bioethanol production: a review
Bibliographic record
Abstract
Biofuels derived from microalgae biomass have received a great deal of attention owing to their high potentials as sustainable alternatives to fossil fuels. Microalgae have a high capacity of CO2 fixation and depending on their growth conditions, they can accumulate different quantities of lipids, proteins, and carbohydrates. Microalgal biomass can, therefore, represent a rich source of fermentable sugars for third generation bioethanol production. The utilization of microalgal carbohydrates for bioethanol production follows three main stages: i) pretreatment, ii) saccharification, and iii) fermentation. One of the most important stages is the pretreatment, which is carried out to increase the accessibility to intracellular sugars, and thus plays an important role in improving the overall efficiency of the bioethanol production process. Diverse types of pretreatments are currently used including chemical, thermal, mechanical, biological, and their combinations, which can promote cell disruption, facilitate extraction, and result in the modification the structure of carbohydrates as well as the production of fermentable sugars. In this review, the different pretreatments used on microalgae biomass for bioethanol production are presented and discussed. Moreover, the methods used for starch and total carbohydrates quantification in microalgae biomass are also briefly presented and compared.
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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 | 0.003 | 0.003 |
| 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) | 0.004 | 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".