Disruption and Wet Extraction of the Microalgae <i>Chlorella vulgaris</i> Using Room-Temperature Ionic Liquids
Bibliographic record
Abstract
Recently, ionic liquids have been demonstrated to increase the efficiency of solvent extraction of lipids from microalgae. However, to date, mostly imidazolium-based ionic liquids have been investigated. This report extends the range of cations studied to over 30, including imidazolium, ammonium, phosphonium, and pyridinium derivatives, which were screened for their ability to increase hexane extraction efficiency of lipids from freeze-dried microalgae Chlorella vulgaris at ambient temperature. Promising ionic liquids were first identified using gravimetric analysis of total extractable oils. Oils extracted after ionic liquid pretreatment were further characterized with respect to fatty acid methyl ester (FAME) equal to biodiesel yield, FAME composition, and chlorophyll content. With few exceptions, all of the tested ionic liquids had lower chlorophyll content than standard solvent extraction techniques. The effect of process parameters such as mass ratio of algae to ionic liquid, incubation time, water content, and cosolvents were investigated for 1-ethyl-3-methylimidazolium ethylsulfate [C 2 mim][EtSO 4 ]. The results indicate that this ionic liquid can disrupt C. vulgaris in conjunction with methanol and allow facile recovery of lipids over a large degree of dewatered microalgae (0–82 wt % water), in a small amount of time (75 min) at room temperature, resulting in the development of a low energy, water compatible, biodiesel production scheme.
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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 | 0.000 | 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".