Enhancement of Lipid and Biomass Production in Microalgae Scenedesmus abundans by Microwave Irradiation
Bibliographic record
Abstract
Due to the rising depletion of fossil fuels and increased energy demands, human society is looking for clean sustainable energy. Commercially produced algal biodiesel is limited by the expense and difficulty of oil extraction and subsequent biodiesel conversion technologies. Microalgae with high oil content are only alternatives for decreasing fossil fuel supplies, but more work remains to be done to improve the lipid content of microalgae strains. In this study, strain improvement is done using microwave radiation in Scenedesmus abundans to increase the production of triacylglycerol, which is the main source of biodiesel. Microalgal cultures were exposed to varied microwave irradiation over different time periods. Under microwave irradiation, 20-25 mins reaction time seems suitable for the complete in situ transesterification reaction. Microwave heating transesterification has been shown to be more effective for adequate biodiesel yield compared to the conventional transesterification process. Maximum increase of 2.22-fold in biomass, and 2.5-fold in triacylglycerol was observed for microwave irradiation of 25 mins and 20 mins intervals respectively. The percentage of some monounsaturated fatty acids increased in gas chromatographic examination of neutral lipid fractions from total lipids of microwave irradiated samples, is considered as one of the preferable properties of biodiesel. According to our study findings, Scenedesmus abundans qualifies as the most efficient feedstock for biodiesel production, and microwave-assisted in situ transesterification reduces the requirement for a large amount of solvents, longer reaction times, and high reaction temperatures and pressures.
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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) | 0.000 | 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".