Investigation of biomass concentration, lipid production, and cellulose content in <i>Chlorella vulgaris</i> cultures using response surface methodology
Bibliographic record
Abstract
The microalgae Chlorella vulgaris produce lipids that after extraction from cells can be converted into biodiesel. However, these lipids cannot be efficiently extracted from cells due to the presence of the microalgae cell wall, which acts as a barrier for lipid removal when traditional extraction methods are employed. Therefore, a microalgae system with high lipid productivity and thinner cell walls could be more suitable for lipid production from microalgae. This study addresses the effect of culture conditions, specifically carbon dioxide and sodium nitrate concentrations, on biomass concentration and the ratio of lipid productivity/cellulose content. Optimization of culture conditions was done by response surface methodology. The empirical model for biomass concentration (R(2) = 96.0%) led to a predicted maximum of 1123.2 mg dw L(-1) when carbon dioxide and sodium nitrate concentrations were 2.33% (v/v) and 5.77 mM, respectively. For lipid productivity/cellulose content ratio (R(2) = 95.2%) the maximum predicted value was 0.46 (mg lipid L(-1) day(-1) )(mg cellulose mg biomass(-1) )(-1) when carbon dioxide concentration was 4.02% (v/v) and sodium nitrate concentration was 3.21 mM. A common optimum point for both variables (biomass concentration and lipid productivity/cellulose content ratio) was also found, predicting a biomass concentration of 1119.7 mg dw L(-1) and lipid productivity/cellulose content ratio of 0.44 (mg lipid L(-1) day(-1) )(mg cellulose mg biomass(-1) )(-1) for culture conditions of 3.77% (v/v) carbon dioxide and 4.01 mM sodium nitrate. The models were experimentally validated and results supported their accuracy. This study shows that it is possible to improve lipid productivity/cellulose content by manipulation of culture conditions, which may be applicable to any scale of bioreactors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".