Effect of nitrate feeding strategies on lipid and biomass productivities in fed‐batch cultures of <i>Nannochloropsis gaditana</i>
Bibliographic record
Abstract
Abstract Controlled nitrate feeding strategies for fed‐batch cultures of microalgae were applied for the enhancement of lipid production and microalgal growth rates. In particular, in this study, the effect of nitrate feeding rates on lipid and biomass productivities in fed‐batch cultures of Nannochloropsis gaditana were investigated using three feeding modes (i.e., pulse, continuous, and staged) and under two light variations on both lipid productivity and fatty acid compositions. Higher nitrate levels negatively affected lipid production in the study. Increasing the light intensity increased the lipid contents of the microalgae in all three fed‐batch feeding modes. A maximum of 58.3% lipid‐ to dry weight ratio was achieved when using pulse‐fed cultures at an illumination of 200 μmol photons m −2 s −1 and 10 mg/day of nitrate feeding. This condition also resulted in the maximum lipid productivity of 44.6 mg L −1 day −1 . The fatty acid compositions of the lipids consisted predominantly of long‐chain fatty acids (C:16 and C:18) and accounted for 70% of the overall fatty acid methyl esters. Pulse feeding mode was found to significantly enhance the biomass and lipid production. The other two feeding modes (continuous and staged) were not ideal for lipid and biomass production. This study demonstrates the applicability of pulse feeding strategies in fed‐batch cultures as an appropriate cultivation strategy that can increase both lipid accumulation and biomass production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".