<scp><i>Chlorella minutissima</i></scp> grown with xylose and arabinose in tubular photobioreactors: Evaluation of kinetics, carbohydrate production, and protein profile
Bibliographic record
Abstract
Abstract Lignocellulosic waste is the most abundant global renewable biomass source and contains significant amounts of pentoses. Thus, pentoses can be considered potential carbon sources for the culture media for microalgae cultivation. The present study aimed to determine whether the addition of D‐xylose and L‐arabinose and lighting variations influence the carbohydrate and protein profiles of Chlorella minutissima grown in tubular photobioreactors. The highest biomass concentration of 1.55 g L−1 was attained by the control cultures exposed to a light intensity of 40.50 μmol m−2 s−1. The highest carbohydrate accumulation (66.4%) was obtained through the combined use of 40.50 μmol m−2 s−1 light intensity, 19.16 mg L−1 D‐xylose, 0.89 mg L−1 L‐arabinose, and 0.125 g L−1 KNO3. A reduction in luminosity and the addition of pentoses altered the protein profile of Chlorella minutissima. Thus, growth and carbohydrate production can be stimulated by pentoses and adequate luminous intensity. Therefore, Chlorella minutissima can be considered a potential source of biomass for bioethanol 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) | 0.000 | 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".