Mixotrophic microalgal mixed cultures for cheese whey valorization
Bibliographic record
Abstract
Abstract Microalgae cultivation, and phycoremediation, can be a polishing step in wastewater treatment. Depending on the stream utilized for microalgal cultivation, biomass can be contaminated with considerable quantities of heavy metals and xenobiotics. Given the economic value of microalgae bioproducts, we suggest coupling anaerobic fermentation with microalgae mixotrophic growth. Cheese whey, a product from cheese production, has a 2022 forecast production of 160.7 million m 3 year −1 in which about 66.5 million m 3 y −1 is used as animal feed, fertilizers or illegally discharged causing eutrophication. Anaerobic fermentation of cheese whey produces volatile fatty acids (VFAs) such as acetate which serves as an organic carbon source for photoorganoheterotrophic microalgal biomass growth. Our work selected three organic sources derived from cheese whey: 40% demineralized whey powder (WPC40), lactose, and acetate. In photolitoautotrophic conditions, green phototrophic growth was successful. In batch tests, acetate was the best organic carbon source among photoorganoheterotrophs with a higher yield of 1.15 mg VSS mg Carbon −1 (C) in anaerobic conditions. Also, acetate uptake was thought to be via the glyoxylate cycle. When upscaling the experiment in a chemostat, a lower dilution rate of 0.17 d −1 was more suitable for green photoorganoheterotrophs selection, as they were not washed out in the process. These findings show that acetate uptake by microalgae in mixotrophic regimes must be better understood as well as reinforce the advantages of coupling microalgal biomass growth with cheese whey acidogenic fermentation, avoiding contaminations as in phycoremediation processes and fully valorizing cheese whey. Graphical abstract
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".