Cultivation of the Marine Macroalgae <i>Chaetomorpha linum</i> in Municipal Wastewater for Nutrient Recovery and Biomass Production
Bibliographic record
Abstract
Compared to microalgae, macroalgae are larger in size, thereby imposing lower separation and drying costs. This study demonstrates the feasibility of cultivating macroalgae Chaetomorpha linum in different types of municipal wastewaters, their ability to remove nutrient and their biomass composition for downstream biofuel production. Screening experiments indicated that C. linum grew well on primary (PW) and secondary wastewaters (SW), as well as centrate wastewater (CW) diluted to less than 20%. In a subsequent experiment, a step feeding approach was found to significantly increase biomass productivity to 10.7 ± 0.2 g AFDW·m –2 ·d –1 ( p < 0.001), a 26.5% improvement in comparison to the control with single feeding, when grown on 10-CW; meanwhile, nitrogen and phosphorus removal efficiencies rose to 86.8 ± 1.1% ( p < 0.001) and 92.6 ± 0.2% ( p < 0.001), respectively. The CO 2 -supplemented SW cultures (10.1 ± 0.4 g AFDW·m –2 ·d –1 ) were 1.20 times more productive than the corresponding controls without CO 2 supplementation ( p < 0.001); however, similar improvements were not observed in PW ( p = 0.07) and 10-CW cultures ( p = 0.07). Moreover, wastewater type and nutrient concentration influenced biomass composition (protein, carbohydrate and lipid). These findings indicate that the application of the macroalgae C. linum could represent an effective wastewater treatment alternative that could also provide a feedstock for downstream processing to biofuels.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".