Implementation of Dunaliella tertiolecta and Desmodesmus communis in a photobioreactor prototype for treatment of wastewater in a recirculating aquaculture system
Bibliographic record
Abstract
Inline photobioreactors (PBRs) are a promising tool for nutrient removal from aquacultural wastewater and production of valuable algal biomass, yet few PBR systems have been rigorously tested. Optimization of algal growth screening across species and strains of interest under specific water conditions is crucial but time-consuming, limiting PBR implementation. Here, we developed a high-throughput screening system to efficiently test algal growth under various nutrient treatments, with the goal of informing implementation in a PBR designed for wastewater treatment in recirculating aquaculture systems (RAS). We assessed growth of the marine alga Dunaliella tertiolecta and the freshwater alga Desmodesmus communis under a matrix of inorganic nitrogen (N) treatments in 96-well plates. We then tested ammonium transfer within a prototype PBR for RAS wastewater treatment and evaluated the batch growth response of D. tertiolecta to ammonium treatments in the PBR. Both species grew on the provided inorganic N sources, showing significant differences in response to N treatments due to species-driven variations in nitrogen uptake and storage mechanisms. D. tertiolecta thrived when grown individually on either nitrate or ammonium, while D. communis favored a combination of N sources. D. tertiolecta showed a 5.4% higher growth rate in nitrate than ammonium. Both species grew in nutrient-free controls, suggesting potential use of internal nutrient reserves. D. tertiolecta grew within the PBR, serving as proof-of-concept for algal cultivation in the prototype. This study supports PBR technology for enhancing food production systems and protecting food security through RAS wastewater treatment.
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 | 0.001 |
| Research integrity | 0.001 | 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".