Novel Thin-Layer Fountain Photobioreactors for the High-Density Cultivation of <i>Spirulina</i> sp
Bibliographic record
Abstract
Microalgae have been considered great candidates for carbon reduction and sustainable feedstock for foods, biofuels, and biochemicals, but their production usually features low productivity, high costs, and intensive energy inputs. The thick culture layer adopted in conventional cultivation systems (5.0–30 cm) has been established as a significant reason for the above problems. In this study, a novel mixing-based thin-layer fountain photobioreactor (TLF-PBR) was developed. A fountain pump was used to pump and spray microalgal culture for mixing. The results showed that the mixing mode could support sufficient mixing to efficiently cultivate Spirulina sp. in a 50 cm-diameter TLF-PBR, with the lowest mixing time of 13.580 ± 0.522 s, the highest oxygen mass transfer coefficient of 142.555 ± 5.791 h –1, and the highest biomass concentration of 3.118 ± 0.009 g L –1 in the 1.0 cm layer. The TLF-PBR was successfully scaled to a diameter of 1.5 m (1.766 m 2 ), with a maximum biomass density of 3.955 ± 0.037 g L –1, which was 71.0 and 44.7% higher than that of the flat panel PBR and thin-layer cascade system, respectively. This study provides a novel approach for developing thin-layer, scalable PBRs that could support cost-effective, efficient microalgae production.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".