Mini-Hydrocyclone Separation of Cyanobacterial and Green Algae: Impact on Cell Viability and Chlorine Consumption
Bibliographic record
Abstract
The co-occurrence of non-toxic phytoplankton alongside cyanobacteria adds to the challenge of treating source waters with harmful algal blooms. The non-toxic species consume the oxidant and, thereby, reduce the efficacy of oxidation of both the extracellular and intracellular cyanotoxins. In this work, a 3D printed mini-hydrocyclone was used to separate a mixture of non-toxic green algae, Scenedesmus obliquus, from a toxic species of cyanobacteria, Microcystis aeruginosa. When water is pumped through the mini-hydrocyclone, cells exit through an overflow or underflow port depending on their size, shape, and density relative to the other cells and particles in the water matrix. The overflow port contains the cells that are smaller and less dense since these particles move toward the center of the hydrocyclone. In this work, the majority (>93%) of Microcystis cells were found in the overflow while the underflow contained primarily the Scenedesmus (>80%). This level of separation efficiency was maintained over the 30-min experiment and the majority of both cells (>86%) remained viable following the separation, which indicates that the pumping combined with forces exerted within the mini-hydrocyclone were not sufficient to cause cell death. The impact of free chlorine on the cells both pre-separation and post-separation was evaluated at two doses (1 and 2 mg/L). After separation, the overflow, which contained primarily Microcystis, had at least a 24% reduction in the free chlorine decay rate as compared to the feed water, which contained both species. This reduction in chlorine consumption shows that the cells separated via mini-hydrocyclone would likely require lower doses of oxidant to produce a similar level of degradation of the cyanotoxins present in either the extracellular or intracellular form. However, future work should be undertaken to evaluate this effect in natural bloom samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".