Evaluating the treatment effectiveness of copper-based algaecides on toxic algae Microcystis aeruginosa using single cell-inductively coupled plasma-mass spectrometry
Bibliographic record
Abstract
Single cell-inductively coupled plasma-mass spectrometry (SC-ICP-MS) is an emerging technology. In this work, we have developed a novel SC-ICP-MS method to quantify metal ions in individual cells of a toxic cyanobacterial species, Microcystis aeruginosa (M. aeruginosa), without complicated post-dosing sample preparation, and applied this method to study the treatment effectiveness of copper-based algaecides (cupric sulfate and EarthTec®) on the toxic algae M. aeruginosa. The developed SC-ICP-MS method uses new intrinsic metal element magnesium to determine real transport efficiency and cell concentration. The cell viability and microcystin-LR release by algaecide treatment were studied by flow cytometry and ultra-fast liquid chromatography-tandem mass spectrometry, respectively. The results showed that this novel method was very rapid, highly sensitive (detection limits of intracellular copper and magnesium were 65 ag/cell and 98 ag/cell, respectively), and reproducible (relative standard deviation within 12%). The algaecide effectiveness study further demonstrated that copper in the forms of cupric sulfate and copper-based algaecide EarthTec® successfully diminished M. aeruginosa populations. The higher the copper concentration used to treat the cells, the faster the speeds of copper uptake and cell lysis in the copper concentrations ranged from 0 to 200 μg/L of copper-based algaecide. The cells exhibit obvious heterogeneity in copper uptake. The result suggests that M. aeruginosa cells uptake and cumulate copper followed by cellular lysis and microcystin-LR release. These novel results indicated that though the copper-based algaecides could control this type of harmful algal bloom, further treatment to remove the released algal toxin from the treated water would be needed. Graphical abstract.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".