A strategy for quantifying microplastic particles in membrane filtration processes using flow cytometry
Bibliographic record
Abstract
Microplastic (MP) pollution is ubiquitous in the aquatic environment, with significant quantities of MPs originating from municipal wastewater treatment plants. Efforts to evaluate and implement MP removal processes are underway, with membrane technologies often recommended as an “ideal” solution. A key challenge in evaluating these technologies involves efficiently quantifying MP concentrations in samples. Here, flow cytometry (FC) is demonstrated as an effective technique to obtain concentration measurements of plastic microbeads (MBs; 1–5 μm) suspended in water with/without added humic acid. Regardless of solution conditions, MB concentrations were easily quantified via FC. Subsequently, two microfiltration membranes were challenged to these suspensions. As measured via FC, the 0.45 μm membrane demonstrated effective MB rejection (>99%) whereas the 5 μm membrane exhibited a broad range of rejections (40% to >95%) depending on solution conditions and filtration time. Finally, a model was formulated utilizing FC forward light scattering intensity measurements to estimate MB sizes in samples. Using the model, a 33% reduction in median MB size, on average, was noted across the 5 μm membrane when filtering MBs suspended in humic acid solution, affirming a preferential permeation of smaller particles. Overall, this study advances MP quantification techniques towards validating removal processes. • Fluorescent microplastic particle concentrations were measured via flow cytometry. • The presence of autofluorescent humic acid did not hinder microplastic detection. • Microfiltration membranes demonstrated a high rejection of microplastic particles. • Membrane fouling by humic acid mitigated microplastic particle transmission. • Smaller particle size distributions in membrane permeate vs. feed samples were seen.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".