Efficient separation and concentration of nanoplastics by tangential flow filtration: process evaluation and quantification using nanoparticle tracking analysis and dynamic light scattering
Bibliographic record
Abstract
Increasing awareness of the presence of nanosized plastic particles (nanoplastics) in the environment has raised interest in their physical and chemical properties and concerns about their environmental effects and potential impact on drinking water and food. Some of the major challenges of environmental nanoplastics research are the difficulty of extracting sufficient amounts of nanoparticles from natural sources for various analytical techniques and the determination of their concentration and size distribution. Overcoming these challenges requires developing robust protocols and methodologies for the extraction and concentration enrichment of nanoparticles. To address this, we explored a tangential flow filtration (TFF) technology to separate and recover 50-nm-in-diameter polystyrene nanoparticles (PS50) from their mixture with larger, 350-nm-in-diameter particles (PS350). We used nanoparticle tracking analysis (NTA) and dynamic light scattering (DLS) to quantify separation and recovery efficiency and assessed the feasibility of using DLS for particle concentration measurement. With sufficient diafiltration volumes, we were able to separate and concentrate PS50 particles with recovery efficiency greater than 95%. Our work shows that TFF is capable of efficiently separating and concentrating nanoplastics from potentially complex environmental matrices, which makes it an essential platform for advancing research in this field. DLS can be used to monitor the nanoparticle separation and concentration efficiency and, when aided by other techniques such as NTA or calibrated using a suitable reference material, reliably estimate the nanoparticle concentration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".