Separation, detection and characterisation of engineered nanoparticles in natural waters using hydrodynamic chromatography and multi-method detection (light scattering, analytical ultracentrifugation and single particle ICP-MS)
Bibliographic record
Abstract
Environmental context The effects of engineered nanoparticles on the environment and on human health are difficult to evaluate largely because nanoparticles are so difficult to measure. The main problems are that concentrations are low and the engineered nanoparticles are often difficult to distinguish from the environmental matrices in which they are found. We report a separation technique that facilitates the detection of engineered nanoparticles in natural waters. Abstract Few analytical techniques are presently able to detect and quantify engineered nanoparticles (ENPs) in the environment. The major challenges result from the complex matrices of environmental samples and the low concentrations at which the ENPs are expected to be found. Separation techniques such as asymmetric flow field flow fractionation (AF4) and more recently, hydrodynamic chromatography (HDC) have been used to partly resolve ENPs from their complex environmental matrices. In this paper, HDC was first coupled to light scattering detectors in order to develop a method that would allow the separation and detection of ENPs spiked into a natural water. Size fractionated samples were characterised using off-line detectors including analytical ultracentrifugation (AUC), dynamic light scattering (DLS) and single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). HDC was able to separate a complex mixture of polystyrene, silver and gold nanoparticles (radii of 60, 40, 20 and 10 nm) contained within a river water matrix. Furthermore, the feasibility of using HDC coupled to SP-ICP-MS was demonstrated by detecting 4 µg L–1 of a 20-nm (radius) nAg in a river water sample.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".