Nanoparticle characterisation for ecotoxicological studies using imaging and non-imaging based tools
Bibliographic record
Abstract
There is a great deal of concern about the safety of nanoparticles and their effect on the environment; this has led to a recent Organisation for Economic Co-operation and Development (OECD) drive for testing the toxicity of industrially relevant nanomaterials.A fundamental aspect of the research is the need to measure particle size and particle concentration accurately and reliably, as these two parameters will infl uence toxicity.The current paper presents a study that aims to characterise CeO 2 nanoparticles for the purpose of ecotoxicological studies, and this has resulted in a two-part study that is presented in this paper.The aim of the fi rst part of the study was to observe if small nanosized clusters (<1 μm) existed when dispersed in four ecotoxicological media (i.e.fi sh, daphnia, seawater and de-ionised water); this was done qualitatively using Scanning Electron Microscopy.The second part of the study aimed to explore the feasibility of Nanoparticle Tracking Analysis (NTA) as a suitable tool to characterise not only particle size but also particle number concentration.This involved dispersing CeO 2 in DI water and diluting the stock solution into nine different concentrations.Particle size and number data were then acquired using NTA; particle size data were compared to the corresponding Dynamic Light Scattering (DLS) response.The results were as follows: (a) although the majority of the nanomaterials were large aggregates (at least few microns in dimensions), smaller clusters (<800 nm) were shown to be present in all four media; it is the fate of these small size clusters that should be monitored if hypothesis relating toxicity to particle size holds true, (b) as a tool NTA yielded small particle size compared to DLS measurements and the limit of quantifi cation is shown to be >0.1 mg/L, as NTA tracks individual particles and does not suffer limitations observed with DLS, in which larger particles can potentially mask signal of smaller particles and (c) calibration curve for number concentration by NTA did not yield a linear response; the non-linear response observed should be further investigated if NTA is going to be used to measure particle number concentration, particularly in the low concentration range.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".