Analyse des nanoparticules de dioxyde de cérium à l’aide de l’ICP-MS en mode particule unique
Bibliographic record
Abstract
Due to their unique properties, engineered nanomaterials are now widely used in numerous commercial products. Cerium oxide (CeO2) nanoparticles (NPs) are among the most commonly used engineered NPs, with applications in surface coatings, catalysis, the manufacturing of semiconductors, biomedicine and agriculture. With the significant increase in the production and use of CeO2 NPs, concern is increasing over their release into the environment and their subsequent fate and toxicity. In order to evaluate their environmental risk, it is necessary to detect, quantify and characterize the NPs in all environmental compartments. Unfortunately, analyses of NPs in natural systems are challenging due to their small sizes, their low concentrations (∼ ng L-1) and the complexity of environmental matrices, which also contain natural colloids. Single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) is a specific and sensitive technique that enables the detection of very low concentrations (∼ ng L-1) of NPs and it can provide information on their number concentrations, sizes, and size distributions. This technique is often limited by high size detection limits (SDL). However, it is especially important to obtain rigorous size, concentration, and fate data for the smallest NPs, since they are expected to have the greatest environmental risk. To that end, the specific objective of this thesis was to develop an improved method for the detection, quantification, and characterization of CeO2 NPs in complex natural waters using SP-ICP-MS. The project was then divided into several objectives: (1) decrease the SDL for CeO2 NPs; (2) optimize the preparation method for natural water samples; (3) apply the preparation and the analysis methods to detect, quantify, and characterize CeO2 NPs in several natural water samples and commercial products, such as a paint and a stain; (4) identify the origin (natural or engineered) of the detected CeO2 NPs; (5) quantify and characterize the release of CeO2 NPs from paint and stain under natural weathering scenarios; and (6) evaluate the effect of different physicochemical conditions (pH, ionic strength, and NOM) on the fate of CeO2 NPs after their release. A high sensitivity sector field ICP-MS (SF-ICP-MS) with microsecond dwell times (50 μs) was used to lower the SDL of CeO2 NPs to below 4.0 nm. While filtration is often used as a preparation method for SP-ICP-MS, its effect on the concentrations and sizes of NPs is unknown. For this purpose, the interactions between six different membrane filters and CeO2 NPs in aqueous samples were examined. The highest recoveries were observed for polypropylene membranes, where 60 % of the pre-filtration NPs were found in a rainwater and 75% were found in a river water. Recoveries could be increased to over 80% by pre-conditioning the filtration membranes with a multi-element solution. Similar recoveries were obtained when samples were centrifuged at low centrifugal forces (≤1000xg). SF-ICP-MS was then used to detect CeO2 NPs in Montreal rainwater, St. Lawrence River water, a paint, and a stain. A significant decrease in the concentrations of CeO2 NPs, initially contained in paint and stain, was measured over time under different conditions, which was attributed to agglomeration and/or dissolution. Finally, when painted and stained panels were placed outside, the released Ce in the precipitation was mainly in the dissolved form with no significant release of CeO2 NPs.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".