Analytical Developments for the Characterization of Nanomaterials inConsumer Products, Environmental and Medicinal Samples
Bibliographic record
Abstract
open huge prospects for innovation in different fields such as medicine, electronics, cosmetics and materials. However, their uses raise questions about possible risks to the environment and humans. The development of suitable protocols for the physicochemical characterization (size distribution, shape and chemical composition) of such materials is a fundamental issue for coming years. To meet the needs of various industrials producing or using NM, UT2A has developed new analytical approaches. The first one is focused on the determination of the size distribution of nano-scale particles using Dynamic Light Scattering detector (DLS) and a splitting system (by size and weight) such as Asymmetrical Flow Field Flow Fractionation hyphenated with a Multi Angle Laser Light Scattering detector (A4F-MALLS). The second approach is based on a comprehensive physicochemical characterization made by the combination of A4F-MALLS with an UV detector and an Inductively Coupled Plasma Mass Spectrometer (ICP-MS). The Single Particle-ICPMS has also been used to characterize NM. This study is first focused on the characterization of NM in consumer products such as sunscreens, candies or juices. The results obtained by the different analytical approaches are also discussed. Then the same techniques were used for environmental (colloids and NPs in effluent processes) and pharmaceutical applications. This work has enabled to develop and validate an approach to global physicochemical characterization of nanomaterials in complex matrices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".