Assessment of the Contribution of Electron Microscopy to Nanoparticle Characterization Sampled with Two Cascade Impactors
Bibliographic record
Abstract
This study assessed the contribution of electron microscopy to the characterization of nanoparticles and compared the degree of variability in sizes observed within each stage when sampled by two cascade impactors: an Electrical Low Pressure Impactor (ELPI) and a Micro-Orifice Uniform Deposit Impactor (MOUDI). A TiO(2) nanoparticle (5 nm) suspension was aerosolized in an inhalation chamber. Nanoparticles sampled by the impactors were collected on aluminum substrates or TEM carbon-coated copper grids using templates, specifically designed in our laboratories, for scanning and transmission electron microscopy (SEM, TEM) analysis, respectively. Nanoparticles were characterized using both SEM and TEM. Three different types of diameters (inner, outer, and circular) were measured by image analysis based on count and volume, for each impactor stage. Electron microscopy, especially TEM, is well suited for the characterization of nanoparticles. The MOUDI, probably because of the rotation of its collection stages, which can minimize the resuspension of particles, gave more stable results and smaller geometric standard deviations per stage. Our data suggest that the best approach to estimate particle size by electron microscopy would rely on geometric means of measured circular diameters. Overall, the most reliable data were provided by the MOUDI and the TEM sampling technique on carbon-coated copper grids for this specific experiment. This study indicates interesting findings related to the assessment of impactors combined with electron microscopy for nanoparticle characterization. For future research, since cascade impactors are extensively used to characterize nano-aerosol exposure scenarios, high-performance field emission scanning electron microscopy (FESEM) should also be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".