Electrospray-dynamic mobility analysis for characterization of engineered nanomaterials in aqueous samples
Bibliographic record
Abstract
Understanding the behavior and fate of engineered nanomaterials (ENMs) released to the environment requires measurement of their physicochemical properties in relevant media. Specialized instrumentation is required to be able to detect their changes in physicochemical characteristics (such as agglomeration and dissolution) as ENMs move from the manufactured state to the exposure pathway and ultimately interact with biological systems. This study combined electrospray with dynamic mobility analysis (ES-DMA) for characterizing ENMs in dispersions. The capability of this approach to measure different size ranges of silica, gold, silver and cerium dioxide nanoparticles in dispersions was evaluated. The proposed approach was found to be capable of accurately characterizing nanoparticle size and size distributions in monomodal, polymodal and polydispersed samples. The capability of ES-DMA to resolve polymodal nanoparticle size distributions over a wide size range (from 6 to 217 nm) in a single run facilitates the detection of aggregation/agglomeration processes. Results obtained using ES-DMA were compared with those using single particle inductively-coupled plasma mass spectrometry (SP-ICP-MS). Measurements of particle size and size distribution obtained using ES-DMA compared well with reference values and with results obtained using SP-ICP-MS, showing that this technique is capable of reliable characterization of dispersed ENMs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".