Measuring the bipolar charge distribution of nanoparticles: Review of methodologies and development using the Aerodynamic Aerosol Classifier
Bibliographic record
Abstract
A review of methodologies to measure the bipolar charge distribution of nanoparticles is completed, including their advantages/disadvantages and sequential development. This summary also provides context for a new development, which uses an Aerodynamic Aerosol Classifier (AAC) and Differential Mobility Analyzer (DMA) in tandem for a similar purpose. It is demonstrated that the tandem AAC-DMA system overcomes some significant limitations of the previous methodologies, such as multiply-charged particle artefacts and low measurement signals. The tandem AAC-DMA methodology also has the sensitivity to detect other charging phenomena, such as the effects of different sample flow rates through the charger, free-ions downstream of the charger, the inlet insert on the 85Kr charger and different particle chargers (x-ray, old 85Kr and new 85Kr). The charge fractions of the particles at low-flow (0.6 L/min) through the new 85Kr charger agreed well (average absolute difference of 0.007) with widely-used charging theory. However, significant deviations from theory (up to a 0.044 difference in charge fractions) were found with a higher sample flow rate (1.2 L/min), with different exposure times to free-ions downstream of the charger, or with the inlet insert on the new 85Kr charger. It was found that regardless of flow rate, a soft x-ray charger resulted in charge fractions which deviated significantly from theory (up to a 0.084 difference in charge fractions), producing higher fractions of positively charged particles and lower fractions of negatively charged particles relative to theory. All of these deviations are likely due to the simplifying assumptions made by the charging theory. Therefore, rigorous measurement of particle charge distributions are necessary for accurate aerosol characterization, such as standard SMPS measurements.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".