Effect of Electrostatic Charge on Deposition of Uniformly Charged Monodisperse Particles in the Nasal Extrathoracic Airways of an Infant
Bibliographic record
Abstract
BACKGROUND: The fraction of inhaled particles depositing in the nasal extrathoracic airways determines the amount of particles delivered to the lungs of infants. Electrostatic charge on particles can affect this deposition, and for this reason, deposition of charged aerosol particles in the Alberta Idealized Infant nasal geometry is examined. METHODS: Charged aerosol particles were generated via Plateau-Rayleigh jet breakup atomization with induction charging. Nasal deposition was measured by collecting particles on a filter membrane at the inlet and outlet of the airway and measuring their mass with an ultramicrobalance. The experiments were carried out using monodisperse, uniformly charged particles with aerodynamic diameters of 3-6 μm at two flow rates of 7.5 and 15 L/min, for a charge range of 0-10,000 e per particle. RESULTS: Electrostatic charge effects are largest for the lowest flow rate, smallest particle size, and highest charge level, with deposition in this case being approximately three times that for neutral particles. Higher flow rates and larger particle size result in much weaker electrostatic effects, with even the highest charge levels giving only a few percent higher deposition for the largest particle size and flow rate considered in this study. A dimensionless empirical relation based on the experimental data was developed for predicting deposition of charged particles in the idealized infant airway. CONCLUSION: Electrostatic charge on inhaled aerosol particles has only a minor effect on deposition for large particles at higher flow rates, because in this case inertial impaction dominates deposition. However, for particles with low inertia, for example, small particles or low flow rates, large values of electrostatic charge strongly increase nasal deposition in the present infant extrathoracic airway.
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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 | 0.000 | 0.001 |
| 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 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".