Evidence map›Paper›PMID 24481172›Full record

ArticlePharmaceutics2014

Electrostatic charge effects on pharmaceutical aerosol deposition in human nasal-laryngeal airways.

Jinxiang Xi, Xiuhua Si, Worth Longest

Abstract read
In one paragraph

Article in Pharmaceutics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Review
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  4. Application of the market-ready NAVETTA electrodeposition chamber for controlledComputational and structural biotechnology journal · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jinxiang XiDepartment of Mechanical and Biomedical Engineering, Central Michigan University, Mt Pleasant, MI 48858, USA. xi1j@cmich.edu.
Xiuhua SiDepartment of Engineering, Calvin College, Grand Rapids, MI 49546, USA. xs22@calvin.edu.
Worth LongestDepartment of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA. pwlongest@vcu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrostatic charging occurs in most aerosol generation processes and can significantly influence subsequent particle deposition rates and patterns in the respiratory tract through the image and space forces. The behavior of inhaled aerosols with charge is expected to be most affected in the upper airways, where particles come in close proximity to the narrow turbinate surface, and before charge dissipation occurs as a result of high humidity. The objective of this study was to quantitatively evaluate the deposition of charged aerosols in an MRI-based nasal-laryngeal airway model. Particle sizes of 5 nm-30 µm and charge levels ranging from neutralized to ten times the saturation limit were considered. A well-validated low Reynolds number (LRN) k-ω turbulence model and a discrete Lagrangian tracking approach that accounted for electrostatic image force were employed to simulate the nasal airflow and aerosol dynamics. For ultrafine aerosols, electrostatic charge was observed to exert a discernible but insignificant effect. In contrast, remarkably enhanced depositions were observed for micrometer particles with charge, which could be one order of magnitude larger than no-charge depositions. The deposition hot spots shifted towards the anterior part of the upper airway as the charge level increased. Results of this study have important implications for evaluating nasal drug delivery devices and for assessing doses received from pollutants, which often carry a certain level of electric charges.

Identifiers

PMID24481172
PMCPMC3978523

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.