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Laboratory studies of particle nucleation: Initial results for H2SO4, H2O, and NH3 vapors

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Ball, S. M., Hanson, D. R., Eisele, F. L., McMurry, P. H.·1999·Journal of Geophysical Research: Atmospheres

Abstract

Particle formation in the binary H 2 SO 4‐H 2 O vapor system was studied at 295 K in a series of experiments employing a flow reactor. The concentration of H 2 SO 4 was detected by chemical ionization mass spectrometry, and an ultrafine particle condensation nucleus counter was used to count the newly nucleated particles. Results yield a particle formation rate that is approximately proportional to [H 2 SO 4] raised to the eighth power and to [H 2 O] raised to the fifth power. The power dependencies measured here are significantly different from those determined in previous experimental work, and furthermore, the water dependence is markedly different from that predicted from current theories. The effect of adding ammonia vapor to the binary system was investigated; concentrations of NH 3 in the many tens of parts per trillion by volume range were observed to promote dramatically the rate of particle nucleation.

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Metadata

Authors
Ball, S. M., Hanson, D. R., Eisele, F. L., & McMurry, P. H.
Venue
Journal of Geophysical Research: Atmospheres
Published
1999-10
Volume
104
Issue
D19
Pages
23709-23718
ISSN
0148-0227 (ISSN-L)
OpenAlex
W2003723869
Cited by
320
References
0
FWCI
6.25
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Laboratory studies of particle nucleation: Initial results for H2SO4, H2O, and NH3 vapors. https://app.cochat.ai/papers/10.1029/1999jd900411
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Ball, S. M., Hanson, D. R., Eisele, F. L., & McMurry, P. H. (1999). Laboratory studies of particle nucleation: Initial results for H2SO4, H2O, and NH3 vapors. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/1999jd900411

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.