Chemical ionization of clusters formed from sulfuric acid and dimethylamine or diamines

Coty N. Jen, Jun Zhao, Peter H. McMurry, David R. Hanson

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Chemical ionization (CI) mass spectrometers are used to study atmospheric nucleation by detecting clusters produced by reactions of sulfuric acid and various basic gases. These instruments typically use nitrate to deprotonate and thus chemically ionize the clusters. In this study, we compare cluster concentrations measured using either nitrate or acetate. Clusters were formed in a flow reactor from vapors of sulfuric acid and dimethylamine, ethylene diamine, tetramethylethylene diamine, or butanediamine (also known as putrescine). These comparisons show that nitrate is unable to chemically ionize clusters with high base content. In addition, we vary the ion-molecule reaction time to probe ion processes which include proton-transfer, ion-molecule clustering, and decomposition of ions. Ion decomposition upon deprotonation by acetate/nitrate was observed. More studies are needed to quantify to what extent ion decomposition affects observed cluster content and concentrations, especially those chemically ionized with acetate since it deprotonates more types of clusters than nitrate.

Model calculations of the neutral and ion cluster formation pathways are also presented to better identify the cluster types that are not efficiently deprotonated by nitrate. Comparison of model and measured clusters indicate that sulfuric acid dimers with two diamines and sulfuric acid trimers with two or more base molecules are not efficiently chemical ionized by nitrate. We conclude that acetate CI provides better information on cluster abundancies and their base content than nitrate CI.

Original languageEnglish (US)
Pages (from-to)12513-12529
Number of pages17
JournalAtmospheric Chemistry and Physics
Issue number19
StatePublished - Oct 7 2016

Bibliographical note

Funding Information:
Support from NSF Awards AGS1068201, AGS1338706, and AGS0943721 is gratefully acknowledged. Coty N. Jen acknowledges support from NSF GRFP award 00006595, UMN DDF, and NSF AGS Postdoctoral Fellowship award 1524211. Jun Zhao acknowledges support from SYSU 100 Talents Program.

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