Abstract
Criegee intermediates in the atmosphere serve as oxidizing agents to initiate aerosol formation, which are particularly important for atmospheric modeling, and understanding their kinetics is one of the current outstanding challenges in climate change modeling. Because experimental kinetics are still limited, we must rely on theory for the complete picture, but obtaining absolute rates from theory is a formidable task. Here, we report the bimolecular reaction kinetics of carbonyl oxide with ammonia, hydrogen sulfide, formaldehyde, and water dimer by designing a triple-level strategy that combines (i) benchmark results close to the complete-basis limit of coupled-cluster theory with the single, double, triple, and quadruple excitations (CCSDTQ/CBS), (ii) a new hybrid meta density functional (M06CR) specifically optimized for reactions of Criegee intermediates, and (iii) variational transition-state theory with both variable rection coordinates and optimized reaction paths, with multidimensional tunneling, and with pressure effects. For (i) we have found that quadruple excitations are required to obtain quantitative reaction barriers, and we designed new composite methods and strategies to reach CCSDTQ/CBS accuracy. The present findings show that (i) the CH2OO + HCHO reaction can make an important contribution to the sink of HCHO under wide atmospheric conditions in the gas phase and that (ii) CH2OO + (H2O)2 dominates over the CH2OO + H2O reaction below 10 km.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 8402-8413 |
| Number of pages | 12 |
| Journal | Journal of the American Chemical Society |
| Volume | 143 |
| Issue number | 22 |
| DOIs | |
| State | Published - Jun 9 2021 |
Bibliographical note
Funding Information:This work was supported in part by the National Natural Science Foundation of China (41775125, 91961123), by the Science and Technology Foundation of Guizhou Province, China ([2019]5648) by the Science and Technology Foundation of Guizhou Provincial Department of Education, China (KY[2021]014), and by Guizhou Minzu University ([2018]5773-ZD01). Y.W. would like to thank the financial support from the National Natural Science Foundation of China (21903024) Huxiang High-Level Talent Gathering Project of Hunan Province (2019RS1034) and the Natural Science Foundation of Hunan Province (2020JJ5349). X.H. would like to thank the financial support from the National Natural Science Foundation of China (Grant Nos. 21922301, 21761132022, and 21673074), the National Key R&D Program of China (Grant Nos. 2016YFA0501700 and 2019YFA0905201), Shanghai Municipal Natural Science Foundation (Grant No. 18ZR1412600), and the Fundamental Research Funds for the Central Universities. J.L.B. acknowledges the financial support provided by the Boston College start-up funding. D.G.T. was supported in part by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award DE-SC0015997. The authors would also like to thank the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001), the Minnesota Supercomputing Institute, Boston College Linux Cluster, and the National Energy Research Scientific Computing Center for computational resources.
Funding Information:
This work was supported in part by the National Natural Science Foundation of China (41775125, 91961123), by the Science and Technology Foundation of Guizhou Province, China ([2019]5648), by the Science and Technology Foundation of Guizhou Provincial Department of Education, China (KY[2021]014), and by Guizhou Minzu University ([2018]5773-ZD01). Y.W. would like to thank the financial support from the National Natural Science Foundation of China (21903024), Huxiang High-Level Talent Gathering Project of Hunan Province (2019RS1034), and the Natural Science Foundation of Hunan Province (2020JJ5349). X.H. would like to thank the financial support from the National Natural Science Foundation of China (Grant Nos. 21922301, 21761132022, and 21673074), the National Key R&D Program of China (Grant Nos. 2016YFA0501700 and 2019YFA0905201), Shanghai Municipal Natural Science Foundation (Grant No. 18ZR1412600), and the Fundamental Research Funds for the Central Universities. J.L.B. acknowledges the financial support provided by the Boston College start-up funding. D.G.T. was supported in part by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award DE-SC0015997. The authors would also like to thank the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001), the Minnesota Supercomputing Institute, Boston College Linux Cluster, and the National Energy Research Scientific Computing Center for computational resources.
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