Articles | Volume 21, issue 21
https://doi.org/10.5194/acp-21-16387-2021
https://doi.org/10.5194/acp-21-16387-2021
Research article
 | 
08 Nov 2021
Research article |  | 08 Nov 2021

A predictive thermodynamic framework of cloud droplet activation for chemically unresolved aerosol mixtures, including surface tension, non-ideality, and bulk–surface partitioning

Nønne L. Prisle

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nonne Prisle on behalf of the Authors (31 Jul 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (05 Aug 2020) by Barbara Ervens
RR by Anonymous Referee #2 (22 Aug 2020)
RR by Anonymous Referee #1 (26 Aug 2020)
RR by Anonymous Referee #3 (29 Sep 2020)
ED: Reconsider after major revisions (01 Oct 2020) by Barbara Ervens
AR by Nonne Prisle on behalf of the Authors (25 Jan 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (12 Feb 2021) by Barbara Ervens
AR by Nonne Prisle on behalf of the Authors (31 Jul 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (03 Aug 2021) by Barbara Ervens
AR by Nonne Prisle on behalf of the Authors (22 Aug 2021)  Manuscript 
Download
Short summary
A mass-based Gibbs adsorption model is presented to enable predictive Köhler calculations of droplet growth and activation with considerations of surface partitioning, surface tension, and non-ideal water activity for chemically complex and unresolved surface active aerosol mixtures, including actual atmospheric samples. The model is used to calculate cloud condensation nuclei (CCN) activity of aerosol particles comprising strongly surface-active model atmospheric humic-like substances (HULIS).
Altmetrics
Final-revised paper
Preprint