Articles | Volume 19, issue 16
https://doi.org/10.5194/amt-19-5373-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Curve fitting algorithm for multimodal particle size distributions – a theoretical basis
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- Final revised paper (published on 18 Aug 2026)
- Supplement to the final revised paper
- Preprint (discussion started on 19 Dec 2025)
- Supplement to the preprint
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2025-4222', Anonymous Referee #2, 06 Jan 2026
- AC1: 'Reply on RC1', Christopher N. Rapp, 08 Jun 2026
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RC2: 'Comment on egusphere-2025-4222', Anonymous Referee #1, 05 Feb 2026
- AC2: 'Reply on RC2', Christopher N. Rapp, 08 Jun 2026
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Christopher N. Rapp on behalf of the Authors (08 Jun 2026)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (12 Jun 2026) by Wiebke Frey
RR by Anonymous Referee #2 (12 Jun 2026)
RR by Anonymous Referee #3 (30 Jun 2026)
ED: Publish as is (30 Jun 2026) by Wiebke Frey
AR by Christopher N. Rapp on behalf of the Authors (13 Jul 2026)
Post-review adjustments
AA – Author's adjustment | EA – Editor approval
AA by Christopher N. Rapp on behalf of the Authors (14 Aug 2026)
Author's adjustment
Manuscript
EA: Adjustments approved (14 Aug 2026) by Wiebke Frey
The work is done and presented well. There are no major questions about the quality of the work. The main question this reviewer has is "why"? The manuscript is about fitting functions to a multimodal particle size distribution. This is basic data analysis and all major scientific software suites include this capability. An argument was made that the presented work simplifies the process so that the user does not have to learn how to find the peaks (which in itself could be problematic since now there's a black box doing the analysis). However, the code is written in R (using existing R packages for analysis - so again, where's the novelty?) which limits its usefulness to those using R (as opposed to e.g., Python or Matlab). As such, there is little scientific merit to be reviewed here. In conclusion, this manuscript does not offer anything novel to meet the standards of AMT and qualify as a full research paper. The manuscript could be re-submitted as a technical note or the focus could be shifted to analyzing the data from SPL using the R functions described.