0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessIt is well known that the use of different methods for measuring particle size distributions may lead to very different results. This is completely understood in the case of solid particles which may differ in several other aspects beyond their size (e.g. shape). However, the explanation is more difficult for the case of small size emulsion droplets where the shape of all droplets is spherical. Here, the idea that the error distribution with respect to droplet size is the origin of different results from different measuring method, is proposed. Each measuring method relies on a physical principle and so the error is relatively uniformly distributed through the values of the variable related to this physical principle. Any attempt to transform this variable leads to severe error maldistribution. The present work not only explains the reason of deviations of droplet size distribution between measuring methods but also suggests the compilation of information from two measuring methods to find a particle size distribution (PSD) with increased accuracy compared to the two individual methods. Several approaches in this direction are proposed and tested.
Margaritis Kostoglou, Thodoris Karapantsios, Angeliki P. Chondrou, Maria C. Vlachou (2021). Towards an accurate size distribution of emulsion droplets by merging distributions estimated from different measuring methods. , 46, DOI: https://doi.org/10.1016/j.colcom.2021.100569.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2021
Authors
4
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1016/j.colcom.2021.100569
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access