Data underlying the publication: Asymmetric polyelectrolyte multilayer nanofiltration membranes: Structural characterisation via transport phenomena.
DOI: 10.4121/c101c837-555a-4c2c-8290-69980f520bdd
Datacite citation style
Dataset
This dataset is meant as supplementary information to the publication: Junker, Moritz A., et al. "Asymmetric polyelectrolyte multilayer nanofiltration membranes: Structural characterisation via transport phenomena." Journal of Membrane Science 681 (2023): 121718.
In this work, the fabrication and performance of asymmetric polyelectrolyte multilayer based nanofiltration membranes were analysed by a combination of experimental filtration studies and theoretical transport modeling. Experimental methods applied for membrane characterisation include pure water permeability, single salt retention, polyethylene glycol retention, and ternary ion retention. Additionally, polyelectrolyte multilayers were studied on a model surface via spectroscopic ellipsometry. Based on experimental results, different pore flow models were parametrised to describe and predict the filtration behaviour.
The dataset contains all relevant data for the study. This includes experimental and theoretical results. More information on the specific files can be found within the README.txt file.
History
- 2023-06-02 first online, published, posted
Publisher
4TU.ResearchDataFormat
text/txt, text/csv, text/xlsx, text/mAssociated peer-reviewed publication
Asymmetric polyelectrolyte multilayer nanofiltration membranes: Structural characterisation via transport phenomenaFunding
- University of Twente Connecting Industry Program: Oasen (Gouda, Netherlands), NX Filtration (Enschede, Netherlands), TKI HTSM, Netherlands.
Organizations
University of Twente, Faculty of Science and Technology (TNW)DATA
Files (17)
- 2,418 bytesMD5:
71dca3ed4bb6502a0ccea45153260724README.txt - 250,474 bytesMD5:
d0d61c46e386823685288e2da18b8c38ExperimentalResults.csv - 308,713 bytesMD5:
fdac1fc8a72215156fc62d77f9f718a4ExperimentalResults_Excel.xlsx - 1,320,096 bytesMD5:
035c8dcf5c3fe982c799c272bb8030e4GPCRawData.csv - 1,486,579 bytesMD5:
52f449667694b98259177a51d1825c2bGPCRawData_Excel.xlsx - 56,454 bytesMD5:
a5ab401bb59b36a0da48a6c6bcc95568ModelResults.csv - 47,090 bytesMD5:
9c09ddefe2e4cd8d8eab60bf9d6a4724ModelResults_Excel.xlsx - 68,551 bytesMD5:
bc31590a65e7323e1d468babf62dd78aPlots.m - 68,551 bytesMD5:
bc31590a65e7323e1d468babf62dd78aPlots.txt - 23,616 bytesMD5:
09a90b99a3889ad3d42bcb55ede750eaSingleSaltPrediction.m - 23,616 bytesMD5:
09a90b99a3889ad3d42bcb55ede750eaSingleSaltPrediction.txt - 76,530 bytesMD5:
206a25c9a05ff347e9b1a1ca916fe2abTernaryIonFit.m - 76,530 bytesMD5:
206a25c9a05ff347e9b1a1ca916fe2abTernaryIonFit.txt - 24,135 bytesMD5:
d53e81e84a1f199e2e506c80768804eaUnchargedFit.m - 24,135 bytesMD5:
d53e81e84a1f199e2e506c80768804eaUnchargedFit.txt - 10,004 bytesMD5:
766891029fc0bcc787504ee45291f4f9UnchargedPrediction.m - 10,004 bytesMD5:
766891029fc0bcc787504ee45291f4f9UnchargedPrediction.txt -
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