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dc.contributor.author
Goldberg, Eli
dc.contributor.author
Scheringer, Martin
dc.contributor.author
Bucheli, Thomas
dc.contributor.author
Hungerbühler, Konrad
dc.date.accessioned
2023-07-14T08:20:28Z
dc.date.available
2017-06-11T18:55:23Z
dc.date.available
2023-07-14T08:20:28Z
dc.date.issued
2015-08-01
dc.identifier.issn
2051-8161
dc.identifier.issn
2051-8153
dc.identifier.other
10.1039/C5EN00050E
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/103471
dc.identifier.doi
10.3929/ethz-b-000103471
dc.description.abstract
In the last 15 years, the development of advection–dispersion particle transport models (PTMs) for the transport of nanoparticles in porous media has focused on improving the fit of model results to experimental data by inclusion of empirical parameters. However, the use of these PTMs has done little to elucidate the complex behavior of nanoparticles in porous media and has failed to provide the mechanistic insights necessary to predictively model nanoparticle transport. The most prominent weakness of current PTMs stems from their inability to consider the influence of physicochemical conditions of the experiments on the transport of nanoparticles in porous media. Qualitative physicochemical influences on particle transport have been well studied and, in some cases, provide plausible explanations for some aspects of nanoparticle transport behavior. However, quantitative models that consider these influences have not yet been developed. With the current work, we intend to support the development of future mechanistic models by relating the physicochemical conditions of the experiments to the experimental outcome using ensemble machine learning (random forest) regression and classification. Regression results demonstrate that the fraction of nanoparticle mass retained over the column length (retained fraction, RF; a measure of nanoparticle transport) can be predicted with an expected mean squared error between 0.025–0.033. Additionally, we find that RF prediction was insensitive to nanomaterial type and that features such as concentration of natural organic matter, ζ potential of nanoparticles and collectors and the ionic strength and pH of the dispersion are strongly associated with the prediction of RF and should be targets for incorporation into mechanistic models. Classification results demonstrate that the shape of the retention profile (RP), such as hyperexponential or linearly decreasing, can be predicted with an expected F1-score between 60–70%. This relatively low performance in the prediction of the RP shape is most likely caused by the limited data on retention profile shapes that are currently available.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Royal Society of Chemistry
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Prediction of nanoparticle transport behavior from physicochemical properties: machine learning provides insights to guide the next generation of transport models
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2015-06-18
ethz.journal.title
Environmental Science: Nano
ethz.journal.volume
2
en_US
ethz.journal.issue
4
en_US
ethz.journal.abbreviated
Environ. Sci., Nano
ethz.pages.start
352
en_US
ethz.pages.end
360
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.notes
Received 17 March 2015, Accepted 17 June 2015, First published online 18 June 2015.
en_US
ethz.identifier.wos
ethz.identifier.nebis
010024878
ethz.publication.place
Cambridge
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03402 - Hungerbühler, Konrad (emeritus) / Hungerbühler, Konrad (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03402 - Hungerbühler, Konrad (emeritus) / Hungerbühler, Konrad (emeritus)
ethz.date.deposited
2017-06-11T18:56:27Z
ethz.source
ECIT
ethz.identifier.importid
imp593653685947482976
ethz.ecitpid
pub:161717
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-20T15:28:01Z
ethz.rosetta.lastUpdated
2024-02-03T01:41:03Z
ethz.rosetta.versionExported
true
ethz.COinS
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