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dc.contributor.author
Jose, Nicholas A.
dc.contributor.author
Kovalev, Mikhail
dc.contributor.author
Bradford, Eric
dc.contributor.author
Schweidtmann, Artur M.
dc.contributor.author
Chun Zeng, Hua
dc.contributor.author
Lapkin, Alexei A.
dc.date.accessioned
2021-07-30T08:19:11Z
dc.date.available
2021-07-30T02:43:30Z
dc.date.available
2021-07-30T08:17:44Z
dc.date.available
2021-07-30T08:19:11Z
dc.date.issued
2021-12-15
dc.identifier.issn
0300-9467
dc.identifier.issn
1385-8947
dc.identifier.issn
1873-3212
dc.identifier.issn
0923-0467
dc.identifier.other
10.1016/j.cej.2021.131345
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/498488
dc.description.abstract
Novel materials are the backbone of major technological advances. However, the development and wide-scale introduction of new materials, such as nanomaterials, is limited by three main factors—the expense of experiments, inefficiency of synthesis methods and complexity of scale-up. Reaching the kilogram scale is a hurdle that takes years of effort for many nanomaterials. We introduce an improved methodology for materials development, combining state-of-the-art techniques—multi-objective machine learning optimization, high yield microreactors and high throughput analysis. We demonstrate this approach through the optimization of ZnO nanoparticle synthesis, simultaneously targeting high yield and high antibacterial activity. In fewer than 100 experiments, we developed a 1 kg day−1 continuous synthesis for ZnO (with a space-time-yield of 62.4 kg day−1 m−3), having an antibacterial activity comparable to hydrothermally synthesized nano-ZnO and cetrimonium bromide. Following this, we provide insights into the mechanistic factors underlying the performance-yield tradeoffs of synthesis and highlight the need for benchmarking machine learning models with traditional chemical engineering methods. Methods for increasing model accuracy at steep pareto fronts, in this case at yields close to 1 kg per day, should also be improved. To project the next steps for process scale-up and the potential advantages of this methodology, we conduct a scalability analysis in comparison to conventional batch production methods, in which there is a significant reduction in degrees of freedom. The proposed method has the potential to significantly reduce experimental costs, increase process efficiency and enhance material performance, which culminate to form a new pathway for materials discovery. © 2021 Elsevier Ltd.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Machine learning
en_US
dc.subject
Scale-up
en_US
dc.subject
Nanomaterials
en_US
dc.subject
Antibacterial
en_US
dc.subject
Reactor
en_US
dc.title
Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis
en_US
dc.type
Journal Article
dc.date.published
2021-07-17
ethz.journal.title
Chemical Engineering Journal
ethz.journal.volume
426
en_US
ethz.journal.abbreviated
Chem. Eng. J.
ethz.pages.start
131345
en_US
ethz.size
12 p.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-07-30T02:43:34Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-07-30T08:17:49Z
ethz.rosetta.lastUpdated
2022-03-29T10:49:33Z
ethz.rosetta.versionExported
true
ethz.COinS
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