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ZnO-TSEMO_ACSCS_Preprint.pdf (2.79 MB)

Pushing Nanomaterials past the Kilogram Scale—a Targeted Approach Integrating Scalable Microreactors, Machine Learning and High-Throughput Analysis

preprint
submitted on 29.07.2020 and posted on 30.07.2020 by Nicholas Jose, mikhail Kovalev, Eric Bradford, Artur Schweidtmann, Hua Chun Zeng, Alexei Lapkin
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 by efficiently developing a kg per day reaction process for highly active antibacterial ZnO nanoparticles. 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.

History

Email Address of Submitting Author

aal35@cam.ac.uk

Institution

University of Cambridge

Country

UK

ORCID For Submitting Author

0000-0001-7621-0889

Declaration of Conflict of Interest

Authors declare no conflict of interest

Version Notes

Submitted for peer review

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