Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry
[1];
- Department of Chemical and Biomolecular Engineering University of California, Berkeley Berkeley CA 94720 USA
- Department of Process Engineering Tiradentes University Aracaju SE 49010-390 Brazil
- Chemometrics Eigenvector Research, Inc. Manson WA 98831 USA
- Department of Chemical and Biomolecular Engineering University of California, Berkeley Berkeley CA 94720 USA, Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
- Department of Materials Science and Engineering Stanford University Stanford CA 94305 USA
Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 2309766
- Alternate ID(s):
- OSTI ID: 2309770
- Journal Information:
- Angewandte Chemie, Journal Name: Angewandte Chemie Vol. 136 Journal Issue: 13; ISSN 0044-8249
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Germany
- Language:
- English
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