Abstract
It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms’ results offers the insight that reduced Na2O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).
| Original language | English (US) |
|---|---|
| Article number | 3152 |
| Journal | Nature communications |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
Bibliographical note
Funding Information:We are indebted to Prof. Constantine Frangakis of the Biostatistics Department of Johns Hopkins University for several useful conversations about the statistical analysis of our data. We acknowledge partial support from the Catalysis Center for Energy Innovation, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, and Office of Basic Energy Sciences under Award No. DE-SC0001004 and support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award No. DE-SC0023403 (Separation Science Program). Partial support was also provided by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences (Award DE-FG02-12ER16362), and by the U.S. Department of Energy, Office of Basic Energy Science, Catalysis Science Program (Award DE-SC00019028). Parts of this work were carried out in the Characterization Facility, University of Minnesota, which receives partial NSF support through the MRSEC and NNIN programs (DMR-1420013). Solid-state MAS NMR measurements were provided by the NMR facility at Caltech. The synchrotron XRD data were collected through the mail-in program at Beamline 17-BM of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
Funding Information:
We are indebted to Prof. Constantine Frangakis of the Biostatistics Department of Johns Hopkins University for several useful conversations about the statistical analysis of our data. We acknowledge partial support from the Catalysis Center for Energy Innovation, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, and Office of Basic Energy Sciences under Award No. DE-SC0001004 and support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award No. DE-SC0023403 (Separation Science Program). Partial support was also provided by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences (Award DE-FG02-12ER16362), and by the U.S. Department of Energy, Office of Basic Energy Science, Catalysis Science Program (Award DE-SC00019028). Parts of this work were carried out in the Characterization Facility, University of Minnesota, which receives partial NSF support through the MRSEC and NNIN programs (DMR-1420013). Solid-state MAS NMR measurements were provided by the NMR facility at Caltech. The synchrotron XRD data were collected through the mail-in program at Beamline 17-BM of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility, operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
Publisher Copyright:
© 2023, The Author(s).
PubMed: MeSH publication types
- Journal Article