Surface characterization of cerium oxide catalysts using deep learning with infrared spectroscopy of CO

Characterization of material surfaces is crucial for understanding their properties and behavior. In this work, we utilized a deep learning technique, along with infrared (IR) spectrum of CO as a probe molecule, to explore the surface properties of cerium oxide (CeO2) catalysts. Through systematic d...

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Published inMaterials Today Sustainability Vol. 24; p. 100534
Main Authors Yu, H.-Y., Muthiah, B., Li, S.-C., Yu, W.-Y., Li, Y.-P.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.12.2023
Subjects
Adsorption energy
CeO2 facets
CO-derived adspecies
Deep neural network
Vibrational spectroscopy
CO-derived adspecies
Deep neural network
Adsorption energy
CeO2 facets
Vibrational spectroscopy
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Abstract Characterization of material surfaces is crucial for understanding their properties and behavior. In this work, we utilized a deep learning technique, along with infrared (IR) spectrum of CO as a probe molecule, to explore the surface properties of cerium oxide (CeO2) catalysts. Through systematic density functional theory (DFT) investigation of CO-derived adspecies on various CeO2 facets, we obtained an extensive dataset containing vibrational frequencies, intensities, and adsorption energies of CO on CeO2. This dataset was used to synthesize large quantities of complex IR spectra to train deep learning models for predicting surface structures, including the distribution of CeO2 facets, CO-derived adspecies, and binding energies. These models were successful in analyzing experimental IR spectra of CO adsorbed on different types of CeO2, and their predictions were consistent with experimental observations in most cases. This work provides a machine learning approach in understanding the morphology, local environmental arrangement, interaction behavior of probe molecules, and catalytic characteristics of diverse CeO2 materials. [Display omitted]
AbstractList Characterization of material surfaces is crucial for understanding their properties and behavior. In this work, we utilized a deep learning technique, along with infrared (IR) spectrum of CO as a probe molecule, to explore the surface properties of cerium oxide (CeO2) catalysts. Through systematic density functional theory (DFT) investigation of CO-derived adspecies on various CeO2 facets, we obtained an extensive dataset containing vibrational frequencies, intensities, and adsorption energies of CO on CeO2. This dataset was used to synthesize large quantities of complex IR spectra to train deep learning models for predicting surface structures, including the distribution of CeO2 facets, CO-derived adspecies, and binding energies. These models were successful in analyzing experimental IR spectra of CO adsorbed on different types of CeO2, and their predictions were consistent with experimental observations in most cases. This work provides a machine learning approach in understanding the morphology, local environmental arrangement, interaction behavior of probe molecules, and catalytic characteristics of diverse CeO2 materials. [Display omitted]
ArticleNumber 100534
Author Li, Y.-P.
Yu, H.-Y.
Yu, W.-Y.
Li, S.-C.
Muthiah, B.
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  email: yipeili@ntu.edu.tw
  organization: Department of Chemical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan
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Keywords CO-derived adspecies
Deep neural network
Adsorption energy
CeO2 facets
Vibrational spectroscopy
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Snippet Characterization of material surfaces is crucial for understanding their properties and behavior. In this work, we utilized a deep learning technique, along...
SourceID crossref
elsevier
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Publisher
StartPage 100534
SubjectTerms Adsorption energy
CeO2 facets
CO-derived adspecies
Deep neural network
Vibrational spectroscopy
Title Surface characterization of cerium oxide catalysts using deep learning with infrared spectroscopy of CO
URI https://dx.doi.org/10.1016/j.mtsust.2023.100534
Volume 24
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