Iron Impurity Impairs the CO2 Capture Performance of MgO: Insights from Microscopy and Machine Learning Molecular Dynamics

Ke Yuan; Nikhil Rampal; Sai Adapa; Barbara R. Evans; Jacquelyn N. Bracco; Matthew G. Boebinger; Andrew G. Stack; Juliane Weber

doi:10.1021/acsami.4c13597

Iron Impurity Impairs the CO₂ Capture Performance of MgO: Insights from Microscopy and Machine Learning Molecular Dynamics

Ke Yuan, Nikhil Rampal, Sai Adapa, Barbara R. Evans, Jacquelyn N. Bracco, Matthew G. Boebinger, Andrew G. Stack, Juliane Weber

Research output: Contribution to journal › Article › peer-review

Abstract

Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using ab initio and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH^-) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC.

Original language	English
Journal	ACS Applied Materials and Interfaces
DOIs	https://doi.org/10.1021/acsami.4c13597
State	Accepted/In press - 2024

Funding

This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Material Sciences and Engineering Division. This research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC05-00OR22725. This research used resources from the ORNL Research Cloud Infrastructure at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract DE-AC02-05CH11231, using NERSC Award ERCAP0025633. TEM characterization was conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. The authors thank James Kolopus for providing the MgO single-crystal samples used in this study. Oak Ridge National Laboratory is operated by UT-Battelle LLC under Contract No. DE-AC05-00OR22725 for the U. S. Department of Energy. This manuscript has been authored in part by UT-Battelle, LLC, under Contract DE-AC0500OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so for U.S. government purposes. The U.S. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Funders	Funder number
Basic Energy Sciences
Oak Ridge National Laboratory
Data Environment for Science
U.S. Department of Energy
Office of Science
UT-Battelle
CADES	DE-AC0500OR22725
Lawrence Berkeley National Laboratory	DE-AC02-05CH11231, ERCAP0025633
Lawrence Berkeley National Laboratory

Keywords

direct air capture
machine learning potential
magnesium oxide
metadynamics simulations
metal impurity

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1021/acsami.4c13597

Cite this

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title = "Iron Impurity Impairs the CO2 Capture Performance of MgO: Insights from Microscopy and Machine Learning Molecular Dynamics",

abstract = "Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using ab initio and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH-) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC.",

keywords = "direct air capture, machine learning potential, magnesium oxide, metadynamics simulations, metal impurity",

author = "Ke Yuan and Nikhil Rampal and Sai Adapa and Evans, {Barbara R.} and Bracco, {Jacquelyn N.} and Boebinger, {Matthew G.} and Stack, {Andrew G.} and Juliane Weber",

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doi = "10.1021/acsami.4c13597",

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journal = "ACS Applied Materials and Interfaces",

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T2 - Insights from Microscopy and Machine Learning Molecular Dynamics

AU - Yuan, Ke

AU - Rampal, Nikhil

AU - Adapa, Sai

AU - Evans, Barbara R.

AU - Bracco, Jacquelyn N.

AU - Boebinger, Matthew G.

AU - Stack, Andrew G.

AU - Weber, Juliane

PY - 2024

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N2 - Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using ab initio and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH-) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC.

AB - Magnesium oxide (MgO) is a promising sorbent for direct air capture (DAC) of carbon dioxide. Iron (Fe) is a common impurity in naturally occurring MgO and minerals used to produce MgO, yet a molecular-scale understanding of Fe-doping effects on carbonation is lacking. Here, we observed reduced carbonation performance in Fe-doped MgO experimentally. The energetics of adsorbing a (bi)carbonate ion on pristine and Fe-doped MgO(001) surfaces were further investigated using ab initio and machine learning potential molecular dynamics coupled with metadynamics simulations. Both pristine and Fe-doped surfaces exhibited a basic (OH-) hydration layer, where the (bi)carbonate ion adsorption is thermodynamically favorable. However, the dissolution of surface Fe had smaller energy barriers and was more favorable than Mg. Leached Fe likely neutralized the near-surface basicity, yielding reduced reactivity on Fe-doped MgO. Our observations offer critical insights for material selection and emphasize the importance of evaluating the geologic origin of earth materials used for DAC.

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KW - machine learning potential

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KW - metadynamics simulations

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