Unveiling the catalytic potential of the Fe(<scp>iv</scp>)oxo species for the oxidation of hydrocarbons in the solid state

Saiz, Fernan; Bernasconi, Leonardo

doi:10.1039/d1cy00551k

Cited by 5 publications

(11 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the role of temperature has been studied systematically combining static and AIMD approaches for the abstraction, rebound, and detachment steps for the methane oxidation with Fe-MOF74. 155 This study has unveiled the central role of entropic effects in the three reaction steps, which leads to significant differences between enthalpy Δ H and free energy Δ G barriers (Fig. 7).…”

Section: Can Computational Approaches Guide the Experimental Design O...mentioning

confidence: 81%

“…36,[151][152][153] Although this reaction paradigm is widely accepted in biological systems and in inorganic (gas-phase and solvated) species, only recently have DFT calculations confirmed its validity also in solid-state systems, particularly MOFs. 23,154,155 The first step of the rebound mechanism is the transfer of a hydrogen atom from the substrate (R-H), through a protoncoupled electron transfer reaction, 106 to the O atom of the Fe(IV)O moiety (O oxo ), which produces an Fe(III)hydroxo intermediate and a R˙radical.…”

Section: Catalysis Science and Technology Mini Reviewmentioning

confidence: 99%

“…186 Dispersive interactions have been shown to constitute an important part of the atomic forces in Fe(IV)O/MOF systems. 23,154,155 For instance, the dispersion-corrected B3LYP functional (B3LYP-D3) correctly predicts the relative energies of the spin states of Fe(IV)O, at variance with B3LYP with no dispersion corrections. B3LYP-D3 also yields lower energy barrier heights for transition states compared to B3LYP, evidencing that dispersion effects are needed for a more complete description of the reaction thermodynamics and kinetics.…”

Section: Inter-electron Interactionsmentioning

confidence: 99%

“…36,151–153 Although this reaction paradigm is widely accepted in biological systems and in inorganic (gas-phase and solvated) species, only recently have DFT calculations confirmed its validity also in solid-state systems, particularly MOFs. 23,154,155…”

Section: The Origin Of the Fe(iv)o Reactivitymentioning

confidence: 99%

“…In summary, according to the standard rebound mechanism, the hydroxylation of saturated hydrocarbons RCH 3 to alcohols RCH 2 OH in the presence of Fe( iv )O species,involves (a) a proton-coupled electron transfer reaction, which abstracts an H atom from the hydrocarbon to generate a an alkyl radical,RCH 3 + FeO → (RCH 2 ˙ + FeO–H)and occurs with a free energy Δ G abs ; (b) a RCH 2 ˙ rebound step(RCH 2 ˙ + FeO–H)˙ → RCH 2 OH–Fe( iii ),with a free energy Δ G reb , which is expected to be small, but, in general, non-negligible; 155 and (c) the detachment of the CH 3 OH molecule from the Fe catalytic siteRCH 2 OH–Fe( iii ) → RCH 2 OH + Fe( iii )with a free energy barrier Δ G det . After the hydroxylation and the detachment of the product from the reactive site, the Fe( iv )O moiety can be regenerated with a suitable oxidant such as N 2 O, 152 O 2 , 157 or H 2 O 2 (ref.…”

Section: The Origin Of the Fe(iv)o Reactivitymentioning

confidence: 99%

See 4 more Smart Citations

Catalytic properties of the ferryl ion in the solid state: a computational review

Saiz

Bernasconi

2022

Catal. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Can Computational Approaches Guide the Experimental Design O...mentioning

confidence: 81%

Section: Catalysis Science and Technology Mini Reviewmentioning

confidence: 99%

Section: Inter-electron Interactionsmentioning

confidence: 99%

Section: The Origin Of the Fe(iv)o Reactivitymentioning

confidence: 99%

Section: The Origin Of the Fe(iv)o Reactivitymentioning

confidence: 99%

See 3 more Smart Citations

Catalytic properties of the ferryl ion in the solid state: a computational review

Saiz

Bernasconi

2022

Catal. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

Metal–organic frameworks as catalysts and biocatalysts for methane oxidation: The current state of the art

Andrade

Lima

Silva

et al. 2023

Coordination Chemistry Reviews

View full text Add to dashboard Cite

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Achar

Wardzala

Bernasconi

et al. 2022

J. Chem. Theory Comput.

Self Cite

View full text Add to dashboard Cite

Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal−organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal− organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate−adsorbate interactions were modeled with Lennard−Jones (LJ) potentials, the adsorbent−adsorbent interactions were described by the DP, and the adsorbent−adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent−adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP−classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for adsorbates to model adsorption and diffusion within the porous material, including adsorbate-induced changes to the framework.

show abstract

Unveiling the catalytic potential of the Fe(iv)oxo species for the oxidation of hydrocarbons in the solid state

Abstract: We present the computational study of the ferryl-catalysed oxidation of methane into methanol in a solid-state system, the metal-organic framework MOF-74 with Fe(IV)O moieties in its cavities. We use spin-polarised...

Cited by 5 publications

References 68 publications

Catalytic properties of the ferryl ion in the solid state: a computational review

Catalytic properties of the ferryl ion in the solid state: a computational review

Metal–organic frameworks as catalysts and biocatalysts for methane oxidation: The current state of the art

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Contact Info

Product

Resources

About