Computational catalysis on the conversion of CO<sub>2</sub> to methane—an update

doi:10.1007/s11705-024-2484-3

Front. Chem. Sci. Eng.

2024, Vol. 18

Issue (11) : 132 https://doi.org/10.1007/s11705-024-2484-3

Computational catalysis on the conversion of CO₂ to methane—an update

Prince Joby¹, Yesaiyan Manojkumar², Antony Rajendran³, Rajadurai Vijay Solomon¹(

)

¹. Department of Chemistry, Madras Christian College (Autonomous), (Affiliated to the University of Madras), Chennai 600059, “Tamil Nadu”, India
². Department of Chemistry, Bishop Heber College, Tiruchirappalli 620017, “Tamil Nadu”, India
³. Department of Chemistry, Mepco Schlenk Engineering College (Autonomous), Sivakasi 626005, “Tamil Nadu”, India

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Abstract

The reliance on fossil fuels intensifies CO₂ emissions, worsening political and environmental challenges. CO₂ capture and conversion present a promising solution, influenced by industrialization and urbanization. In recent times, catalytic conversion of CO₂ into fuels and chemical precursors, particularly methane, are gaining traction for establishing a sustainable, carbon-neutral economy due to methane’s advantages in renewable energy applications. Though homogeneous and heterogeneous catalysts are available for the conversion of CO₂ to methane, the efficiency is found to be higher in heterogeneous catalysts. Therefore, this review focuses only on the heterogeneous catalysts. In this context, the efficient heterogeneous catalysts with optimum utility are yet to be obtained. Therefore, the quest for suitable catalyst for the catalytic conversion of CO₂ to CH₄ is still continuing and designing efficient catalysts requires assessing their synthetic feasibility, often achieved through computational methods like density functional theory simulations, providing insights into reaction mechanisms, rate-limiting steps, catalytic cycle, activation of C=O bonds and enhancing understanding while lowering costs. In this context, this review examines the conversion of CO₂ to CH₄ using seven distinct types of catalysts, including single and double atom catalysts, metal organic frameworks, metalloporphyrins, graphdiyne and graphitic carbon nitrite and alloys with some case studies. The main focus of this review is to offer a detailed and extensive examination of diverse catalyst design approaches and their utilization in CH₄ production, with a specific emphasis on computational aspects. It explores the array of design methodologies used to identify reaction pathways and investigates the critical role of computational tools in their refinement and enhancement. We believe this review will help budding researchers to explore the possibilities of designing catalysts for the CO₂ to CH₄ conversion from computational framework.

Keywords computational catalysis single atom catalyst CO₂ reduction metalloporphyrins double-atom catalyst graphitic carbon nitrite

Corresponding Author(s): Rajadurai Vijay Solomon

Just Accepted Date: 31 May 2024 Issue Date: 02 September 2024

Cite this article:

Rajadurai Vijay Solomon,Antony Rajendran,Yesaiyan Manojkumar, et al. Computational catalysis on the conversion of CO₂ to methane—an update[J]. Front. Chem. Sci. Eng., 2024, 18(11): 132.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-024-2484-3
https://academic.hep.com.cn/fcse/EN/Y2024/V18/I11/132

Fig.1 Different computational methods and parameters employed in the study of different catalysts for the conversion of CO₂ to CH₄ in literature (PBE: Perdew-Burke-Ernzerhof; PAW: projector augmented-wave; AIMD: ab initio molecular dynamics; DNP: double numeric polarization).

Fig.2 Schematic representation of possible reaction pathways reported in literature for the production of methane from CO₂.

Fig.3 Schematic representation of the study conducted by Yang et al. [106], signifying the ways of finding the most suitable catalyst for methane formation.

Fig.4 Optimized structure of M-GYs: (a) top view and (b) side view (The blue and gray spheres represent C and metal atoms, respectively). Reprinted with permission from ref [107], copyright 2021, Elsevier.

Fig.6 Free energy pathway for all the intermediates formed. The optimal reaction path (red), the secondary favorable reaction path (orange), formation of CH₃OH as the final product (blue), and formation of HCOOH as the final product (green). Reprinted with permission from ref [107], copyright 2021, Elsevier.

Fig.7 Schematic representation of the study conducted by Fu et al. [107], signifying the ways of finding the most suitable catalyst for methane formation.

Fig.9 A schematic representation performed by Tian el al. [114], demonstrating methodologies for identifying the optimal catalyst for methane production

Fig.10 Potential energy profiles for the reduction of CO₂ to CH₄ on Rh₃C₁₂S₁₂ (Color code: Rh, green; S, yellow; C, gray; O, red; and H, white). Reprinted with permission from ref [114], copyright 2019, Royal Society of Chemistry.

Fig.12 Free energy diagram for CO₂RR on (a) Cr₃(HHB)₂, (b) Mo₃(HHB)₂, and (c) W₃(HHB)₂ (Red lines indicates the intermediates that require higher energy, black line indicates the optimal reaction pathway). Reprinted with permission from ref [115], copyright 2021, Elsevier.

Fig.13 A schematic representation of the study performed by Xing et al. [115], demonstrating methodologies for identifying the optimal catalyst for methane production.

Fig.15 The computed activation barriers (E_a) for the initial reduction step along both the COOH* and HCOO* pathway on various metal atom-modified TP substrates. Reprinted with permission from ref [108], copyright 2018, Royal Society of Chemistry.

Fig.16 A schematic representation of the research conducted by Chen et al. [108], illustrating approaches for determining the best catalyst for methane generation.

Fig.17 The top and side views of the optimized structures of (a) CoP, (b) Co-PMOF, (c) Zn-PMOF, and (d) Zr-PMOF monolayers in a 2 × 2 supercell (The red dotted lines indicate a unit cell). Reprinted with permission from ref [124], copyright 2019, American Chemical Society.

Fig.19 The PDOS of *CHO intermediates adsorbed on (a) CoP, (b) Co-PMOF, (c) Zn-PMOF, and (d) Zr-PMOF monolayers (The blue line denotes the p-orbital of the C atom in *CHO, and the red line denotes the d-orbital of the Co atom in porphyrin ring which interacts with *CHO. The Fermi level is denoted with a gray dashed line). Reprinted with permission from ref [124], copyright 2019, American Chemical Society.

Fig.20 A schematic representation of the research conducted by Wang et al. [124], illustrating approaches for determining the best catalyst for methane generation.

Fig.23 A schematic representation of the investigation done by Guo et al. [135], demonstrating methods for identifying the optimal catalyst for methane production.

Fig.24 Top view of (a) monolayer g-C₃N₄, (b) B-doped g-C₃N₄, and (c) SnS₂ nanosheet; (d, e) top and (f, g) side representation for heterostructures g-C₃N₄/SnS₂ and B-doped g-C₃N₄/SnS₂ respectively. Reprinted with permission from ref [96], copyright 2018, Royal Society of Chemistry.

Fig.26 Calculated free energy diagram for CO₂ conversion on Z-scheme. (a) B-doped g-C₃N₄/SnS₂ heterostructure CO₂ conversion on the Z-scheme, (b) g-C₃N₄/SnS₂ heterostructure. Reprinted with permission from ref [96], copyright 2018, Royal Society of Chemistry.

Fig.27 A schematic representation of the investigation done by Wang et al. [96], demonstrating methods for identifying optimal catalyst for methane production.

Fig.28 The optimized structures of pristine and doped GDYs with B or N at different sites (The gray, pink, and blue balls represent carbon, boron, and nitrogen atoms). Reprinted with permission from ref [140], copyright 2019, Royal Society of Chemistry.

Fig.30 The free energy profiles of CO₂RR on B2, B3, N1, and N2 surfaces along the most favorable pathway as well as the ΔG values of the PDS. Reprinted with permission from ref [140], copyright 2019, Royal Society of Chemistry.

Fig.31 A schematic representation of the research conducted by Zhao et al. [140], illustrating methodologies employed in identifying the optimal catalyst for methane production.

Fig.32 Geometric structures of (a) pristine GDY, (b) Cu@GDY, (c) Cu@N₁-GDY, (d) Cu@N₂-GDY, (e) Cu@N_b-GDY, (f) Cu@N_b-GDY’, (g) Cu@B₁-GDY, (h) Cu@B₂-GDY, and (i) Cu@B_b-GDY monolayers (The pink, gray, blue, and brown balls represent B, C, N, and Cu atoms, respectively). Reprinted with permission from ref [141], copyright 2021, Elsevier.

Fig.33 TDOS and PDOS of (a) pristine GDY, (b) Cu@GDY, (c) Cu@N₁-GDY, (d) Cu@N₂-GDY, (e) Cu@N_b-GDY, (f) Cu@N_b-GDY’, (g) Cu@B₁-GDY, (h) Cu@B₂-GDY, and (i) Cu@B_b-GDY. Reprinted with permission from ref [141], copyright 2021, Elsevier.

Fig.34 Free energy diagrams of Cu@N_b-GDY monolayer. Path 1: formation of HCOOH, Path 2: formation of CO, Path 3: HCHO, Path 4: formation of CH₃OH, Path 5: formation of CH₄. Reprinted with permission from ref [141], copyright 2021, Elsevier.

Fig.35 A schematic representation of the research conducted by Feng et al. [141], illustrating methodologies employed in identifying the optimal catalyst for methane production.

Fig.38 Gibbs free energy diagram for CO₂RR on CuCo@C₂N. Blue colored pathway indicated the optimal reaction path (Cyan and pink represented the highest energy intermediates formed and black color indicates the all possible intermediates formed during the CO₂ conversion process). Reprinted with permission from ref [149], copyright 2021, Elsevier.

Fig.39 A schematic representation of the research conducted by Liu et al. [149], illustrating methodologies employed in identifying the optimal catalyst for methane production.

Fig.41 Free Energy diagrams of CO₂ hydrogenation on Fe₂ and Ni₂@BN nanosheet at 0 V (Blue color indicates the higher energy reaction pathway, red color indicates the optimal reaction pathway and green color indicates the higher energy intermediate that is predicted to be formed). Reprinted with permission from ref [150], copyright 2020, Elsevier.

Fig.43 A schematic representation of the research conducted by Huang et al., illustrating methodologies employed in identifying the optimal catalyst for methane production.

Fig.45 A schematic representation of the study done by Zhao and Lu [156], illustrating methodologies employed in identifying the optimal catalyst for methane production.

Fig.47 A schematic representation of the study done by Mohan et al., illustrating methodologies employed in identifying the optimal catalyst for methane production.

Types of catalysts	Stability	Charge transfer	Surface area	Porosity	Disadvantages
SACs	The bonding ability of single metal atoms on the supports shows good stability for many chemical processes. In the case of the Pt₁/FeO_x catalyst, Pt atoms are present on FeO_x, forming Pt-O-Fe structures, indicating its catalytic stability [100].	The metal atom bonded with exposed sites on the support causes the charge transfer between metal atoms and the support due to differences in chemical potentials. Depending on the charge of the metal atom, the efficiency of the charge transfer increases [100].			Occupying a single atom at particular sites selectively imposes a constraint on the formation of multimetal active sites, thereby limiting its efficiency [143].
MOFs	The efficient catalysis process attributed to MOFs is mainly because of their porous structure. Such tunability in the porous structure helps in designing MOFs that can encapsulate various functional groups into ligands [158].	In photocatalysis, MOFs can be used as carriers for photocatalytically active species, which act as a medium in the ligand-to-cluster charge transfer process [158] .	MOFs possess high surface area, having active metal centers. The higher surface facilitates convenient access for species like O₂, while the active metal centers serve as the sites for catalytic reactions to occur [158].	MOF derivatives (reduced graphene oxide, heteroatom-doped) demonstrate greater stability, even in extreme conditions [159].	Pristine MOFs (MOFs with TMs and elements like C, H, O, N) exhibit lower stability in highly acidic or alkaline conditions [159].
Metalloporphyrins	In the review done by Zou et al., it is can be noted that the metalloporphyr-in catalysts that were discussed have higher activity and stability for CO₂ reduction process [117].	The effective π-conjugation involved in the porphyrin ring results in higher charge transfer rate for metalloporphyr-ins [117].	Metalloporphyrins are said to have high surface area containing open cobalt centers which are arranged to form channel walls that can capture CO₂ [119].	Since they fall in the category of MOFs it is understood that they possess structures having tunable pore sizes which provides more options in creating new CO₂ capture materials [119].	The drawback existing for metalloporphyrin is that they will get decomposed in the presence of oxidants in the medium [160].The synthesis of CMPs from metalloporphyrins faces challenges due to hindered polymerization of porphyrin rings via direct covalent linkages and steric hindrance present between porphyrin molecule, making morphology control difficult [70].
g-C₃N₄	It is depicted that g-C₃N₄ is said to exhibit high thermal and chemical stability because of its N-bridged tri-s-triazine based structure and the van der Waals interaction found between the g-C₃N₄ single layers [127].	It is observed that efficient charge transfer is found in direct Z-scheme and type II g-C₃N₄ heterostructures due to the efficient separation of photogenerated charge carriers (electron-hole pairs) [128].	Many novel nanorstructured g-C₃N₄ based photocatalysts show high surface area indicating the exposed reactive sites existing within the catalyst [127].	The highly tunable porous nature of g-C₃N₄ creates new possibilities in designing catalysts with improved surface reactivity [127].	Bulk g-C₃N₄ show low photocatalytic efficiency because of certain disadvantages associated with g-C₃N₄ which are the high electron-hole recombination rate, slow surface reaction kintetics, low charge mobility which disturb the electron delocalization. These defects can be confronted by introducing altrations to g-C₃N₄ thereby increasing its catalytic activity [127].
GDY	GDY is reported as the efficient support which has an enhanced catalytic activity and higher stability than other carbon allotropes [137].	The adsorption of TM atoms can modify the electronic structures of GDY which results in a charge transfer between adsorbed TM atom and GDY redistributing the electrons in s, p, and d-orbitals of the TM atoms [137].	The large surface area of GDY indicates its ability to accommodate metal ions like lithium showing their ability to be used as energy storage devices [137].	GDY possess a 2D porous framework which has the ability to separate mixed gases. Based on the number of acetylenic linkages the pore size of the triangular pores are determined which allows selective permeation of molecules with different sizes [137].	There are particular drawbacks that observed during modications on GDY which are given as follows: difficulty in controlling the quantity of the doped elements, maintaining the quality of GDY during modification process is difficult [161].
DACs	DACs are introduced as the extension of SACs to increase the active sites and single-atom loading [144].	DACs possess the feature of synergistic catalysis which has the advantage of higher atom utilization and has higher performance in CO₂ reduction than SACs [147].	Among the DACs heteronulcear DACs (with two different metal atoms) are more advantageous than the homonuclear DACs (with two same metal atoms) [142].		The primary challenge with DACs lies in the precise control of metal dimers, complicating experimental studies and making them more challenging [143]. Hence new techniques for the control in synthesis of DACs are being developed.
Alloys in catalysis	SAAs provide a simplified way for rational designing of catalyst which bridges the structural and pressure gaps between ultra-high vacuum and industrially relevant conditions [162].	They were able to address other issues such as CO poisoning, resistance to coking, and achieving high activity alongside exceptional selectivity indicates the uniqueness of SAAs [162].	Alloys offer a significant advantage by providing access to multifunctional active sites required for complex reactions and those with selectivity challenges [163].		Ternary alloy systems pose challenges for high throughput screening due to complex compositional and geometric characteristics, unlike binary systems. Computational demands and time increase significantly due to the involvement of three elements [163].

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