About this Research Topic
This collection addresses one of the defining environmental, energy, and societal challenges of the 21st century: CO2 capture, utilization and recycling. Anthropogenic CO2 emissions have emerged as a grave threat to mankind and are widely recognized as underpinning global warming. In a sobering milestone, the atmospheric concentration of CO2 recently surpassed 400 pm, a massive increase of over 120 ppm since pre-industrial times. CO2 capture, storage and recycling to useful fuels or chemicals that is compatible with the existing energy infrastructure is an excellent strategy for addressing both increasing CO2 emissions and decreasing fossil supplies. While CO2 is an inexpensive, abundant and renewable C1 source for synthesizing many chemicals in particular to fuels such as CO, methanol, formic acid and methane, the activation and subsequent chemical transformation of CO2 is a formidable challenge due to its chemical inertness. In this context of global warming due to CO2 emissions and depletion of fossil fuel resources, the development of low-cost novel materials that can capture and catalytically recycle CO2 to useful chemicals and fuels is an important step towards carbon-free sustainable energy world.
With the advances in high-performance computational facilities, electronic structure calculations in particular density functional theory (DFT) as well as molecular dynamic (MD) methods have become affordable and accurate enough for scientists to describe catalytic reactions with the detail and accuracy necessary to guide and even ultimately replace experimental catalytic screening work. Moreover, recently there is a significant attraction towards Machine Learning (ML) assisted material discovery for various catalytic applications. Data-drive AI material discovery can analyse huge amount of data and can provide high-level accurate predictions on many material and molecular properties without requiring domain knowledge or domain insights. The goal of the proposed research topic is to leverage the power of DFT, MD and ML for the design of novel high-efficient materials and catalysts for CO2 capture and storage as well as it's recycling into useful chemicals.
The overall aim of this Research Topic is to attract high-quality work in the research field of computer-aided material discovery for CO2 capture and recycling employing the principles of electronic structure calculations, molecular dynamic simulations and data-driven ML. Some of the interesting class of materials for the proposed collection in CO2 capture and recycling are given below:
• Metal-free Carbo catalysts: graphene-based materials as well as other carbon materials.
• Molecular catalysts including inorganic complexes: homogeneous catalysts in particular molecular complexes.
• Non-graphene 2D material-based catalysts: 2D materials other than graphene-based materials for CO2 capture and recycling.
• Electrocatalysts: electrocatalytic reduction of CO2 including homogeneous and heterogeneous catalysts.
• Photocatalysts: photocatalytic reduction of CO2 including homogeneous and heterogeneous catalysts.
• Nanocatalysts: nanocatalysts including larger nanoparticles as well as size-selected small nanocluster-based catalysts.
• Metal organic framework (MOF) materials: MOFs which are a new class of porous materials that have attracted much recent attention owing to their unique capability in CO2 capture processes.
With the advances in high-performance computational facilities, electronic structure calculations in particular density functional theory (DFT) as well as molecular dynamic (MD) methods have become affordable and accurate enough for scientists to describe catalytic reactions with the detail and accuracy necessary to guide and even ultimately replace experimental catalytic screening work. Moreover, recently there is a significant attraction towards Machine Learning (ML) assisted material discovery for various catalytic applications. Data-drive AI material discovery can analyse huge amount of data and can provide high-level accurate predictions on many material and molecular properties without requiring domain knowledge or domain insights. The goal of the proposed research topic is to leverage the power of DFT, MD and ML for the design of novel high-efficient materials and catalysts for CO2 capture and storage as well as it's recycling into useful chemicals.
The overall aim of this Research Topic is to attract high-quality work in the research field of computer-aided material discovery for CO2 capture and recycling employing the principles of electronic structure calculations, molecular dynamic simulations and data-driven ML. Some of the interesting class of materials for the proposed collection in CO2 capture and recycling are given below:
• Metal-free Carbo catalysts: graphene-based materials as well as other carbon materials.
• Molecular catalysts including inorganic complexes: homogeneous catalysts in particular molecular complexes.
• Non-graphene 2D material-based catalysts: 2D materials other than graphene-based materials for CO2 capture and recycling.
• Electrocatalysts: electrocatalytic reduction of CO2 including homogeneous and heterogeneous catalysts.
• Photocatalysts: photocatalytic reduction of CO2 including homogeneous and heterogeneous catalysts.
• Nanocatalysts: nanocatalysts including larger nanoparticles as well as size-selected small nanocluster-based catalysts.
• Metal organic framework (MOF) materials: MOFs which are a new class of porous materials that have attracted much recent attention owing to their unique capability in CO2 capture processes.
Keywords: CO2 reduction, CO2 recycling, CO2 storage, DFT, Catalysts, Electrocatalys, CO2RR, Computational Catalysis, Machine Learning
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