Perspectives on Advancing Sustainable CO₂ Conversion Processes: Trinomial Technology, Environment, and Economy

ACS Sustainable Chemistry & Engineering, Journal Year: 2024, Volume and Issue: 12(14), P. 5357 - 5382

Published: March 22, 2024

CO2 can be converted into value-added products such as fuels, chemicals, and building materials, adding an economic incentive for capture green economy, while also reducing the environmental footprint of hard-to-abate industries aviation, construction, metallurgy. Nonetheless, most available technologies direct conversion, promising, are still in early development stages, facing technical challenges their scale-up, questioning viability to truly instill a timely impact on global emissions. Furthermore, clear benefit should obtained new processes versus traditional ones they replacing market. In this perspective, we examine range conversion using thermal, electrical, photochemical routes mineralization including advancements role synthesizing resulting from methanol, methane, carbon monoxide, solid carbonates. We offer insights trends current research required direction expedite technological readiness attractive terms catalytic material reactor design. highlight important modeling (molecular process levels) enabling tool deploy these at commercial scale originating understanding behavior molecular level. Lastly, significance carrying out reliable life cycle analysis identifying hotspots well gaps technology that allows improving attractiveness.

Language: Английский

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Machine learning aided design of single-atom alloy catalysts for methane cracking

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 17, 2024

Abstract The process of CH 4 cracking into H 2 and carbon has gained wide attention for hydrogen production. However, traditional catalysis methods suffer rapid deactivation due to severe deposition. In this study, we discover that effective can be achieved at 450 °C over a Re/Ni single-atom alloy via ball milling. To explore catalysis, construct library 10,950 transition metal surfaces screen candidates based on C–H dissociation energy barriers predicted by machine learning model. Experimental validation identifies Ir/Ni as top performers. Notably, the non-noble achieves yield 10.7 gH gcat –1 h with 99.9% selectivity 7.75% conversion °C, 1 atm. Here, show mechanical boosts clearly sustained 240 is achieved, significantly surpassing other approaches in literature.

Language: Английский

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Machine learning for CO2 capture and conversion: A review

Energy and AI, Journal Year: 2024, Volume and Issue: 16, P. 100361 - 100361

Published: March 30, 2024

Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential enhance energy- cost-efficiency by circumventing amine regeneration step. However, optimizing coupled system is more challenging than handling separated because its complexity, caused incorporation solvent heterogeneous catalysts. Nevertheless, deployment machine learning can be immensely beneficial, reducing both time cost ability simulate describe complex with numerous parameters involved. In this review, we summarized techniques employed in development solvents such as ionic liquids, well To optimize a system, these two separately developed will need combined via future.

Language: Английский

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AI in single-atom catalysts: a review of design and applications

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Language: Английский

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Targeted design of advanced electrocatalysts by machine learning

CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION), Journal Year: 2021, Volume and Issue: 43(1), P. 11 - 32

Published: Nov. 17, 2021

Language: Английский

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