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3D Hierarchically Porous Nanostructured Catalyst Helps Efficiently Reduce CO2
- This new catalyst will bring CO2 one step closer to serving as a sustainable energy source. - KAIST researchers developed a three-dimensional (3D) hierarchically porous nanostructured catalyst with carbon dioxide (CO2) to carbon monoxide (CO) conversion rate up to 3.96 times higher than that of conventional nanoporous gold catalysts. This new catalyst helps overcome the existing limitations of the mass transport that has been a major cause of decreases in the CO2 conversion rate, holding a strong promise for the large-scale and cost-effective electrochemical conversion of CO2 into useful chemicals. As CO2 emissions increase and fossil fuels deplete globally, reducing and converting CO2 to clean energy electrochemically has attracted a great deal of attention as a promising technology. Especially due to the fact that the CO2 reduction reaction occurs competitively with hydrogen evolution reactions (HER) at similar redox potentials, the development of an efficient electrocatalyst for selective and robust CO2 reduction reactions has remained a key technological issue. Gold (Au) is one of the most commonly used catalysts in CO2 reduction reactions, but the high cost and scarcity of Au pose obstacles for mass commercial applications. The development of nanostructures has been extensively studied as a potential approach to improving the selectivity for target products and maximizing the number of active stable sites, thus enhancing the energy efficiency. However, the nanopores of the previously reported complex nanostructures were easily blocked by gaseous CO bubbles during aqueous reactions. The CO bubbles hindered mass transport of the reactants through the electrolyte, resulting in low CO2 conversion rates. In the study published in the Proceedings of the National Academy of Sciences of the USA (PNAS) on March 4, a research group at KAIST led by Professor Seokwoo Jeon and Professor Jihun Oh from the Department of Materials Science and Engineering designed a 3D hierarchically porous Au nanostructure with two different sizes of macropores and nanopores. The team used proximity-field nanopatterning (PnP) and electroplating techniques that are effective for fabricating the 3D well-ordered nanostructures. The proposed nanostructure, comprised of interconnected macroporous channels 200 to 300 nanometers (nm) wide and 10 nm nanopores, induces efficient mass transport through the interconnected macroporous channels as well as high selectivity by producing highly active stable sites from numerous nanopores. As a result, its electrodes show a high CO selectivity of 85.8% at a low overpotential of 0.264 V and efficient mass activity that is up to 3.96 times higher than that of de-alloyed nanoporous Au electrodes. “These results are expected to solve the problem of mass transfer in the field of similar electrochemical reactions and can be applied to a wide range of green energy applications for the efficient utilization of electrocatalysts,” said the researchers. This work was supported by the National Research Foundation (NRF) of Korea. Image credit: Professor Seokwoo Jeon and Professor Jihun Oh, KAIST Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only. Publication: Hyun et al. (2020) Hierarchically porous Au nanostructures with interconnected channels for efficient mass transport in electrocatalytic CO2 reduction. Proceedings of the National Academy of Sciences of the USA (PNAS). Available online at https://doi.org/10.1073/pnas.1918837117 Profile: Seokwoo Jeon, PhD Professor firstname.lastname@example.org http://fdml.kaist.ac.kr Department of Materials Science and Engineering (MSE) https://www.kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea Profile: Jihun Oh, PhD Associate Professor email@example.com http://les.kaist.ac.kr Department of Materials Science and Engineering (MSE) Department of Energy, Environment, Water and Sustainability (EEWS) KAIST Profile: Gayea Hyun PhD Candidate firstname.lastname@example.org http://fdml.kaist.ac.kr Flexible Devices and Metamaterials Laboratory (FDML) Department of Materials Science and Engineering (MSE) KAIST Profile: Jun Tae Song, PhD Assistant Professor email@example.com http://www.cstf.kyushu-u.ac.jp/~ishihara-lab/ Department of Applied Chemistry https://www.kyushu-u.ac.jp Kyushu UniversityFukuoka, Japan (END)
Bioengineers develop a new strategy for accurate prediction of cellular metabolic fluxes
A team of pioneering South Korean scientists has developed a new strategy for accurately predicting cellular metabolic fluxes under various genotypic and environmental conditions. This groundbreaking research is published in the journal Proceedings of the National Academy of Sciences of the USA (PNAS) on August 2, 2010. To understand cellular metabolism and predict its metabolic capability at systems-level, systems biological analysis by modeling and simulation of metabolic network plays an important role. The team from the Korea Advanced Institute of Science and Technology (KAIST), led by Distinguished Professor Sang Yup Lee, focused their research on the development of a new strategy for more accurate prediction of cellular metabolism. “For strain improvement, biologists have made every effort to understand the global picture of biological systems and investigate the changes of all metabolic fluxes of the system under changing genotypic and environmental conditions,” said Lee. The accumulation of omics data, including genome, transcriptome, proteome, metabolome, and fluxome, provides an opportunity to understand the cellular physiology and metabolic characteristics at systems-level. With the availability of the fully annotated genome sequence, the genome-scale in silico (means “performed on computer or via computer simulation.”) metabolic models for a number of organisms have been successfully developed to improve our understanding on these biological systems. With these advances, the development of new simulation methods to analyze and integrate systematically large amounts of biological data and predict cellular metabolic capability for systems biological analysis is important. Information used to reconstruct the genome-scale in silico cell is not yet complete, which can make the simulation results different from the physiological performances of the real cell. Thus, additional information and procedures, such as providing additional constraints (constraint: a term to exclude incorrect metabolic fluxes by restricting the solution space of in silico cell) to the model, are often incorporated to improve the accuracy of the in silico cell. By employing information generated from the genome sequence and annotation, the KAIST team developed a new set of constraints, called Grouping Reaction (GR) constraints, to accurately predict metabolic fluxes. Based on the genomic information, functionally related reactions were organized into different groups. These groups were considered for the generation of GR constraints, as condition- and objective function- independent constraints. Since the method developed in this study does not require complex information but only the genome sequence and annotation, this strategy can be applied to any organism with a completely annotated genome sequence. “As we become increasingly concerned with environmental problems and the limits of fossil resources, bio-based production of chemicals from renewable biomass has been receiving great attention. Systems biological analysis by modeling and simulation of biological systems, to understand cellular metabolism and identify the targets for the strain improvement, has provided a new paradigm for developing successful bioprocesses,” concluded Lee. This new strategy for predicting cellular metabolism is expected to contribute to more accurate determination of cellular metabolic characteristics, and consequently to the development of metabolic engineering strategies for the efficient production of important industrial products and identification of new drug targets in pathogens.”
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