Periodic nanostructures: preparation, properties and applications

Yin, Hang; Xing, Kaijian; Zhang, Yurou; Dissanayake, Darshanamala; Lu, Ziyang; Zhao, Haitao; Zeng, Zhiyuan; Yun, Jung‐Ho; Qi, Dongchen; Yin, Zongyou

doi:10.1039/d0cs01146k

Cited by 41 publications

(36 citation statements)

References 343 publications

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“…Therefore, future studies should also more broadly consider the combination of multiple strategies to improve the activity of photocatalysts, [232,233] such as introducing cocatalysts on heterojunction photocatalysts, [81] constructing functional group modification and defects on the surface of semiconductors, [15,93,232,233] nanocatalysts phase engineering, [234,235] local surface plasmonic enhancement, [236][237][238][239] and superlattice architectures. [240] At the same time, it is necessary to conduct rigorous investigations to examine the specific interactions and synergies between the multiple strategies employed and establish structure-activity relationships, including quantitative elucidation of mass transport effects and intrinsic chemical kinetics. [241] In tandem with the pursuit of photocatalysts with improved CH 4 selectivity and yield, the overall process sustainability should also be an important concern, especially given the need for industrial application on a large scale to achieve carbon neutrality.…”

Section: Discussionmentioning

confidence: 99%

Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Cheng

Sun

Lim

et al. 2022

Advanced Energy Materials

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been described as a key metric for understanding the effects of global warming due to its direct impact on climate change. [2] Extensive modeling with energy-economyenvironment scenarios or projections to keep the global temperature from rising above 1.5 °C by the year 2100 shows that such a positive outlook is only possible if the global energy system is completely decarbonized (i.e., net-zero global CO 2 emissions) by mid-century, followed by active CO 2 removal (i.e., carbon-negative) in the second half of the century. [3] The catalytic conversion of CO 2 to valuable energy-related products (e.g., syngas, CH 4 , CH 3 OH, and longer-chain hydrocarbons) [4] by thermal reforming and hydrogenation, [5][6][7][8][9][10][11] electrocatalysis, [12][13][14] or photocatalysis [15] could occupy an important position in a future carbon-negative economy by directly replacing nonrenewable sources of these molecules, provided that the energy inputs to these processes are themselves derived from renewable sources. [16,17] In this regard, photocatalytic strategies stand out by not requiring a secondary medium of energy storage, and being able to realize the CO 2 conversion into high value-added fuels directly via renewable solar energy without external energy input. [18] Indeed, photocatalytic CO 2 reduction has been considered to be a "kill two birds with one stone" approach for sustainable energy production and greenhouse gas reduction. [19] In contrast, thermocatalytic approachesThe solar-energy-driven photoreduction of CO 2 has recently emerged as a promising approach to directly transform CO 2 into valuable energy sources under mild conditions. As a clean-burning fuel and drop-in replacement for natural gas, CH 4 is an ideal product of CO 2 photoreduction, but the development of highly active and selective semiconductor-based photocatalysts for this important transformation remains challenging. Hence, significant efforts have been made in the search for active, selective, stable, and sustainable photocatalysts. In this review, recent applications of cutting-edge experimental and computational materials design strategies toward the discovery of novel catalysts for CO 2 photocatalytic conversion to CH 4 are systematically summarized. First, insights into effective experimental catalyst engineering strategies, including heterojunctions, defect engineering, cocatalysts, surface modification, facet engineering, and single atoms, are presented. Then, datadriven photocatalyst design spanning density functional theory (DFT) simulations, high-throughput computational screening, and machine learning (ML) is presented through a step-by-step introduction. The combination of DFT, ML, and experiments is emphasized as a powerful solution for accelerating the discovery of novel catalysts for photocatalytic reduction of CO 2 . Last, challenges and perspectives concerning the interplay between experiments and data-driven rational design strategies for the industrialization of large-scale CO 2 photoreduction technologies are described.

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Section: Discussionmentioning

confidence: 99%

Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Cheng

Sun

Lim

et al. 2022

Advanced Energy Materials

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“…Moreover, reliable and sufficient data sources are critical for ML assistance in CO 2 RR, and there is a current lack of sufficiently diverse and extensive experimental datasets for model training and validation. The experimental complexity grows exponentially with the number of variables, limiting most searches to narrow regions of the material space 49,60,61 . Therefore, designing materials directly by trial and error is costly.…”

Section: Challenges Of ML In Electrocatalyst For Co2 Reductionmentioning

confidence: 99%

“…The experimental complexity grows exponentially with the number of variables, limiting most searches to narrow regions of the material space. 49,60,61 Therefore, designing materials directly by trial and error is costly. Interestingly, the emergence of high-throughput experiments performed by robotic chemists, 49 chemical synthesis machines, 62 and a programmable system of materials synthesis 63 could pave a new way for the generation of high-quality datasets.…”

Section: Challenges Of ML In Electrocatalyst For Co 2 Reductionmentioning

confidence: 99%

Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction

et al. 2022

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In the past decades, machine learning (ML) has impacted the field of electrocatalysis. Modern researchers have begun to take advantage of ML-based data-driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design. Hence, significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO 2 reduction. This review discusses recent applications of ML to discover, design, and optimize novel electrocatalysts. First, insights into ML aided in accelerating calculation are presented. Then, ML aided in the rational design of the electrocatalyst is introduced, including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model. Finally, the opportunities and future challenges are summarized for the future design of electrocatalyst for CO 2 reduction with the assistance of ML.

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“…Recently, a series of breakthroughs have been accomplished by tuning the physicochemical properties of materials to screen the best catalytic ability as the universally accepted modification strategy. [ 6,7 ] Moreover, the Sabatier principle states that the catalytic performance relies heavily on the adsorption strength of the reactants, intermediates and products on the catalyst surface. [ 8 ] Simultaneously, many descriptors have proposed the four‐electron charge transfer steps on surface‐active sites according to the volcano‐shaped, such as Fermi level, charge‐transfer energy, orbital occupancy.…”

Section: Introductionmentioning

confidence: 99%

Ru Coordinated ZnIn₂S₄ Triggers Local Lattice‐Strain Engineering to Endow High‐Efficiency Electrocatalyst for Advanced Zn‐Air Batteries

Hou

Sun

Cui

et al. 2022

Adv Funct Materials

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Developing bifunctional electrocatalysts is the primary challenge to improve the reaction efficiency of zinc-air batteries. Lattice-strain engineering constructs high-efficiency oxygen redox catalysts by tuning the physicochemical properties of nanomaterials. However, the randomness and complexity of lattice mismatch make it difficult to effectively identify the structure-activity relationship between the strain and catalyst. Herein, a strategy of Ru triggered partial coordination environment mutation of ZnIn 2 S 4 (R 0.1 ZIS) to regulate the d-band center of catalytic sites is provided, which dramatically activates intrinsic activity and accelerates electron transfer. Density functional theory calculations and system characterizations reveal that local lattice strain causes anti-bonding orbital to occupy more electrons and narrower bandwidth, reduce the migration energy barrier of * OH deprotonation and optimize the adsorption/desorption process of oxygen-containing intermediates, thus demonstrating extraordinary catalytic performance in oxygen reduction reaction and oxygen evolution reaction. Expectedly, the R 0.1 ZIS-based cell delivers the open circuit potential of 1.587 V almost identical to the theoretical voltage, and an ultralow voltage gap of 0.71 V after undergoing 262 h operation. This work offers a promising avenue for building lattice-strain engineering to realize robust bifunctional electrocatalysts.

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Periodic nanostructures: preparation, properties and applications

Abstract: This review has summarized and discussed the recent advances of periodic nanostructures, consisting of multiple identical nano units/components periodically arranged in good order, from their preparation and properties to applications.

Cited by 41 publications

References 343 publications

Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction

Ru Coordinated ZnIn₂S₄ Triggers Local Lattice‐Strain Engineering to Endow High‐Efficiency Electrocatalyst for Advanced Zn‐Air Batteries

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Periodic nanostructures: preparation, properties and applications

Abstract: This review has summarized and discussed the recent advances of periodic nanostructures, consisting of multiple identical nano units/components periodically arranged in good order, from their preparation and properties to applications.

Cited by 41 publications

References 343 publications

Emerging Strategies for CO2 Photoreduction to CH4: From Experimental to Data‐Driven Design

Emerging Strategies for CO2 Photoreduction to CH4: From Experimental to Data‐Driven Design

Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction

Ru Coordinated ZnIn2S4 Triggers Local Lattice‐Strain Engineering to Endow High‐Efficiency Electrocatalyst for Advanced Zn‐Air Batteries

Contact Info

Product

Resources

About

Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Emerging Strategies for CO₂ Photoreduction to CH₄: From Experimental to Data‐Driven Design

Machine learning accelerated calculation and design of electrocatalysts for CO₂ reduction

Ru Coordinated ZnIn₂S₄ Triggers Local Lattice‐Strain Engineering to Endow High‐Efficiency Electrocatalyst for Advanced Zn‐Air Batteries