Electrochemical Approaches for CO₂ Conversion to Chemicals: A Journey toward Practical Applications

Accounts of Chemical Research, Год журнала: 2022, Номер 55(5), С. 638 - 648

Опубликована: Янв. 18, 2022

ConspectusCarbon capture, utilization, and sequestration play an essential role to address CO2 emissions. Among all carbon utilization technologies, electroreduction has gained immense interest due its potential for directly converting a variety of valuable commodity chemicals using clean, renewable electricity as the sole energy source. The research community witnessed rapid advances in electrolysis technology recent years, including highly selective catalysts, larger-scale reactors, specific process modeling, well mechanistic understanding reduction reaction. field brings promise commercial application rollout chemical manufacturing.This Account focuses on our contributions both fundamental applied electrocatalytic CO reactions. We first discuss (1) development novel electrocatalysts CO2/CO enhance product selectivity lower consumption. Specifically, we synthesized nanoporous Ag homogeneously mixed Cu-based bimetallic catalysts enhanced production from multicarbon products CO, respectively. Then, review efforts (2) reactor engineering, dissolved H-type cell, vapor-fed three-compartment flow membrane electrode assembly, enhancing reaction rates scalability. Next, describe (3) investigation mechanisms situ operando techniques, such surface-enhanced vibrational spectroscopies electrochemical mass spectroscopy. revealed participation bicarbonate Au attenuated total-reflectance infrared absorption spectroscopy, presence "oxygenated" surface Cu under conditions Raman origin oxygen acetaldehyde other electrolyzer spectrometry. Lastly, examine (4) technology, pollutant effects developing techno-economic analysis. SO2 NOx Cu, Ag, Sn catalysts. also identify technical barriers that need be overcome offer perspective accelerating deployment technology.

Язык: Английский

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Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

Energy & Environmental Science, Год журнала: 2021, Номер 14(12), С. 6122 - 6157

Опубликована: Янв. 1, 2021

A review of the state-of-the-art applications machine learning for CO 2 capture, transport, storage, and utilisation.

Язык: Английский

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Electrochemical reduction of carbon dioxide to multicarbon (C₂₊) products: challenges and perspectives

Energy & Environmental Science, Год журнала: 2023, Номер 16(11), С. 4714 - 4758

Опубликована: Янв. 1, 2023

This review analyzes advanced catalysts and C 2+ synthesis mechanisms based on theoretical explorations in situ / operando characterizations. Triphasic interface optimization is discussed for the potential of industry-compatible stability.

Язык: Английский

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Structure Sensitivity in Single-Atom Catalysis toward CO₂ Electroreduction

ACS Energy Letters, Год журнала: 2021, Номер 6(2), С. 713 - 727

Опубликована: Янв. 28, 2021

Owing to unique electronic structure and high atom utilization, single-atom catalysts (SACs) have displayed unprecedented activity selectivity toward a wide range of catalytic reactions, including electrocatalytic CO2 reduction reaction (CO2RR), which holds great promise in reducing carbon emission storing renewable energy. The CO2RR is closely related structural characteristics specific SACs. Here we discuss the fundamental understanding sensitivity catalysis using selected examples. influences speciation metal centers coordination environments on performances are summarized. importance situ operando characterizations reveal dynamic evolution under conditions identify active sites SACs highlighted. mechanistic role tandem also discussed. We illustrate existing challenges research opportunities investigating structure–performance correlations for CO2RR.

Язык: Английский

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2D Materials Bridging Experiments and Computations for Electro/Photocatalysis

Advanced Energy Materials, Год журнала: 2021, Номер 12(4)

Опубликована: Фев. 22, 2021

Abstract The exploration of catalysts for energy conversion lies at the center sustainable development. combination experimental and computational approaches can provide insights into inner laws between catalytic performance structural electronic properties catalysts. Owing to inherent advantages 2D materials over their 3D counterparts, including high specific surface area abundant defects that could sufficient active sites, are promising candidates have attracted wide interest in catalysis. Importantly, most widely computationally investigated models with which relate prediction confirmation conveniently. Recently, more been prepared experiments while accurate methods used disclose performance, explore mechanism an atomic level. In this review, recent advances summarized related development design electro/photocatalysts. main emphasis is put on unique by computations. Computational closer environments introduced particular attention bridge gap addition, challenges computations also discussed

Язык: Английский

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Machine learning for advanced energy materials

Energy and AI, Год журнала: 2021, Номер 3, С. 100049 - 100049

Опубликована: Янв. 24, 2021

The screening of advanced materials coupled with the modeling their quantitative structural-activity relationships has recently become one hot and trending topics in energy due to diverse challenges, including low success probabilities, high time consumption, computational cost associated traditional methods developing materials. Following this, new research concepts technologies promote development necessary. latest advancements artificial intelligence machine learning have therefore increased expectation that data-driven science would revolutionize scientific discoveries towards providing paradigms for Furthermore, current advances engineering also demonstrate application technology not only significantly facilitate design but enhance discovery deployment. In this article, importance necessity contributing global carbon neutrality are presented. A comprehensive introduction fundamentals is provided, open-source databases, feature engineering, algorithms, analysis model. Afterwards, progress alkaline ion battery materials, photovoltaic catalytic dioxide capture discussed. Finally, relevant clues successful applications remaining challenges highlighted.

Язык: Английский

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Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Angewandte Chemie International Edition, Год журнала: 2021, Номер 60(46), С. 24354 - 24366

Опубликована: Июль 1, 2021

Abstract Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies have led to a focus on data‐driven research. This Minireview summarizes the application of ML rechargeable batteries, from microscale macroscale. Specifically, offers strategy explore new functionals for density functional theory calculations potentials molecular dynamics simulations, which expected significantly enhance challenging descriptions interfaces amorphous structures. also possesses great potential mine unveil valuable information both experimental theoretical datasets. A quantitative “structure–function” correlation can thus be established, used predict ionic conductivity solids as well battery lifespan. exhibits advantages optimization, such fast‐charge procedures. The future combination multiscale experiments, is discussed role humans research highlighted.

Язык: Английский

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Theory-guided electrocatalyst engineering: From mechanism analysis to structural design

CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION), Год журнала: 2022, Номер 43(12), С. 2987 - 3018

Опубликована: Ноя. 9, 2022

Язык: Английский

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

Advanced Energy Materials, Год журнала: 2022, Номер 12(20)

Опубликована: Март 29, 2022

Abstract The solar‐energy‐driven photoreduction of CO 2 has recently emerged as a promising approach to directly transform into valuable energy sources under mild conditions. As clean‐burning fuel and drop‐in replacement for natural gas, CH 4 is an ideal product photoreduction, but the development highly active selective semiconductor‐based photocatalysts this important transformation remains challenging. Hence, significant efforts have been made in search active, selective, stable, sustainable photocatalysts. In review, recent applications cutting‐edge experimental computational materials design strategies toward discovery novel catalysts photocatalytic conversion are systematically summarized. First, insights effective catalyst engineering strategies, including heterojunctions, defect engineering, cocatalysts, surface modification, facet single atoms, presented. Then, data‐driven photocatalyst spanning density functional theory (DFT) simulations, high‐throughput screening, machine learning (ML) presented through step‐by‐step introduction. combination DFT, ML, experiments emphasized powerful solution accelerating reduction . Last, challenges perspectives concerning interplay between rational industrialization large‐scale technologies described.

Язык: Английский

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C₂₊ Selectivity for CO₂ Electroreduction on Oxidized Cu-Based Catalysts

Journal of the American Chemical Society, Год журнала: 2023, Номер 145(26), С. 14335 - 14344

Опубликована: Июнь 21, 2023

Design for highly selective catalysts CO2 electroreduction to multicarbon (C2+) fuels is pressing and important. There is, however, presently a poor understanding of selectivity toward C2+ species. Here we report the first time method judiciously combined quantum chemical computations, artificial-intelligence (AI) clustering, experiment development model relationship between product composition oxidized Cu-based catalysts. We 1) evidence that Cu surface more significantly facilitates C-C coupling, 2) confirm critical potential condition(s) this oxidation state under different metal doping components viaab initio thermodynamics computation, 3) establish an inverted-volcano experimental Faradaic efficiency using multidimensional scaling (MDS) results based on physical properties dopant elements, 4) demonstrate design electrocatalysts selectively generate product(s) through co-doping strategy early late transition metals. conclude combination theoretical AI can be used practically relationships descriptors complex reactions. Findings will benefit researchers in designing conversions products.

Язык: Английский

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