Graphynes and Graphdiynes for Energy Storage and Catalytic Utilization: Theoretical Insights into Recent Advances

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(8), P. 4795 - 4854

Published: March 15, 2023

Carbon allotropes have contributed to all aspects of people's lives throughout human history. As emerging carbon-based low-dimensional materials, graphyne family members (GYF), represented by graphdiyne, a wide range potential applications due their superior physical and chemical properties. In particular, graphdiyne (GDY), as the leader family, has been practically applied various research fields since it was first successfully synthesized. GYF large surface area, both sp sp2 hybridization, certain band gap, which considered originate from overlap carbon 2pz orbitals inhomogeneous π-bonds atoms in different hybridization forms. These properties mean GYF-based materials still many be developed, especially energy storage catalytic utilization. Since most yet synthesized not developed for long time, theoretical results application should shared experimentalists attract more intentions. this Review, we summarized discussed synthesis, structural properties, insights, hoping provide viewpoints comments.

Language: Английский

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Machine Learning: A New Paradigm in Computational Electrocatalysis

The Journal of Physical Chemistry Letters, Journal Year: 2022, Volume and Issue: 13(34), P. 7920 - 7930

Published: Aug. 18, 2022

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, uncovering scientific insights lie the center of development electrocatalysis. Despite certain success in experiments computations, it is still difficult to achieve above objectives due complexity systems vastness chemical space for candidate electrocatalysts. With advantage machine learning (ML) increasing interest electrocatalysis energy conversion storage, data-driven research motivated by artificial intelligence (AI) has provided new opportunities discover promising investigate dynamic reaction processes, extract knowledge from huge data. In this Perspective, we summarize recent applications ML electrocatalysis, including electrocatalysts simulation processes. Furthermore, interpretable methods are discussed accelerate generation. Finally, blueprint envisaged future

Language: Английский

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Nano-Micro Letters, Journal Year: 2023, Volume and Issue: 15(1)

Published: Oct. 13, 2023

Abstract Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method producing advanced is not only cost-ineffective but also time-consuming labor-intensive. Fortunately, advancement of machine learning brings new opportunities discovery design. By analyzing experimental theoretical data, can effectively predict their evolution reaction (HER) performance. This review summarizes recent developments in low-dimensional electrocatalysts, including zero-dimension nanoparticles nanoclusters, one-dimensional nanotubes nanowires, two-dimensional nanosheets, as well other electrocatalysts. In particular, effects descriptors algorithms on screening investigating HER performance highlighted. Finally, future directions perspectives electrocatalysis discussed, emphasizing potential to accelerate electrocatalyst discovery, optimize performance, provide insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding current state its research.

Language: Английский

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Cost-effective electrocatalysts for Hydrogen Evolution Reactions (HER): Challenges and Prospects

International Journal of Hydrogen Energy, Journal Year: 2022, Volume and Issue: 47(92), P. 38964 - 38982

Published: Oct. 5, 2022

Language: Английский

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Modeling Single‐Atom Catalysis

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(46)

Published: Sept. 25, 2023

Electronic structure calculations represent an essential complement of experiments to characterize single-atom catalysts (SACs), consisting isolated metal atoms stabilized on a support, but also predict new catalysts. However, simulating SACs with quantum chemistry approaches is not as simple often assumed. In this work, the factors that reliable simulation activity are examined. The Perspective focuses importance precise atomistic characterization active site, since even small changes in atom's surroundings can result large reactivity. dynamical behavior and stability under working conditions, well adopting appropriate methods solve Schrödinger equation for quantitative evaluation reaction energies addressed. relevance model adopted. For electrocatalysis must include effects solvent, presence electrolytes, pH, external potential. Finally, it discussed how similarities between coordination compounds may intermediates usually observed electrodes. When these aspects adequately considered, predictive power electronic quite limited.

Language: Английский

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AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

ACS Nano, Journal Year: 2023, Volume and Issue: 17(11), P. 9763 - 9792

Published: June 2, 2023

Zero-carbon energy and negative emission technologies are crucial for achieving a carbon neutral future, nanomaterials have played critical roles in advancing such technologies. More recently, due to the explosive growth data, adoption exploitation of artificial intelligence (AI) as part materials research framework had tremendous impact on development nanomaterials. AI has enabled revolutionary next-generation paradigms significantly accelerate all stages material discovery facilitate exploration enormous design space. In this review, we summarize recent advancements applications discovery, with special emphasis selected nanotechnology net-zero future including solar cells, hydrogen energy, battery renewable CO2 capture conversion capture, utilization storage (CCUS) addition, discuss limitations challenges current area by identifying gaps that exist development. Finally, present prospect directions order large-scale

Language: Английский

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Hybrid Double Atom Catalysts for Hydrogen Evolution Reaction: A Sweet Marriage of Metal and Nonmetal

Advanced Energy Materials, Journal Year: 2023, Volume and Issue: 14(2)

Published: Nov. 27, 2023

Abstract Searching for active and low‐cost electrocatalysts the hydrogen evolution reaction (HER) is crucial to develop sustainable energy, yet it remains a significant challenge. Based on density functional theory calculations, new kind of double atom catalysts (HDACs) with hybrid metal nonmetal center embedded in g‐CN reported HER. It demonstrated that introduction atoms (B, C, Si, P, S) near sites enables unique charge communication between them, which offers diatomic very different catalytic activity than single counterparts. Out 130 HDACs, Pd‐B, Ti‐C, Ir‐C, Cr‐Si, Mn‐Si, Co‐Si, Rh‐Si, Au‐Si, Ir‐P, Fe‐S, Ni‐S pairs are identified as high‐performance nearly ideal adsorption strength proton. Machine learning analysis allows directly identify key characteristics affect establish predictable framework fast screen unknown chemical space HDACs. This work paves avenue designing developing potential HER catalysts.

Language: Английский

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First row transition metal doped B12P12 and Al12P12 nanocages as excellent single atom catalysts for the hydrogen evolution reaction

International Journal of Hydrogen Energy, Journal Year: 2023, Volume and Issue: 48(44), P. 16663 - 16677

Published: Feb. 1, 2023

Language: Английский

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Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(28)

Published: Feb. 11, 2024

Abstract The conversion of atmospheric nitrogen (N 2 ) into ammonia (NH 3 ), known as fixation, plays a crucial role in sustaining life on Earth, facing innovation with electrocatalytic and photocatalytic methods. These approaches promise gentler conversions from to ammonia, diverging the energy‐intensive Haber‐Bosch process, which requires complex plant infrastructure. Vitality lies eco‐friendly, cost‐effective, energy‐efficient pathways. challenge is that electrocatalysts photocatalysts for reduction have shown low Faraday efficiency, hampered by hydrogen evolution. This work delves recent strides electro/photo‐catalytic fixation/reduction, deciphering mechanisms, catalysts, prospects. By unveiling core principles steering these processes, it dissects efficiency drivers. Experimental theoretical studies, ranging density functional calculations/simulations machine learning‐based catalyst screening, mark path toward highly efficient including single/multi‐atom catalysts embedded 2D materials. journey explores diverse assessing their performance, spotlighting emerging nanomaterials, heterostructures, co‐catalyst techniques. Perspectives future directions potential applications fixation/reduction are offered, emphasizing sustainable management implications global agriculture environmental sustainability.

Language: Английский

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Machine-Learning-Driven High-Throughput Screening of Transition-Metal Atom Intercalated g-C₃N₄/MX₂ (M = Mo, W; X = S, Se, Te) Heterostructures for the Hydrogen Evolution Reaction

ACS Applied Materials & Interfaces, Journal Year: 2024, Volume and Issue: 16(10), P. 12437 - 12445

Published: March 4, 2024

Rising global energy demand, accompanied by environmental concerns linked to conventional fossil fuels, necessitates a shift toward cleaner and sustainable alternatives. This study focuses on the machine-learning (ML)-driven high-throughput screening of transition-metal (TM) atom intercalated g-C3N4/MX2 (M = Mo, W; X S, Se, Te) heterostructures unravel rich landscape possibilities for enhancing hydrogen evolution reaction (HER) activity. The stability intercalation within substrates are verified through adhesion binding energies, showcasing significant impact chalcogenide selection interaction properties. Based adsorption Gibbs free (ΔGH) computed via density functional theory (DFT) calculations, several ML models were evaluated, particularly random forest regression (RFR) emerges as robust tool in predicting HER activity with low mean absolute error (MAE) 0.118 eV, thereby paving way accelerated catalyst screening. Shapley Additive exPlanation (SHAP) analysis elucidates pivotal descriptors that influence activity, including C site (HC), MX layer (HMX), S (HS), TM atoms at N (IN). Overall, our integrated approach utilizing DFT effectively identifies (site-3) g-C3N4 active site, exceptional Sc Ti, underscoring their potential advancing catalytic performance.

Language: Английский

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