A general machine-learning framework for high-throughput screening for stable and efficient RuO2-based acidic oxygen evolution reaction catalysts DOI Creative Commons
Zhe Shang,

Qian Dang,

Fengmei Wang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 12, 2024

Abstract Doping guest elements is an effective way to increase activity and stability of RuO2 catalysts in acidic oxygen evolution reaction (OER). However, due the vastness doping space, it challenging for either high-cost experiments or density functional theory (DFT) calculations screen out doped structures with optimized catalytic performance. Herein, we reported a machine-learning (ML) framework that aims realize high-throughput screening both doped-RuO2 OER from mono-doping triple-doping at low level computational cost. Compared d-band some other previous models, our ML model was constructed based on more general input features realized higher prediction accuracy mean absolute errors (MAEs) 0.074, 0.142 0.082 eV *OH, *O *OOH adsorption, respectively. Through three structures, Ru41Zn7O96, Ru41Zn4Fe3O96, Ru39Zn4Cu4Co1O96 were found possess extraordinarily high comparable than previously catalysts. This work provided efficient study paradigm fields material useful guide experimental synthesis.

Language: Английский

Exsolved Ru-mediated stabilization of MoO2-Ni4Mo electrocatalysts for anion exchange membrane water electrolysis and unbiased solar-driven saline water splitting DOI
Sang Eon Jun,

Shin‐Woo Myeong,

Byeong‐Gwan Cho

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2024, Volume and Issue: 358, P. 124364 - 124364

Published: July 2, 2024

Language: Английский

Citations

6
Harnessing Physics-inspired Machine Learning to Design Nanocluster Catalysts for Dehydrogenating Liquid Organic Hydrogen Carriers DOI
Chuhong Lin, Bryan Lee,

Uzma Anjum

et al.

Applied Catalysis B Environment and Energy, Journal Year: 2025, Volume and Issue: unknown, P. 125192 - 125192

Published: Feb. 1, 2025

Language: Английский

Citations

0
Digital Descriptors in Predicting Catalysis Reaction Efficiency and Selectivity DOI

Qin Zhu,

Yuming Gu, Jing Ma

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: 16(9), P. 2357 - 2368

Published: Feb. 26, 2025

Accurately controlling the interactions and dynamic changes between multiple active sites (e.g., metals, vacancies, lone pairs of heteroatoms) to achieve efficient catalytic performance is a key issue challenge in design complex reactions involving 2D metal-supported catalysts, metal-zeolites, metal–organic metalloenzymes. With aid machine learning (ML), descriptors play central role optimizing electrochemical elucidating essence activity, predicting more thereby avoiding time-consuming trial-and-error processes. Three kinds descriptors─active center descriptors, interfacial reaction pathway descriptors─are crucial for understanding designing catalysts. Specifically, as sites, synergize with metals significantly promote reduction energy-relevant small molecules. By combining some physical interpretable can be constructed evaluate performance. Future development ML models faces constructing vacancies multicatalysis systems rationally selectivity, stability Utilization generative artificial intelligence multimodal automatically extract would accelerate exploration mechanisms. The transferable from catalysts metalloenzymes provide innovative solutions energy conversion environmental protection.

Language: Английский

Citations

0
Overcoming Interfacial Hydrogen Site-Blocking during Alkaline Formate Oxidation: Insights from Lattice-Compressed PdZr/C Catalysts DOI

Lanlan Shi,

Feike Zhang,

Xiaojun Wang

et al.

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 27, 2025

Improving the electrocatalytic conversion of formate in alkaline solutions is crucial for commercial application fuel cells. However, palladium-based catalysts used oxidation reactions (FOR) face challenges due to strong adsorption hydrogen intermediates, resulting lower catalytic efficiency environments. Herein, we prepared a PdZr/C catalyst aimed at employing doping-induced strain strategy reduce binding energy palladium and release more active sites formate. Through density functional theory calculations experimental investigations, find that lattice compression induced by Zr doping regulates electronic structure Pd. Specifically, incorporation dopant shifts d-band center Pd downward, weakening sites. This adjustment promotes desorption thus accelerating FOR kinetics alleviating site-blocking effect. As result, exhibited 2.4-fold increase activity compared conventional Pd/C catalyst. It also achieved peak potential delivered significantly higher current 1917 mA mg–1. These findings highlight critical role tuning properties offer valuable insights into design high-performance electrocatalysts technologies.

Language: Английский

Citations

0
First-principles calculations insight into non-noble-metal bifunctional electrocatalysts for zinc–air batteries DOI

W.W. Zhang,

Yue Wang, Yongjun Li

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 391, P. 125925 - 125925

Published: April 13, 2025

Language: Английский

Citations

0
Revealing critical features determining the hydrogen adsorption behaviors in ferric oxide films via machine learning DOI

Gang Wu,

Hong‐Hui Wu, Yanjing Su

et al.

Applied Surface Science, Journal Year: 2025, Volume and Issue: unknown, P. 163408 - 163408

Published: May 1, 2025

Language: Английский

Citations

0
Imidacloprid degradation activated by peroxydisulfate with NiCoAl layered metal oxide catalysts: The unique role of Al DOI
Xiaolong Dong, Qiang Fu, Guorui Liu

et al.

Separation and Purification Technology, Journal Year: 2024, Volume and Issue: unknown, P. 129845 - 129845

Published: Sept. 1, 2024

Language: Английский

Citations

2
Mechanism Investigation of Cu Doping Improvement on the Stability of Water Molecule Binding in Zr-Based Mofs DOI
Weidong Man, Shuo Zhang,

Weitian Yang

et al.

Published: Jan. 1, 2024

The respiration and transpiration of postharvest fruits elevate the relative humidity within packaging microenvironment, fostering proliferation microorganisms. Consequently, developing materials characterized by high water adsorption stability is imperative for maintaining quality food. In this study, a bimetallic organic framework with exceptional (i.e., Fe-MOF-801), was synthesized employing transition metal (Fe) doping strategy subsequently fabricated into (FeMGCF). Grand Canonical Monte Carlo simulations demonstrated that as H2O loading increased, migrated from T1 T2 cavities to T3 cavity (which possesses weaker binding capability) MOF-801. However, primarily adsorbed in Fe-MOF-801. Density functional theory indicated that, comparison MOF-801, electron accumulation occurred at O site Fe-MOF-801, resulting an increased interaction force O-H (in H2O), dynamic vapor thermogravimetric analysis revealed energy Fe-MOF-801 29.6%. As validation experiment, prolonged shelf life tomatoes 6 days delayed degradation lycopene anthocyanin modulating microenvironment. conclusion, work developed material improves water-binding capacity, which anticipated offer novel approach microenvironment preserving nutritional fruits.

Language: Английский

Citations

0
Iron doping enhances the bind stability of transpiration water to Zr-based MOFs packaging DOI
Weidong Man, Shuo Zhang, Yunlong Wang

et al.

Food Chemistry, Journal Year: 2024, Volume and Issue: unknown, P. 141714 - 141714

Published: Oct. 1, 2024

Language: Английский

Citations

0
A general machine-learning framework for high-throughput screening for stable and efficient RuO2-based acidic oxygen evolution reaction catalysts DOI Creative Commons
Zhe Shang,

Qian Dang,

Fengmei Wang

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 12, 2024

Abstract Doping guest elements is an effective way to increase activity and stability of RuO2 catalysts in acidic oxygen evolution reaction (OER). However, due the vastness doping space, it challenging for either high-cost experiments or density functional theory (DFT) calculations screen out doped structures with optimized catalytic performance. Herein, we reported a machine-learning (ML) framework that aims realize high-throughput screening both doped-RuO2 OER from mono-doping triple-doping at low level computational cost. Compared d-band some other previous models, our ML model was constructed based on more general input features realized higher prediction accuracy mean absolute errors (MAEs) 0.074, 0.142 0.082 eV *OH, *O *OOH adsorption, respectively. Through three structures, Ru41Zn7O96, Ru41Zn4Fe3O96, Ru39Zn4Cu4Co1O96 were found possess extraordinarily high comparable than previously catalysts. This work provided efficient study paradigm fields material useful guide experimental synthesis.

Language: Английский

Citations

0