Transition Metal Anchored Novel Holey Boron Nitride Analogues as Single-Atom Catalysts for the Hydrogen Evolution Reaction

被引:3
作者
Karthikraja, Esackraj [1 ,2 ]
Chowdhury, Chandra [1 ]
Nulakani, Naga Venkateswara Rao [3 ,4 ]
Ramanujam, Kothandaraman [5 ]
Vaidyanathan, V. G. [1 ,2 ]
Subramanian, Venkatesan [5 ]
机构
[1] CSIR Cent Leather Res Inst CSIR CLRI, Adv Mat Lab, Sardar Patel Rd, Chennai 600020, India
[2] Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, India
[3] Khalifa Univ Sci & Technol, Dept Chem, Abu Dhabi, U Arab Emirates
[4] Khalifa Univ Sci & Technol, Ctr Catalysis & Separat, Abu Dhabi, U Arab Emirates
[5] Indian Inst Technol Madras, Dept Chem, Chennai 600036, India
关键词
Single-atom catalyst; 2D-material; Density functional theory; Machine learning; Sustainable energy; TOTAL-ENERGY CALCULATIONS; ELECTRONIC-PROPERTIES; MOLECULAR-DYNAMICS; REGRESSION; ELECTROCATALYSTS; CARBON; CLASSIFICATION; OXIDATION; EXCHANGE;
D O I
10.1002/asia.202401256
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The increasing global energy demand and environmental pollution necessitate the development of alternative, sustainable energy sources. Hydrogen production through electrochemical methods offers a carbon-free energy solution. In this study, we have designed novel boron nitride analogues (BNyne) and investigated their stability and electronic properties. Furthermore, the incorporation of transition metals (TM) at holey sites in these analogues was explored, revealing their potential as promising electrocatalysts for the hydrogen evolution reaction (HER). The inclusion of transition metals significantly enhances their structural stability and electronic properties. The TM-anchored BNynes exhibit optimal Gibbs free energy changes (Delta GH) for effective HER performance. Additionally, the favorable alignment of d-band centers near the Fermi level supports efficient hydrogen adsorption. Machine learning models, particularly the Random Forest model, have also been employed to predict Delta GH values with high accuracy, capturing the complex relationships between material properties and HER efficiency. This dual approach underscores the importance of integrating advanced computational techniques with material design to accelerate the discovery of effective HER catalysts. Our findings highlight the potential of these tailored boron nitride analogues to enhance electrocatalytic applications and improve HER efficiency.
引用
收藏
页数:12
相关论文
共 98 条
[1]   Atomically thin hexagonal boron nitride probed by ultrahigh-resolution transmission electron microscopy [J].
Alem, Nasim ;
Erni, Rolf ;
Kisielowski, Christian ;
Rossell, Marta D. ;
Gannett, Will ;
Zettl, A. .
PHYSICAL REVIEW B, 2009, 80 (15)
[2]   Permutation importance: a corrected feature importance measure [J].
Altmann, Andre ;
Tolosi, Laura ;
Sander, Oliver ;
Lengauer, Thomas .
BIOINFORMATICS, 2010, 26 (10) :1340-1347
[3]   STRUCTURE-PROPERTY PREDICTIONS FOR NEW PLANAR FORMS OF CARBON - LAYERED PHASES CONTAINING SP2 AND SP ATOMS [J].
BAUGHMAN, RH ;
ECKHARDT, H ;
KERTESZ, M .
JOURNAL OF CHEMICAL PHYSICS, 1987, 87 (11) :6687-6699
[4]   PROJECTOR AUGMENTED-WAVE METHOD [J].
BLOCHL, PE .
PHYSICAL REVIEW B, 1994, 50 (24) :17953-17979
[5]   Support Vector Machines for classification and regression [J].
Brereton, Richard G. ;
Lloyd, Gavin R. .
ANALYST, 2010, 135 (02) :230-267
[6]   Defect-induced efficient dry reforming of methane over two-dimensional Ni/h-boron nitride nanosheet catalysts [J].
Cao, Yang ;
Maitarad, Phornphimon ;
Gao, Min ;
Taketsugu, Tetsuya ;
Li, Hongrui ;
Yan, Tingting ;
Shi, Liyi ;
Zhang, Dengsong .
APPLIED CATALYSIS B-ENVIRONMENTAL, 2018, 238 :51-60
[7]   Hexagonal boron nitride is an indirect bandgap semiconductor [J].
Cassabois, G. ;
Valvin, P. ;
Gil, B. .
NATURE PHOTONICS, 2016, 10 (04) :262-+
[8]   Electronic structure engineering on two-dimensional (2D) electrocatalytic materials for oxygen reduction, oxygen evolution, and hydrogen evolution reactions [J].
Chandrasekaran, Sundaram ;
Ma, Dingtao ;
Ge, Yanqi ;
Deng, Libo ;
Bowen, Chris ;
Roscow, James ;
Zhang, Yan ;
Lin, Zhiqun ;
Misra, R. D. K. ;
Li, Jianqing ;
Zhang, Peixin ;
Zhang, Han .
NANO ENERGY, 2020, 77
[9]   Engineering Nanostructured Interfaces of Hexagonal Boron Nitride- Based Materials for Enhanced Catalysis [J].
Chen, Hao ;
Jiang, De-en ;
Yang, Zhenzhen ;
Dai, Sheng .
ACCOUNTS OF CHEMICAL RESEARCH, 2022, :52-65
[10]   Introduction of defects in hexagonal boron nitride for vacancy-based 2D memristors [J].
Chen, Haohan ;
Kang, Yu ;
Pu, Dong ;
Tian, Ming ;
Wan, Neng ;
Xu, Yang ;
Yu, Bin ;
Jie, Wenjing ;
Zhao, Yuda .
NANOSCALE, 2023, 15 (09) :4309-4316