Karthikraja Esackraj, Chowdhury Chandra, Nulakani Naga Venkateswara Rao, Ramanujam Kothandaraman, Vaidyanathan V G, Subramanian Venkatesan
Advanced Materials Laboratory, CSIR-Central Leather Research Institute (CSIR-CLRI), Sardar Patel Road, Adyar, Chennai, 600 020, India.
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
Chem Asian J. 2025 Feb 3;20(3):e202401256. doi: 10.1002/asia.202401256. Epub 2024 Dec 4.
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 (ΔG) 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 ΔG 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.
全球能源需求的不断增加和环境污染促使人们开发替代的可持续能源。通过电化学方法制氢提供了一种无碳能源解决方案。在本研究中，我们设计了新型氮化硼类似物（BNyne），并研究了它们的稳定性和电子性质。此外，还探索了在这些类似物的多孔位点引入过渡金属（TM），揭示了它们作为析氢反应（HER）有前景的电催化剂的潜力。过渡金属的加入显著提高了它们的结构稳定性和电子性质。TM锚定的BNyne表现出用于有效HER性能的最佳吉布斯自由能变化（ΔG）。此外，费米能级附近d带中心的有利排列支持高效的氢吸附。机器学习模型，特别是随机森林模型，也被用于高精度预测ΔG值，捕捉材料性质与HER效率之间的复杂关系。这种双重方法强调了将先进计算技术与材料设计相结合以加速发现有效HER催化剂的重要性。我们的研究结果突出了这些定制氮化硼类似物在增强电催化应用和提高HER效率方面的潜力。
