In this study, we employ a multi-scale computational modeling approach, combining density functional theory (DFT) and self-consistent charge density functional tight binding (SCC-DFTB), to investigate hydrogen (H${}_2$) production and dissociation mechanisms from ammonia (NH${}_3$) and methane (CH${}_4$) on pristine and nickel-doped graphene. These two-dimensional materials hold significant potential for applications in advanced gas sensing and catalysis. Our analysis reveals that Ni-doped graphene, validated through work function calculations, is a promising material for gas separation and hydrogen production. The samples with adsorbed molecules are characterized by calculating chemical potential, chemical hardness, electronegativity, electrophilicity, vibrational frequencies, adsorption and Gibbs energies by DFT calculations. Methane molecules preferentially adsorb at the hexagonal ring centers of graphene, while ammonia interacts more strongly with carbon atoms, highlighting distinct molecular doping mechanisms for CH${}_4$ and NH${}_3$. Dynamic simulations show that CH${}_4$ splits into CH${}_3$+H, with Ni-doped graphene facilitating enhanced hydrogen transmission, while NH${}_3$ dissociates into NH${}_2$+H, which may lead to N${}_2$H${}_4$ formation. Our non-equilibrium Green’s function (NEGF) simulations demonstrate increased H-atom transmission on Ni-doped graphene during gas interactions. These findings suggest that Ni-doped graphene is superior to pristine graphene for applications in gas separation, hydrogen production, and high-sensitivity sensors.

中文翻译：

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NH3 和 CH4 对 Ni 掺杂石墨烯的解离机制：调整电子和光学性质


在这项研究中，我们采用多尺度计算建模方法，结合密度泛函理论 （DFT） 和自洽电荷密度泛函紧密结合 （SCC-DFTB），研究氨 （NH） 和甲烷 （CH） 在原始和镍掺杂石墨烯上的氢 （H） 产生和解离机制。这些二维材料在高级气体传感和催化方面具有巨大的应用潜力。我们的分析表明，通过功函数计算验证的 Ni 掺杂石墨烯是一种很有前途的气体分离和制氢材料。通过 DFT 计算计算化学势、化学硬度、电负性、亲电性、振动频率、吸附和吉布斯能量，对吸附分子的样品进行表征。甲烷分子优先吸附在石墨烯的六方环中心，而氨与碳原子的相互作用更强烈，突出了 CH 和 NH 的不同分子掺杂机制。动力学模拟表明，CH 分裂成 CH+H，Ni 掺杂石墨烯有助于增强氢传输，而 NH 解离成 NH+H，这可能导致 NH 形成。我们的非平衡格林函数 （NEGF） 模拟表明，在气体相互作用期间，氢原子在掺杂镍石墨烯上的传输增加。这些发现表明，Ni 掺杂石墨烯在气体分离、制氢和高灵敏度传感器方面的应用优于原始石墨烯。