Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations

被引:69
作者
Hellstrom, Matti [1 ]
Behler, Jorg [1 ]
机构
[1] Ruhr Univ Bochum, Lehrstuhl Theoret Chem, D-44780 Bochum, Germany
关键词
HYDROXIDE SOLVATION; IONS; TRANSPORT; WATER; POTENTIALS; CHEMISTRY; POLYHEDRA; MECHANISM; HYDRATION; NA+;
D O I
10.1039/c6cp06547c
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Sodium hydroxide, NaOH, is one of the most widely-used chemical reagents, but the structural properties of its aqueous solutions have only sparingly been characterized. Here, we automatically classify the cation coordination polyhedra obtained from molecular dynamics simulations. We find that, for example, with increasing concentration, octahedral coordination geometries become less favored, while the opposite is true for the trigonal prism. At high concentrations, the coordination polyhedra frequently deviate considerably from "ideal'' polyhedra, because of an increased extent of interligand hydrogen-bonding, in which hydrogen bonds between two ligands, either OH2 or OH-, around the same Na+ are formed. In saturated solutions, with concentrations of about 19 mol L-1, ligands are frequently shared between multiple Na+ ions as a result of the deficiency of solvent molecules. This results in more complex structural patterns involving certain "characteristic'' polyhedron connectivities, such as octahedra sharing ligands with capped trigonal prisms, and tetrahedra sharing ligands with trigonal bipyramids. The simulations were performed using a density-functional-theory-based reactive high-dimensional neural network potential, that was extensively validated against available neutron and X-ray diffraction data from the literature.
引用
收藏
页码:82 / 96
页数:15
相关论文
共 57 条
[1]   Protons and Hydroxide Ions in Aqueous Systems [J].
Agmon, Noam ;
Bakker, Huib J. ;
Campen, R. Kramer ;
Henchman, Richard H. ;
Pohl, Peter ;
Roke, Sylvie ;
Thaemer, Martin ;
Hassanali, Ali .
CHEMICAL REVIEWS, 2016, 116 (13) :7642-7672
[2]  
[Anonymous], 1995, ACTA CRYSTALLOGR, DOI DOI 10.1107/S0108767395099958
[3]   Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials [J].
Artrith, Nongnuch ;
Kolpak, Alexie M. .
NANO LETTERS, 2014, 14 (05) :2670-2676
[4]   Hydration structure of Na+ and K+ from ab initio molecular dynamics based on modern density functional theory [J].
Bankura, Arindam ;
Carnevale, Vincenzo ;
Klein, Michael L. .
MOLECULAR PHYSICS, 2014, 112 (9-10) :1448-1456
[5]   Representing potential energy surfaces by high-dimensional neural network potentials [J].
Behler, J. .
JOURNAL OF PHYSICS-CONDENSED MATTER, 2014, 26 (18)
[6]   Generalized neural-network representation of high-dimensional potential-energy surfaces [J].
Behler, Joerg ;
Parrinello, Michele .
PHYSICAL REVIEW LETTERS, 2007, 98 (14)
[7]   Constructing high-dimensional neural network potentials: A tutorial review [J].
Behler, Joerg .
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, 2015, 115 (16) :1032-1050
[8]   Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations [J].
Behler, Joerg .
PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2011, 13 (40) :17930-17955
[9]   Atom-centered symmetry functions for constructing high-dimensional neural network potentials [J].
Behler, Joerg .
JOURNAL OF CHEMICAL PHYSICS, 2011, 134 (07)
[10]   Metadynamics simulations of the high-pressure phases of silicon employing a high-dimensional neural network potential [J].
Behler, Joerg ;
Martonak, Roman ;
Donadio, Davide ;
Parrinello, Michele .
PHYSICAL REVIEW LETTERS, 2008, 100 (18)