Structures, electronic and thermodynamic properties of NiB2n (n=7-11) and their anions: A theoretical study

Based on the Crystal structure Analysis by Particle Swarm Optimization (CALYPSO) searching method and density functional theory (DFT), theoretical studies about structures, electronic and thermodynamic properties have been investigated systematically at the TPSSh/6-311+G(d) level for NiB2n0/- (n=7-11) clusters. Results found that the lowest energy structures possess a Ni atom-centered double ring tubular boron structures, NiB180/- except. Relative stabilities were analyzed via computing their vertical ionization potentials (VIP), vertical electronic affinity (VEA), adiabatic electronic affinity (AEA), HOMO-LUMO gaps and hardness. The infrared spectra, Raman spectra and photoelectron spectra were computationally simulated to facilitate their experimental characterizations. At last, aromatic properties (Nucleus independent chemical shift) and thermodynamic properties (enthalpy and entropy) with temperature were discussed in detailed for studied systems.

Simulation of Biochemical Reactions with ANN-Dependent Kinetic Parameter Extraction Method

The measurement of thermodynamic properties of chemical or biological reactions were often confined to experimental means, which produced overall measurements of properties being investigated, but were usually susceptible to pitfalls of being too general. Among the thermodynamic properties that are of interest, reaction rates hold the greatest significance, as they play a critical role in reaction processes where speed is of essence, especially when fast association may enhance binding affinity of reaction molecules. Association reactions with high affinities often involve the formation of a intermediate state, which can be demonstrated by a hyperbolic reaction curve, but whose low abundance in reaction mixture often preclude the possibility of experimental measurement. Therefore, we resorted to computational methods using predefined reaction models that model the intermediate state as the reaction progresses. Here, we present a novel method called AKPE (ANN-Dependent Kinetic Parameter Extraction), our goal is to investigate the association/dissociation rate constants and the concentration dynamics of lowly-populated states (intermediate states) in the reaction landscape. To reach our goal, we simulated the chemical or biological reactions as system of differential equations, employed artificial neural networks (ANN) to model experimentally measured data, and utilized Particle Swarm Optimization (PSO) algorithm to obtain the globally optimum parameters in both the simulation and data fitting. In the Results section, we have successfully modeled a protein association reaction using AKPE, obtained the kinetic rate constants of the reaction, and constructed a full concentration versus reaction time curve of the intermediate state during the reaction. Furthermore, judging from the various validation methods that the method proposed in this paper has strong robustness and accuracy.