The highly exothermic hydrogen abstraction reaction H2Te + OH → H2O + TeH: comparison with analogous reactions for H2Se and H2S.

The "gold standard" CCSD(T) method is adopted along with the correlation consistent basis sets up to aug-cc-pV5Z-PP to study the mechanism of the hydrogen abstraction reaction H2Te + OH. The predicted geometries and vibrational frequencies for reactants and products are in good agreement with the available experimental results. With the ZPVE corrections, the transition state in the favorable pathway of this reaction energetically lies 1.2 kcal mol-1 below the reactants, which is lower than the analogous relative energies for the H2Se + OH reaction (-0.7 kcal mol-1), the H2S + OH reaction (+0.8 kcal mol-1) and the H2O + OH reaction (+9.0 kcal mol-1). Accordingly, the exothermic reaction energies for these related reactions are predicted to be 47.8 (H2Te), 37.7 (H2Se), 27.1 (H2S), and 0.0 (H2O) kcal mol-1, respectively. Geometrically, the low-lying reactant complexes for H2Te + OH and H2Se + OH are two-center three-electron hemibonded structures, whereas those for H2S + OH and H2O + OH are hydrogen-bonded. With ZPVE and spin-orbit coupling corrections, the relative energies for the reactant complex, transition state, product complex, and the products for the H2Te + OH reaction are estimated to be -13.1, -1.0, -52.0, and -52.6 kcal mol-1, respectively. Finally, twenty-eight DFT functionals have been tested systematically to assess their ability in describing the potential energy surface of the H2Te + OH reaction. The best of these functionals for the corresponding energtics are -9.9, -1.4, -46.4, and -45.4 kcal mol-1 (MPWB1K), or -13.1, -2.4, -57.1, and -54.6 kcal mol-1 (M06-2X), respectively.

Structure, stability, infrared spectra, and bonding of OHm(H2O)7 (m = 0, ±1) clusters: ab initio study combining the particle swarm optimization algorithm.

The various structural candidates of anionic, neutral, and cationic water clusters OHm(H2O)7 (m = 0, ±1) have been globally predicted by combining the particle swarm optimization method and quantum chemical calculations. Geometry optimization and vibrational analysis for the optimal structures were performed with the MP2/aug-cc-pVDZ method, and the energy profile was further refined at the CCSD(T)/CBS level. Special attention was paid to the relationships between configurations and energies, particularly the first solvation shell coordination number of OH- and OH. For OH-(H2O)7, OH(H2O)7, and OH+(H2O)7 clusters, the most stable species at room temperature are predicted to be the tetra-solvated multi-ring structure A6, the tri-solvated hemibond cage structure N1, and the single five-membered ring structure C2, respectively. The temperature effects on the stability of these three systems were also explored via Gibbs free energies. Furthermore, for the OH-(H2O)7 clusters, the assignments of vibrational transitions in the OH stretching region are in good agreement with the studies of small hydroxide ion-water clusters, and the IR spectra of two isomers (tetra-solvated multi-ring A6 and penta-solvated cage A3) may match future experimental observation well. By topological analysis and reduced density gradient analysis, the structural characteristics and bonding strengths of the studied clusters were investigated. This work indicates the excellent performance of the PSO search algorithm and CALYPSO on water clusters, and may further provide extensive insights into the chemical behavior such as the transport mechanism of OH- ions and OH radicals in the aqueous phase.

Neuromorphic Vision Based Contact-Level Classification in Robotic Grasping Applications.

In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.

Study of the thermodynamic properties of CeO2 from ab initio calculations: the effect of phonon-phonon interaction.

The thermodynamic properties of CeO2 have been reevaluated by a simple but accurate scheme. All our calculations are based on the self-consistent ab initio lattice dynamical (SCAILD) method that goes beyond the quasiharmonic approximation. Through this method, the effects of phonon-phonon interactions are included. The obtained thermodynamic properties and phonon dispersion relations are in good agreement with experimental data when considering the correction of phonon-phonon interaction. We find that the correction of phonon-phonon interaction is equally important and should not be neglected. At last, by comparing with quasiharmonic approximation, the present scheme based on SCAILD method is probably more suitable for high temperature systems.