14,446 research outputs found

    Cobalt-Porphyrin Catalyzed Electrochemical Reduction of Carbon Dioxide in Water II: Mechanism from First Principles

    Full text link
    We apply first principles computational techniques to analyze the two-electron, multi-step, electrochemical reduction of CO2 to CO in water using cobalt porphyrin as a catalyst. Density Functional Theory calculations with hybrid functionals and dielectric continuum solvation are used to determine the steps at which electrons are added. This information is corroborated with ab initio molecular dynamics simulations in an explicit aqueous environment which reveal the critical role of water in stabilizing a key intermediate formed by CO2 bound to cobalt. Using potential of mean force calculations, the intermediate is found to spontaneously accept a proton to form a carboxylate acid group at pH<9.0, and the subsequent cleavage of a C-OH bond to form CO is exothermic and associated with a small free energy barrier. These predictions suggest that the proposed reaction mechanism is viable if electron transfer to the catalyst is sufficiently fast. The variation in cobalt ion charge and spin states during bond breaking, DFT+U treatment of cobalt 3d orbitals, and the need for computing electrochemical potentials are emphasized.Comment: 33 pages, 7 figure

    3D scanning of cultural heritage with consumer depth cameras

    Get PDF
    Three dimensional reconstruction of cultural heritage objects is an expensive and time-consuming process. Recent consumer real-time depth acquisition devices, like Microsoft Kinect, allow very fast and simple acquisition of 3D views. However 3D scanning with such devices is a challenging task due to the limited accuracy and reliability of the acquired data. This paper introduces a 3D reconstruction pipeline suited to use consumer depth cameras as hand-held scanners for cultural heritage objects. Several new contributions have been made to achieve this result. They include an ad-hoc filtering scheme that exploits the model of the error on the acquired data and a novel algorithm for the extraction of salient points exploiting both depth and color data. Then the salient points are used within a modified version of the ICP algorithm that exploits both geometry and color distances to precisely align the views even when geometry information is not sufficient to constrain the registration. The proposed method, although applicable to generic scenes, has been tuned to the acquisition of sculptures and in this connection its performance is rather interesting as the experimental results indicate

    Path integral policy improvement with differential dynamic programming

    Get PDF
    Path Integral Policy Improvement with Covariance Matrix Adaptation (PI2-CMA) is a step-based model free reinforcement learning approach that combines statistical estimation techniques with fundamental results from Stochastic Optimal Control. Basically, a policy distribution is improved iteratively using reward weighted averaging of the corresponding rollouts. It was assumed that PI2-CMA somehow exploited gradient information that was contained by the reward weighted statistics. To our knowledge we are the first to expose the principle of this gradient extraction rigorously. Our findings reveal that PI2-CMA essentially obtains gradient information similar to the forward and backward passes in the Differential Dynamic Programming (DDP) method. It is then straightforward to extend the analogy with DDP by introducing a feedback term in the policy update. This suggests a novel algorithm which we coin Path Integral Policy Improvement with Differential Dynamic Programming (PI2-DDP). The resulting algorithm is similar to the previously proposed Sampled Differential Dynamic Programming (SaDDP) but we derive the method independently as a generalization of the framework of PI2-CMA. Our derivations suggest to implement some small variations to SaDDP so to increase performance. We validated our claims on a robot trajectory learning task
    • …
    corecore