A novel CoN4-driven self-assembled molecular engineering for oxygen reduction reaction

The poor kinetics of oxygen reduction reaction (ORR) invites a quest for the development of low-cost and efficient non-Pt electrocatalysts for fuel cells. Herein, a nanocomposite (g-[Co₂N₈]) was synthesized by coordination assembly of CoN₄ macrocyclic moieties on graphene surface. The CoN₄ macrocyclic complex was characterized by UV–Vis, FT-IR, Mass, ¹H NMR and ¹³C NMR spectral studies, whereas UV–Vis, FT-IR, and Mass spectral, Raman, XRD and TEM studies were utilized to characterize the nanocomposite g-[Co₂N₈]. The results suggested that [CoN₄] units are present in self-assembled [Co₂N₈] species. Further, the nanocomposite g-[Co₂N₈] was examined for ORR activity by employing cyclic and linear sweep voltammetry and found that the formal potential (E_1/2) of g-[Co₂N₈] (+0.90 V) was more positive than 20% Pt/C (+0.86 V), indicating a remarkable ORR performance of g-[Co₂N₈] in comparison to 20% Pt/C, followed by 4e-mechanism. Moreover, the nanocomposite (g-[Co₂N₈]) displayed better ORR activity in comparison to [CoN₄] complex which can be attributed to the synergistic incorporation of endo and exo N₄–Co²⁺ moieties in the [Co₂N₈] species. In addition, g-[Co₂N₈] electrocatalyst exhibited a comparable stability to 20% Pt/C catalyst after 5000 cycles. This work will help to design multi-metallic coordination polymers with similar or different metal ions in N₄-arrangement for various energy related electrocatalysis.     

A Quotient Space Formulation for Generative Statistical Analysis of Graphical Data

Complex analyses involving multiple, dependent random quantities often lead to graphical models—a set of nodes denoting variables of interest, and corresponding edges denoting statistical interactions between nodes. To develop statistical analyses for graphical data, especially towards generative modeling, one needs mathematical representations and metrics for matching and comparing graphs, and subsequent tools, such as geodesics, means, and covariances. This paper utilizes a quotient structure to develop efficient algorithms for computing these quantities, leading to useful statistical tools, including principal component analysis, statistical testing, and modeling. We demonstrate the efficacy of this framework using datasets taken from several problem areas, including letters, biochemical structures, and social networks.

     

Computing Equilibrium Wealth Distributions in Models with Heterogeneous-Agents, Incomplete Markets and Idiosyncratic Risk

This paper describes an accurate, fast and robust fixed point method for computing the stationary wealth distributions in macroeconomic models with a continuum of infinitely-lived households who face idiosyncratic shocks with aggregate certainty. The household wealth evolution is modeled as a mixture Markov process and the stationary wealth distributions are obtained using eigen structures of transition matrices by enforcing the conditions for the Perron–Frobenius theorem by adding a perturbation constant to the Markov transition matrix. This step is utilized repeatedly within a binary search algorithm to find the equilibrium state of the system. The algorithm suggests an efficient and reliable framework for studying dynamic stochastic general equilibrium models with heterogeneous agents.