Electrosynthesized CuMgAl Layered Double Hydroxides as New Catalysts for the Electrochemical Reduction of CO2

In this study, new nanostructured CuMgAl Layered Double Hydroxide (LDH) based materials are synthesized on a 4 cm² sized carbonaceous gas diffusion membrane. By means of microscopic and spectroscopic techniques, the catalysts are thoroughly investigated, revealing the presence of several species within the same material. By a one-step, reproducible potentiodynamic deposition it is possible to obtain a composite with an intimate contact between a ternary CuMgAl LDH and Cu⁰/Cu₂O species. The catalyst compositions are investigated by varying: the molar ratio between the total amount of bivalent cations and Al³⁺, the amount of loading, and the molar ratios among the three cations in the electrolyte. Each electrocatalyst has been evaluated based on the catalytic performances toward the electrochemical CO₂ reduction to CH₃COOH at −0.4 V versus reversible hydrogen electrode  in liquid phase. The optimized catalyst, that is, CuMgAl 2:1:1 LDH exhibits a productivity of 2.0 mmol_CH3COOH g_cat⁻¹ h⁻¹. This result shows the beneficial effects of combining a material like the LDHs, alkaline in nature, and thus with a great affinity to CO₂, with Cu⁰/Cu⁺ species, which couples the increase of carbon sources availability at the electrode with a redox mediator capable to convert CO₂ into a C₂ product.     

Preserving the autocovariance of texture tilings using importance sampling

By-example aperiodic tilings are popular texture synthesis techniques that allow a fast, on-the-fly generation of unbounded and non-periodic textures with an appearance matching an arbitrary input sample called the “exemplar”. But by relying on uniform random sampling, these algorithms fail to preserve the autocovariance function, resulting in correlations that do not match the ones in the exemplar. The output can then be perceived as excessively random. In this work, we present a new method which can well preserve the autocovariance function of the exemplar. It consists in fetching contents with an importance sampler taking the explicit autocovariance function as the probability density function (pdf) of the sampler. Our method can be controlled for increasing or decreasing the randomness aspect of the texture. Besides significantly improving synthesis quality for classes of textures characterized by pronounced autocovariance functions, we moreover propose a real-time tiling and blending scheme that permits the generation of high-quality textures faster than former algorithms with minimal downsides by reducing the number of texture fetches.