Bayesian Inversion, Uncertainty Analysis and Interrogation Using Boosting Variational Inference

Geoscientists use observed data to estimate properties of the Earth's interior. This often requires non-linear inverse problems to be solved and uncertainties to be estimated. Bayesian inference solves inverse problems under a probabilistic framework, in which uncertainty is represented by a so-called posterior probability distribution. Recently, variational inference has emerged as an efficient method to estimate Bayesian solutions. By seeking the closest approximation to the posterior distribution within any chosen family of distributions, variational inference yields a fully probabilistic solution. It is important to define expressive variational families so that the posterior distribution can be represented accurately. We introduce boosting variational inference (BVI) as a computationally efficient means to construct a flexible approximating family comprising all possible finite mixtures of simpler component distributions. We use Gaussian mixture components due to their fully parametric nature and the ease with which they can be optimized. We apply BVI to seismic travel time tomography and full waveform inversion, comparing its performance with other methods of solution. The results demonstrate that BVI achieves reasonable efficiency and accuracy while enabling the construction of a fully analytic expression for the posterior distribution. Samples that represent major components of uncertainty in the solution can be obtained analytically from each mixture component. We demonstrate that these samples can be used to solve an interrogation problem: to assess the size of a subsurface target structure. To the best of our knowledge, this is the first method in geophysics that provides both analytic and reasonably accurate probabilistic solutions to fully non-linear, high-dimensional Bayesian full waveform inversion problems

Comparing the contribution of visible-light irradiation, gold nanoparticles, and titania supports in photocatalytic nitroaromatic coupling and aromatic alcohol oxidation

Under visible-light irradiation, gold nanoparticles (Au NPs) supported by titania (TiO₂) nanofibers show excellent activity and high selectivity for both reductive coupling of nitroaromatics to corresponding azobenzene or azoxylbenzene and selective oxidation of aromatic alcohols to corresponding aldehydes. The Au NPs act as active centers mainly due to their localized surface plasmon resonance (LSPR) effect. They can effectively couple the photonic energy and thermal energy to enhance reaction efficiency. Visible-light irradiation has more influence on the reduction than on the oxidation, lowering the activation energy by 24.7 kJ mol⁻¹ and increasing the conversion rate by 88% for the reductive coupling, compared to merely 8.7 kJ mol⁻¹ and 46% for the oxidation. Furthermore, it is found that the conversion of nitroaromatics significantly depends on the particle size and specific surface area of supported Au NPs; and the catalyst on TiO₂(B) support outperforms that on anatase phase with preferable ability to activate oxygen. In contrast, for the selective oxidation, the effect of surface area is less prominent and Au NPs on anatase exhibit higher photo-catalytic activity than other TiO₂ phases. The catalysts can be recovered efficiently because the Au NPs stably attach to TiO₂ supports by forming a well-matched coherent interface observed via high-resolution TEM

An overview of Cu-based heterogeneous electrocatalysts for CO2 reduction

The electrochemical (EC) reduction of CO2 is a promising approach for value-added fuel or chemical production. Cu-based electrodes have been extensively used as a ‘star’ material for CO2 reduction to hydrocarbons. This review mainly focuses on the recent progress of Cu-based heterogeneous electrocatalysts for CO2 reduction from 2013 to 2019. Various morphologies of oxide-derived, bimetallic Cu species and their activity in EC CO2 reduction are reviewed, providing insights for the standardization of Cu-based heterogeneous systems. We also present a tutorial manual to describe parameters for the EC CO2 reduction process, especially for the pretreatment of the reaction system. This will offer useful guidance for newcomers to the field. Aqueous and non-aqueous electrolyte effects based on Cu electrodes are discussed. Finally, an overview of reaction systems of EC/PEC CO2 reduction and H2O oxidation for Cu-based heterogeneous catalysts is provided