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Register a new userInsight into hydrogen production through molecular simulation of an electrode-ionomer electrolyte system
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In this work, we examine metal electrode-ionomer electrolyte systems at high voltage / negative surface charge and at high pH to assess factors that influence hydrogen production efficiency. We simulate the hydrogen evolution electrode interface investigated experimentally in Bates et al., Journal of Physical Chemistry C, 2015 using a combination of first principles calculations and classical molecular dynamics. With this detailed molecular information, we explore the hypotheses posed in Bates et al. In particular we examine the response of the system to increased bias voltage and oxide coverage in terms of the potential profile, changes in solvation and species concentrations away from the electrode, surface concentrations, and orientation of water at reactive surface sites. We discuss this response in the context of hydrogen production.
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Highly crystalline UO2 nanoparticles (NPs) with sizes of 2-3 nm were produced by fast chemical deposition of uranium(IV) under reducing conditions at pH 8-11. The particles were then characterized by microscopic and spectroscopic techniques including high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), high-energy resolution fluorescence detection (HERFD) X-ray absorption spectroscopy at the U M4 edge and extended X-ray absorption fine structure (EXAFS) spectroscopy at the U L3 edge. The results of this investigation show that despite U(IV) being the dominant oxidation state of the freshly prepared UO2 NPs, they oxidize to U4O9 with time and under the X-ray beam, indicating the high reactivity of U(IV) under these conditions. Moreover, it was found that the oxidation process of NPs is accompanied by their growth in size to 6 nm. We highlight here the major differences and similarities of the UO2 NPs properties with PuO2, ThO2 and CeO2 NPs.
The rapid charging and/or discharging of electrochemical cells can lead to localized depletion of electrolyte concentration. This depletion can significantly impact the systems time dependent resistance. For systems with porous electrodes, electrolyte depletion can limit the rate of charging and increase energy dissipation. Here we propose a theory to control and avoid electrolyte depletion by tailoring the value and spatial distribution of resistance in a porous electrode. We explore the somewhat counterintuitive idea that increasing local spatial resistances of the solid electrode itself leads to improved charging rate and minimal change in energy loss. We analytically derive a simple expression for an electrode resistance profile that leads to highly uniform electrolyte depletion. We use numerical simulations to explore this theory and simulate spatiotemporal dynamics of electrolyte concentration in the case of a supercapacitor with various tailored electrode resistance profiles which avoid localized depletion. This increases charging rate up to around 2-fold with minimal effect on overall dissipated energy in the system.
In expansion of our recent proposal (Physics, 2020, 2, 213-276) that the solar systems evolution occurred in two stages -- during the first stage, the gaseous giants formed (via disk instability), and, during the second stage (caused by an encounter with a particular stellar-object leading to in-system fission-driven nucleogenesis), the terrestrial planets formed (via accretion) -- we emphasize here that the mechanism of formation of such stellar-objects is generally universal and therefore encounters of such objects with stellar-systems may have occurred elsewhere across galaxies. If so, their aftereffects may perhaps be observed as puzzling features in the spectra of individual stars (such as idiosyncratic chemical enrichments) and/or in the structures of exoplanetary systems (such as unusually high planet densities or short orbital periods). This paper reviews and reinterprets astronomical data within the fission-events framework. Classification of stellar systems as pristine or impacted is offered.
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The concept of chemical bonding can ultimately be seen as a rationalization of the recurring structural patterns observed in molecules and solids. Chemical intuition is nothing but the ability to recognize and predict such patterns, and how they transform into one another. Here we discuss how to use a computer to identify atomic patterns automatically, so as to provide an algorithmic definition of a bond based solely on structural information. We concentrate in particular on hydrogen bonding -- a central concept to our understanding of the physical chemistry of water, biological systems and many technologically important materials. Since the hydrogen bond is a somewhat fuzzy entity that covers a broad range of energies and distances, many different criteria have been proposed and used over the years, based either on sophisticate electronic structure calculations followed by an energy decomposition analysis, or on somewhat arbitrary choices of a range of structural parameters that is deemed to correspond to a hydrogen-bonded configuration. We introduce here a definition that is univocal, unbiased, and adaptive, based on our machine-learning analysis of an atomistic simulation. The strategy we propose could be easily adapted to similar scenarios, where one has to recognize or classify structural patterns in a material or chemical compound.
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