Metal Oxidation Kinetics and the Transition from Thin to Thick Films

We report an investigation of growth kinetics and transition from thin to thick films during metal oxidation. In the thin film limit (< 20 nm), Cabrera and Mott's theory is usually adopted by explicitly considering ionic drift through the oxide in response to electric fields, where the growth kinetics follow an inverse logarithmic law. It is generally accepted that Wagner's theory, involving self-diffusion, is valid only in the limit of thick film regime and leads to parabolic growth kinetics. Theory presented here unifies the two models and provides a complete description of oxidation including the transition from thin to thick film. The range of validity of Cabrera and Mott's theory and Wagner's theory can be well defined in terms of the Debye-Huckel screening length. The transition from drift-dominated ionic transport for thin film to diffusion-dominated transport for thick film is found to strictly follow the direct logarithmic law that is frequently observed in many experiments

Implementation and Validation of Constrained Density Functional Theory Forces in the CP2K Package

Constrained density functional theory (CDFT) is a powerful tool for the prediction of electron transfer parameters in condensed phase simulations at a reasonable computational cost. In this work we present an extension to CDFT in the popular mixed Gaussian/plane wave electronic structure package CP2K, implementing the additional force terms arising from a constraint based on Hirshfeld charge partitioning. This improves upon the existing Becke partitioning scheme, which is prone to give unphysical atomic charges. We verify this implementation for a variety of systems: electron transfer in (H_{2}O)_{2}^{+} in a vacuum, electron tunnelling between oxygen vacancy centers in solid MgO, and electron self-exchange in aqueous Ru^{2+}-Ru^{3+}. We find good agreement with previous plane-wave CDFT results for the same systems, but at a significantly lower computational cost, and we discuss the general reliability of condensed phase CDFT calculations

Multi-heme Cytochromes in Shewanella oneidensis MR-1:Structures, functions and opportunities

Multi-heme cytochromes are employed by a range of microorganisms to transport electrons over distances of up to tens of nanometers. Perhaps the most spectacular utilization of these proteins is in the reduction of extracellular solid substrates, including electrodes and insoluble mineral oxides of Fe(III) and Mn(III/IV), by species of Shewanella and Geobacter. However, multi-heme cytochromes are found in numerous and phylogenetically diverse prokaryotes where they participate in electron transfer and redox catalysis that contributes to biogeochemical cycling of N, S and Fe on the global scale. These properties of multi-heme cytochromes have attracted much interest and contributed to advances in bioenergy applications and bioremediation of contaminated soils. Looking forward there are opportunities to engage multi-heme cytochromes for biological photovoltaic cells, microbial electrosynthesis and developing bespoke molecular devices. As a consequence it is timely to review our present understanding of these proteins and we do this here with a focus on the multitude of functionally diverse multi-heme cytochromes in Shewanella oneidensis MR-1. We draw on findings from experimental and computational approaches which ideally complement each other in the study of these systems: computational methods can interpret experimentally determined properties in terms of molecular structure to cast light on the relation between structure and function. We show how this synergy has contributed to our understanding of multi-heme cytochromes and can be expected to continue to do so for greater insight into natural processes and their informed exploitation in biotechnologies

Cell adhesion of Shewanella oneidensis to iron oxide minerals: Effect of different single crystal faces

The results of experiments designed to test the hypothesis that near-surface molecular structure of iron oxide minerals influences adhesion of dissimilatory iron reducing bacteria are presented. These experiments involved the measurement, using atomic force microscopy, of interaction forces generated between Shewanella oneidensis MR-1 cells and single crystal growth faces of iron oxide minerals. Significantly different adhesive force was measured between cells and the (001) face of hematite, and the (100) and (111) faces of magnetite. A role for electrostatic interactions is apparent. The trend in relative forces of adhesion generated at the mineral surfaces is in agreement with predicted ferric site densities published previously. These results suggest that near-surface structure does indeed influence initial cell attachment to iron oxide surfaces; whether this is mediated via specific cell surface-mineral surface interactions or by more general interfacial phenomena remains untested

Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation

Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknown that whether synthetic XRD based models are capable to solve u-XRD mapping data for in-situ experiments involving liquid phase exhibiting lower quality with significant artifacts. In this study, we collected u-XRD mapping data from an LaCl3-calcite hydrothermal fluid system and trained two categories of models to solve the experimental XRD patterns. The models trained by synthetic XRD patterns show low accuracy (as low as 64%) when solving experimental u-XRD mapping data. The accuracy of the DNN models was significantly improved (90% or above) when training them with the dataset containing both synthetic and small number of labeled experimental u-XRD patterns. This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase.Comment: See link below for supporting information https://docs.google.com/document/d/1m2SyaBDej4BhkWCA38GRXJe5Jd7Di7cp/edit?usp=sharing&ouid=108731997922646321851&rtpof=true&sd=tru