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We apply recurrent neural networks (RNNs) to predict the time evolution of the concentration profile of multiple species resulting from a set of interconnected chemical reactions. As a proof of concept of our approach, RNNs were trained on a synthetic dataset generated by solving the kinetic equations of a system of aqueous inorganic iodine reactions that can follow after nuclear reactor accidents. We examine the minimum dataset necessary to obtain accurate predictions and explore the ability of RNNs to interpolate and extrapolate when exposed to previously unseen data. We also investigate the limits of our RNN by evaluating the robustness of the training initialization on our dataset.
RESUMEN
Reducing operating temperatures is a key step in making solid oxide fuel cell (SOFC) technology viable. A promising strategy for accomplishing this goal is employing mixed ion-electron conducting (MIEC) cathodes. La1-xSrxCo1-yFeyO3-δ (LSCF) is the most widely employed MIEC cathode material; however, rational optimization of the composition of LSCF requires fundamental insight linking its electronic structure to its defect chemistry. To provide the necessary insight, density functional theory plus U (DFT+U) calculations are used to investigate the electronic structure of LSCF (xSr = 0.50, yCo = 0.25). The DFT+U calculations show that LSCF has a significantly different electronic structure than La1-xSrxFeO3 because of the addition of cobalt, but that minimal electronic structure differences exist between La0.5Sr0.5Co0.25Fe0.75O3 and La0.5Sr0.5Co0.5Fe0.5O3. The oxygen vacancy formation energy (ΔEf,vac) is calculated for residing in different local environments within La0.5Sr0.5Co0.25Fe0.75O3. These results show that configurations have the highest ΔEf,vac, while have the lowest ΔEf,vac and may act as traps for . We conclude that compositions with more Fe than Co are preferred because the additional sites would lead to higher overall ΔEf,vac (and lower concentrations), while the trapping strength of the sites is relatively weak (â¼0.3 eV).
RESUMEN
[Re(bpy)(CO)3](-) is a well-established homogeneous electrocatalyst for the reduction of CO2 to CO. Recently, substitution of the more abundant transition metal Mn for Re yielded a similarly active electrocatalyst, [Mn(bpy)(CO)3](-). Compared to the Re catalyst, this Mn catalyst operates at a lower applied reduction potential but requires the presence of a weak acid in the solution for catalytic activity. In this study, we employ quantum chemistry combined with continuum solvation and microkinetics to examine the mechanism of CO2 reduction by each catalyst. We use cyclic voltammetry experiments to determine the turnover frequencies of the Mn catalyst with phenol as the added weak acid. The computed turnover frequencies for both catalysts agree to within one order of magnitude of the experimental ones. The different operating potentials for these catalysts indicate that different reduction pathways may be favored during catalysis. We model two different pathways for both catalysts and find that, at their respective operating potentials, the Mn catalyst indeed is predicted to take a different reaction route than the Re catalyst. The Mn catalyst can access both catalytic pathways, depending on the applied potential, while the Re catalyst does not show this flexibility. Our microkinetics analysis predicts which intermediates should be observable during catalysis. These intermediates for the two catalyzed reactions have qualitatively different electronic configurations, depending on the applied potential. The observable intermediate at higher applied potentials possesses an unpaired electron and therefore should be EPR-active; however, the observable intermediate at lower applied potentials, accessible only for the Mn catalyst, is diamagnetic and therefore should be EPR-silent. The differences between both catalysts are rationalized on the basis of their electronic structure and different ligand binding affinities.
RESUMEN
CONSPECTUS: Global advances in industrialization are precipitating increasingly rapid consumption of fossil fuel resources and heightened levels of atmospheric CO2. World sustainability requires viable sources of renewable energy and its efficient use. First-principles quantum mechanics (QM) studies can help guide developments in energy technologies by characterizing complex material properties and predicting reaction mechanisms at the atomic scale. QM can provide unbiased, qualitative guidelines for experimentally tailoring materials for energy applications. This Account primarily reviews our recent QM studies of electrode materials for solid oxide fuel cells (SOFCs), a promising technology for clean, efficient power generation. SOFCs presently must operate at very high temperatures to allow transport of oxygen ions and electrons through solid-state electrolytes and electrodes. High temperatures, however, engender slow startup times and accelerate material degradation. SOFC technologies need cathode and anode materials that function well at lower temperatures, which have been realized with mixed ion-electron conductor (MIEC) materials. Unfortunately, the complexity of MIECs has inhibited the rational tailoring of improved SOFC materials. Here, we gather theoretically obtained insights into oxygen ion conductivity in two classes of perovskite-type materials for SOFC applications: the conventional La1-xSrxMO3 family (M = Cr, Mn, Fe, Co) and the new, promising class of Sr2Fe2-xMoxO6 materials. Using density functional theory + U (DFT+U) with U-J values obtained from ab initio theory, we have characterized the accompanying electronic structures for the two processes that govern ionic diffusion in these materials: (i) oxygen vacancy formation and (ii) vacancy-mediated oxygen migration. We show how the corresponding macroscopic oxygen diffusion coefficient can be accurately obtained in terms of microscopic quantities calculated with first-principles QM. We find that the oxygen vacancy formation energy is a robust descriptor for evaluating oxide ion transport properties. We also find it has a direct relationship with (i) the transition metal-oxygen bond strength and (ii) the extent to which electrons left behind by the departing oxygen delocalize onto the oxygen sublattice. Design principles from our QM results may guide further development of perovskite-based MIEC materials for SOFC applications.
RESUMEN
We use ab initio density functional theory + U calculations to characterize the oxide ion diffusion process in bulk Sr2Fe1.5Mo0.5O(6-δ) (SFMO) by analyzing the formation and migration of oxygen vacancies. We show that SFMO's remarkable ionic conductivity arises from its intrinsic content of oxygen vacancies and a predicted very low migration barrier of such vacancies. Theoretical analysis of the electronic structure reveals a crucial role played by strongly hybridized Fe 3d/O 2p states to achieve the attendant mixed ion-electron conductor character so important for intermediate temperature fuel cell operation. We predict a next-nearest-neighbor-type migration pathway for the O(2-) ion should dominate. The low energy barrier of this pathway is mainly related to electrostatic interactions with homogeneously distributed Mo in the SFMO sublattice. We identify the reasons why Fe-rich perovskites, with the key addition of a certain concentration of Mo, produce excellent electronic and ionic transport properties so crucial for efficient operation of intermediate temperature solid oxide fuel cells.