Authors: Hasti Vahidi | Alejandro Mejia | Shengquan Xuan | Angel Cassiadoro | Abdnego Abdi | David S. Mebane | William J. Bowman
Source: ECS Meeting Abstracts (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Which Interfaces Matter Most? Variability in Grain Boundary Defect Chemistry and Conductivity in a Concentrated Solid Electrolyte Inorganic solid electrolytes with fast ionic conductivity are critical components of fuel/electrolysis cells, all-solid-state batteries, membrane and separation reactors, sensors and other electrochemical devices. For example, solid oxygen ion (O 2- ) conductors are used in solid oxide fuel/electrolysis cells (SOFCs/SOECs) for high-efficiency conversion between chemical and electrical energy and chemical production from CO 2 . One major challenge in engineering such materials is the diminished grain boundary (GB) conductivity in polycrystalline oxide ceramics. This phenomenon, which was initially accredited to highly resistive silica-based glassy impurity phases, is attributed to the formation of intrinsic space charge layers (SCLs) directly adjacent to GB cores. The positive potential at the GB core (due to large concentrations of oxygen vacancies) results in a depletion of oxygen vacancies in the space charge layer as well as the segregation of charge-compensating acceptor solutes from grain interiors which ultimately reduces GB conductivity. Several strategies have been explored to improve GB conductivity by understanding and mitigating the SCL effects. While the standard Poisson–Boltzmann approach models the near-grain boundary defect behavior in dilute solid solutions and recent models by Mebane et al [1] and Virkant et al [2] incorporate the effects of defect-defect interactions (in concentrated solid solutions) and chemomechanical stress, respectively, there is yet to be a model developed that can predict individual GB conductivities in concentrated solid solutions based on direct experimental measurements of near-GB defect chemistry. Here, we present an experimental-computational framework that predicts which GBs in a polycrystalline electrolyte likely facilitate ionic conductivity. We developed a thermodynamic phase-field modeling framework and applied it to a model oxygen electrolyte Gd 0.25 Ce 0.75 O 2-δ , wherein the GB-to-GB variability in defect concentrations (Gd 3+ solutes, electrons localized at Ce 3+ , and oxygen vacancies) was measured microscopically using scanning transmission electron microscopy electron energy-loss spectroscopy (STEM EELS). These data were used to predict the GB-to-GB variability in cross-GB ionic conduction using a unique model, which prioritizes reproducing microscopically observed GB defect concentrations and considers defect-defect interactions in highly concentrated solid solutions, making the framework applicable to other technologically relevant solid electrolytes. Across the GBs studied, we revealed a non-monotonic relationship between depletion and GB conductivity, with the highest conductivity predicted for intermediate depletion amounts. This work lays the foundation for future experimental-computational research and advanced data-driven design of solid electrolytes needed for functional and energy storage/conversion devices. [1] D. S. Mebane, R. A. D. Souza, Energy &amp; Environmental Science , 2015. [2] K. S. N. Vikrant, R. Edwin García, Energy &amp; Environmental Science , 2018. [3] H. Vahidi, W.J. Bowman (Submitted). Acknowledgments HV and WJB acknowledge primary support from the UCI new faculty startup funding. This research was partially supported by the National Science Foundation Materials Research Science and Engineering Center program through the UC Irvine Center for Complex and Active Materials (DMR-2011967). We acknowledge the use of facilities and instrumentation at the UC Irvine Materials Research Institute (IMRI) supported in part by the same NSF MRSEC (DMR-2011967). AM, DM, and AC acknowledge funding from the NSF [WB1] [hv2] and the WVU MAE research department for the use of the Thorny Flat HPC Cluster for the numerical modeling work and computational resources. Electrolyte ; Conductivity ; Grain boundary ; Materials science ; Chemical physics ; Chemistry ; Chemical engineering ; Physical chemistry ; Metallurgy ; Microstructure ; Electrode ; Engineering Advancements in Solid Oxide Fuel Cells ; Chemical and Physical Properties in Aqueous Solutions ; Ionic liquids properties and applications