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  1. Free, publicly-accessible full text available September 1, 2024
  2. Epoxy-based polymer networks from step-growth polymerizations are ubiquitous in coatings, adhesives, and as matrices in composite materials. Dynamic covalent bonds in the network allow its degradation into small molecules and thus, enable chemical recycling; however, such degradation often requires elevated temperatures and costly chemicals, resulting in various small molecules. Here, we design crosslinked polyesters from structurally similar epoxy and anhydride monomers derived from phthalic acid. We achieve selective degradation of the polyesters through transesterification reactions at near-ambient conditions using an alkali carbonate catalyst, resulting in a singular phthalic ester. We also demonstrate upcycling the network polyesters to photopolymers by one-step depolymerization using a functional alcohol. 
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    Free, publicly-accessible full text available June 27, 2024
  3. Free, publicly-accessible full text available May 9, 2024
  4. Free, publicly-accessible full text available April 1, 2024
  5. Samples of 6 mol%Sc2O3 ? 1 mol% CeO2 co ? doped ZrO2 were fabricated by conventional ceramic processing methods and sintered at various temperatures from 1000?C to 1650?C in air. The sintering conditions on microstructure and phase content are investigated using various characterization methods, including pycnometry, diffraction, and spectroscopy. The electrical conductivity of samples was investigated using electrochemical impedance spectroscopy (EIS). The effect of inductive load (measured from room temperature to 800?C) is discussed in low to high temperature regimes. At T < 400?C since the arc is not a complete semicircle, the high-frequency arc could be fit using a constant phase element (CPE), while by subtraction of inductive load, a good fit is achieved using a capacitor element instead of CPE. The Arrhenius conductivity plot of samples reveals that the specimen sintered at 1600?C for 6 hours exhibits the highest conductivity. The activation energy (Ea) and conductivity pre-exponential (??0) factor are calculated from a linear fit to data that decreases by the increase in sintering temperature. 
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  6. In recent years, multi-phase materials capable of multi-ion transport have emerged as attractive candidates for a variety of electrochemical devices. Here, we provide experimental results for fabricating a composite electrolyte made up of a one-dimensional fast sodium-ion conductor, sodium zirconogallate, and an oxygen-ion conductor, yttria-stabilized zirconia. The composite is synthesized through a vapor phase conversion mechanism, and the kinetics of this process are discussed in detail. The samples are characterized using diffraction, electron microscopy, and electrochemical impedance spectroscopy techniques. Samples with a finer grain structure exhibit higher kinetic rates due to larger three-phase boundaries (TPBs) per unit area. The total conductivity is fitted to an Arrhenius type equation with activation energies ranging from 0.23 eV at temperatures below 550 ° C to 1.07 eV above 550 ° C . The electrochemical performance of multi-phase multi-species, mixed Na + and O 2 − conductor, is tested under both oxygen chemical potential gradient as well as sodium chemical potential gradient are discussed using the Goldman-Hodgkin-Kats (GHK) and the Nernst equation. 
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  7. We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR) (https://www.github.com/sparks-baird/mat_discover), a Python tool for identifying and assessing high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model. In this work, we create pairwise distance matrices between compounds via Element Mover's Distance (ElMD) and use these to create 2D density-aware embeddings for chemical compositions via Density-preserving Uniform Manifold Approximation and Projection (DensMAP). Because ElMD assigns distances between compounds that are more chemically intuitive than Euclidean-based distances, the compounds can then be clustered into chemically homogeneous clusters via Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN*). In combination with performance predictions via Compositionally-Restricted Attention-Based Network (CrabNet), we introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multi-objective Pareto front plots and assign a weighted score to each composition that encompasses the trade-off between performance and density-based chemical uniqueness. In addition to density-based metrics, we explore an additional uniqueness proxy related to property gradients in DensMAP space. As a validation study, we use DiSCoVeR to screen materials for both performance and uniqueness to extrapolate to new chemical spaces. Top-10 rankings are provided for the compound-wise density and property gradient uniqueness proxies. Top-ranked compounds can be further curated via literature searches, physics-based simulations, and/or experimental synthesis. Finally, we compare DiSCoVeR against the naive baseline of random search for several parameter combinations in an adaptive design scheme. To our knowledge, this is the first time automated screening has been performed with explicit emphasis on discovering high-performing, novel materials. 
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