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XRD dataset for 169 unique CuNiAl compositions analyzed as-sputtered and annealed. The CuNiAl alloys were annealed at 400 C for 3 hours in a vacuum furnace. Compositions ranged from 21.2 - 77.1 at% Cu, 13.4 - 51.2 at% Ni and 8.4 - 46.1 at% Al. 

Abstract The effect of target geometry on coating microstructure and morphology is correlated to changes in deposition conditions, plasma characteristics, and film growth during planar and hollow cathode sputtering. The sputtering plasma properties for the two target geometries were characterized via Langmuir probe analysis as a function of power density and Ar pressure to determine the evolution of ion density for each configuration. Films were then synthesized at the low (0.4 W cm⁻²) and high (1.2 W cm⁻²) power densities and characterized using x-ray diffraction, scanning electron microscopy, and electron backscatter diffraction to link changes in texturing, morphology, and microstructure with variations in ion density and sputtering deposition conditions caused by target geometry. It was observed that varying target geometry led to an over threefold increase in deposition rate, homologous temperature, and ion density, which altered the morphology and texture of the film without significant changes to the grain size. 

Powder X‐ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high‐throughput discovery in self‐driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low‐quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X‐ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, for example, through transfer learning methods. We collected 92,552 diffractograms, 2179 of them labeled, from a wide spectrum of material classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self‐driving materials labs. 

Bistable electroactive polymers (BSEP) combine shape memory with large-strain actuation at the rubbery state to achieve rigid-to-rigid actuation. The stiffness of the BSEP is tunable via glass transition or phase changing. The reversible melting-crystallization of the polymer chains in the phase changing BSEP contributes to the stiffness change within a narrow temperature range. A modulus change of more than 1000 folds can be achieved within 3 °C. Additionally, large actuation strains rivaling those of VHB acrylic elastomers can be obtained at the rubbery state. Explorations regarding potential applications of this material have been focused on tactile displays. In one design, Joule heating of a serpentine-shaped compliant electrode coated on a BSEP film, coupled with a pneumatic pressure source has been employed to raise diaphragm dots with 1.5 mm base diameter to heights up to 0.7 mm. The resulting Braille electronic readers could thus be actuated with low voltages.