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Functional properties of transition-metal oxides strongly depend on crystallographic defects; crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Yet, localized lattice distortions remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here, we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SrIrO3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO3, and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments. 

We report the first experimental demonstration of ferroelectric field-effect transistor (FEFET) based spiking neurons. A unique feature of the ferroelectric (FE) neuron demonstrated herein is the availability of both excitatory and inhibitory input connections in the compact 1T-1FEFET structure, which is also reported for the first time for any neuron implementations. Such dual neuron functionality is a key requirement for bio-mimetic neural networks and represents a breakthrough for implementation of the third generation spiking neural networks (SNNs)-also reported herein for unsupervised learning and clustering on real world data for the first time. The key to our demonstration is the careful design of two important device level features: (1) abrupt hysteretic transitions of the FEFET with no stable states therein, and (2) the dynamic tunability of the FEFET hysteresis by bias conditions which allows for the inhibition functionality. Experimentally calibrated, multi-domain Preisach based FEFET models were used to accurately simulate the FE neurons and project their performance at scaled nodes. We also implement an SNN for unsupervised clustering and benchmark the network performance across analog CMOS and emerging technologies and observe (1) unification of excitatory and inhibitory neural connections, (2) STDP based learning, (3) lowest reported power (3.6nW) during classification, and (4) a classification accuracy of 93%. 

Abstract Epitaxial strain has been shown to produce dramatic changes to the orbital structure in transition metal perovskite oxides and, in turn, the rate of oxygen electrocatalysis therein. Here, epitaxial strain is used to investigate the relationship between surface electronic structure and oxygen electrocatalysis in prototypical fuel cell cathode systems. Combining high‐temperature electrical‐conductivity‐relaxation studies and synchrotron‐based X‐ray absorption spectroscopy studies of La_0.5Sr_0.5CoO₃and La_0.8Sr_0.2Co_0.2Fe_0.8O₃thin films under varying degrees of epitaxial strain reveals a strong correlation between orbital structure and catalysis rates. In both systems, films under biaxial tensile strain simultaneously exhibit the fastest reaction kinetics and lowest electron occupation in thed_z²orbitals. These results are discussed in the context of broader chemical trends and electronic descriptors are proposed for oxygen electrocatalysis in transition metal perovskite oxides. 

Abstract Solid‐oxide fuel/electrolyzer cells are limited by a dearth of electrolyte materials with low ohmic loss and an incomplete understanding of the structure–property relationships that would enable the rational design of better materials. Here, using epitaxial thin‐film growth, synchrotron radiation, impedance spectroscopy, and density‐functional theory, the impact of structural parameters (i.e., unit‐cell volume and octahedral rotations) on ionic conductivity is delineated in La_0.9Sr_0.1Ga_0.95Mg_0.05O_3–δ. As compared to the zero‐strain state, compressive strain reduces the unit‐cell volume while maintaining large octahedral rotations, resulting in a strong reduction of ionic conductivity, while tensile strain increases the unit‐cell volume while quenching octahedral rotations, resulting in a negligible effect on the ionic conductivity. Calculations reveal that larger unit‐cell volumes and octahedral rotations decrease migration barriers and create low‐energy migration pathways, respectively. The desired combination of large unit‐cell volume and octahedral rotations is normally contraindicated, but through the creation of superlattice structures both expanded unit‐cell volume and large octahedral rotations are experimentally realized, which result in an enhancement of the ionic conductivity. All told, the potential to tune ionic conductivity with structure alone by a factor of ≈2.5 at around 600 °C is observed, which sheds new light on the rational design of ion‐conducting perovskite electrolytes.