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Structure–activity relationships built on descriptors of bulk and bulk-terminated surfaces are the basis for the rational design of electrocatalysts. However, electrochemically driven surface transformations complicate the identification of such descriptors. Here we demonstrate how the as-prepared surface composition of (001)-terminated LaNiO 3 epitaxial thin films dictates the surface transformation and the electrocatalytic activity for the oxygen evolution reaction. Specifically, the Ni termination (in the as-prepared state) is considerably more active than the La termination, with overpotential differences of up to 150 mV. A combined electrochemical, spectroscopic and density-functional theory investigation suggests that this activity trend originates from a thermodynamically stable, disordered NiO 2 surface layer that forms during the operation of Ni-terminated surfaces, which is kinetically inaccessible when starting with a La termination. Our work thus demonstrates the tunability of surface transformation pathways by modifying a single atomic layer at the surface and that active surface phases only develop for select as-synthesized surface terminations. Structure–activity relationships built on descriptors of surfaces can help to design electrocatalysts, but their identification for electrochemically driven surface transformations is challenging. The composition of LaNiO 3 thin film surfaces can now dictate surface transformation and activity of the oxygen evolution reaction.

In this study, we investigated the influence of oxygen non-stoichiometry on the resistive switching performance of tantalum oxide based memristive devices. Thin-films of tantalum oxide were deposited with varying sputter power and oxygen partial pressure. The electroforming voltage was found to decrease with increasing power density or decreased oxygen partial pressure, while the endurance remained stable and the resistance window R OFF / R ON was found to increase. In-depth XPS analysis connects these observations to a controllable oxygen sub-stoichiometry in the sputter-deposited films. Our analysis shows that the decrease of the forming voltage results from an increase in carrier density in the as-prepared thin-films, which is induced by the presence of oxygen vacancies.

Electrically induced resistive switching in metal insulator-metal structures is a subject of increasing scientific interest because it is one of the alternatives that satisfies current requirements for universal non-volatile memories. However, the origin of the switching mechanism is still controversial. Here we report the fabrication of a resistive switching device inside a transmission electron microscope, made from a Pt/SiO 2 / a -Ta 2 O 5− x / a -TaO 2− x /Pt structure, which clearly shows reversible bipolar resistive switching behaviour. The current–voltage measurements simultaneously confirm each of the resistance states (set, reset and breakdown). In situ scanning transmission electron microscope experiments verify, at the atomic scale, that the switching effects occur by the formation and annihilation of conducting channels between a top Pt electrode and a TaO 2− x base layer, which consist of nanoscale TaO 1− x filaments. Information on the structure and dimensions of conductive channels observed in situ offers great potential for designing resistive switching devices with the high endurance and large scalability. Despite its importance for non-volatile memory, the origin of resistive switching in a metal insulator-metal structure is unclear. Park et al. fabricate such a structure inside a transmission electron microscope to show that switching occurs via oxygen-vacancy migration, which changes the conduction channels.

Memristive devices are novel electronic devices, which resistance can be tuned by an external voltage in a non-volatile way. Due to their analog resistive switching behavior, they are considered to emulate the behavior of synapses in neuronal networks. In this work, we investigate memristive devices based on the field-driven redox process between the p-conducting Pr 0.7 Ca 0.3 MnO 3 (PCMO) and different tunnel barriers, namely, Al 2 O 3 , Ta 2 O 5 , and WO 3 . In contrast to the more common filamentary-type switching devices, the resistance range of these area-dependent switching devices can be adapted to the requirements of the surrounding circuit. We investigate the impact of the tunnel barrier layer on the switching performance including area scaling of the current and variability. Best performance with respect to the resistance window and the variability is observed for PCMO with a native Al 2 O 3 tunnel oxide. For all different layer stacks, we demonstrate a spike timing dependent plasticity like behavior of the investigated PCMO cells. Furthermore, we can also tune the resistance in an analog fashion by repeated switching the device with voltage pulses of the same amplitude and polarity. Both measurements resemble the plasticity of biological synapses. We investigate in detail the impact of different pulse heights and pulse lengths on the shape of the stepwise SET and RESET curves. We use these measurements as input for the simulation of training and inference in a multilayer perceptron for pattern recognition, to show the use of PCMO-based ReRAM devices as weights in artificial neural networks which are trained by gradient descent methods. Based on this, we identify certain trends for the impact of the applied voltages and pulse length on the resulting shape of the measured curves and on the learning rate and accuracy of the multilayer perceptron.

Exsolution reactions enable the synthesis of oxide-supported metal nanoparticles, which are desirable as catalysts in green energy conversion technologies. It is crucial to precisely tailor the nanoparticle characteristics to optimize the catalysts’ functionality, and to maintain the catalytic performance under operation conditions. We use chemical (co)-doping to modify the defect chemistry of exsolution-active perovskite oxides and examine its influence on the mass transfer kinetics of Ni dopants towards the oxide surface and on the subsequent coalescence behavior of the exsolved nanoparticles during a continuous thermal reduction treatment. Nanoparticles that exsolve at the surface of the acceptor-type fast-oxygen-ion-conductor SrTi 0.95 Ni 0.05 O 3− δ (STNi) show a high surface mobility leading to a very low thermal stability compared to nanoparticles that exsolve at the surface of donor-type SrTi 0.9 Nb 0.05 Ni 0.05 O 3− δ (STNNi). Our analysis indicates that the low thermal stability of exsolved nanoparticles at the acceptor-doped perovskite surface is linked to a high oxygen vacancy concentration at the nanoparticle-oxide interface. For catalysts that require fast oxygen exchange kinetics, exsolution synthesis routes in dry hydrogen conditions may hence lead to accelerated degradation, while humid reaction conditions may mitigate this failure mechanism. Exsolved metal nanoparticles are widely believed to exhibit an exceptional robustness against coarsening. Here, the authors demonstrate that the coarsening behavior depends on the surface defect chemistry of the respective oxide support as well as the oxophilicity of the exsolved metal.

The continuing revolutionary success of mobile computing and smart devices calls for the development of novel, cost- and energy-efficient memories. Resistive switching is attractive because of, inter alia, increased switching speed and device density. On electrical stimulus, complex nanoscale redox processes are suspected to induce a resistance change in memristive devices. Quantitative information about these processes, which has been experimentally inaccessible so far, is essential for further advances. Here we use in operando spectromicroscopy to verify that redox reactions drive the resistance change. A remarkable agreement between experimental quantification of the redox state and device simulation reveals that changes in donor concentration by a factor of 2–3 at electrode-oxide interfaces cause a modulation of the effective Schottky barrier and lead to >2 orders of magnitude change in device resistance. These findings allow realistic device simulations, opening a route to less empirical and more predictive design of future memory cells. Resistive switching in metal oxides is related to the migration of donor defects. Here Baeumer et al. use in operando X-ray spectromicroscopy to quantify the doping locally and show that small local variations in the donor concentration result in large variations in the device resistance.

Exsolution-active catalysts allow for the formation of highly active metallic nanoparticles, yet recent work has shown that their long-term thermal stability remains a challenge. In this work, the dynamics of exsolved Ni nanoparticles are probed in-situ with atomically resolved secondary electron imaging with environmental scanning transmission electron microscopy. Pre-characterization shows embedded NiO x nanostructures within the parent oxide. Subsequent in-situ exsolution demonstrates that two populations of exsolved particles form with distinct metal-support interactions and coarsening behaviors. Nanoparticles which precipitate above embedded nanostructures are observed to be more stable, and are prevented from migrating on the surface of the support. Nanoparticle migration which fits random-walk kinetics is observed, and particle behavior is shown to be analogous to a classical wetting model. Additionally, DFT calculations indicate that particle motion is facilitated by the support oxide. Ostwald ripening processes are visualized simultaneously to migration, including particle redissolution and particle ripening. Exsolution enables the formation of active catalytic nanoparticles, but their thermal stability remains limited. Here, the authors use secondary-electron imaging at atomic resolution to directly visualize the coarsening of exsolved nanoparticles.

With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the sensors, the so-called “edge computing.” Augmenting these chips with emerging memory technologies enables these edge devices with non-volatile and adaptive properties which are desirable for low power and online learning operations. However, an energy- and area-efficient realization of these systems requires disruptive hardware changes. Memristor-based solutions for these concepts are in the focus of research and industry due to their low-power and high-density online learning potential. Specifically, the filamentary-type valence change mechanism (VCM memories) have shown to be a promising candidate In consequence, physical models capturing a broad spectrum of experimentally observed features such as the pronounced cycle-to-cycle (c2c) and device-to-device (d2d) variability are required for accurate evaluation of the proposed concepts. In this study, we present an in-depth experimental analysis of d2d and c2c variability of filamentary-type bipolar switching HfO 2 /TiO x nano-sized crossbar devices and match the experimentally observed variabilities to our physically motivated JART VCM compact model. Based on this approach, we evaluate the concept of parallel operation of devices as a synapse both experimentally and theoretically. These parallel synapses form a synaptic array which is at the core of neuromorphic chips. We exploit the c2c variability of these devices for stochastic online learning which has shown to increase the effective bit precision of the devices. Finally, we demonstrate that stochastic switching features for a pattern classification task that can be employed in an online learning neural network.

The demand for highly scalable, low-power devices for data storage and logic operations is strongly stimulating research into resistive switching as a novel concept for future non-volatile memory devices. To meet technological requirements, it is imperative to have a set of material design rules based on fundamental material physics, but deriving such rules is proving challenging. Here, we elucidate both switching mechanism and failure mechanism in the valence-change model material SrTiO 3 , and on this basis we derive a design rule for failure-resistant devices. Spectromicroscopy reveals that the resistance change during device operation and failure is indeed caused by nanoscale oxygen migration resulting in localized valence changes between Ti 4+ and Ti 3+ . While fast reoxidation typically results in retention failure in SrTiO 3 , local phase separation within the switching filament stabilizes the retention. Mimicking this phase separation by intentionally introducing retention-stabilization layers with slow oxygen transport improves retention times considerably. Memristive devices offer a future low-power solution to data storage and logic operations, but there is still a lack of suitable material design rules. Here, the authors present a design rule for retention-failure-resistant devices based on spectromicroscopic studies of strontium titanate.

Using low-temperature electrical measurements, the interrelation between electron transport, magnetic properties, and ionic defect structure in complex oxide interface systems is investigated, focusing on NdGaO3/SrTiO3 (100) interfaces. Field-dependent Hall characteristics (2–300 K) are obtained for samples grown at various growth pressures. In addition to multiple electron transport, interfacial magnetism is tracked exploiting the anomalous Hall effect (AHE). These two properties both contribute to a nonlinearity in the field dependence of the Hall resistance, with multiple carrier conduction evident below 30 K and AHE at temperatures ≲10K . Considering these two sources of nonlinearity, we suggest a phenomenological model capturing the complex field dependence of the Hall characteristics in the low-temperature regime. Our model allows the extraction of the conventional transport parameters and a qualitative analysis of the magnetization. The electron mobility is found to decrease systematically with increasing growth pressure. This suggests dominant electron scattering by acceptor-type strontium vacancies incorporated during growth. The AHE scales with growth pressure. The most pronounced AHE is found at increased growth pressure and, thus, in the most defective, low-mobility samples, indicating a correlation between transport, magnetism, and cation defect concentration.