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The manipulation of ions in complex oxide materials can be used to mimic brain-like plasticity through changes to the resistivity of a neuromorphic device. Advances in the design of more energy efficient devices require improved understanding of how ions migrate within a material and across its interface. We investigate the exchange of oxygen and hydrogen in a model SrCoOx epitaxial film—a material that transitions between a ferromagnetic metal and antiferromagnetic insulator depending on the oxygen concentration. Changes to the film during ionic liquid gating were measured by in situ synchrotron x-ray techniques as a function of time and gate voltage, examining the reversibility of the oxide over one complete gating cycle. We find that the out-of-plane lattice constant and oxygen vacancy concentration of SrCoOx are largely reversible although changes were observed in the ordered vacancy structure. Our results provide much needed insight into electrolyte-gated phase behavior in the transition metal oxides. 

Yttrium iron garnet (YIG) magnonics has garnered significant research interest because of the unique properties of magnons (quasiparticles of collective spin excitation) for signal processing. In particular, hybrid systems based on YIG magnonics show great promise for quantum information science due to their broad frequency tunability and strong compatibility with other platforms. However, their broad applications have been severely constrained by substantial microwave loss in the gadolinium gallium garnet (GGG) substrate at cryogenic temperatures. In this study, we demonstrate that YIG thin films can be spalled from YIG/GGG samples. Our approach is validated by measuring hybrid devices comprising superconducting resonators and spalled YIG films, which exhibit anti-crossing features that indicate strong coupling between magnons and microwave photons. Such new capability of separating YIG thin films from GGG substrates via spalling and the integrated superconductor-YIG devices represent a significant advancement for integrated magnonic devices, paving the way for advanced magnon-based coherent information processing. 

Abstract One of the most common approaches for quenching single-photon avalanche diodes is to use a passive resistor in series with it. A drawback of this approach has been the limited recovery speed of the single-photon avalanche diodes. High resistance is needed to quench the avalanche, leading to slower recharging of the single-photon avalanche diodes depletion capacitor. We address this issue by replacing a fixed quenching resistor with a bias-dependent adaptive resistive switch. Reversible generation of metallic conduction enables switching between low and high resistance states under unipolar bias. As an example, using a Pt/Al 2 O 3 /Ag resistor with a commercial silicon single-photon avalanche diodes, we demonstrate avalanche pulse widths as small as ~30 ns, 10× smaller than a passively quenched approach, thus significantly improving the single-photon avalanche diodes frequency response. The experimental results are consistent with a model where the adaptive resistor dynamically changes its resistance during discharging and recharging the single-photon avalanche diodes. 

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m3/m3). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.