The electric double layer effect and its strong suppression at Li+ solid electrolyte/hydrogenated diamond interfaces

Redox-based ion-gating reservoir consisting of (104) oriented LiCoO2 film, assisted by physical masking

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1-xCoO2 + xLi+ + xe-) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.

Electric Double Layer Based Epidermal Electronics for Healthcare and Human-Machine Interface

Epidermal electronics, an emerging interdisciplinary field, is advancing the development of flexible devices that can seamlessly integrate with the skin. These devices, especially Electric Double Layer (EDL)-based sensors, overcome the limitations of conventional electronic devices, offering high sensitivity, rapid response, and excellent stability. Especially, Electric Double Layer (EDL)-based epidermal sensors show great potential in the application of wearable electronics to detect biological signals due to their high sensitivity, fast response, and excellent stability. The advantages can be attributed to the biocompatibility of the materials, the flexibility of the devices, and the large capacitance due to the EDL effect. Furthermore, we discuss the potential of EDL epidermal electronics as wearable sensors for health monitoring and wound healing. These devices can analyze various biofluids, offering real-time feedback on parameters like pH, temperature, glucose, lactate, and oxygen levels, which aids in accurate diagnosis and effective treatment. Beyond healthcare, we explore the role of EDL epidermal electronics in human-machine interaction, particularly their application in prosthetics and pressure-sensing robots. By mimicking the flexibility and sensitivity of human skin, these devices enhance the functionality and user experience of these systems. This review summarizes the latest advancements in EDL-based epidermal electronic devices, offering a perspective for future research in this rapidly evolving field.

Carbonization of a Molybdenum Substrate Surface and Nanoparticles by a One-Step Method of Femtosecond Laser Ablation in a Hexane Solution

Molybdenum carbides (MoC and Mo₂C) are being reported for various applications, for example, catalysts for sustainable energies, nonlinear materials for laser applications, protective coatings for improving tribological performance, and so on. A one-step method for simultaneously fabricating molybdenum monocarbide (MoC) nanoparticles (NPs) and MoC surfaces with a laser-induced periodic surface structure (LIPSS) was developed by using pulsed laser ablation of a molybdenum (Mo) substrate in hexane. Spherical NPs with an average diameter of 61 nm were observed by scanning electron microscopy. The X-ray diffraction pattern and electron diffraction (ED) pattern results indicate that a face-centered cubic MoC was successfully synthesized for the NPs and on the laser-irradiated area. Notably, the ED pattern suggests that the observed NPs are nanosized single crystals, and a carbon shell was observed on the surface of MoC NPs. The X-ray diffraction pattern of both MoC NPs and LIPSS surface indicates the formation of FCC MoC, agreeing with the results of ED. The results of X-ray photoelectron spectroscopy also showed the bonding energy attributed to Mo-C, and the sp²-sp³ transition was confirmed on the LIPSS surface. The results of Raman spectroscopy have also supported the formation of MoC and amorphous carbon structures. This simple synthesis method for MoC may provide new possibilities for preparing Mo _x C-based devices and nanomaterials, which may contribute to the development of catalytic, photonic, and tribological fields.

Edge-of-chaos learning achieved by ion-electron-coupled dynamics in an ion-gating reservoir

Physical reservoir computing has recently been attracting attention for its ability to substantially reduce the computational resources required to process time series data. However, the physical reservoirs that have been reported to date have had insufficient computational capacity, and most of them have a large volume, which makes their practical application difficult. Here, we describe the development of a Li⁺ electrolyte-based ion-gating reservoir (IGR), with ion-electron-coupled dynamics, for use in high-performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which were stored as transient charge density patterns in an electric double layer, at the Li⁺ electrolyte/diamond interface. Performance for a second-order nonlinear dynamical equation task is one order of magnitude higher than memristor-based reservoirs. The edge-of-chaos state of the IGR enabled the best computational capacity. The IGR described here opens the way for high-performance and integrated neural network devices.