Bioinspired Synaptic Branched Network within Quasi-Solid Polymer Electrolyte for High-Performance Microsupercapacitors.

The branched network-driven ion solvating quasi-solid polymer electrolytes (QSPEs) are prepared via one-step photochemical reaction. A poly(ethylene glycol diacrylate) (PEGDA) is combined with an ion-conducting solvate ionic liquid (SIL), where tetraglyme (TEGDME), which acts like interneuron in the human brain and creates branching network points, is mixed with EMIM-NTf2 and Li-NTf2. The QSPE exhibits a unique gyrified morphology, inspired by the cortical surface of human brain, and features well-refined nano-scale ion channels. This human-mimicking method offers excellent ion transport capabilities through a synaptic branched network with high ionic conductivity (σDC ≈ 1.8 mS cm-1 at 298 K), high dielectric constant (εs ≈ 125 at 298 K), and strong ion solvation ability, in addition to superior mechanical flexibility. Furthermore, the interdigitated microsupercapacitors (MSCs) based on the QSPE present excellent electrochemical performance of high energy (E  =  5.37 µWh cm-2) and power density (P  =  2.2 mW cm-2), long-term cycle stability (≈94% retention after 48 000 cycles), and mechanical stability (>94% retention after continuous bending and compressing deformation). Moreover, these MSC devices have flame-retarding properties and operate effectively in air and water across a wide temperature range (275 to 370 K), offering a promising foundation for high-performance, stable next-generation all-solid-state energy storage devices.

Machine Learning-Enabled Environmentally Adaptable Skin-Electronic Sensor for Human Gesture Recognition.

Stretchable sensors have been widely investigated and developed for the purpose of human motion detection, touch sensors, and healthcare monitoring, typically converting mechanical/structural deformation into electrical signals. The viscoelastic strain of stretchable materials often results in nonlinear stress-strain characteristics over a broad range of strains, consequently making the stretchable sensors at the body joints less accurate in predicting and recognizing human gestures. Accurate recognition of human gestures can be further deteriorated by environmental changes such as temperature and humidity. Here, we demonstrated an environment-adaptable high stress-strain linearity (up to ε = 150%) and high-durability (>100,000 cycles) stretchable sensor conformally laminated onto the body joints for human gesture recognition. The serpentine configuration of our ionic liquid-based stretchable film enabled us to construct broad data sets of mechanical strain and temperature changes for machine learning-based gesture recognition. Signal recognition and training of distinct strains and environmental stimuli using a machine learning-based algorithm analysis successfully measured and predicted the joint motion in a temperature-changing environment with an accuracy of 92.86% (R-squared). Therefore, we believe that our serpentine-shaped ion gel-based stretchable sensor harmonized with machine-learning analysis will be a significant achievement toward environmentally adaptive and multianalyte sensing applications. Our proposed machine learning-enabled multisensor system may enable the development of future electronic devices such as wearable electronics, soft robotics, electronic skin, and human-machine interaction systems.

Centrifugal-Gravity-Enforced Deposition of MXene Electrodes for High-Performance and Ultrastable Microsupercapacitors.

Two-dimensional (2D) transition metal carbides, known as MXenes, have captured much attention for their excellent electrical conductivity and electrochemical capability. However, the susceptibility of MXenes to oxidation, particularly Ti3C2Tx transforming into titanium dioxide upon exposure to ambient air, hinders their utilization for extended operational life cycles. This work introduces a simple and straightforward method for producing ultrathin MXene electrode films tailored for energy storage applications, employing centrifugal-gravity force. Our approach significantly suppresses the oxidation phenomenon that arises in MXene materials and also effectively prevents the recrystallization of potentially residual LiF during the film formation. Additionally, the utilization of this MXene electrode in an all-solid-state microsupercapacitor (MSC) with an interdigitated pattern demonstrates an exceptionally improved and stable electrochemical performance. This includes a high volumetric capacitance of approximately 467 F cm-3, an energy density of around 65 mWh cm-3, and impressive long-term cycle stability, retaining about 94% capacity after 10 000 cycles. Moreover, a downsized MSC device exhibits remarkable mechanical durability, retaining over 98% capacity even when folded and sustaining stability over extended periods. Therefore, we believe that this study provides valuable insights for advancing highly integrated energy storage devices, ensuring exceptional electrochemical efficiency and prolonged functionality in diverse environments, whether ambient or humid.

First demonstration of 2T0C-FeDRAM: a-ITZO FET and double gate a-ITZO/a-IGZO FeFET with a record-long multibit retention time of >4-bit and >2000 s.

Conventional DRAM, consisting of one transistor and one capacitor (1T1C), requires periodic data refresh processes due to its limited retention time and data-destructive read operation. Here, we propose and demonstrate a novel 3D-DRAM memory scheme available with a single transistor and a single ferroelectric field-effect transistor (FeFET) DRAM (2T0C-FeDRAM), which offers extended retention time and non-destructive read operation. This architecture uses a back-end-of-line (BEOL)-compatible amorphous oxide semiconductor (AOS) that is suitable for increasing DRAM cell density. Notably, the device structures of a double gate a-ITZO/a-IGZO FeFET, used for data storage and reading, are engineered to achieve an enlarged memory window (MW) of 1.5 V and a prolonged retention time of 104 s. This is accomplished by a double gate and an a-ITZO/a-IGZO heterostructure channel to enable efficient polarization control in hafnium-zirconium oxide (HZO) layers. We present successful program/erase operations of the double gate a-ITZO/a-IGZO FeFET through incremental step pulse programming (ISPP), demonstrating multi-level states with remarkable retention characteristics. Most importantly, we perform 2T0C-FeDRAM operations by electrically connecting the double gate a-ITZO/a-IGZO FeFET and the a-ITZO FET. Leveraging the impressive performance of the double gate a-ITZO/a-IGZO FeFET technology, we have effectively showcased an exceptionally record-long retention time exceeding 2000 s and 4-bit multi-level states, positioning it as a robust contender among emerging memory solutions in the era of artificial intelligence.