A hybrid supercapacitor with high energy and power densities is reported. It comprises a composite anode of anatase TiO2 and reduced graphene oxide and an activated carbon cathode in a non‐aqueous electrolyte. While intercalation compounds can provide high energy typically at the expense of power, the anatase TiO2 nanoparticles are able to sustain both high energy and power in the hybrid supercapacitor. At a voltage range from 1.0 to 3.0 V, 42 W h kg−1 of energy is achieved at 800 W kg−1. Even at a 4‐s charge/discharge rate, an energy density as high as 8.9 W h kg−1 can be retained. The high energy and power of this hybrid supercapacitor bridges the gap between conventional batteries with high energy and low power and supercapacitors with high power and low energy.

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Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.

An extremely simple bulk sheet made of a piezoresistive carbon nanotube (CNT)-Ecoflex composite can act as a smart keypad that is portable, disposable, and flexible enough to be carried crushed inside the pocket of a pair of trousers. Both a rigid-button-imbedded, rollable (or foldable) pad and a patterned flexible pad have been introduced for use as portable keyboards. Herein, we suggest a bare, bulk, macroscale piezoresistive sheet as a replacement for these complex devices that are achievable only through high-cost fabrication processes such as patterning-based coating, printing, deposition, and mounting. A deep-learning technique based on deep neural networks (DNN) enables this extremely simple bulk sheet to play the role of a smart keypad without the use of complicated fabrication processes. To develop this keypad, instantaneous electrical resistance change was recorded at several locations on the edge of the sheet along with the exact information on the touch position and pressure for a huge number of random touches. The recorded data were used for training a DNN model that could eventually act as a brain for a simple sheet-type keypad. This simple sheet-type keypad worked perfectly and outperformed all of the existing portable keypads in terms of functionality, flexibility, disposability, and cost.

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We present a comprehensive introduction to the photorheological (PR) fluids whose rheological behavior can be changed by ultraviolet (UV) light with a wavelength of 365 nm. When the PR fluid was exposed to UV light, the viscosity of the fluid decreased, while the viscosity recovered to its initial value when UV light was turned off, indicating that the viscosity of these types of fluids can be reversible and tunable by UV light. Contrary to conventional smart fluids, such as electrorheological and magnetorheological fluids, PR fluid does not suffer from a phase splitting problem because it exists in a single-phase solution. Additionally, the PR fluid does not require any contact component, such as electrodes, and electric wires that are essential components for conventional smart fluids. In this work, the PR fluids were synthesized by doping lecithin/sodium deoxycholate reverse micelles with a photo-chromic spiropyran compound. It is demonstrated that the viscosity changes of PR fluids can be induced by UV light, and their rheological properties are examined in detail. In addition, an example of tailoring rheological properties using photoluminescence was introduced for improved response time. One of the potential applications, such as microfluidic flow control using the PR fluids, is also briefly presented.

Herein, data‐driven symmetry identification, property prediction, and low‐dimensional embedding from powder X‐Ray diffraction (XRD) patterns of inorganic crystal structure database (ICSD) and materials project (MP) entries are reported. For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). A task‐specified small dataset that focuses on a narrow material system, knowledge (rule)‐based descriptor extraction, and significant data dimension reduction are not the main focus of this study. Conventional powder XRD patterns, which are most widely used in materials research, can be used as a significantly informative material descriptor for deep learning. Both the FCN and T‐encoder outperform the CGCNN for symmetry classification. For property prediction, the performance of the FCN concatenated with multilayer perceptron reaches the performance level of CGCNN. Machine‐learning‐driven material property prediction from the powder XRD pattern deserves appreciation because no such attempts have been made despite common XRD‐driven symmetry (and lattice size) prediction and phase identification. The ICSD and MP data are embedded in the 2D (or 3D) latent space through the VAE, and well‐separated clustering according to the symmetry and property is observed.

Here, we describe the utility of a carbon fiber (CF) electrode that is inexpensive, simple, and flexible and can be embedded with elastomeric nanocomposite piezo-resistive sensors fabricated from silicone rubber (Ecoflex) blended with carbon nanotubes (CNTs) and various wt % of silicone thinner to tune the sensitivity and softness range. The performance of the CF electrode was evaluated on the basis of piezo-resistive responses from the sensors subjected to dynamic sinusoidal compressive strains at different levels and frequencies. The responses were positive-pressure effects with rate-dependent asymmetric nonlinear hysteresis characteristics. Developing a mathematical model to describe the rate-dependent asymmetric nonlinear hysteresis behavior is technically impossible; therefore, we employed artificial intelligence-based hysteresis modeling, long short-term memory recurrent neural network, to describe the hysteresis nonlinearity. The debonding strength of the CF electrode was determined in the pull-off testing and was found to be much higher than that of a copper wire electrode. The debonding mechanism was further elucidated via an in situ resistance profile. The importance of a robust conductive interface between a CF electrode and a nanocomposite was experimentally demonstrated. It was found that the inherent piezo-resistance of the CF was negligible compared with the piezo-resistance of the sensor; therefore, the signals from the sensor were free of interference. We believe CF-embedded tunable piezo-resistive sensors could be used in biomedical devices, artificial e-skins, robotic touch applications, and flexible keyboards where the required stretchability of the electrode can be introduced via an appropriate geometrical design.