National Institute for Materials Science IAI

A team of researchers has developed a gel electret capable of stably retaining a large electrostatic charge. The team then combined this gel with highly flexible electrodes to create a sensor capable of perceiving low-frequency vibrations (e.g., vibrations generated by human motion) and converting them into output voltage signals. This device may potentially be used as a wearable healthcare sensor. A team of researchers from NIMS, Hokkaido University and Meiji Pharmaceutical University has developed a gel electret capable of stably retaining a large electrostatic charge. The team then combined this gel with highly flexible electrodes to create a sensor capable of perceiving low-frequency vibrations (e.g., vibrations generated by human motion) and converting them into output voltage signals. This device may potentially be used as a wearable healthcare sensor. Interest in the development of soft, lightweight, power-generating materials has been growing in recent years for use in soft electronics designed for various purposes, such as healthcare and robotics. Electret materials capable of stably retaining electrostatic charge may be used to develop vibration-powered devices without external power sources. NIMS has been leading efforts to develop a low-volatility, room-temperature alkyl-π liquid composed of a π-conjugated dye moiety and flexible yet branched alkyl chains (a type of hydrocarbon compound). The alkyl-π liquids exhibit excellent charge retention properties, can be applied to other materials (e.g., through painting and impregnation) and are easily formable. However, when these liquids have been combined with electrodes to create flexible devices, they have proven difficult to immobilize and seal, resulting in leakage issues. Moreover, the electrostatic charge retention capacities of alkyl-π liquids needed to be increased in order to improve their power generation capabilities. The research team recently succeeded in creating an alkyl-π gel by adding a trace amount of a low-molecular-weight gelator to an alkyl-π liquid. The elastic storage modulus of this gel was found to be 40 million times that of its liquid counterpart, and it could be simplified fixation and sealed. Moreover, the gel-electret obtained by charging this gel achieved a 24% increase in charge retention compared to the base material (i.e., the alkyl-π liquid), thanks to the improved confinement of electrostatic charges within the gel. The team then combined flexible electrodes with the gel-electret to create a vibration sensor. This sensor was able to perceive vibrations with frequencies as low as 17 Hz and convert them into an output voltage of 600 mV -- 83% higher than the voltage generated by an alkyl-π liquid electret-based sensor. In future research, the team aims to develop wearable sensors capable of responding to subtle vibrations and various strain deformations by further improving the charging electret characteristics (i.e., charge capacity and charge life) and strength of the alkyl-π gel. Additionally, since this gel is recyclable and reusable as a vibration sensor material, its use is expected to help promote a circular economy.

Optical analysis and machine learning techniques can now readily detect microplastics in marine and freshwater environments using inexpensive porous metal substrates. Optical analysis and machine learning techniques can now readily detect microplastics in marine and freshwater environments using inexpensive porous metal substrates. Details of the method, developed by researchers at Nagoya University with collaborators at the National Institute for Materials Sciences in Japan and others, are published in the journal Nature Communications. Detecting and identifying microplastics in water samples is essential for environmental monitoring but is challenging due in part to the structural similarity of microplastics with natural organic compounds derived from biofilms, algae, and decaying organic matter. Existing detection methods generally require complex separation techniques that are time-consuming and costly. "Our new method can simultaneously separate and measure the abundance of six key types of microplastics -- polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon and polyethylene terephthalate," says Dr. Olga Guselnikova of the National Institute for Materials Science (NIMS). The system uses a porous metal foam to capture microplastics from solution and detect them optically using a process called surface-enhanced Raman spectroscopy (SERS). "The SERS data obtained is highly complex," explains Dr. Joel Henzie of NIMS, "but it contains discernible patterns that can be interpreted using modern machine learning techniques." To analyse the data, the team created a neural network computer algorithm called SpecATNet. This algorithm learns how to interpret the patterns in the optical measurements to identify the target microplastics more quickly and with higher accuracy than traditional methods. "Our procedure holds immense potential for monitoring microplastics in samples obtained directly from the environment, with no pretreatment required, while being unaffected by possible contaminants that could interfere with other methods," says Professor Yusuke Yamauchi of Nagoya University. The researchers hope their innovation will greatly assist society in evaluating the significance of microplastic pollution on public health and the health of all organisms in marine and freshwater environments. By creating inexpensive microplastic sensors and open-source algorithms to interpret data, they hope to enable the rapid detection of microplastics, even in resource-limited labs. Currently, materials required for the new system bring cost savings of 90 to 95% compared to commercially available alternatives. The group plans to drive the cost of these sensors down even further and make the methods simple to replicate without the need for expensive facilities. In addition, the researchers hope to expand the capability of the SpecATNet neural network to detect a broader range of microplastics and even accept different kinds of spectroscopic data in addition to SERS data.

A collaborative research team has successfully developed a cutting-edge artificial intelligence (AI) device that executes brain-like information processing through few-molecule reservoir computing. This innovation utilizes the molecular vibrations of a select number of organic molecules. By applying this device for the blood glucose level prediction in patients with diabetes, it has significantly outperformed existing AI devices in terms of prediction accuracy. A collaborative research team from NIMS and Tokyo University of Science has successfully developed a cutting-edge artificial intelligence (AI) device that executes brain-like information processing through few-molecule reservoir computing. This innovation utilizes the molecular vibrations of a select number of organic molecules. By applying this device for the blood glucose level prediction in patients with diabetes, it has significantly outperformed existing AI devices in terms of prediction accuracy. With the expansion of machine learning applications in various industries, there's an escalating demand for AI devices that are not only highly computational but also feature low-power consumption and miniaturization. Research has shifted towards physical reservoir computing, leveraging physical phenomena presented by materials and devices for neural information processing. One challenge that remains is the relatively large size of the existing materials and devices. Our research has pioneered the world's first implementation of physical reservoir computing that operates on the principle of surface-enhanced Raman scattering, harnessing the molecular vibrations of merely a few organic molecules. The information is inputted through ion-gating, which modulates the adsorption of hydrogen ions onto organic molecules (p-mercaptobenzoic acid, pMBA) by applying voltage. The changes in molecular vibrations of the pMBA molecules, which vary with hydrogen ion adsorption, serve the function of memory and nonlinear waveform transformation for calculation. This process, using a sparse assembly of pMBA molecules, has learned approximately 20 hours of a diabetic patient's blood glucose level changes and managed to predict subsequent fluctuations over the next 5 minutes with an error reduction of about 50% compared to the highest accuracy achieved by similar devices to date. The outcome of this study indicates that a minimal quantity of organic molecules can effectively perform computations comparable to a computer. This technological breakthrough of conducting sophisticated information processing with minimal materials and in tiny spaces presents substantial practical benefits. It paves the way for the creation of low-power AI terminal devices that can be integrated with a variety of sensors, opening avenues for broad industrial use.