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.

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.

Scientist have developed a hot-melt tissue adhesive (i.e., medical glue that is applied in a molten state) capable of healing operative wounds. This adhesive has excellent medical material properties in terms of its ease of use, adhesiveness to tissues, biocompatibility and ability to prevent postoperative complications. NIMS has developed a hot-melt tissue adhesive (i.e., medical glue that is applied in a molten state) capable of healing operative wounds. This adhesive has excellent medical material properties in terms of its ease of use, adhesiveness to tissues, biocompatibility and ability to prevent postoperative complications. Post-surgical complications (e.g., adhesions, bleeding, inflammation and infections) are major issues in clinical medicine. For example, post-surgical adhesion -- the binding of wounded tissue to a neighboring organ as it heals -- can cause ileus, infertility and pelvic pain. This complication also may negatively affect patients' quality of life, prolong hospitalization and necessitate further corrective surgery. Existing medical materials designed to prevent postoperative adhesion are known to have a number of disadvantages, including poor adhesiveness to tissues, difficulty in handling during endoscopy, cumbersome preparation and difficulty in preparing a homogeneous liquid mixture. It is therefore highly desirable to develop medical materials capable of minimizing the risk of postoperative complications with high degrees of adhesiveness to tissues, biocompatibility and manipulability. This research team recently developed a single-syringe hot-melt tissue adhesive with an adjustable sol-gel transition temperature. Porcine skin-derived gelatin -- a commonly used medical material -- was initially found to be unsuitable for use as a tissue adhesive because its sol-gel transition temperature was around 32°C, causing it to become a sol at body temperature (37°C). The team therefore used porcine tendon-derived gelatin instead and added a specific number of ureidopyrimidinone (UPy) groups to it, thereby adjusting the number and strength of intermolecular hydrogen bonds within it. Through this process, the team synthesized a new hot-melt tissue adhesive made of biopolymers (UPy gelatin) whose sol-gel transition temperature can be freely controlled. The team then designed it to melt to a sol above 40°C and solidify into a gel at body temperature. This tissue adhesive is expected to be able to transform into a stable gel within the human body, strongly bind to tissues and eventually decompose and be absorbed by the body, thereby preventing postoperative adhesions from developing and eliminating the need for secondary surgery. In fact, animal testing using rat cecum-abdominal wall models demonstrated that the use of this adhesive caused no postoperative adhesion. In future research, the team plans to conduct preclinical studies and biological safety testing with the goal of putting the tissue adhesive into practical use in clinical medicine.