In a recent study published in the journal Science Advances, Dr. Caltech researchers led by Wei Gao have developed a 3D-printed epifluidic electronic skin using machine learning (ML) for multi-modal health monitoring. This platform allows real-time monitoring of physical and chemical health.

Wearable medical devices can revolutionize the medical world by offering real-time monitoring, personalized treatment and early diagnosis of diseases.

However, one of the main problems of these devices is that they do not track data at the molecular level and their fabrication is a difficult task. Dr. Gao explained why this became a motivation for his teams.

“Research interest in personalized healthcare designed to revolutionize traditional medical practices is now growing. To overcome these challenges, we are using our 3D printing technology to create the necessary components such as physical sensors, chemical sensors, microfluidics and supercapacitors for our device platform,” said Dr. . Gao.

Dr. Gao and his team did just that by mass producing a wearable platform called e3-skin, produced by 3D printing on special materials.

e3-skin: 3D printed epifluidic electronic skin.

The name E3-skin comes from “epifluidic elastic electronic skin”. It is a 3D printed system that continuously monitors various physiological parameters and predicts behavioral responses.

Dr. Gao explained the various components of the e3 skin: “All major components of the wearable platform, including physical sensors, chemical sensors, microfluidics, and micro supercapacitors for energy storage, can be easily fabricated by extrusion 3D printing of various functional materials.”

What sets e3-skin apart from others is its 3D printed biochemical sensors and microfluidic system. Integration of 3D printing technology is an important aspect of creating the e3 look. 3D printing provides precision and customization, allowing researchers to precisely design and manufacture critical components. This simplified fabrication and enabled the integration of complex structures and materials, including 3D printed biochemical sensors and microfluidics.

Dr. Gao continued to elaborate: “Bochemical sensors can provide important information about health at the molecular level. “When combined with biophysical sensors, they can provide more complete information about our health.”

Most importantly, the use of microfluidics, the science of manipulating and controlling small amounts of fluid in tiny channels or devices, helped them analyze biomarkers in human sweat. Microfluidics can automatically induce sweat via iontophoresis, collect it without the need for strenuous activity, minimize sweat evaporation, and facilitate real-time biochemical analysis using fresh sweat samples.

ML-enabled wearable medical technology

e3-skin’s capabilities go beyond hardware components. It integrates machine learning algorithms that play an important role in its functionality. But before diving into machine learning, it’s important to understand the amazing thing that makes e3 skins possible: MXene.

MXene, a family of 2D materials, is a versatile material known for its unique properties. Aqueous Ti3C2Tx (MXene) served as ink for 3D printing of interconnects and biophysical sensors in e3 skin.

The team used MXene to overcome the limitations of existing systems. Dr. According to Gao, “Most wearable systems today are powered by batteries, which are hard, bulky, and insufficient and need to be replaced frequently.”

To address this limitation, the e3-skin incorporates a solar cell that harvests energy from ambient light and efficiently stores it in 3D printed MXene-based micro supercapacitors. This innovation provides stable battery-free operation for long-term health monitoring during daily activities.

MXene nanosheets have properties such as a negatively charged surface and hydrophilicity that allows them to disperse and remain stable in water. This enables precision printing with MXene filaments that have adjustable line widths and the ability to adhere to flexible materials such as human skin.

Dr. Gao also emphasized: “Printed MXene filaments can form uniform arrays with complex patterns, allowing complex structures to be formed inside the e3 skin.”

The future of wearable devices

to³-skin shows great promise by combining the best of machine learning, materials, and medicine. “to³-skin offers exciting opportunities for translating wearable biosensors into practical applications in modern healthcare,” emphasized Dr. Gao.

It has the potential to predict cognitive and behavioral disorders and monitor various aspects of health through continuous monitoring of vital biomarkers and comprehensive data collection. Collected data³-skin can improve personalized healthcare by enabling early warning, early diagnosis, and timely intervention to maximize health outcomes.

Dr. “Large data sets collected by such multi-modal wearable devices on daily activities, combined with advanced machine learning algorithms, can reveal the underlying relationship of biomarker levels with complex health conditions,” Gao concluded.

“Thus, it promises to revolutionize wearable health monitoring and power data-driven personalized healthcare.” Source