The invention of wearable health devices is considered one of the breakthroughs of modern science, as they can potentially revolutionize the medical world. These devices can provide real-time tracking, customized treatments, and early diagnosis of diseases.
However, they fail to track data at the molecular level, making their fabrication difficult to achieve. These challenges served as the motivation for the team of scientists at the California Institute of Technology.
3D-Printed Wearable System
Led by Dr. Wei Gao, the researchers employ 3D printing technology in creating essential components for their wearable platform, including supercapacitors, microfluidics, chemical sensors, and physical sensors. The group has done precisely that by realizing the mass production of a wearable platform called e3-skin.
The name e3-skin is derived from "epifluidic elastic electronic skin." This device is a 3D-printed wearable system that can continuously monitor different physiological parameters and predict behavioral responses.
Since 3D printing technology provides precision and customization, researchers can design and manufacture important components precisely. However, what sets the e3-skin apart from other wearable devices are the 3D-printed biochemical sensors and microfluidics system.
As described by Dr. Gao, wearable biochemical sensors can offer crucial health data at molecular levels. Combined with biophysical sensors, they can give more comprehensive information about a person's health.
The use of microfluidics has also helped them analyze the biomarkers in human sweat. Microfluidics also enables other processes, such as automatic sweat production through iontophoresis, minimizing sweat evaporation, and facilitating real-time biochemical analysis.
Most current wearable systems depend on usually bulky, rigid, and insufficient batteries. This limitation is addressed in e3-skin by integrating a solar cell, which allows it to harvest energy from ambient light and store it efficiently in micro-supercapacitors.
READ ALSO : Fabricated Electronic Skin Perceives Pain Like Humans, Paving Way for Prosthetics and Robotics
Assistance From Machine Learning
The capabilities of e3-skin extend beyond its hardware components. It integrates machine learning algorithms, which play a significant role in its functionality. Getting assistance from machine learning involves using MXene, a remarkable material that makes the wearable device possible.
MXene is a versatile medium from a family of 2D materials known for their unique properties. It is the ink to 3D print the interconnects and biophysical sensors in the e3-skin.
MXene nanosheets exhibit properties that allow them to disperse and remain stable in water. This leads to precise printing where MXene filaments demonstrate adjustable line widths and the ability to adhere to flexible substrates.
The versatility of MXene can be used in temperature sensing since the sensors exhibit a negative temperature coefficient and wear stability. MXene can also be combined with carbon nanotubes for pulse monitoring, forming highly sensitive and durable sensors. Moreover, the capabilities of e3-skin are useful in predicting an individual's behavioral responses to alcohol consumption.
These data are analyzed by machine learning to predict the response time and degree of impairment of an individual. Sweat alcohol is crucial in predicting response time, while heart rate complements a more accurate impairment prediction.
Check out more news and information on Electronic Skin in Science Times.
