Cloud Integrated Sensor Detects Physiological Signals in Real-Time

Wearable, stretchable, and flexible strain sensors can withstand large strains. In an article recently published in the journal npj Flexible Electronics, researchers developed graphene/ecoflex composites-based strain sensors via multiscale/hierarchical wrinkles modulations on the flexible substrate-ecoflex, for the detection of a broad range of physiological signals like pulse monitoring, body motions, and speech recognition.

Cloud Integrated Sensor Detects Physiological Signals in Real-Time

Study: Multiscale and hierarchical wrinkle enhanced graphene/Ecoflex sensors integrated with human-machine interfaces and cloud-platform. Image Credit: amgun/Shutterstock.com

Graphene sensors designed in the present study were ultrahigh sensitive with a gauge factor (GF) of 1078.1 and showed 650% stretchability with a response time of approximately 140 milliseconds and enhanced cycling durability. These graphene-based sensors were integrated into a cloud platform to monitor human respiration, showing the potential of graphene sensors in healthcare applications. 

The constructed graphene sensors also enabled the precise detection of complex sign languages or gestures. Furthermore, integrating graphene sensors into the glove of the human-machine interface could remotely control a manipulator to defuse a bomb from a distance, which is applicable in military fields and manufacturing industries.

Properties of Strain Sensors

The mechanical deformations caused due to the application of large strain on bendable/ stretchable/ flexible/curved substrates are converted into electrical signals, promoting their applications in monitoring systems in the healthcare field, soft robotics, and human-machine interactions. The strain sensors that are flexible/stretchable/wearable can withstand significant deformation and strain with up to 500% compared to their rigid counterparts.

Stretchable strain sensors are commercially available as resistive, capacitive, triboelectric, and piezoelectric sensor types. Resistive strain sensors are used for wearable sensing applications due to their simple structures, reliable read-out circuit, and cost-effective microfabrication processes. Moreover, they offer high sensitivity, good stretchability, and flexibility.

Graphene has extraordinary properties, including high carrier mobility, superior electrical and thermal conductivities, large surface area, high Young's modulus, high optical transmittance, and excellent mechanical flexibility.

Graphene is a promising two-dimensional (2D) material for various applications, especially in developing wearable sensors and implantable devices that could be applied in health monitoring.

To achieve practical applications of sensors, it is vital that these sensors possess large stretchability and high sensitivity. Since stretchability and sensitivity are contradictory parameters, various interface and surface engineering methods are proposed to achieve both high sensitivity and stretchability.

Wrinkle structure application is one of the reliable strategies to achieve the requirements of sensitivity and stretchability. Although various researchers have proposed different wrinkled structure-based strain sensors, their sensitivity and stretchability were limited.

Wrinkle Enhanced Graphene/Ecoflex Sensors

In the present study, flexible graphene/ecoflex composite-based strain sensors were fabricated via an interface engineering strategy, wherein a wrinkling mechanism was applied to the ecoflex substrate. A cost-effective two-step method was utilized for the ecoflex substrate’s surface treatment to form micro to nanoscale wrinkle patterns.

The characterization of designed graphene/ecoflex composites using a scanning electron microscope (SEM) revealed that after surface treatment of ecoflex, it showed micron-sized wrinkles in large numbers on its surface. These were formed via rapid volatilization of ethanol, leading to uneven surface morphology, which increased the surface area of ecoflex. This surface treatment enhances the specific surface areas on ecoflex and improves the interfacial bonding between graphene and ecoflex, thus improving the strain sensitivity of graphene strain sensors.

The fabricated graphene strain sensors exhibited high sensitivity with GF of 1078.1, significantly large stretchability with strain withholding capacity up to 650%, good cycling durability, and response time as small as 140 milliseconds.

The designed graphene-based strain sensors could capture and detect a broad range of physiological signals from speech sound, pulse vibration, and vigorous body motions. These graphene sensors could be applied to monitor human respiration with multiple-user flexibility and a real-time cloud platform.

A human-machine interface integrated with the designed graphene sensors could demonstrate the detection of complex sign languages and gestures and can perform remote bomb defusing. The sensor-integrated glove could remotely control the external manipulator that performed the tests for sensor detection.

Conclusion

To summarize, the methodology of modulating multiscale and hierarchical wrinkles on flexible ecoflex substrate was successful in their integration into strain sensors. The optimized design could achieve sensors with high sensitivity. The fabricated graphene sensors showed a GF of 1078.1, stretchability of up to 650%, and a short response time of approximately 140 milliseconds.

The obtained graphene sensors could detect various physiological signals. Furthermore, these sensors were applied for remote monitoring of healthcare devices. The integration of the graphene-based sensors into a real-time cloud platform and human-machine interface glove indicated their potential application in the healthcare industry and remote bomb defusing, respectively.

The present work highlighted the application of graphene sensors in long-range and real-time medical diagnoses. These graphene sensors also helped perform dangerous tasks from a distance, which is useful in industrial and military fields.

Reference

Zhou, J., Long, X., Huang, J., Jiang, C., Zhuo, F., Guo, C., Li, H. et al. (2022). Multiscale and hierarchical wrinkle enhanced graphene/Ecoflex sensors integrated with human-machine interfaces and cloud-platform. npj Flexible Electronicshttps://www.nature.com/articles/s41528-022-00189-1

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Bhavna Kaveti

Written by

Bhavna Kaveti

Bhavna Kaveti is a science writer based in Hyderabad, India. She has a Masters in Pharmaceutical Chemistry from Vellore Institute of Technology, India, and a Ph.D. in Organic and Medicinal Chemistry from Universidad de Guanajuato, Mexico. Her research work involved designing and synthesizing heterocycle-based bioactive molecules, where she had exposure to both multistep and multicomponent synthesis. During her doctoral studies, she worked on synthesizing various linked and fused heterocycle-based peptidomimetic molecules that are anticipated to have a bioactive potential for further functionalization. While working on her thesis and research papers, she explored her passion for scientific writing and communications.

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