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Electronic skins need to be versatile and able to detect multiple inputs beyond simple pressure and touch while having attributes of transparency and facile manufacturability. Herein, we demonstrate a versatile nanostructured transparent sensor capable of detecting wide range of pressures and proximity as well as novel nonoptical detection of printed patterns. The architecture and fabrication processes are straightforward and show robustness to repeated cycling and testing. The sensor displays good sensitivity and stability from 30 Pa to 5 kPa without the use of microstructuration and is conformal and sensitive to be utilized as a wrist-based heartrate monitor. Highly sensitive proximity detection is shown from a distance of 9 cm. Finally, a unique nonoptical pattern recognition dependent on the difference in the dielectric constant between ink and paper is also demonstrated, indicating the multifunctionality of this simple architecture.
Electronic skins need to be versatile and able to detect multiple inputs beyond simple pressure and touch while having attributes of transparency and facile manufacturability. Herein, we demonstrate a versatile nanostructured transparent sensor capable of detecting wide range of pressures and proximity as well as novel nonoptical detection of printed patterns. The architecture and fabrication processes are straightforward and show robustness to repeated cycling and testing. The sensor displays good sensitivity and stability from 30 Pa to 5 kPa without the use of microstructuration and is conformal and sensitive to be utilized as a wrist-based heartrate monitor. Highly sensitive proximity detection is shown from a distance of 9 cm. Finally, a unique nonoptical pattern recognition dependent on the difference in the dielectric constant between ink and paper is also demonstrated, indicating the multifunctionality of this simple architecture.
(a) Structure of sensor (b) photo of sensor displaying its transparency
1 .Pressure sensor: Piezo - capacitive pressure sensor
1 .Pressure sensor: Piezo - capacitive pressure sensor
(a) Normalized change in capacitance with applied pressure; inset shows the mechanism. (b) Change in capacitance in nearby eight pixels for pressure applied only at the central pixel. (c) Stability test of capacitance with 10 and 50 g standard weights kept for 210 min. (d) Normalized change in capacitance after each 50 bending cycles for a small pressure of 29 Pa. (e) Measurement assembly used for heart-rate measurement. (f) Waveform of the measured heart rate.
2. Proximity sensing:
2. Proximity sensing:
(a) Equivalent circuit of capacitance for the measurement of proximity. (b) Absolute normalized change in capacitance with respect to the distance of approaching finger. (c) Frequency dependence on proximity sensing. (d) Absolute change in capacitance for different pixels in the vicinity of the central pixel with immediate proximity (finger is at a distance of 0.5 cm).
3 .Non optical sensing and water salinity/ contamination check:
3 .Non optical sensing and water salinity/ contamination check:
(a) Pattern drawn on paper with pencil. (b) Pattern of “9” detected by sensor. (c) Equivalent capacitance and dielectric formation for ink detection.
4. Strain sensor
4. Strain sensor
Flexible and transparent strain sensor has attracted a great deal of attention since it has bright future applications such as in the field of soft robotics, smart textile or body-integrated electronics. In this work we systematically studied the fabrication and characterization of flexible and transparent strain sensor utilizing piezo-capacitive and piezoresistive effect.
The experiments resulted in strain sensors with sensitivity value of ~1 and good linearity profile. The strain sensors also showed good stability up to 10 kHz for carbon grease-based and up to 100 kHz for ionic gel-based flexible strain sensor. High transparency of 96% was also achieved by employing conductive ionic gel.
Materials used for exploited sensors:
Piezo - resistive strain sensor : using AgNW and PDMS, CNT on PDMS
Piezo - capacitive strain sensor: VHB and ionic gel carbon grease
This work is done with my student Elmo Octavian.
5. Temperature Sensor: PEDOT PSS+MoS2
5. Temperature Sensor: PEDOT PSS+MoS2
The rising interest in wearable technology due to its diverse potential applications in healthcare is responsible for advancement in sensing and wireless communication technology. Continuous monitoring of human vitals requires sensors to be flexible and sensitive to physical stimuli. As temperature is one of the main vitals of body, a flexible temperature sensor possessing relatively high sensitivity needed to be realized. Comparison of poly(3,4-ethylenedioxythiopene) poly(styrenesulfonicacid) (PEDOT:PSS) or PEDOT:PSS + Molybdenum Disulfide (MoS2) performance as a thin film for flexible temperature sensor was studied. The measured temperature data from the thin film will be transmitted using Arduino to mobile device over Bluetooth communication. The transferred data was then displayed and stored using a mobile application which was developed in-house specifically for temperature monitoring. The change in resistance decreases with increase in temperature. The curing temperature of PEDOT:PSS at 200 oC gave a significant resistance decrease within temperature range of 25 oC to 35 oC with sensitivity as high as 0.121 oC-1 . Furthermore, it was also found that the sensitivity of PEDOT:PSS + MoS2 composite was found to be around 0.24 oC-1 which was much higher compared to standard commercial platinum temperature sensor(39.2x10-4 oC-1) and PEDOT:PSS temperature sensor within certain range. This work is done with my student Samuel Jior.
Structure of temperature sensor
Block diagram for temperature sensor
Exhibited sensitivity change of PEDOT:PSS + MoS2 and PEDOT:PSS with respect to temperature. Dotted line referred to cooling curve while solid line refers to heating region.
Raman analysis of the prepared MoS2
6. Sensing glove for object recognition
6. Sensing glove for object recognition
The in-house all-sewed glove capable to identify objects held.
Usage of machine learning- python, Arduino, processing 3.
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