Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications
Abstract
:1. Introduction
2. Design and Fabrication Process
2.1. Mathematical Modeling of the Soft Sensor
2.2. Liquid Metal Preparation
2.3. Device Working Mechanism
- is the value of resistance when the sensor is stretched,
- represents the electrical resistivity of the Galinstan,
- l is the length of the micro-channel,
- l is the channel length when the sensor is stretched,
- w is the width of the sensor, and
- h is the cross-section of the micro-channel.
3. Applications of Soft Sensors
4. Results and Discussion
4.1. Stretchability
4.2. Gauge Factors
4.3. Linearity
4.4. Durability
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Material Name | Electrical Conductivity (S·m) | Thermal Conductivity (W·m·K) | Viscosity (Pa·s) |
---|---|---|---|
Mercury [14] | 1.06 × 10 | 8.54 | 1.5 × 10 |
EGaIn [15] | 3.04 × 10 | 26.6 | 1.9 × 10 |
Galinstan [16] | 3.04 × 10 | 16.5 | 2 × 10 |
Properties | Galinstan |
---|---|
Melting point/°C | −19 |
Boiling point/°C | >1300 |
Vapor pressure/mmHg | <10 at 500 °C |
Specific Heat/kJ·kg °C | 0.200 |
Density/kgm | 6440 |
Thermal | 16.5 |
Surface tension/N·m | 0.718 at 25 °C |
Solubility in water | Insoluble |
Viscosity/Pa·s | 2.4 × 10 at 20 °C |
Materials | Methods | Stretching (%) | GF | Linearity | Applications |
---|---|---|---|---|---|
EGaIn-PDMS [39] | Printing technology | 350 | 1.6-3.2 | partial | Wearable devices |
AgNW-PDMS [40] | Micro-modeling | 70 | 2–14 | Partly linear | Motion detection |
CNT-CB-PDMS [41] | Micromolding method | 22.6 | 29 | Nonlinear | Wound monitoring |
Graphene-PDMS [42] | Coating techniques | 7.1 | 2.4–14 | linear | Wearable devices |
GWFs-PDMS [43] | Coating techniques | 30 | 106 | Nonlinear | In vitro diagnostic |
MWCNTs-PDMS [44] | Liquid phase | 45 | 1.2 | Nonlinear | Pressure measurement |
AgNP-PDMS [45] | Printing technology | 20 | 4.7–12.5 | Two linear | Motions |
CNT-PDMS [46] | Printing technology | 100 | 104 | Nonlinear | Bending angle |
GO-PDMS [47] | Filtration method | (5–2.5 cm) | NA | Nonlinear | Human interactive |
Graphene-PDMS [48] | Filtration method | 100 | 7.1 | Partly linear | Drug-induced changes |
Ti/Au-PDMS [49] | Micromolding method | NA | NA | Nonlinear | Electronic skin |
GnPs-PDMS [50] | Micromolding method | 10 | 27.7–164.5 | Nonlinear | Human–machine interface |
This work | Micromolding method | 175 | 8.6 | Partly linear | Robotics, health monitoring |
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Ghosh, P.K.; Sundaravadivel, P. Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications. Sensors 2023, 23, 4039. https://doi.org/10.3390/s23084039
Ghosh PK, Sundaravadivel P. Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications. Sensors. 2023; 23(8):4039. https://doi.org/10.3390/s23084039
Chicago/Turabian StyleGhosh, Prosenjit Kumar, and Prabha Sundaravadivel. 2023. "Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications" Sensors 23, no. 8: 4039. https://doi.org/10.3390/s23084039
APA StyleGhosh, P. K., & Sundaravadivel, P. (2023). Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications. Sensors, 23(8), 4039. https://doi.org/10.3390/s23084039