3D-Printed Metamaterial-Based Soft Sensors: Materials, Design, and Fabrications

Area of Science:

• Materials Science
• Robotics
• Additive Manufacturing

Background:

• Additive manufacturing (AM) enables complex 3D soft bodies, crucial for advanced soft matter systems.
• Soft robotics demands compliant, distributed, and deformation-driven sensing solutions.
• 3D-printed metamaterial-based soft sensors show promise for high-performance mechano-sensing.

Purpose of the Study:

• To provide a comprehensive framework for 3D-printed metamaterial-based soft sensors.
• To review AM technologies, material systems, and sensing mechanisms.
• To offer design frameworks for perceptive soft machines.

Main Methods:

• Discussion of available 3D printing technologies, focusing on resolution, multi-material capabilities, and structural fidelity.
• Examination of various 3D printing materials (polymer-based, functional composites, smart responsive materials) and their structure-property relationships.
• Classification of metamaterial transduction mechanisms (resistive, capacitive, inductive) and emerging multimodal sensing.

Main Results:

• AM enables sophisticated soft bodies with tunable stiffness and controlled instability.
• Material-structure interactions are key to achieving desired sensing performance.
• Metamaterial-based sensors offer efficient electromechanical transduction.

Conclusions:

• Synthesizing AM, materials, and sensing architectures provides design frameworks for soft sensors.
• This review highlights the potential of 3D-printed metamaterial sensors for perceptive soft machines.
• Outlook provided for real-world applications of advanced soft sensing technologies.