journal article·2023-09-01Open access

Highly conductive laser-induced graphene through the deposition of liquid metal particles for flexible electronics

Halil Tetik, Emmy Markgraf, Kohya Kato, Valerie N Chan, Mohammad H Malakooti

Abstract

Abstract The rapid and simple fabrication process of laser-induced graphene (LIG) has enabled the creation of flexible sensors for emerging applications such as wearable electronics and intelligent systems. Although LIG can be designed to be a sensitive strain sensor due to the large change in its resistance under deformation, this property can also limit its use as a printed conductor on flexible substrates. Here, we present a versatile technique to enhance the electrical conductivity and resistive heating ability of LIG for use as flexible conductors in printed electronics. The highly conductive traces are prepared by direct writing of LIG onto a polyimide film using a CO2 laser, upon which the functionalized liquid metal (LM)—eutectic gallium indium (EGaIn)—particles are deposited and activated. This results in a ∼400 times increase in electrical conductivity of LIG traces while maintaining mechanical flexibility and manufacturing scalability without the need for soldering. Electromechanical characterization of the LIG-LM traces shows low resistance change (less than 0.3 Ω) under large bending deformations. At the same time, the enhanced electrical conductivity contributes to the resistive heating performance as it reduces the input voltage requirement by ∼15 times to achieve similar surface temperatures compared to pure LIG traces. By combining EGaIn LM with laser-synthesized graphene, we can fabricate flexible hybrid electronics. We demonstrate the practicality of this technique by fabrication of flexible conductors and heating devices with highly customizable patterns.
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Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Thermal Radiation and Cooling Technologies · Thermal properties of materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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