Highly sensitive and conformal sensors are essential for the implementation of human-machine interfaces, health monitoring, and rehabilitation prostheses. The proper adjustment of conductive pathways in the sensing materials is essential for their sensitive transduction of mechanical stimuli into electrical signals. However, the rational, precise, and wide-range control of electrical networks within traditional conductive composites is difficult due to the randomly distributed fillers. Herein, we adopt an indirect 3D-printing method to fabricate pressure sensors with various microchannels for liquid metal (LM) to form consistent and tunable conductive pathways. LM is highly conductive, fluidic, and incompressible at ambient conditions, which guarantees the reliable regulation and function of our pressure sensor. Additive manufacturing provides a facile way to construct complicated microchannels with different lengths, different orientations, cross-sectional sizes, depth-width ratios, and shapes, which can effectively modulate the sensitivity and the sensing range. Under the optimized structural configurations, our sensor achieves a high sensitivity of 1.139 kPa-1, a detection range of 0-68 kPa (loading process), and stability of over 5000 cycles, whose sensing performance is better than most microchannel-filled LM sensors. It can achieve high-accuracy monitoring of pulse, speaking and gestures, and exhibit a full recognition of objects under the assistance of machine learning. This work can provide new ideas on the design of conductive pathways in flexible electronics and expand the application of recyclable LM in human-machine interfaces.
Keywords: 3D structure design; liquid metal; object recognition; pressure sensor; soluble template.