3D-Printed Gradient Shape-Memory Strain Sensors with Rewritable Gauge Factors

The gauge factor (GF) of a flexible strain sensor is typically fixed during fabrication, constraining its dynamic range to a single operating window. Here, we report a material–structure co-programming strategy that transforms GF from a static fabrication parameter into a dynamically reconfigurable sensing state. Using a polycaprolactone shape-memory polymer/carbon black composite, thermomechanical programming allows a single unmodified device to access a continuum of gain states, tuning GF from 4.69 to 238.4 through preset strain. Thermal erasing resets the baseline state, enabling repeated rewriting of the sensing gain without altering material composition or electrical layout. To expand this design space, direct ink writing adds an independent architectural axis: printed hourglass and tapered tetra-pyramid geometries concentrate strain into localized sensing zones, amplifying the programmed response and pushing peak GF beyond 1000. Recognizing that high GF is not universally optimal, we show that high-gain states resolve faint physiological signals, whereas low-gain states provide stable output under large-amplitude motion. Finally, we demonstrate a rewritable dual-channel interface that reassigns gain states across channels to re-task a single sensor on demand.