This 3D-Printed Meta-Material Squishes Under Pressure — Delivering High-Performance Sensors

Researchers from the Seoul National University of Science and Technology have developed a 3D-printed smart material that can be used to build high-performance pressure sensor for wearable devices.

"The proposed sensor platform can be integrated into smart insoles for gait monitoring and pronation analysis, robotic hands for precise object manipulation, and wearable health monitoring systems that require comfortable sensing without disrupting daily life," says associate professor Soonjae Pyo of the potential for the team's creation. "Importantly, the auxetic structure preserves its sensitivity and stability even when confined within rigid housings, such as insole layers, where conventional porous lattices typically lose performance. Its scalability and compatibility with various transduction modes also make it suitable for pressure mapping surfaces, rehabilitation devices, and human-robot interaction interfaces that require high sensitivity and mechanical robustness."

The pressure sensor developed by the researchers is based on the concept of auxetic mechanical metamaterials (AMMs), which posses a negative Poisson's ratio — the amount by which a material deforms in one direction when compressed or elongated in the other, named for mathematician and physicist Siméon Poisson. With a negative Poisson's ratio, the materials compress inwards at the sides when compressed vertically — the opposite of what you would expect — but efforts to use this property for sensors have hit difficulties in fabricating functional devices.

That's where the team's work comes in: a 3D-printed AMM-based tactile sensing platform that takes the form of a cubic lattice containing spherical holes. Depending on how the material is functionalized, the resulting sensor can be capacitive or piezoresistive — and to prove its real-world potential the team built two proof-of-concept prototypes, a 4×4 tactile array for spatial pressure mapping feeding into an object classification model and a wearable insole with gait monitoring and pronation type detection.

"The unique negative Poisson's ratio behavior utilized by our technology induces inward contraction under compression, concentrating strain in the sensing region and enhancing sensitivity. Beyond this fundamental mechanism, our auxetic design further strengthens sensor performance in three critical aspects: sensitivity enhancement through localized strain concentration, exceptional performance stability when embedded within confined structures, and crosstalk minimization between adjacent sensing units," says project lead and first author Mingyu Kang.

"Unlike conventional porous structures, this design minimizes lateral expansion, improving wearability and reducing interference when integrated into devices such as smart insoles or robotic grippers. Furthermore, the use of digital light processing-based 3D printing enables precise structural programming of sensor performance, allowing geometry-based customization without changing the base material."

The team's work has been published in the journal Advanced Functional Materials under open-access terms.