Session: 04-07-01: Material Processing of Flexible/Emerging Electronics, Sensors, and Devices

Paper Number: 173402

Integration of 3d Printing With Acoustic-Assisted Assembly of Nanomaterials for Tunable Strain Sensors 

The demand for flexible, cost-effective, and high-performance strain sensors continues to grow in healthcare monitoring, soft robotics, and wearable electronics. Traditional strain sensors typically require large amounts of conductive nanomaterials embedded within polymer matrices, leading to high production costs and limited tunability in sensitivity. To overcome these limitations, we propose a novel approach that integrates 3D printing with acoustic-assisted nanomaterial assembly to fabricate strain sensors with tunable mechanical and electrical properties. This method not only reduces nanomaterial usage but also expands the design space for sensor sensitivity and geometry.

Our study introduces a hybrid manufacturing framework that combines Direct Ink Writing (DIW)-based 3D printing for fabricating the sensor substrate with acoustic-assisted dip-coating for assembling a conductive graphene network on its surface. The substrate is made from polydimethylsiloxane (PDMS) filled with silica nanoparticles to adjust rheological and mechanical properties, while the graphene layer acts as the primary sensing element. By strategically designing alternating wide and narrow strips along the substrate length, we are able to induce localized strain amplification, thereby enhancing the sensor's overall responsiveness.

To optimize the substrate printability and mechanical strength, we varied the silica nanoparticle loading and evaluated the inks using Dual-Layer Printability Analysis (DLPA). The optimal composition was identified at a 20:100 weight ratio of SiO₂ to PDMS, yielding improved shape fidelity and an increased Young’s modulus (from 0.62 MPa to 0.80 MPa). The dip-coating process was then used to deposit graphene flakes onto the printed substrates, where we systematically tuned the coating time and evaluated the resulting film thickness and conductivity. A 10-minute assembly time produced a continuous graphene network with a resistance of approximately 0.25 Ω·m and a layer thickness of ~94 nm, as confirmed by SEM and AFM measurements.

To evaluate sensing performance, we fabricated strain sensors with variable width ratios (r = W/N) between wide and narrow segments. By maintaining the wide strip width at 4.8 mm and reducing the narrow strip width down to 0.3 mm, the gauge factor (GF) was effectively tuned from 8.53 (r = 1:1) to 33.15 (r = 16:1). Furthermore, the incorporation of softer PDMS in the narrow strips (heterogeneous design) resulted in additional sensitivity enhancement compared to homogeneous structures. Sensors with a fixed width ratio (r = 8:1) showed a gauge factor increase from 24.90 to 28.26 when soft PDMS was used in the narrow segments.

Our results highlight that sensitivity can be precisely tailored through geometrical design and mechanical contrast, while significantly reducing the amount of conductive material required. The dual benefits of tunability and material efficiency position this integrated approach as a promising platform for scalable, customizable sensor fabrication.

In conclusion, this work demonstrates a new paradigm in hybrid sensor manufacturing by combining the geometric versatility of 3D printing with the precision and efficiency of acoustic-assisted nanomaterial assembly. This platform enables low-cost, tunable strain sensors and provides a foundation for broader applications in flexible electronics, wearable devices, and biomedical monitoring. The methodology can be generalized to other hybrid systems, enabling new routes for multifunctional soft device development.

Presenting Author: Bo Li Villanova University

Presenting Author Biography: Associate Professor, Director of HNAM Lab
Department of Mechanical Engineering
Villanova University

Authors:

Yun Li Villanova University
Deana Yuan Villanova University
Mingyuan Sun Villanova University
Kathryn Feddish Villanova University
Liang Zhao Villanova University
Bo Li Villanova University