| Abstrakt: |
Medical training simulations that utilize 3D-printed, patient-specific tissue models improve practitioner and patient understanding of individualized procedures and capacitate pre-operative, patient-specific rehearsals. The impact of these novel constructs in medical training and pre-procedure rehearsals has been limited, however, by the lack of effectively embedded sensors that detect the location, direction, and amplitude of strains applied by the practitioner on the simulated structures. The monolithic fabrication of strain sensors embedded into lifelike tissue models with customizable orientation and placement could address this limitation. The demonstration of 3D printing of an ionogel as a stretchable, piezoresistive strain sensor embedded in an elastomer is presented as a proof-of-concept of this integrated fabrication for the first time. The significant hysteresis and drift inherent to solid-phase piezoresistive composites and the dimensional instability of low-hysteresis piezoresistive liquids inspired the adoption of a 3D-printable piezoresistive ionogel composed of reduced graphene oxide and an ionic liquid. The shear-thinning rheology of the ionogel obviates the need to fabricate additional structures that define or contain the geometry of the sensing channel. Sensors are printed on and subsequently encapsulated in polydimethylsiloxane (PDMS), a thermoset elastomer commonly used for analog tissue models, to demonstrate seamless fabrication. Strain sensors demonstrate geometry- and strain-dependent gauge factors of 0.54-2.41, a high dynamic strain range of 350% that surpasses the failure strain of most dermal and viscus tissue, low hysteresis (<3.5% degree of hysteresis up to 300% strain) and baseline drift, a single-value response, and excellent fatigue stability (5000 stretching cycles). In addition, we fabricate sensors with stencil-printed silver/PDMS electrodes in place of wires to highlight the potential of seamless integration with printed electrodes. The compositional tunability of ionic liquid/graphene-based composites and the shear-thinning rheology of this class of conductive gels endows an expansive combination of customized sensor geometry and performance that can be tailored to patient-specific, high-fidelity, monolithically fabricated tissue models. |
