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Surgical simulators are valuable educational tools for physicians, enhancing their proficiency and improving patient safety. However, they typically still suffer from a lack of realism as they do not emulate dynamic tissue biomechanics haptically and fail to convincingly mimic real‐time physiological reactions. This study presents a dynamic tactile synthetic tissue, integrating both sensory and actuatory capabilities within a fully soft unit, as a core component for soft robotics and future hybrid surgical simulators utilizing dynamic physical phantoms. The adaptive surface of the tissue replica, actuated via hydraulics, is assessed by an embedded carbon black silicone sensor layer using electrical impedance tomography to determine internally or externally induced deformations. The integrated fluid chambers enable pressure and force measurements. The combination of these principles enables real‐time tissue feedback as well as closed loop operation, allowing optimal interaction with the environment. Based on the concepts of soft robotics, such artificial tissues find broad applicability, demonstrated via a soft gripper and surgical simulation applications including a dynamic, artificial brain phantom as well as a synthetic, beating heart. These advancements pave the way toward enhanced realism in surgical simulators including reliable performance evaluation and bear the potential to transform the future of surgical training methodologies.
Surgical simulators are valuable educational tools for physicians, enhancing their proficiency and improving patient safety. However, they typically still suffer from a lack of realism as they do not emulate dynamic tissue biomechanics haptically and fail to convincingly mimic real‐time physiological reactions. This study presents a dynamic tactile synthetic tissue, integrating both sensory and actuatory capabilities within a fully soft unit, as a core component for soft robotics and future hybrid surgical simulators utilizing dynamic physical phantoms. The adaptive surface of the tissue replica, actuated via hydraulics, is assessed by an embedded carbon black silicone sensor layer using electrical impedance tomography to determine internally or externally induced deformations. The integrated fluid chambers enable pressure and force measurements. The combination of these principles enables real‐time tissue feedback as well as closed loop operation, allowing optimal interaction with the environment. Based on the concepts of soft robotics, such artificial tissues find broad applicability, demonstrated via a soft gripper and surgical simulation applications including a dynamic, artificial brain phantom as well as a synthetic, beating heart. These advancements pave the way toward enhanced realism in surgical simulators including reliable performance evaluation and bear the potential to transform the future of surgical training methodologies.
Stretchable electronic conductors are vital components in soft robotics and flexible electronics. One method for producing these is combining conductive filler with a nonconductive elastomer. These composites commonly exhibit significant piezoresistivity. This work examines various mechanisms that may underlie this effect. These composites are generally analyzed through percolation theory, which describes the nonlinear relationship between filler volume fraction and conductivity. However, it is unclear whether percolation theory can explain their piezoresistivity or whether mechanisms such as rearrangement of the conductive network under deformation must be considered. This work compares volumetric change in the context of percolation theory against network rearrangement to examine the relative significance of these factors in determining piezoresistivity. Digital image correlation is utilized to investigate volumetric changes in carbon‐black silicone composites and finds that the typical assumption of incompressibility is reasonable, suggesting that volumetric changes alone cannot account for the behavior. A computational model is also developed, which implies that network rearrangement is likely a more significant factor and that interparticle interactions are crucial in understanding this effect. It was found that the most realistic modeling results were achieved only when both rigid and attractive interparticle interactions were accounted for in the model.
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