RESUMEN
Developing an electronic skin (e-skin) is becoming popular due to its capability to mimic human skin's ability to detect various stimuli. Mostly, such skins are tactile-based sensors. However, the exploration of nontactile-based sensing capability in the e-skin is still in a nascent stage. Herein, we report an approach toward developing electrical hysteresis- and cross-interference-free nontactile e-skin using liquid polyisoprene with an ultralow concentration of multiwalled carbon nanotubes (Ï = 0.006 volume fraction) by leveraging the stencil printing technique. The impact of cross-linking the samples was studied. Uncross-linked samples demonstrated higher electrical conductivity than the cross-linked samples. A coarse-grained phenomenological model with molecular dynamics simulation was utilized to investigate filler network formation and percolation that dictate the conductivity of uncross-linked and cross-linked samples. Simulation studies supported the fidelity of the experimental findings. The uncross-linked e-skin demonstrated a higher temperature sensitivity (-1.103%/°C) than the cross-linked e-skin (-0.320%/°C) in the thermal conduction mode. Despite the superior sensitivity of the uncross-linked e-skin, the cross-linked systems demonstrated superior cyclic stability (35 thermal cycles), ensuring reliable sensor readings over extended usage. Judicious choice of encapsulant warranted the cross-linked e-skin sensor to nullify the impact of moisture on signal output, thereby providing cross-interference-free results. The optimized e-skin sample retained a similar thermal sensitivity even when used in the nontactile mode. From the application purview, the utility of the developed sensor was tested successfully for nontactile sensing of human body temperature. Additionally, the sensor was utilized to determine the respiratory profile by integrating the developed sensor into a wearable mask. This study advances nontactile e-skin-based sensing technology and opens new avenues for creating wearable and IoT devices for healthcare and human-machine interactions.