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 (ϕ = 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.