Electronic skins (e-skins) with high sensitivity to multidirectional mechanical stimuli are crucial for healthcare monitoring devices, robotics, and wearable sensors. In this study, we present piezoresistive e-skins with tunable force sensitivity and selectivity to multidirectional forces through the engineered microstructure geometries (i.e., dome, pyramid, and pillar). Depending on the microstructure geometry, distinct variations in contact area and localized stress distribution are observed under different mechanical forces (i.e., normal, shear, stretching, and bending), which critically affect the force sensitivity, selectivity, response/relaxation time, and mechanical stability of e-skins. Microdome structures present the best force sensitivities for normal, tensile, and bending stresses. In particular, microdome structures exhibit extremely high pressure sensitivities over broad pressure ranges (47,062 kPa⁻¹ in the range of <1 kPa, 90,657 kPa⁻¹ in the range of 1–10 kPa, and 30,214 kPa⁻¹ in the range of 10–26 kPa). On the other hand, for shear stress, micropillar structures exhibit the highest sensitivity. As proof-of-concept applications in healthcare monitoring devices, we show that our e-skins can precisely monitor acoustic waves, breathing, and human artery/carotid pulse pressures. Unveiling the relationship between the microstructure geometry of e-skins and their sensing capability would provide a platform for future development of high-performance microstructured e-skins.

Herein, we report the fabrication of a highly stretchable, transparent gas sensor based on silver nanowire–graphene hybrid nanostructures. Due to its superb mechanical and optical characteristics, the fabricated sensor demonstrates outstanding and stable performances even under extreme mechanical deformation (stable until 20% of strain). The integration of a Bluetooth system or an inductive antenna enables the wireless operation of the sensor. In addition, the mechanical robustness of the materials allows the device to be transferred onto various nonplanar substrates, including a watch, a bicycle light, and the leaves of live plants, thereby achieving next-generation sensing electronics for the ‘Internet of Things’ area.

Accurate transmission of biosignals without interference of surrounding noises is a key factor for the realization of human-machine interfaces (HMIs). We propose frequency-selective acoustic and haptic sensors for dual-mode HMIs based on triboelectric sensors with hierarchical macrodome/micropore/nanoparticle structure of ferroelectric composites. Our sensor shows a high sensitivity and linearity under a wide range of dynamic pressures and resonance frequency, which enables high acoustic frequency selectivity in a wide frequency range (145 to 9000 Hz), thus rendering noise-independent voice recognition possible. Our frequency-selective multichannel acoustic sensor array combined with an artificial neural network demonstrates over 95% accurate voice recognition for different frequency noises ranging from 100 to 8000 Hz. We demonstrate that our dual-mode sensor with linear response and frequency selectivity over a wide range of dynamic pressures facilitates the differentiation of surface texture and control of an avatar robot using both acoustic and mechanical inputs without interference from surrounding noise.

Skin‐interfaced electronics have emerged as a promising frontier in personalized healthcare. However, existing skin‐interfaced patches often struggle to simultaneously achieve robust skin adhesion, adaptability to dynamic body motions, seamless integration of bulky devices, and on‐demand, damage‐free detachment. Here, a hybrid strategy that synergistically combines these critical features within a thin, flexible patch platform is introduced. This design leverages shape memory polymers (SMPs) arranged in a tessellated array, comprising both rigid and compliant SMPs. This configuration enables exceptional deformability, motion adaptability, and ultra‐strong, repeatable skin adhesion while offering on‐demand adhesion control. Furthermore, the design facilitates the seamless integration of bulky electronics without compromising skin adhesion. By incorporating sizeable electronics including signal acquisition circuits, sensors, and a battery, it is demonstrated that the proposed tessellated patch can be securely mounted on the skin, accommodate dynamic body motions, precisely detect physiological signals with an outstanding signal‐to‐noise ratio (SNR), wirelessly transmit data, and be effortlessly released from the skin.

This paper presents an energy-efficient intelligent multi-sensor system for hazardous gases, whose performance can be adaptively optimized through a multi-mode structure and a learning-based pattern recognition algorithm. The multi-mode operation provides control capability on trade-off relationship of accuracy and power consumption. In-house micro-electro-mechanical (MEMS) devices with a suspended nanowire structure are manufactured to provide desired characteristics of small size, low power, and high sensitivity. The pattern recognition to combine the dimensionality reduction and the neural network is adopted to improve the selectivity of MEMS gas sensors. Moreover, potential deviations in sensing characteristics are calibrated through a proposed self-calibration zooming structure. Reconfigurable circuits for these key features are integrated into an adaptive readout integrated circuit (ROIC) which is fabricated in a 180-nm complementary metal-oxide semiconductor (CMOS) process. For its system-level verification, a wireless multi-channel gas-sensor system prototype is implemented and experimentally verified to achieve 2.6 times efficiency improvement.