A wearable skin hydration sensor in the form of a capacitor is demonstrated based on skin impedance measurement. The capacitor consists of two interdigitated or parallel electrodes that are made of silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix. The flexible and stretchable nature of the AgNW/PDMS electrode allows conformal contact to the skin. The hydration sensor is insensitive to the external humidity change and is calibrated against a commercial skin hydration system on an artificial skin over a wide hydration range. The hydration sensor is packaged into a flexible wristband, together with a network analyzer chip, a button cell battery, and an ultralow power microprocessor with Bluetooth. In addition, a chest patch consisting of a strain sensor, three electrocardiography electrodes, and a skin hydration sensor is developed for multimodal sensing. The wearable wristband and chest patch may be used for low-cost, wireless, and continuous monitoring of skin hydration and other health parameters.

We present our efforts towards enabling a wearable sensor system that allows for the correlation of individual environmental exposures to physiologic and subsequent adverse health responses. This system will permit a better understanding of the impact of increased ozone levels and other pollutants on chronic asthma conditions. We discuss the inefficiency of existing commercial off-the-shelf components to achieve continuous monitoring and our system-level and nano-enabled efforts towards improving the wearability and power consumption. Our system consists of a wristband, a chest patch, and a handheld spirometer. We describe our preliminary efforts to achieve a sub-milliwatt system ultimately powered by the energy harvested from thermal radiation and motion of the body with the primary contributions being an ultra-low power ozone sensor, an volatile organic compounds sensor, spirometer, and the integration of these and other sensors in a multimodal sensing platform. The measured environmental parameters include ambient ozone concentration, temperature, and relative humidity. Our array of sensors also assesses heart rate via photoplethysmography and electrocardiography, respiratory rate via photoplethysmography, skin impedance, three-axis acceleration, wheezing via a microphone, and expiratory airflow. The sensors on the wristband, chest patch, and spirometer consume 0.83, 0.96, and 0.01 milliwatts respectively. The data from each sensor is continually streamed to a peripheral data aggregation device and is subsequently transferred to a dedicated server for cloud storage. Future work includes reducing the power consumption of the system-on-chip including radio to reduce the entirety of each described system in the sub-milliwatt range.

Mechanoluminescence (ML) is one of the most important routes to realize remote sensing of stress distribution, but has never been used in temperature sensing. Traditionally, stress sensing and temperature sensing are separately realized through different methods in multifunctional sensors, which definitely makes the structure more complicated. In this work, the remote stress-temperature dual-modal sensing is proposed by using the double-lanthanide-activated ML material SrZnSO:Tb,Eu, where the stress is read by the integral intensity of ML and the temperature is displayed by the green to red emission ratio (I Tb /I Eu ) of ML in one material. The dual sensing mode in SrZnSO:Tb,Eu enables building of a new imaging system, providing a facile, reliable, and more sensitive way to remotely visualize the distribution of stress and temperature. It opens up a novel approach to develop advanced artificial skins with simplified structures in human-machine interfaces, structural health monitoring, and biomedical engineering applications.

Proportionally converting the applied mechanical energy into photons by individual mechanoluminescent (ML) micrometer‐sized particles opens a new way to develop intelligent electronic skins as it promises high‐resolution stress distribution visualization and fast response. However, a big challenge for ML sensing technology is its low sensitivity in detecting stress. In this work, a novel stress distribution sensor with the detection sensitivity enhanced by two orders of magnitude is developed by combining a proposed near‐distance ML imaging scheme with an improved mechano‐to‐photon convertor. The enhanced sensitivity is the main contributor to the realization of a maximum photon harvesting rate of ≈80% in the near‐distance ML imaging scheme. The developed near‐distance ML sensor shows a high sensitivity with a detection limit down to ≈kPa level, high spatial resolution of 254 dpi, and fast response with an interval of 3.3 ms, which allows for high‐resolution and real‐time visualization of complex mechanical actions such as irregular solid contacts or fluid impacts, and thus enables use in intelligent electronic skin, structural health monitoring, and human–computer interaction.

Mechanoluminescent (ML) materials with the characteristics of photon emission under mechanical stimulation show broad application prospects in building structural health diagnosis, biomechanical engineering, and wearable devices. However, existing ML materials cannot fully meet the requirements of different stress sensing applications due to the limited understanding of the structure and mechano-to-photon conversion mechanism of ML materials. Herein, we report novel ML materials with excellent self-recoverable ML performance in the family of mixed-anion compounds RE 2 O 2 S:Ln 3+ (RE = Y, Lu, La, Gd). The ML intensity is linearly related to the applied force, and the ML wavelength is tunable over a broad range of 514−1550 nm. More importantly, we construct a polyhedron distortion model that describes the local symmetry breaking. This model well explains the origin of ML and piezoelectricity in the compounds with centrosymmetric crystal structures. The findings may deepen the understanding of the microstructure and the mechano-to-photon conversion mechanism in ML materials and are expected to provide important guidance for the development of high-performance ML materials.