For
the mimicry of human skin, one of the challenges is how to
detect and recognize different stimulus by electronic device, while
still has the ability of skin self-recovery at the same time. Because
of the excellent elasticity and flexibility, strong self-healing ability,
in this paper, we reported a bifunctional self-healing e-skin with
polyurethane (PU) and polyurethane@multiwalled carbon nanotubes (PU@CNT)
as the sensing materials by integrating a resistance temperature sensor
on top of a capacitive pressure sensor on the same flexible cellulose
nanocrystals@carboxylated nitrile rubber@polyethylenimine (CNC@XNBR)
substrate. Studies found that each type of sensor exhibited fast and
superior response to only the target stimuli. Meanwhile, due to the
self-recovery properties of PU and CNC@XNBR, as-fabricated e-skin
has the self-healing ability after damage and remains excellent sensitivity
to temperature and pressure after healing. A 5 × 5 device array
was also fabricated, which can simultaneously image the pressure and
temperature distribution.
The sensitivities (S) of Y2O3 (YAG/LaAlO3):Yb3+/Er3+ phosphors increased with increasing average bond covalency and the calculated values were basically in good agreement with our experimental results.
Strain and temperature are important physiological parameters for health monitoring, providing access to the respiration state, movement of joints, and inflammation processes. The challenge for smart wearables is to unambiguously discriminate strain and temperature using a single sensor element assuring a high degree of sensor integration. Here, a dual-mode sensor with two electrodes and tubular mechanically heterogeneous structure enabling simultaneous sensing of strain and temperature without cross-talk is reported. The sensor structure consists of a thermocouple coiled around an elastic strain-to-magnetic induction conversion unit, revealing a giant magnetoelastic effect, and accommodating a magnetic amorphous wire. The thermocouple provides access to temperature and its coil structure allows to measure impedance changes caused by the applied strain. The dual-mode sensor also exhibits interference-free temperature sensing performance with high coefficient of 54.49 µV °C−1 , low strain and temperature detection limits of 0.05% and 0.1 °C, respectively. The use of these sensors in smart textiles to monitor continuously breathing, body movement, body temperature, and ambient temperature is demonstrated. The developed multifunctional wearable sensor is needed for applications in early disease prevention, health monitoring, and interactive electronics as well as for smart prosthetics and intelligent soft robotics.
The accurate measurement of pressure sensors realizes the idea of non-interference environmental monitoring, which is very important for the application of electronic skins (e-skins).
This paper focuses on a comprehensive comparison of the European Centre for Medium-Range Weather Forecasts (ECMWF) significant wave height (SWH) forecasts with buoy data in the China Sea, and analysis of accuracy characteristics varying with related variables (SWH, water depth, and distance from shore) and different scenarios (each month, different sea area, and typhoon- and cold air activity–induced waves). This is the first time that observations from the Chinese Ocean Monitoring Network have been used to verify ECMWF wave forecasts in the China Sea. Two years’ worth of data from 24 hydrometeorological buoys are used. The comparison shows good accuracy and predictive stability of SWH forecasts for the Chinese buoys, which is consistent with Korean and global buoys. However, the accuracy in the Bohai Sea and Taiwan Strait is worse than that in the South and East China Sea. SWH forecasts for QF206 in the southern Taiwan Strait have systematically underestimated observations, which may be mainly due to the coarse resolution of wave forecasts. Besides, forecasts underestimate observations when 1.5 m < SWH ≤ 6.5 m. The accuracy and predictive stability in spring and summer are worse than those in winter, especially in April, which is the worst in 12 months. The accuracy increases with water depth and distance from shore. During typhoon conditions, the accuracy is worse than during cold air conditions.
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