Developing fabric-based electronics with good wearability is undoubtedly an urgent demand for wearable technologies. Although the state-of-the-art fabric-based wearable devices have shown unique advantages in the field of e-textiles, further efforts should be made before achieving "electronic clothing" due to the hard challenge of optimally unifying both promising electrical performance and comfortability in single device. Here, we report an all-fiber triboferroelectric synergistic e-textile with outstanding thermal-moisture comfortability. Owing to a tribo-ferroelectric synergistic effect introduced by ferroelectric polymer nanofibers, the maximum peak power density of the e-textile reaches 5.2 W m −2 under low frequency motion, which is 7 times that of the state-of-the-art breathable triboelectric textiles. Electronic nanofiber materials form hierarchical networks in the e-textile hence lead to moisture wicking, which contributes to outstanding thermal-moisture comfortability of the e-textile. The all-fiber electronics is reliable in complicated real-life situation. Therefore, it is an idea prototypical example for electronic clothing.

No abstract

Extreme heat events are mainly responsible for weather-related human mortality due to climate change. However, there is a lack of outdoor thermal management for protecting people from extreme heat events. We present a novel infrared-radiation-enhanced nanofiber membrane (NFM) that has good infrared resonance absorption and selectively radiates thermal radiation of the human body through the atmosphere and into the cold outer space. The NFM comprises polyamide 6 (PA6) nanofibers and randomly distributed SiO2 submicron spheres and has sufficient air permeability and thermal–moisture comfortability because of its interconnect nanopores and micropores. We measure the sky radiative cooling performance under a clear sky, and PA6/SiO2 NFM produces temperatures that are about 0.4–1.7 °C lower than those of commercial textiles when covering dry and wet hands and temperatures 1.0–2.5 °C lower than the ambient temperature when thermal conduction and convection are isolated in a closed device. Our processed PA6/SiO2 NFM combines sky radiative cooling with thermal management of the human body very well, which will promote the development of radiative cooling textiles.

Developing functional textiles with a cooling effect is important for personal comfort in human life and activities. Although existing passive cooling fabrics exhibit promising cooling effects, they do not meet the thermal comfort requirements under many practical conditions. Here, we report a nanofiber membrane-based moisture-wicking passive cooling hierarchical metafabric that couples selective optical cooling and wick-evaporation cooling to achieve efficient temperature and moisture management. The hierarchical metafabric showed high sunlight reflectivity (99.16% in the 0.3–0.76 μm wavelength range and 88.60% in the 0.76–2.5 μm wavelength range), selective infrared emissivity (78.13% in the 8–13 μm wavelength range), and good moisture permeability owing to the optical properties of the material and hierarchical morphology design. Cooling performance experiments revealed that covering simulated skin with the hierarchical metafabric prevented overheating by 16.6 °C compared with traditional textiles, including a contribution from management of the humidity (∼8.2 °C). In addition to the personal thermal management ability, the hierarchical metafabric also showed good wearability.

No abstract

Levels of serum BAFF were elevated in patients with IgAN and were associated with clinical and pathological features of the disease. Serum BAFF levels could be a noninvasive biomarker for monitoring disease severity of IgAN.

Sweating during exercise, physical labor, or hot weather leads to a feeling of discomfort. The stuffiness, stickiness, and heaviness brought by sweat may promote negative emotions or disease. Clothing, textiles, and wearable devices exacerbate these problems by restricting evaporation of sweat. Here, a textile that can promote and enhance sweat evaporation by coupling wicking and polarization is reported. The wicking is produced by the wettability gradient and pore size, which make the surface moisture content of the textile in contact with the skin strictly 0%. The polarization is driven by a ferroelectric‐enhanced triboelectric textile. This textile degrades large‐sized water clusters into small‐sized water clusters or water monomers, so that the textiles have an excellent moisture evaporation rate (4.4 and 3.6 times faster than the cotton and polyester textiles, respectively). This work provides a new source of inspiration for quick‐drying textiles and also finds an attractive application for triboelectric technology.

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. The purpose of feature selection is to not only identify a feature subset from an original set of features [without reducing the predictive accuracy of classification algorithm] but also reduce the computation overhead in data mining. In this paper, we present our improved shuffled frog leaping algorithm which introduces a chaos memory weight factor, an absolute balance group strategy and an adaptive transfer factor. Our proposed approach explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant features in high-dimensional biomedical data. To evaluate the effectiveness of our proposed method we have employed the K-nearest neighbor method with a comparative analysis in which we compare our proposed approach with genetic algorithms, particle swarm optimization, and the shuffled frog leaping algorithm. Experimental results show that our improved algorithm achieves improvements in the identification of relevant subsets and in classification accuracy.