Flexible and Robust Triboelectric Nanogenerators with Chemically Prepared Metal Electrodes and a Plastic Contact Interface Based on Low-Cost Pressure-Sensitive Adhesive.

Triboelectric nanogenerators (TENGs) are devices that can harvest energy from mechanical motions; such devices can be used to power wearable sensors and various low-power electronics. To increase the lifetime of the device, scientists mainly use the method of making TENG in a hard skeleton to simplify the complex possible relative movements between two triboelectric parts. However, the hard skeletons cannot be embedded in soft and lightweight clothing. To make matters worse, the materials used in the garments must be able to withstand high mechanical forces when worn, such as the pressure of more than 100 KPa exerted by body pressure or everyday knocks. Notably, the TENGs are usually made of fragile materials, such as vacuum-evaporated metal electrodes and nano-sized coatings, on the contact interface; these electrodes and coatings often chip or wear off under the action of external loads. In this work, we succeeded in creating a thin, light-weight, but extremely robust garment-integrated triboelectric nanogenerator (G-TENG) that can be embedded in clothing and pass the water wash test. First, we chemically deposited a durable electrode with flexible properties for G-TENG using a novel technique called polymer-assisted metal deposition (PAMD). The as-formed metal electrodes are firmly bonded to the plastic substrate by a sub-10 nm adhesive polymer brush and can withstand a pressure of 22.5 MPa and a tear force of 0.7 MPa. We then removed the traditionally used fragile nanoparticle materials and the non-durable poly-dimethylsiloxane (PDMS) layer at the triboelectric interface, and then used a cost-effective, durable and slightly flowable pressure-sensitive adhesive to form a plastic contact interface. Such a soft plastic interface can ensure full contact of the triboelectric materials, which is excellent in complex environments and ultimately improves the power generation efficiency of the devices. The as-formed low-cost energy harvesting device could become an industry standard for future smart clothing.

Detecting association of rare and common variants based on cross-validation prediction error.

Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE). We first propose a PE method based on Ridge regression. Based on PE, we also propose another two tests PE-WS and PE-TOW by testing a weighted combination of variants with two different weighting schemes. PE-WS is the PE version of the test based on the weighted sum statistic (WS) and PE-TOW is the PE version of the test based on the optimally weighted combination of variants (TOW). Using extensive simulation studies, we are able to show that (1) PE-TOW and PE-WS are consistently more powerful than TOW and WS, respectively, and (2) PE is the most powerful test when causal variants contain both common and rare variants.