The
conversion of a biorenewable plant oil (anethole) to a new
fluoropolymer with both low dielectric constant and low water uptake
is reported here. First, cationic polymerization of plant oil by using
CF3SO3H as an initiator gave a polymer, which
was then functionalized by introducing the thermocrosslinkable -OCFCF2 groups via a three-step procedure. The obtained fluoropolymer
can be easily thermally converted to an infusible and insoluble cross-linked
network exhibiting low water uptake (<0.24%, in water of 96 °C
for 4 days) and low dielectric constant (<2.64 at a range of frequencies
varying from 1.0 to at 30 MHz at room temperature). TGA and DMA data
showed that the cross-linked network had 5 wt % loss temperature of
400 °C (in N2) and a T
g of 160 °C, respectively. Nanoindentation tests indicated that
the cross-linked film had an average hardness of 0.239 GPa and a Young’s
modulus of 6.11 GPa. These results mean that the new polymer derived
from biorenewable anethole is comparable to the petroleum-based materials,
implying that the low k polymers widely utilized
in microelectronic industry will have a new sustainable feedstock
supply.
Li CO -passivated Li N with high stability is prepared by aging Li N powder in dry air, and is then used as an electrode additive for a Li(Li Ni Co Mn )O (LLMO) cathode material. The material shows a large irreversible capacity of 800 mA h g during the first charge, with the formation of a Li N intermediate product. Acting as a Li sacrificial salt for a LLMO(+)/graphite(-) Li-ion battery, 2 wt % Li N results in a 10 % increase in discharge capacity. The Li N intermediate product reacts with the electrolyte, forming a uniform and regular surface film on the cathode. Moreover, chemical bonding between LLMO and N improves the electrode stability, resulting in excellent electrochemical performance.
Depth sensors, including Kinect and Xtion, open up a new possibility for future human-computer interaction (HCI).Even though there already are some mature methods of detecting human skeleton and poses using depth sensors, it is still an unsolved problem to detect hands and recognize delicate gestures effectively, because hands are too small a part in the images generated from depth sensor, so the details of hands are hard to extract. In this paper, we present a gesture detecting method that is able to: firstly segment hands through skin color segmentation and K-means clustering; secondly find the convex hull and the contour that form the hand shape; thirdly detect positions of each fingertip; and finally represent gestures using the sets of detected hand data. Having been tested with a series of applications, our method is proved to be robust and effective.
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