Dynamic color-changing nanomaterials have been widely
investigated for applications in fields like optical sensors, wearable
activity monitors, smart electronic devices, and anticounterfeiting
materials due to the excellent ability to change their optical properties
with external variation. Here, a simple metal–insulator–metal
(MIM) trilayer Fabry–Perot resonance cavity with a poly(N-isopropylacrylamide)
(PNIPAm) brush layer as a responsive element is reported as a thermal-induced
colorimetric response platform. The dynamic changes of conformation
and physical properties of PNIPAm brush layer in response to external
signals give rise to a significant color change of the MIM Fabry–Perot
resonance cavity. This MIM Fabry–Perot resonance cavity shows
the advantages of dynamic color change, rapid response, good repeatability,
and simple construction. Additionally, the as-prepared MIM cavity
shows great potential in various applications such as color printing,
multicolor indicator, and information anticounterfeiting.
The detection of thiocyanate (SCN − ) is particularly important in industrial effluents and biological fluids because of the toxic nature of SCN − . Herein, a metal−insulator−metal (MIM) resonator for visual detection of SCN − is presented based on a poly[(2-(methacryloyloxy)ethyl) trimethylammonium chloride] (PMETAC) brush. The MIM resonator exhibits obvious color change as the concentration of SCN − changes, which can be easily distinguished by the naked eyes. In addition, the as-prepared MIM resonator also shows the advantages of good anti-interference, excellent reusability, and fast response rate. Combining the above advantages, the proposed MIM resonator may provide a broad perspective for a wide variety of visible-light applications.
Abstract-For the nonlinear distortion problem of current power amplifiers (PAs) with memory effects, we use goal programming to present a memoryless predistorter matrix model based on limiting baseband predistortion technique, and the normalized mean squared error (NM SE) is limited in a satisfactory range while the output power is maximum. Then we propose a nonlinear power amplifier with memory effects based on back propagation neural network (BPNN) with three tapped delay nodes and six single hidden layer nodes, which is single input -dual output. Simulation results show that the method proposed in this paper makes the experimental precision higher. Further, the linearization effect of power amplifiers becomes better.
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