Two-dimensional (2D) gold nanoparticle (Au NP) monolayer film possesses a lot of fascinating peculiarities, and has shown promising applications in photoelectrical devices, catalysis, spectroscopy, sensors, and anticounterfeiting. Because of the localized surface plasmon resonance (LSPR) property predetermined by the natural structure of metal nanoparticles, it is usually difficult to realize the reversible LSPR transition of 2D film. In this work, we report on the fabrication of a large-area free-standing Au NP monolayer film with dual-responsive switchable plasmonic property using a pH- or thermal-responsive dendronized copolymer as a stimuli-sensitive linker. In this system, an oligoethylene-glycol-based (OEG-based) dendronized copolymer (named PG1A) with pH or temperature sensitivity was first modified onto the surface of a Au NP. Then, polyethylene glycol dibenzyl aldehyde (PEG-DA) was introduced to interact with the amino moieties from PG1A before the process of oil–water interfacial self-assembly of NPs, resulting in an elastic, robust, pH- or temperature-sensitive interpenetrating network among Au NPs in monolayer films. In addition, the film could exhibit reversibly plasmonic shifts of about 77 nm and inherent color changes through varying temperature or pH. The obtained free-standing monolayer film also shows an excellent transferable property, which can be easily transferred onto substrates such as plastic molds, PDMS, copper grids, and silicon wafers. In virtue of these peculiarities of the free-standing property, special plasmonic signal, and homologous macroscopic color, the transferred film was primely applied to an anticounterfeiting security label with clear color change at the designed spots, providing a new avenue to plasmonic nanodevices with various applications.
In this paper, a slot microring with an asymmetric grating structure is proposed. Through the coupling between the grating and the slot microring, a high free spectral range or EIT-like effects with a high quality factor can be achieved in the same device. The grating is designed as an asymmetric structure to realize the modulation of the optical signal and the control of the resonance peak by changing the grid number, and the effect of different grating periods on the output spectrum is explored. The results show that changing the grating on slot sidewalls can increase or decrease the number of resonant peaks. By selecting a specific period of the gratings on both sides of the slot, the distance between adjacent resonance peaks can be increased to achieve modulation of the free spectral range. In this paper, depending on the grating period, we obtain a quality factor of 5016 and an FSR of 137 nm, or a quality factor of 10,730 and an FSR of 92 nm. The refractive index sensing simulation is carried out for one of the periods, which can achieve a sensitivity of 370 nm/RIU. Therefore, the proposed new structure has certain advantages in different sensing applications.
With the increase of spatial resolution of remote sensing images, features of feature imaging become more and more complex, and the change detection methods based on techniques such as texture representation and local semantics are difficult to meet the demand. Most change detection methods usually focus on extracting semantic features and ignore the importance of high-resolution shallow information and fine-grained features, which often lead to uncertainty in edge detection and small target detection. For single-input networks when two temporal images are connected, the shallow layer of the network cannot provide the information of the individual original image to the deep layer features to help reconstruct the image, and therefore, the change detection results may be missing in detail and feature compactness. For this purpose, a twins context aggregation network (TCANet) is proposed to perform change detection on remote sensing images. In order to reduce the loss of spatial accuracy of remote sensing images and maintain high-resolution representation, we introduce HRNet as our backbone network to initially extract the features of interest. Our proposed context aggregation module (CAM) can amplify the convolutional neural network receptive field to obtain more detailed contextual information without significantly increasing the computational effort. The side output embedding module (SOEM) is proposed to improve the accuracy of small volume target change detection as well as to shorten the training process and speed up the detection while ensuring the performance. The method has experimented on the publicly available CDD dataset, the SYSU-CD dataset, and a challenging DSIFN dataset. With significant improvements in precision, recall, F1 score, and overall accuracy, the method outperforms the five methods mentioned in the literature.
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