RESUMO
The advancement of interface engineering has demonstrated remarkable efficacy in overcoming the primary impediment associated with sluggish reaction kinetics in supercapacitor electrodes. In this investigation, we employed a facile co-precipitation method to synthesize NiCoMoO4/MXene heterostructures utilizing Ti3C2Tx MXene nanosheets as carriers. This heterostructure inhibits the restacking of MXene nanosheets and simultaneously enhances the exposure of electrochemically active sites in NiCoMoO4 nanorods, thereby mitigating the reduction in specific capacitance resulting from volumetric fluctuations. The NiCoMoO4/MXene electrode, possessing pseudo-capacitance properties, demonstrates an impressive level of specific capacitance, exceptional performance across various charging rates, and consistent behavior throughout repeated cycles. By optimizing the mass ratio, this electrode achieves a specific capacity of 1900 F/g under a current density of 1 A/g. Even after enduring 10,000 cycles at a significantly higher current density of 5 A/g, it still maintains an impressive retention rate of 94.73 %. Our density functional theory (DFT) calculations indicate that the enhanced electrochemical performance can be attributed to the improved electronic coupling within the NiCoMoO4/MXene heterostructure. The integration of NiCoMoO4/MXene cathode and activated carbon (AC) anode with an alkaline gel electrolyte containing potassium ferricyanide in flexible quasi-solid-state supercapacitors (FSSCs) results in exceptional electrochemical performance and flexibility. These FSSCs demonstrate a maximum energy density of 72.89 Wh kg-1 at a power density of 850 W kg-1, while maintaining an impressive power output of 16,780 W kg-1 with an energy density of 37.28 Wh kg-1. Based on these outstanding properties, it is evident that the NiCoMoO4/MXene heterojunction possesses significant advantages as electrode material for supercapacitors, and the fabricated FSSCs devices pave a new pathway for flexible electronic devices.
RESUMO
In the field of fashion design, designing garment image according to texture is actually changing the shape of texture image, and image-to-image translation based on Generative Adversarial Network (GAN) can do this well. This can help fashion designers save a lot of time and energy. GAN-based image-to-image translation has made great progress in recent years. One of the image-to-image translation models--StarGAN, has realized the function of multi-domain image-to-image translation by using only a single generator and a single discriminator. This paper details the use of StarGAN to complete the task of garment design. Users only need to input an image and a label for the garment type to generate garment images with the texture of the input image. However, it was found that the quality of the generated images is not satisfactory. Therefore, this paper introduces some improvements on the structure of the StarGAN generator and the loss function of StarGAN, and a model was obtained that can be better applied to garment design. It is called GD-StarGAN. This paper will demonstrate that GD-StarGAN is much better than StarGAN when it comes to garment design, especially in texture, by using a set of seven categories of garment datasets.