Influence of Environment on Ageing Behaviour of the Polyurethane Film

In this work, UV-Vis spectra, FT-IR spectra, colour difference, yellowness index, and SEM micrographs were used to study the accelerated ageing behaviour of polyurethane films that exposed to UV radiation, O3 atmosphere, and UV/O3 integrated environment. During 200 hours of exposure in three different environment, the UV absorbance, the colour difference, the yellowness, and the absorption of –NH/–OH and –C=O bands in FT-IR spectra of the films increase gradually with increasing exposure time, respectively, and the films exposed to the three environments have different colour difference, yellowness index, UV-Vis spectra, and FT-IR spectra. The films are vulnerable to degradation and yellowing in the following environment order: O3 < UV < UV/O3. After exposure to UV radiation or O3 atmosphere, some degradation products and blisters are formed on the film surface. After exposure to UV/O3 integrated environment, there are strip blisters and micro-cracks on the film surface, and exists an obvious synergism between UV radiation and O3 atmosphere in accelerating the ageing of the polyurethane films

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challenging, requiring non-trivial prior knowledge of network design. To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way. In DeepMAD, a CNN network is modeled as an information processing system whose expressiveness and effectiveness can be analytically formulated by their structural parameters. Then a constrained mathematical programming (MP) problem is proposed to optimize these structural parameters. The MP problem can be easily solved by off-the-shelf MP solvers on CPUs with a small memory footprint. In addition, DeepMAD is a pure mathematical framework: no GPU or training data is required during network design. The superiority of DeepMAD is validated on multiple large-scale computer vision benchmark datasets. Notably on ImageNet-1k, only using conventional convolutional layers, DeepMAD achieves 0.7% and 1.5% higher top-1 accuracy than ConvNeXt and Swin on Tiny level, and 0.8% and 0.9% higher on Small level.Comment: Accepted by CVPR 202

Preparation of 3D spherical Ni/Al LDHs with significantly enhanced electrochemical performance as a superior cathode material for Ni/MH batteries.

Nickel-based hydroxides with excellent electrochemical performance have been considered as cathode materials for Ni/MH batteries. In this paper, a Ni/Al layered double hydroxides (Ni/Al LDHs) material with three-dimensional (3D) spherical structure is synthesized by a facile stable dual complexation-precipitation method. SEM images show that the obtained Ni/Al LDHs possess 3D spherical structure composed of nanosheets. XRD and CV tests indicate that doping of Al increases the distance between Ni-Al layers, greatly improving the specific capacity of the obtained materials. The electrochemical tests show that the specific capacity of the obtained material with 18% Al is up to 383.4 mAh g-1 at a current density of 1 A g-1. In addition, when the current density is further increased to 10 and 20 A g-1, the specific capacity of this material still maintains 345.0 mAh g-1 and 307.9 mAh g-1, respectively, which implies that this cathode material can provide remarkable power densities. Moreover, the material composed of Ni/Al LDHs keeps 97.6% initial capacity after 5000 cycles at a current density of 10 A g-1, showing an excellent cycling stability and durability

A green and template-free synthesis process of superior carbon material with ellipsoidal structure as enhanced material for supercapacitors.

Metal Organic Frameworks or related carbon materials are the ideal materials for supercapacitors due to their high surface area and unique porous structure. Here, we propose a new green and recyclable synthesis method of porous carbon. Aluminum hydroxide (Al(OH)₃) and trimesic acid (BTC) are employed as raw materials to obtain aluminium trimesic (denoted as Al-BTC) via their covalent reaction. Then, the porous carbon is obtained through carbonization and dissolving process to remove the aluminum oxide (Al₂O₃). Al(OH)₃ is recovered by the Bayer method for the next batch. The SEM images show that the porous carbon has rugby-like morphology with the same of 400 nm wide and 1000 nm long which indicates the porous carbon with c/a ratio of 2.5 providing the largest specific volume surface area. The sample offers 306.4 F gˉ¹at 1 A gˉ¹, and it can retain 72.2% even at the current density of 50 A gˉ¹. In addition, the porous carbon provides excellent durability of 50,000 cycles at 50 A gˉ¹ with only 5.05% decline of capacitance. Moreover, the porous carbon has an ultrafast charge acceptance, and only 4.4 s is required for one single process, which is promising for application in electric vehicles