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To improve the aging resistance and prolong the service life of asphalt pavement, this study utilized modified multi-walled carbon nanotubes (MWCNTs) functionalized with carboxyl (MWCNTs-COOH), hydroxyl (MWCNTs-OH), and amino (MWCNTs-NH2) groups. These nanotubes were incorporated into SBS-modified asphalt at a concentration of 1%. The study sought to examine the impact of MWCNTs and their functional groups on asphalt’s rheological and anti-aging characteristics, alongside exploring their modification mechanisms. The findings indicated that rheological tests showed that MWCNTs/SBS composite-modified asphalt, particularly MWCNTs-OH/SBS, exhibited remarkable resistance to hightemperature deformation, a crucial characteristic for asphalt performance in hot climates. Nevertheless, it is important to note that the addition of MWCNTs led to an increase in asphalt’s creep strength and creep rate, which could potentially reduce its resistance to low-temperature cracking. However, the covalent functionalization of MWCNTs mitigated these adverse effects on low-temperature performance. Moreover, the inclusion of MWCNTs in asphalt served as a barrier, impeding the penetration of oxygen molecules and ultraviolet radiation into the asphalt matrix. This proved to be an effective means of inhibiting the aging process, a critical factor in extending the service life of asphalt pavement. Among the different formulations of MWCNTs modified asphalt, the MWCNTs-OH/SBS composite-modified asphalt exhibited notably effective anti-aging properties. Both rheological and anti-aging evaluations demonstrated that hydroxyl functional groups played a pivotal role in enhancing performance, chiefly by fostering interactions with asphalt molecules.

Abstract The current super‐resolution (SR) deep network is mainly applied to the common image and pays little attention to the image with noise. The remote sensing image contains much noise, so that the SR reconstruction effect is not satisfactory. Therefore, a convolution blind denoising adaptive dense connection SR (CBD‐ADCSR) network for the remote sensing image is proposed in this paper. The whole model is divided into a convolution blind denoising (CBD) network for denoising and an ADCSR network for reconstruction. Firstly, the components of the network are given in detail and are analysed. Secondly, a data set making method is designed combining motion blur, defocusing blur and Gaussian noise, which is used to generate low‐resolution image data sets with complex degradation for the model training. Finally, through the detailed comparative experiment, it is proved that the reconstruction effect of the CBD‐ADCSR model is better than that of the most state‐of‐the‐art algorithms in objective criteria. In addition, compared with the original ADCSR network, CBD‐ADCSR has a stronger ability for noise suppression.