Enhancing Carbon Nanotube Yarns via Infiltration Filling with Polyacrylonitrile in Supercritical Carbon Dioxide.

Carbon nanotube (CNT) fibers are renowned for their exceptional axial tensile strength and modulus. However, in yarn form, they frequently encounter transverse loading in practical applications, which exposes their suboptimal mechanical attributes rooted in inadequate inter-tube interactions and yarn surface defects. Efforts to mitigate micro-slippage among CNTs have encompassed gap-filling methodologies with varied materials, yet the outcomes have fallen short of expectations. This work aimed to enhance the mechanical properties of CNT yarns via infiltration with polyacrylonitrile (PAN) under supercritical carbon dioxide (sc-CO2) conditions. PAN was strategically chosen for its capability to undergo pre-oxidation and subsequent carbonization, leading to robust graphitic reinforcement. Leveraging sc-CO2's swelling and high permeability properties, the infiltration process effectively plugged interstitial spaces, elevating the yarn's tensile strength to 277.50 MPa and Young's modulus to 5094.05 MPa. Additional enhancements were realized after pre-oxidation, conferring a dense, reinforced shell structure that augmented tensile strength by 96.93% and Young's modulus by 298.80%. Scanning electron microscopy (SEM) analyses revealed a homogeneous PAN distribution within the yarn matrix, corroborated by X-ray photoelectron spectroscopy (XPS) evidence of C-N bonding, indicative of a successfully interlaced network. Consequently, this investigation introduces a novel strategy to tackle micro-slippage in CNT yarns, thereby achieving substantial improvements in their mechanical resilience.

Factors Influencing O3 Concentration in Traffic and Urban Environments: A Case Study of Guangzhou City.

Ozone (O3) pollution is a serious issue in China, posing a significant threat to people's health. Traffic emissions are the main pollutant source in urban areas. NOX and volatile organic compounds (VOCs) from traffic emissions are the main precursors of O3. Thus, it is crucial to investigate the relationship between traffic conditions and O3 pollution. This study focused on the potential relationship between O3 concentration and traffic conditions at a roadside and urban background in Guangzhou, one of the largest cities in China. The results demonstrated that no significant difference in the O3 concentration was observed between roadside and urban background environments. However, the O3 concentration was 2 to 3 times higher on sunny days (above 90 µg/m3) than on cloudy days due to meteorological conditions. The results confirmed that limiting traffic emissions may increase O3 concentrations in Guangzhou. Therefore, the focus should be on industrial, energy, and transportation emission mitigation and the influence of meteorological conditions to minimize O3 pollution. The results in this study provide some theoretical basis for mitigation emission policies in China.

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data.

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8-36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.

Layer-by-Layer Self-Assembly Strategy for Surface Modification of Aramid Fibers to Enhance Interfacial Adhesion to Epoxy Resin.

In this work, the layer-by-layer self-assembly technology was used to modify aramid fibers (AFs) to improve the interfacial adhesion to epoxy matrix. By virtue of the facile layer-by-layer self-assembly technique, poly(l-3,4-Dihydroxyphenylalanine) (l-PDOPA) was successfully coated on the surface of AFs, leading to the formation of AFs with controllable layers (nL-AF). Then, a hydroxyl functionalized silane coupling agent (KH550) was grafted on the surface of l-PDOPA coated AFs. The properties such as microstructure and surface morphology of AFs before and after modification were characterized by FTIR, XPS and FE-SEM. The results confirmed that l-PDOPA and KH550 were successfully introduced into the surface of AFs by electrostatic adsorption. The interfacial properties of AFs reinforced epoxy resin composites before and after coating were characterized by interfacial shear strength (IFSS), interlaminar shear strength (ILSS) and FE-SEM, and the results show that the interfacial adhesion properties of the modified fiber/epoxy resin composites were greatly improved.