The core-shell NaYF4/Yb/Tm/TiO2 hollow composite fibers were prepared by coaxial electrospinning and high-temperature calcination. The composite fibers exhibit excellent photocatalytic activity under the dual synergistic of regulating the core-shell hollow microstructure and the composition by doping nanoparticles. Compared with commercial P25 and hollow fiber without nanoparticles, the degradation efficiency of rhodamine B using the core-shell composite fiber was significantly improved up to 99%. Moreover, the nanoparticles in the composite fibers can exist stably and maintain good structure and photocatalytic activity after repeated use. Therefore, the composite fiber has a wide application prospect in photocatalytic degradation of organic pollutants.

Liquid transport is of great significance to industry and life, such as microfluidic chip, liquid separation, fluidic gates, and tissue fluid discharge. However, there are still some challenges to achieve well‐controlled directional transports, and the delamination often occurs for the now existing Janus membranes. Herein, fibrous assembly with hierarchically fibrous helix architecture bioinspired by tendrils through electrospinning combined with mechanical twisting technology is engineered and demonstrated. The liquid transport behavior using as fluidic gates by connecting light‐emitting diode (LED) and the resulting liquid separation mechanism were characterized and investigated, respectively. Different from previous materials, due to the existence of distinct periodic alternate gradient interface topology, the hierarchically fibrous helix exhibits a long‐range order and directional liquid transport trajectory as well as improved water management property. This strategy is cost‐effective and can be extended to other fields. The resultant materials are highly promising for applications in actuators, microfluidic chips, and fluidic gates.

No abstract

No abstract

PurposeThe main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.Design/methodology/approachFor this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.FindingsThe experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.Originality/valueBy constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.