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The cotton textiles with superhydrophobic and flame-retardant properties used in this study were manufactured by combining nano APP@SiO2 with silicone oil. To generate nano APP@SiO2 particles, the APP is coated with nano SiO2. The nano APP@SiO2 improves the flame retardancy of cotton textiles while altering the surface roughness of cotton fabrics, making them superhydrophobic after being treated with silicone oil. Cotton fabrics’ surface topography, chemical components, crystalline structure, thermal stability, flame-retardant, and superhydrophobic properties were investigated. The modified cotton fabric demonstrated not only exceptional superhydrophobicity with a WCA of 151.28°, but also good flame-retardant property. This multifunctional cotton fabric offers a wide range of commercial applications.

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The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to diverse and uncontrollable shape representation cross datasets. To improve discrimination on unseen distribution of point-based geometries in a practical and feasible perspective, this paper proposes a new method of geometry-aware self-training (GAST) for unsupervised domain adaptation of object point cloud classification. Specifically, this paper aims to learn a domain-shared representation of semantic categories, via two novel selfsupervised geometric learning tasks as feature regularization. On one hand, the representation learning is empowered by a linear mixup of point cloud samples with their self-generated rotation labels, to capture a global topological configuration of local geometries. On the other hand, a diverse point distribution across datasets can be normalized with a novel curvature-aware distortion localization. Experiments on the PointDA-10 dataset show that our GAST method can significantly outperform the state-ofthe-art methods. Source codes and pre-trained models are available at https://github.com/zou-longkun/ GAST.