Pressure-Sensitive Nano-Sheet for Optical Pressure Measurement

A flexible nanofiber membrane containing dendritic oxygen probe for visual monitoring pressure distribution

Pressure-sensitive paints (PSP) enable non-intrusive visualization of surface pressure distribution on model surface which is important for aerodynamic studies. However, conventional PSP materials suffer from photobleaching and inadequate sensitivity. In this work, we rationally designed and synthesized novel dendritic oxygen probes (PT1 and PT2) by covalently grafting fluorinated dendrons onto platinum tetrakis(pentafluorophenyl)porphyrin (PT0) (a common oxygen probe). Subsequently, PT2 loaded nanofibers membranes from polycaprolactone (PCL) were fabricated by electrospinning. Fabricated membranes showed high oxygen sensitivity (I0/I100 = 35.3) with excellent flexibility, good reversibility, and outstanding photostability (merely 2.0% intensity loss after prolonged irradiation). The pressure sensitivity was found around 0.73 % per kilopascal. Furthermore, significant variation in emission intensity with respect to the variation in air pressure (1.3-101.32 kPa), facilitates the naked eye visualization of the pressure distribution on the membrane surface. Such excellent oxygen and pressure sensitivity and photostability might be due to high fluorine contents of complex dendritic structure of PT2. This flexible fluorine-functionalized dendritic oxygen probe puts forward a facile and effective strategy to develop advanced PSP materials enabling accurate pressure mapping for aerodynamic studies.

Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures

This paper describes the combined detection of coating and rust damages on painted metal structures through the multiclass image segmentation technique. Our prior works were focused solely on the localization of rust damages and rust segmentation under different ambient conditions (different lighting conditions, presence of shadows, low background/object color contrast). This paper method proposes three types of damages: coating crack, coating flaking, and rust damage. Background, paint flaking, and rust damage are objects that can be separated in RGB color-space alone. For their preliminary classification SVM is used. As for paint cracks, color features are insufficient for separating it from other defect types as they overlap with the other three classes in RGB color space. For preliminary paint crack segmentation we use the valley detection approach, which analyses the shape of defects. A multiclass level-set approach with a developed penalty term is used as a framework for the advanced final damage segmentation stage. Model training and accuracy assessment are fulfilled on the created dataset, which contains input images of corresponding defects with respective ground truth data provided by the expert. A quantitative analysis of the accuracy of the proposed approach is provided. The efficiency of the approach is demonstrated on authentic images of coated surfaces.