Numerical methods, such as finite element or finite difference, have been widely used in the past decades for modeling solid mechanics problems by solving partial differential equations (PDEs). Differently from the traditional computational paradigm employed in numerical methods, physics-informed deep learning approximates the physics domains using a neural network and embeds physics laws to regularize the network. In this work, a physics-informed neural network (PINN) is extended for application to linear elasticity problems that arise in modeling non-uniform deformation for a typical open-holed plate specimen. The main focus will be on investigating the performance of a conventional PINN approach to modeling non-uniform deformation with high stress concentration in relation to solid mechanics involving forward and inverse problems. Compared to the conventional finite element method, our results show the promise of using PINN in modeling the non-uniform deformation of materials with the occurrence of both forward and inverse problems.
Polydopamine fluorescent organic nanomaterials present unique physicochemical and biological properties, which have great potential application in bio-imaging and chemical sensors. Here, folic acid (FA) adjustive polydopamine (PDA) fluorescent organic nanoparticles (FA-PDA FONs) were prepared by a facile one-pot self-polymerization strategy using dopamine (DA) and FA as precursors under mild conditions. The as-prepared FA-PDA FONs had an average size of 1.9 ± 0.3 nm in diameter with great aqueous dispersibility, and the FA-PDA FONs solution exhibit intense blue fluorescence under 365 nm UV lamp, and the quantum yield is ~8.27%. The FA-PDA FONs could be stable in a relatively wide pH range and high ionic strength salt solution, and the fluorescence intensities are constant. More importantly, here we developed a method for rapidly selective and sensitive detection of mercury ions (Hg2+) within 10 s using FA-PDA FONs based probe, the fluorescence intensities of FA-PDA FONs presented a great linear relationship to Hg2+ concentration, the linear range and limit of detection (LOD) were 0–18 µM and 0.18 µM, respectively. Furthermore, the feasibility of the developed Hg2+ sensor was verified by determination of Hg2+ in mineral water and tap water samples with satisfactory results.
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