We demonstrate a method to determine the Brownian motion and the diffusion coefficient of a nanoparticle in water in a plane that is parallel to a solid boundary and as function of the distance normal to that boundary by using an optical tweezers instrument. A solution of 190 nm-diameter fluorescent polystyrene nanoparticles in de-ionized (DI) water is introduced in a micro-chamber built from two thin glass substrates. A single particle is trapped by the tweezers and optically moved in the z-direction normal to a substrate. By analyzing a scatter plot of the time-dependent positions of the nanoparticle in the x-y plane in a histogram, the diffusion coefficient parallel to the substrate of the Brownian particle constrained by the substrate is determined as a function of the distance between the substrate and the nanoparticle. The experimental results indicate the increased drag effect on the nanoparticle when it is close to the substrate, as evidenced by an experimental diffusion coefficient nearby the substrate that is about half of that of the particle in the bulk fluid.
We propose the use of a high-refractive index glass microsphere in combination with a conventional fluorescence microscope for imaging sub-cellular organelles and biomolecules. The microsphere is placed on a sample that is immersed in water, collects the near-field nano-features of the sample and generates a magnified virtual image in the farfield, which is recorded through a conventional water immersion objective. We first investigate the imaging capability of the microspheres on size-calibrated fluorescent micro-/nano-particles. The experimental results obtained from a microsphere with 60 μm in diameter demonstrate imaging capability of features of ~λ/7-size (λ is the wavelength) with a magnification factor of 5.4. The position of the virtual image, the field-of-view (FOV) of the microsphere and the magnification factor are studied by using microspheres with different sizes. Finite Element Method (FEM) simulations are performed, providing key insight into the imaging effect of the microspheres with super-resolution capability. Moreover, the distribution and complex shape of different sub-cellular organelles, like centrioles, mitochondria and chromosomes in the AML12 cell line, are imaged with help of the glass microspheres. Thereafter, the subcellular location of mitochondrial encoded proteins could be studied.
With the development of 3-D imaging techniques, three dimensional point cloud partition becomes one of the key
research fields. In this paper, two data partition algorithms are proposed. Each algorithm includes two parts: data
re-organization and data classification. Two methods for data re-organization are proposed: dimension reduction and
triangle mesh reconstruction. The algorithm of data classification is based on edge detection of depth data. The edge
detection algorithms of gray images are improved for depth data partition. As to the triangulation method, the data
partition is realized by region growing. The simulation result shows that the two methods can achieve point cloud data
partition of standard template and real scene. The result of standard template shows the total error rates of the two
algorithms are both less than 3%.
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