To study slow mass transport in confined environments, we developed a three-dimensional (3D) single-particle localization technique to track their microscopic movements in cylindrical nanopores. Under two model conditions, particles are retained much longer inside the pores: (1) increased solvent viscosity, which slows down the particle throughout the whole pore, and (2) increased pore wall affinity, which slows down the particle only at the wall. In viscous solvents, the particle steps decrease proportionally to the increment of the viscosity, leading to macroscopically slow diffusion. As a contrast, the particles in sticky pores are microscopically active by showing limited reduction of step sizes. A restricted diffusion mode, possibly caused by the heterogeneous environment in sticky pores, is the main reason for macroscopically slow diffusion. This study shows that it is possible to differentiate slow diffusion in confined environments caused by different mechanisms.
Single particle tracking (SPT) has proven to be a powerful technique in studying molecular dynamics in complicated systems. We review its recent development, including three-dimensional (3D) SPT and its applications in probing nanostructures and molecule-surface interactions that are important to analytical chemical processes. Several frequently used 3D SPT techniques are introduced. Especially of interest are those based on point spread function engineering, which are simple in instrumentation and can be easily adapted and used in analytical labs. Corresponding data analysis methods are briefly discussed. We present several important case studies, with a focus on probing mass transport and molecule-surface interactions in confined environments. The presented studies demonstrate the great potential of 3D SPT for understanding fundamental phenomena in confined space, which will enable us to predict basic principles involved in chemical recognition, separation, and analysis, and to optimize mass transport and responses by structural design and optimization.
Diffusion on highly curved surfaces is important to many industrial and biological processes. Despite the progress made in theoretical studies, how diffusion is affected by the curvature is unclear due to experimental challenges. Here, we measured the trajectories of polystyrene nanoparticles diffusing on highly curved water-silicone oil interface, where the oil droplet diameter ranges from several μm to as small as ∼400 nm. To analyze the diffusion coefficients on curved surface, an analytical solution developed by Castro-Villarreal containing an infinite series can be used. Through Monte Carlo simulations, we simplified the Castro-Villarreal equation and defined the conditions that satisfy corresponding approximations. For the experiments, unexpectedly, we found that the diffusion slows down significantly when the oil droplet becomes smaller. Possible reasons were discussed, and a diffusion-induced droplet deformation and interface fluctuation model is consistent with the experimental results. This study reveals an unexpected decrease of particle diffusion on small oil droplet surface and sheds new light on understanding diffusion on highly curved interface.
Three-dimensional single particle tracking (3D SPT) is a powerful tool in various chemical and biological studies. In 3D SPT, z sensitive point spread functions (PSFs) are frequently used to generate different patterns, from which the axial position of the probe can be recovered in addition to its xy coordinates. Conventional linear classifier-based methods, for example, the correlation coefficient method, perform poorly when the signal-to-noise ratio (S/N) drops. In this work, we test deep neural networks (DNNs) in recognizing and differentiating very similar image patterns incurred in 3D SPT. The training of the deep neural networks is optimized, and a procedure is established for 3D localization. We show that for high S/N images, both DNNs and conventional correlation coefficient-based method perform well. However, when the S/N drops close to 1, conventional methods completely fail while DNNs show strong resistance to both artificial and experimental noises. This noise resistance allows us to achieve a camera integration time of 50 μs for 200 nm fluorescent particles without losing accuracy significantly. This study sheds new light on developing robust image data analysis methods and on improving the time resolution of 3D SPT.
Understanding the spatial organization of nano-and micro-sized particle is very important in the fabrication of complex structures having unprecedented properties. Study on self-assembly of submicroscopic colloidal particles at high ionic strength solution at single particle resolution can provide new insight into the nanoscale interactions. In this study, we studied the self assembly behavior of negatively charged 0.2 and 1 μm colloidal particles at high ionic strength on glass-solution interface that is, in situ environment. The self-assembled 0.2 μm particles could not be resolved with conventional confocal and epi-fluorescent microscopy, so a home-built continuous wave stimulated emission depletion (STED) microscope was used for the study. We found that particles self-assemble into ordered and disordered structures at higher and lower ionic strength solution, respectively. The optical imaging methods allowed us to measure inter-particle gap at second energy minimum directly. Interestingly, we found that the inter-particle gap in the wet self-assembly higher than the classical Derjaguin, Landau, Verwey, and Overbeek (DLVO) theory predicted. The in situ investigation of particle self-assembly at high ionic strength will provide more insight for the understanding nanoscale interactions.
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