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Among various preconcentration strategies using nanofluidic platforms, a nanoscale electrokinetic phenomenon called ion concentration polarization (ICP) has been extensively utilized due to several advantages such as high preconcentration factor and no need of complex buffer exchange process. However, conventional ICP preconcentrator had difficulties in the recovery of preconcentrated sample and complicated buffer channels. To overcome these, bufferchannel-less radial micro/nanofluidic preconcentrator was developed in this work. Radially arranged microchannel can maximize the micro/nano membrane interface so that the samples were preconcentrated from each microchannel. All of preconcentrated plugs moved toward the center pipette tip and can be easily collected by just pulling out the tip installed at the center reservoir. For a simple and cost-effective fabrication, a commercial printer was used to print the nanoporous membrane as "Nafion-junction device." Various analytes such as polystyrene particle, fluorescent dye, and dsDNA were preconcentrated and extracted with the recovery ratio of 85.5%, 79.0%, and 51.3%, respectively. Furthermore, we used a super inkjet printer to print the silver electrode instead of nanoporous membrane to preconcentrate either type of charged analytes as "printed-electrode device." A Faradaic reaction was used as the main mechanism, and we successfully demonstrated the preconcentration of either negatively or positively charged analytes. The presented bufferchannelless radial preconcentrator would be utilized as a practical and handy platform for analyzing low-abundant molecules.

While critical to biological processes, molecular diffusion is difficult to quantify, and spatial mapping of local diffusivity is even more challenging. Here we report a machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), to directly extract the diffusion coefficient D from single-molecule images, and consequently enable super-resolved D spatial mapping. Working with single-molecule images recorded at a fixed framerate under typical single-molecule localization microscopy (SMLM) conditions, Pix2D exploits the often undesired yet evident motion blur, i.e., the convolution of single-molecule motion trajectory during the frame recording time with the diffraction-limited point spread function (PSF) of the microscope. Whereas the stochastic nature of diffusion imprints diverse diffusion trajectories to different molecules diffusing at the same given D, we construct a convolutional neural network (CNN) model that takes a stack of single-molecule images as the input and evaluates a D-value as the output. We thus validate robust D evaluation and spatial mapping with simulated data, and with experimental data successfully characterize D differences for supported lipid bilayers of different compositions and resolve gel and fluidic phases at the nanoscale.