Untrained deep learning-based differential phase-contrast microscopy.

Quantitative differential phase-contrast (DPC) microscopy produces phase images of transparent objects based on a number of intensity images. To reconstruct the phase, in DPC microscopy, a linearized model for weakly scattering objects is considered; this limits the range of objects to be imaged, and requires additional measurements and complicated algorithms to correct for system aberrations. Here, we present a self-calibrated DPC microscope using an untrained neural network (UNN), which incorporates the nonlinear image formation model. Our method alleviates the restrictions on the object to be imaged and simultaneously reconstructs the complex object information and aberrations, without any training dataset. We demonstrate the viability of UNN-DPC microscopy through both numerical simulations and LED microscope-based experiments.

Polarization-sensitive intensity diffraction tomography.

Optical anisotropy, which is an intrinsic property of many materials, originates from the structural arrangement of molecular structures, and to date, various polarization-sensitive imaging (PSI) methods have been developed to investigate the nature of anisotropic materials. In particular, the recently developed tomographic PSI technologies enable the investigation of anisotropic materials through volumetric mappings of the anisotropy distribution of these materials. However, these reported methods mostly operate on a single scattering model, and are thus not suitable for three-dimensional (3D) PSI imaging of multiple scattering samples. Here, we present a novel reference-free 3D polarization-sensitive computational imaging technique-polarization-sensitive intensity diffraction tomography (PS-IDT)-that enables the reconstruction of 3D anisotropy distribution of both weakly and multiple scattering specimens from multiple intensity-only measurements. A 3D anisotropic object is illuminated by circularly polarized plane waves at various illumination angles to encode the isotropic and anisotropic structural information into 2D intensity information. These information are then recorded separately through two orthogonal analyzer states, and a 3D Jones matrix is iteratively reconstructed based on the vectorial multi-slice beam propagation model and gradient descent method. We demonstrate the 3D anisotropy imaging capabilities of PS-IDT by presenting 3D anisotropy maps of various samples, including potato starch granules and tardigrade.

Rapid and selective lithium recovery from desalination brine using an electrochemical system.

Due to the steep increase in the use of mobile electronics and electric vehicles, there has been a dramatic rise in the global lithium consumption. Although seawater is considered as an ideal future source of lithium, technological advances are necessary to ensure the economic feasibility of lithium recovery from seawater because the concentration and portion of Li+ are extremely low in seawater. Especially, battery-based electrochemical systems for lithium recovery have been considered as promising lithium recovery methods, though they have not been considered for seawater applications due to the extremely low concentration of Li+. In this study, we demonstrate that an electrochemical system based on a battery electrode material (λ-MnO2) can be used for efficient lithium recovery from desalination brine (2-3 times concentrated seawater). Our approach was able to capture Li+ within a substantially short period of time compared to conventional processes at a rate that was at least 3 times faster than that of adsorption processes, and our approach did not require acid or toxic chemicals unlike the other recovery technologies. Moreover, by consecutive operation of the system, a lithium recovery solution containing 190 mM of Li+ was obtained with only a small consumption of energy (3.07 Wh gLi-1), and the purity of Li+ was increased to 99.0%.