Deep learning-based hologram generation using a white light source.

Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.

A numbering-up metal microreactor for the high-throughput production of a commercial drug by copper catalysis.

Microreactors are emerging as an efficient, sustainable synthetic tool compared to conventional batch reactors. Here, we present a new numbering-up metal microreactor by integrating a flow distributor and a copper catalytic module for high productivity of a commercial synthetic drug. A flow distributor and an embedded baffle disc were manufactured by CNC machining and 3D printing of stainless steel (S/S), respectively, whereas a catalytic reaction module was composed of 25 copper coiled capillaries configured in parallel. Eventually, the numbering-up microreactor system assembled with functional modules showed uniform flow distribution and high mixing efficiency regardless of clogging, and achieved high-throughput synthesis of the drug "rufinamide", an anticonvulsant medicine, via a Cu(i)-catalyzed azide-alkyne cycloaddition reaction under optimized conditions.

Prediction of a typhoon track using a generative adversarial network and satellite images.

Tracks of typhoons are predicted using a generative adversarial network (GAN) with satellite images as inputs. Time series of satellite images of typhoons which occurred in the Korea Peninsula in the past are used to train the neural network. The trained GAN is employed to produce a 6-hour-advance track of a typhoon for which the GAN was not trained. The predicted track image of a typhoon favorably identifies the future location of the typhoon center as well as the deformed cloud structures. Errors between predicted and real typhoon centers are measured quantitatively in kilometers. An averaged error of 95.6 km is achieved for tested 10 typhoons. Predicting sudden changes of the track in westward or northward directions is identified as a challenging task, while the prediction is significantly improved, when velocity fields are employed along with satellite images.

A 3D-printed flow distributor with uniform flow rate control for multi-stacked microfluidic systems.

In the scale-up of chemical production in a microfluidic system, it is challenging to prevent flow maldistribution from a single inlet into stacked multiple microchannel exits. In the present study, a compact flow distributor equipped with a fluidic damper is developed by computational fluid dynamics (CFD) along with experimental validation. A microfluidic flow distributor, which is equipped with an optimized fluidic damper and consists of 25 exit channels, is fabricated as an integrated body using a digital light processing (DLP) type 3D printer. The 3D printed flow distributor with a CFD-optimized fluidic damper is found to achieve a low maldistribution factor (MF) of 2.2% for the average flow rate over 25 exit channels while inducing only a minor increment (<6%) in the pressure drop. A generalized manual is proposed for the design of optimal flow distributors with different scale-up dimensions. Using the manual, an optimal flow distributor with 625 stacked microchannels with a MF of only 1.2% is successfully designed. It is expected that the design manual and the rapid printing platform will allow the efficient development of multi-channel stacked micro-devices such as those in drug delivery and energy conversion systems where equidistribution of fluid flow is highly demanded.

Continuum modeling of deformation and aggregation of red blood cells.

In order to gain better understanding for rheology of an isolated red blood cell (RBC) and a group of multiple RBCs, new continuum models for describing mechanical properties of cellular structures of an RBC and inter-cellular interactions among multiple RBCs are developed. The viscous property of an RBC membrane, which characterizes dynamic behaviors of an RBC under stress loading and unloading processes, is determined using a generalized Maxwell model. The present model is capable of predicting stress relaxation and stress-strain hysteresis, of which prediction is not possible using the commonly used Kelvin-Voigt model. Nonlinear elasticity of an RBC is determined using the Yeoh hyperelastic material model in a framework of continuum mechanics using finite-element approximation. A novel method to model inter-cellular interactions among multiple adjacent RBCs is also developed. Unlike the previous modeling approaches for aggregation of RBCs, where interaction energy for aggregation is curve-fitted using a Morse-type potential function, the interaction energy is analytically determined. The present aggregation model, therefore, allows us to predict various effects of physical parameters such as the osmotic pressure, the thickness of a glycocalyx layer, the penetration depth, and the permittivity, on the depletion and electrostatic energy among RBCs. Simulations for elongation and recovery deformation of an RBC and for aggregation of multiple RBCs are conducted to evaluate the efficacy of the present continuum modeling methods.