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Point-of-care (POC) diagnostic devices have been developing rapidly in recent years, but they are mainly using saliva instead of blood as a test sample. A highly efficient self-separation during the self-driven flow without power systems is desired for expanding the point-of-care diagnostic devices. Microfiltration stands out as a promising technique for blood plasma separation but faces limitations due to blood cell clogging, resulting in reduced separation speed and efficiency. These limitations are mainly caused by the high viscosity and hematocrit in the blood flow. A small increment in the hematocrit of the blood significantly increases the pressure needed for the blood plasma separation in the micro-filters and decreases the separation speed and efficiency. Addressing this challenge, this study explores the feasibility of diluting whole blood within a microfluidic device without external power systems. This study implemented a spiral microchannel utilizing the inertial focusing and Dean vortex effects to focus the red blood cells and extract the blood with lower hematocrit. The inertial migration of the particles during the capillary flow was first investigated experimentally; a maximum of 88% of the particles migrated to the bottom and top equilibrium positions in the optimized 350 × 60 μm (cross-sectional area, 5.8 aspect ratio) microchannel. With the optimized dimension of the microchannel, the whole blood samples within the physiological hematocrit range were tested in the experiments, and more than 10% of the hematocrit reduction was compared between the outer branch outlet and inner branch outlet in the 350 × 60 μm microchannel. 

Two-dimensional materials (e.g., graphene and transition metal dichalcogenides) and their heterostructures have enormous applications in electrochemical energy storage systems such as batteries. A comprehensive and solid understanding of these materials’ thermal transport and mechanism is essential for practical device design. Several advanced experimental techniques have been developed to measure the intrinsic thermal conductivity of materials. However, experiments have challenges in providing improved control and characterization of complex structures, especially for low-dimensional materials. Theoretical and simulation tools, such as first-principles calculations, Boltzmann transport equations, molecular dynamics simulations, lattice dynamics simulation, and nonequilibrium Green’s function, provide reliable predictions of thermal conductivity and physical insights to understand the underlying thermal transport mechanism in materials. However, doing these calculations requires high computational resources. The development of new materials synthesis technology and fast-growing demand for rapid and accurate prediction of physical properties requires novel computational approaches. The machine learning method provides a promising solution to address such needs. This review details the recent development in atomistic/molecular studies and machine learning of thermal transport in two-dimensional materials. The paper also addresses the latest significant experimental advances. However, designing the best two-dimensional materials-based heterostructures is like a multivariate optimization problem. For example, a particular heterostructure may be suitable for thermal transport but can have lower mechanical strength/stability. For bilayer and multilayer structures, the interlayer distance may influence the thermal transport properties and interlayer strength. Therefore, the last part of this review addresses the future research direction in two-dimensional materials-based heterostructure design for thermal transport in energy storage systems. 

Here we report a nitrogen-doped graphene modified metal-organic framework (N-G/MOF) catalyst, a promising metal-free electrocatalyst exhibiting the potential to replace the noble metal catalyst from the electrochemical systems; such as fuel cells and metal-air batteries. The catalyst was synthesized with a planetary ball milling method, in which the precursors nitrogen-functionalized graphene (N-G) and ZIF-8 are ground at an optimized grinding speed and time. The N-G/MOF catalyst not only inherited large surface area from the ZIF-8 structure, but also had chemical interactions, resulting in an improved Oxygen Reduction Reaction (ORR) electrocatalyst. Thermogravimetric Analysis (TGA) curves revealed that the N-G/MOF catalyst still had some unreacted ZIF-8 particles, and the high catalytic activity of N-G particles decreased the decomposition temperature of ZIF-8 in the N-G/MOF catalyst. Also, we present the durability study of the N-G/MOF catalyst under a saturated nitrogen and oxygen environment in alkaline medium. Remarkably, the catalyst showed no change in the performance after 2000 cycles in the N2 environment, exhibiting strong resistance to the corrosion. In the O2 saturated electrolyte, the performance loss at lower overpotentials was as low compared to higher overpotentials. It is expected that the catalyst degradation mechanism during the potential cycling is due to the oxidative attack of the ORR intermediates.