Modern neuroscience increasingly relies on 3D models to study neural circuitry, nerve regeneration, and neural disease. Several different biofabrication approaches have been explored to create 3D neural tissue model structures. Among them, 3D bioprinting has shown to have great potential to emerge as a high-throughput/high precision biofabrication strategy that can address the growing need for 3D neural models. Here, we have reviewed the design principles for neural tissue engineering. The main challenge to adapt printing technologies for biofabrication of neural tissue models is the development of neural bioink, i.e., a biomaterial with printability and gelation properties and also suitable for neural tissue culture. This review shines light on a vast range of biomaterials as well as the fundamentals of 3D neural tissue printing. Also, advances in 3D bioprinting technologies are reviewed especially for bioprinted neural models. Finally, the techniques used to evaluate the fabricated 2D and 3D neural models are discussed and compared in terms of feasibility and functionality.

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Advances in lab-on-a-chip (LOC) devices have led to significant improvements in the on-chip manipulation, separation, sorting, and isolation of particles and cells. Among various Loc-based approaches such as inertia-based methods, acoustophoresis, and magnetophoresis, the planarslanted-electrode dielectrophoresis (Dep) method has demonstrated great potential as a label-free, cost-effective, and user-friendly approach. However, the devices built based on this method suffer from low flow throughput compared to devices functioning based on other LOC-based manipulation approaches. in order to overcome this obstacle, the geometrical parameters of these types of DEP-based devices must be studied to increase the effectiveness of DEP manipulation. With the consideration of both numerical and experimental studies, this paper studies the geometrical factors of a Loc platform consisting of tilted planar electrodes with the goal of achieving higher throughput in continuous manipulation of polystyrene particles. coMSoL Multiphysics software was used to study the effect of the electrodes geometry on the induced electric field. The simulation results show that by increasing the electrode's width and decreasing the electrode's spacing, higher Dep force is generated. furthermore, the experimental outcomes indicated that lower channel height, higher voltage, and larger particle size resulted in the most improvement to Dep manipulation. Additionally, the experimental results demonstrated that slanted electrodes with an angle of 8° with respect to the direction of flow provide a more effective configuration. Cell manipulation, as a preliminary step for cell-based analysis, is a rapidly growing area of interdisciplinary research for the development of single-cell technologies. Over the past two decades, single-cell manipulation and analysis methods have improved significantly due to advances in microfluidic cell manipulation methods 1,2. These methods can be broadly categorized as either passive or active. The passive methods, including microfiltration 3 , inertia-based 4 , contraction-expansion channels 5 , deterministic lateral displacement 6 , and pinched flow fractionation 7 , do not rely on any external forces. On the other hand, the active methods, such as dielectrophoresis (DEP) 8 , magnetophoresis 9 , acoustophoresis 10 , and optical-based manipulation 11 , require an external force to manipulate the cells/particles. Although passive methods can handle higher flow rates, active methods offer more control over cells/particles, real-time tuning, reliability, and higher manipulation efficiency 12-14 .

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In this study, the temperature and viscosity-dependent methods were used to identify the main heat conduction mechanism in nanofluids. Three sets of experiments were conductedto investigate the effects of Brownian motion and aggregation. Image processing approach was used to identify detailed configurations of different nanofluids microstructures. The thermal conductivity of the nanofluids was measured with respect to the dynamic viscosity in the temperature range between 0 and 55 °C. TheResultsclearly indicated thatthe nanoparticle Brownian motion does not play a significant role in heat conduction of nanofluids, which was also supported by the observation that a more viscous sample rendered a higher thermal conductivity. Moreover, the microscopic pictures and the differences in the viscosity between theoretical and experimental valuessuggested the major role of particle aggregation and clustering.