No abstract

The fabrication of a large-scale microfluidic mold with 3D microstructures for manufacturing of the conical microwell chip using a combined projection micro-stereolithography (PµSL) 3D printing/CNC micro-milling method for tumor spheroid formation is presented. The PµSL technique is known as the most promising method of manufacturing microfluidic chips due to the possibility of creating complex three-dimensional microstructures with high resolution in the range of several micrometers. The purpose of applying the proposed method is to investigate the influence of microwell depths on the formation of tumor spheroids. In the conventional methods, the construction of three-dimensional microstructures and multi-height chips is difficult, time-consuming, and is performed using a multi-step lithography process. Microwell depth is an essential parameter for microwell design since it directly affects the shear stress of the fluid flow and the diffusion of nutrients, respiratory gases, and growth factors. In this study, a chip was made with microwells of different depth varying from 100 to 500 µm. The mold of the microwell section is printed by the lab-made PµSL printer with 6 and 1 µm lateral and vertical resolutions. Other parts of the mold, such as the main chamber and micro-channels, were manufactured using the CNC micro-milling method. Finally, different parts of the master mold were assembled and used for PDMS casting. The proposed technique drastically simplifies the fabrication and rapid prototyping of large-scale microfluidic devices with high-resolution microstructures by combining 3D printing with the CNC micro-milling method.

This research studied the effects of climate change on rice yield under irrigation by using the AquaCrop model and the various climate change scenarios for the crop years 2017 and 2018 at the research farm of the Rice Research Institute of Iran, Rasht. The LARS-WG6 model was employed to simulate the meteorological data obtained from Rasht Meteorological Station under the RCP8.5 and RCP4.5 scenarios for the 2020-2050 and 2060-2090 periods. The simulated values were assessed based on the measured values of total biomass and grain yields using the coefficient of determination (R2 ), the relative error parameters, and the normalized root mean square error (RMSEn). The calculated values of RMSEn varied from 7 to 4% for grain yield and from 3 to 7% for biomass yield at the calibration and validation stages, respectively. The results suggested that the AquaCrop model had suitable accuracy in predicting biomass and grain yields. The findings indicate that climate change decreased mean rice grain yield by 17 to 23% under the RCP4.5 scenario and by 18 to 23% under the RCP8.5 scenario for 2050 and 2090, respectively, in non-flooded irrigation management. The lowest simulated mean biomass and grain yields were obtained under irrigation at 11-day intervals in scenario RCP8.5 for the second future period (2060-2090). In general, non-flooded irrigation management had negative effects on rice grain yield under climate change. Moreover, the mean rice growing period under both RCP8.5 and RCP4.5 scenarios declined by 2090. These findings can be used for a broad spectrum of users such as farmers, agricultural engineers, and project managers in practical policymaking and making correct decisions compatible with the region in order to increase rice grain yield productivity under future climate conditions in northern Iran.