The internal fluidic parameters of microfluidic channels must be analyzed to solve fundamental microfluidic problems, including microscale transport problems involving thermal analysis, chemical reactivity, velocity, pressure drop, etc., for developing good-quality chemical and biological products. Therefore, the characterization and optimization of the interaction of chemical and biological solutions through microfluidic channels are vital for fluid flow design and engineering for quality assurance in microfluidic platforms. As the internal structures and kinetics of microfluidic channels are becoming increasingly complex, experiments involving optimal fluidic and transport designs are challenging to perform with high accuracy. However, highly integrated simulation tools can guide researchers without specialized computational fluid backgrounds to design numerical prototypes of highly integrated devices. In this study, a microfluidic chip with two inlet wells and one outlet well was fabricated from polydimethylsiloxane following which simulations were performed using an ANSYS Fluent tool influenced by computational fluid dynamics at a nearly identical scale. The pressure drop and velocity profiles of the interaction of two pH buffer solutions (pH 4 and 10) through the designed microfluidic chip were qualitatively estimated from experimental data analysis and validated with the simulation results obtained from the CFD-influenced ANSYS Fluent tool.

Polydimethylsiloxane (PDMS)-based mold prepared using a 3D-printed structure is a cost-effective material and tool to fabricate robust microfluidic chips (MCs) without requiring expensive clean room facilities. A capillary-based MC made of PDMS can be attached onto a glass substrate to visualize the chemical reactions in different types of pH buffer solutions (BSs) flowing through microflow channels (MFCs) using terahertz (THz) image sensing technology. In this study, we designed a microfluidic structure with two inlet wells and an outlet well, equipped with a Si:sapphire substrate to visualize the chemical interaction between BSs injected at different pH values (4 and 10) through an MFC. THz imaging maps were captured during the flow of the BSs using a THz chemical microscope, and the fluid dynamics was studied. We determined and plotted the variation in the THz amplitude data with respect to the BS concentration and analyzed the characteristics of the data.

The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 μm microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of $6\times {10}^{-5}$ using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters.

A Terahertz Chemical Microscope has been proposed and developed for the visualization of chemical reactions in the solutions. To visualize chemical reactions with higher imaging quality and higher scanning speed, it is required to improve the contrast of the images. In this study, we applied an adaptive filter to the TCM system and evaluate the imaging quality qualitatively. The contrasts without filter and with filter are 0.33 and 0.74 respectively. This result shows that our system with the filter could improve the imaging quality without losing the scanning speed of the microscope.