Hemp-Based Microfluidics

Abstract

Hemp is a sustainable, recyclable, and high-yield annual crop that can be used to produce textiles, plastics, composites, concrete, fibers, biofuels, bionutrients, and paper. The integration of microfluidic paper-based analytical devices (µPADs) with hemp paper can improve the environmental friendliness and high-throughputness of µPADs. However, there is a lack of sufficient scientific studies exploring the functionality, pros, and cons of hemp as a substrate for µPADs. Herein, we used a desktop pen plotter and commercial markers to pattern hydrophobic barriers on hemp paper, in a single step, in order to characterize the ability of markers to form water-resistant patterns on hemp. In addition, since a higher resolution results in densely packed, cost-effective devices with a minimized need for costly reagents, we examined the smallest and thinnest water-resistant patterns plottable on hemp-based papers. Furthermore, the wicking speed and distance of fluids with different viscosities on Whatman No. 1 and hemp papers were compared. Additionally, the wettability of hemp and Whatman grade 1 paper was compared by measuring their contact angles. Besides, the effects of various channel sizes, as well as the number of branches, on the wicking distance of the channeled hemp paper was studied. The governing equations for the wicking distance on channels with laser-cut and hydrophobic side boundaries are presented and were evaluated with our experimental data, elucidating the applicability of the modified Washburn equation for modeling the wicking distance of fluids on hemp paper-based microfluidic devices. Finally, we validated hemp paper as a substrate for the detection and analysis of the potassium concentration in artificial urine.

Deep Learning-Enabled Technologies for Bioimage Analysis

“…Microfluidics allows for multiplexing biotechnological techniques and enabling applications ranging from single-cell analysis [60][61][62][63][64] to on-chip applications [65,66]. It is commonly used in biomedical and chemical research [67][68][69][70][71][72][73] to transcend traditional tech- Five popular deep CNNs for feature extraction and classification purposes are AlexNet, visual geometry group network (VGGNet), GoogLeNet, U-Net, and residual network (ResNet) [55]. AlexNet was the first CNN to achieve good performance for object detection and classification purposes [55].…”

“…Microfluidics allows for multiplexing biotechnological techniques and enabling applications ranging from single-cell analysis [60][61][62][63][64] to on-chip applications [65,66]. It is commonly used in biomedical and chemical research [67][68][69][70][71][72][73] to transcend traditional techniques with the capability of trapping, aligning, and manipulating single cells for cell combination [74], phenotyping [75][76][77], cell classification [78][79][80][81], and flow-based cytometry [82][83][84], cell capture [85,86], such as circulating tumor cells [87], and cell motility (e.g., sperm movement [88,89], mass [90], and volume sensing [91]). These applications generate high volumes of data of diverse types [92,93].…”

Biomedical Applications of Magnetic Levitation

“…[134] A number of viable methods to decrease the equilibrium time are the following: increasing the magnetic field strength, augmenting the concentration of the paramagnetic medium, decreasing the working temperature, reducing the distance traveled by the objects during levitation (i.e., smaller MagLev platforms), and gentle injection of objects into the medium to prevent dispersion. [134] In addition, the integration of MagLev with microfluidic chips [136][137][138][139][140][141] and multiplexed biomedical platforms [126,[142][143][144][145] enables the development of flow-assisted devices for real-time monitoring and continuous separation of samples.…”

Glioma-on-a-Chip Models

“…Owing to high analytical potential and the increasing presence of microfluidic devices in scientific studies [ 76 , 77 , 78 , 79 , 80 , 81 ], a diversity of fabrication methods are proposed for microchip production [ 82 , 83 , 84 , 85 , 86 ]. For 3D laminate microfluidic devices are produced by stacking (using adhesives or thermal bonding) independent 2D cut layers (e.g., interface, flow, and bottom layers) to form a final 3D structure [ 87 ].…”