Hydrodynamic Assembly of Astrocyte Cells in Conductive Hollow Microfibers.

The manufacturing of 3D cell scaffoldings provides advantages for modeling diseases and injuries as it enables the creation of physiologically relevant platforms. A triple-flow microfluidic device is developed to rapidly fabricate alginate/graphene hollow microfibers based on the gelation of alginate induced with CaCl2 . This five-channel microdevice actualizes continuous mild fabrication of hollow fibers under an optimized flow rate ratio of 300:200:100 µL min-1 . The polymer solution is 2.5% alginate in 0.1% graphene and a 30% polyethylene glycol solution is used as the sheath and core solutions. The biocompatibility of these conductive microfibers by encapsulating mouse astrocyte cells (C8D1A) within the scaffolds is investigated. The cells can successfully survive both the manufacturing process and prolonged encapsulation for up to 8 days, where there is between 18-53% of live cells on both the alginate microfibers and alginate/graphene microfibers. These unique 3D hollow scaffolds can significantly enhance the available surface area for nutrient transport to the cells. In addition, these conductive hollow scaffolds illustrate unique advantages such as 0.728 cm3  gr-1 porosity and two times more electrical conductivity in comparison to alginate scaffolds. The results confirm the potential of these scaffolds as a microenvironment that supports cell growth.

Machine learning-assisted E-jet printing for manufacturing of organic flexible electronics.

Electrohydrodynamic-jet (E-jet) printing technique enables the high-resolution printing of complex soft electronic devices. As such, it has an unmatched potential for becoming the conventional technique for printing soft electronic devices. In this study, the electrical conductivity of the E-jet printed circuits was studied as a function of key printing parameters (nozzle speed, ink flow rate, and voltage). The collected experimental dataset was then used to train a machine learning algorithm to establish models capable of predicting the characteristics of the printed circuits in real-time. A decision tree was applied to the data set and resulted in an accuracy of 0.72, and further evaluations showed that pruning the tree increased the accuracy while sensitivity decreased in the highly pruned trees. The k-fold cross-validation (CV) method was used in model selection to test the ability of the model to get trained on data. The accuracy of CV method was the highest for random forest at 0.83 and K-NN model (k = 10) at 0.82. Precision parameters were compared to evaluate the supervised classification models. According to F-measure values, the K-NN model (k = 10) and random forest are the best methods to classify the conductivity of electrodes.

Advancement of Sensor Integrated Organ-on-Chip Devices.

Organ-on-chip devices have provided the pharmaceutical and tissue engineering worlds much hope since they arrived and began to grow in sophistication. However, limitations for their applicability were soon realized as they lacked real-time monitoring and sensing capabilities. The users of these devices relied solely on endpoint analysis for the results of their tests, which created a chasm in the understanding of life between the lab the natural world. However, this gap is being bridged with sensors that are integrated into organ-on-chip devices. This review goes in-depth on different sensing methods, giving examples for various research on mechanical, electrical resistance, and bead-based sensors, and the prospects of each. Furthermore, the review covers works conducted that use specific sensors for oxygen, and various metabolites to characterize cellular behavior and response in real-time. Together, the outline of these works gives a thorough analysis of the design methodology and sophistication of the current sensor integrated organ-on-chips.