Capacitance of Flexible Polymer/Graphene Microstructures with High Mechanical Strength.

Carbon-modified fibrous structures with high biocompatibility have attracted much attention due to their low cost, sustainability, abundance, and excellent electrical properties. However, some carbon-based materials possess low specific capacitance and electrochemical performance, which pose significant challenges in developing electronic microdevices. In this study, we report a microfluidic-based technique of manufacturing alginate hollow microfibers incorporated by water dispersed modified graphene (bovine serum albumin-graphene). These architectures successfully exhibited enhanced conductivity ∼20 times higher than alginate hollow microfibers without any significant change in the inner dimension of the hollow region (220.0 ± 10.0 µm) compared with pure alginate hollow microfibers. In the presence of graphene, higher specific surface permeability, active ion adsorption sites, and shorter pathways were created. These continuous ion transport networks resulted in improved electrochemical performance. The desired electrochemical properties of the microfibers make alginate/graphene hollow fibers an excellent choice for further use in the development of flexible capacitors with the potential to be used in smart health electronics.

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.

Minute-sensitive real-time monitoring of neural cells through printed graphene microelectrodes.

Real-time and high-throughput cytometric monitoring of neural cells exposed to injury mechanisms is invaluable for in-vitro studies. Electrical impedance spectroscopy via microelectrode arrays is a label-free technique for monitoring of neural growth and their detachment upon death. In this method, the interface material plays a vital role to provide desirable attachment cues for the cell network. Thus, here we demonstrate the electrohydrodynamic patterning of aqueous graphene for microelectrode fabrication. We investigated whether the wrinkled surface morphology of the electrodes fabricated by this deposition method expands their electroactive surface area and thus enables a rapid response time. The nano-scale quality of the graphene lattice is characterized by Raman spectroscopy and Transmittance electron microscopy. N27 rat dopaminergic neural cells were cultured on the chips and the surface morphology of the microelectrodes during cellular growth was investigated by Scanning electrode spectroscopy. Attachment of the neural population on the graphene microelectrodes was parametrized and the change in the impedance spectrum of this cell population was quantified at 10 Hz to 10 kHz frequencies along with the change in TUBB3 gene expression. The viability test of the cell population on the biosensor demonstrated no significant difference in comparison to the control, and a cell density of 2289 cell/mm2 was achieved. As a proof of concept, the confluent N27 cell population was exposed to UV and its cytotoxic impact on neural detachment and lift-off was monitored. The multiplexed detection of cellular activity was reported with a temporal resolution of one minute.

Microfluidic Seeding of Cells on the Inner Surface of Alginate Hollow Microfibers.

Mimicking microvascular tissue microenvironment in vitro calls for a cytocompatible technique of manufacturing biocompatible hollow microfibers suitable for cell-encapsulation/seeding in and around them. The techniques reported to date either have a limit on the microfiber dimensions or undergo a complex manufacturing process. Here, a microfluidic-based method for cell seeding inside alginate hollow microfibers is designed whereby mouse astrocytes (C8-D1A) are passively seeded on the inner surface of these hollow microfibers. Collagen I and poly-d-lysine, as cell attachment additives, are tested to assess cell adhesion and viability; the results are compared with nonadditive-based hollow microfibers (BARE). The BARE furnishes better cell attachment and higher cell viability immediately after manufacturing, and an increasing trend in the cell viability is observed between Day 0 and Day 2. Swelling analysis using percentage initial weight and width is performed on BARE microfibers furnishing a maximum of 124.1% and 106.1%, respectively. Degradation analysis using weight observed a 62% loss after 3 days, with 46% occurring in the first 12 h. In the frequency sweep test performed, the storage modulus (G') remains comparatively higher than the loss modulus (G″) in the frequency range 0-20 Hz, indicating high elastic behavior of the hollow microfibers.

The influence of spacer composition on thermomechanical properties, crystallinity, and morphology in ionene segmented copolymers.

A series of segmented ammonium ionenes with varying weight fractions of 2000 g mol-1 poly(ethylene glycol) (PEG) or poly(tetramethylene oxide) (PTMO) soft segments were synthesized, and a simplified coarse-grained model of these materials was implemented using molecular dynamics simulations. In addition to varying soft segment type (PTMO vs. PEG), charge density and soft segment content were varied to create a comprehensive series of segmented ammonium ionenes; thermogravimetric analysis reveals that all segmented ionenes in the series are thermally stable up to 240 °C. Differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) show the formation of phase separated microdomains at low soft segment content. In particular, DSC shows that the hard and soft domains have distinct glass transition temperatures. Similarly, simulations show that reduced soft segment content induces stronger microphase separation, reduces soft segment mobility, and increases ionic aggregate connectivity and size. These increased ionic associations result in elastomeric behavior, as evidenced by the higher rubbery plateau moduli observed at lower soft segment contents through DMA. Moreover, simulations show that ionic aggregation increases when switching from PEG to the less polar PTMO repeat units, which is consistent with DMA results showing higher plateau moduli for PTMO-based ionenes relative to PEG ionenes. DSC and X-ray diffraction determined that the degree of crystallinity increased with soft segment content regardless of segment type. Overall, these results suggest a semi-crystalline microphase-separated morphology strongly influenced by charge density, the degree of ionic aggregation, and the resulting level of confinement and mobility of the soft segments.

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.