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Strategies to stir and mix reagents in microfluid devices have evolved concomitantly with advancements in manufacturing techniques and sensing. While there is a large array of reported designs to combine and homogenize liquids, most of the characterization has been focused on setups with two inlets and one outlet. While this configuration is helpful to directly evaluate the effects of features and parameters on the mixing degree, it does not portray the conditions for experiments that involve more than two substances required to be subsequently combined. In this work, we present a mixing characterization methodology based on particle tracking as an alternative to the most common approach to measure homogeneity using the standard deviation of pixel intensities from a grayscale image. The proposed algorithm is implemented on a free and open-source mobile application (MIQUOD) for Android devices, numerically tested on COMSOL Multiphysics, and experimentally tested on a bidimensional split and recombine micromixer and a three-dimensional micromixer with sinusoidal grooves for different Reynolds numbers and geometrical features for samples with fluids seeded with red, blue, and green microparticles. The application uses concentration field data and particle track data to evaluate up to eleven performance metrics. Furthermore, with the insights from the experimental and numerical data, a mixing index for particles (mp) is proposed to characterize mixing performance for scenarios with multiple input reagents.
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Despite the copious amount of research on the design and operation of micromixers, there are few works regarding manufacture technology aimed at implementation beyond academic environments. This work evaluates the viability of xurography as a rapid fabrication tool for the development of ultra-low cost microfluidic technology for extreme Point-of-Care (POC) micromixing devices. By eschewing photolithographic processes and the bulkiness of pumping and enclosure systems for rapid fabrication and passively driven operation, xurography is introduced as a manufacturing alternative for asymmetric split and recombine (ASAR) micromixers. A T-micromixer design was used as a reference to assess the effects of different cutting conditions and materials on the geometric features of the resulting microdevices. Inspection by stereographic and confocal microscopy showed that it is possible to manufacture devices with less than 8% absolute dimensional error. Implementation of the manufacturing methodology in modified circular shape- based SAR microdevices (balanced and unbalanced configurations) showed that, despite the precision limitations of the xurographic process, it is possible to implement this methodology to produce functional micromixing devices. Mixing efficiency was evaluated numerically and experimentally at the outlet of the microdevices with performances up to 40%. Overall, the assessment encourages further research of xurography for the development of POC micromixers.
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Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin-Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.
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Light-based bioprinter manufacturing technology is still prohibitively expensive for organizations that rely on accessing three-dimensional biological constructs for research and tissue engineering endeavors. Currently, most of the bioprinting systems are based on commercial-grade-based systems or modified DIY (do it yourself) extrusion apparatuses. However, to date, few examples of the adoption of low-cost equipment have been found for light-based bioprinters. The requirement of large volumes of bioinks, their associated cost, and the lack of information regarding the parameter selection have undermined the adoption of this technology. This paper showcases the retrofitting and assessing of a low-cost Light-Based 3D printing system for tissue engineering. To evaluate the potential of a proposed design, a manufacturability test for different features, machine parameters, and Gelatin Methacryloyl (GelMA) concentrations for 7.5% and 10% was performed. Furthermore, a case study of a previously seeded hydrogel with C2C12 cells was successfully implemented as a proof of concept. On the manufacturability test, deviational errors were found between 0.7% to 13.3% for layer exposure times of 15 and 20 s. Live/Dead and Actin-Dapi fluorescence assays after 5 days of culture showed promising results in the cell viability, elongation, and alignment of 3D bioprinted structures. The retrofitting of low-cost equipment has the potential to enable researchers to create high-resolution structures and three-dimensional in vitro models.
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The strategy of embedding conductive materials on polymeric matrices has produced functional and wearable artificial electronic skin prototypes capable of transduction signals, such as pressure, force, humidity, or temperature. However, these prototypes are expensive and cover small areas. This study proposes a more affordable manufacturing strategy for manufacturing conductive layers with 6 × 6 matrix micropatterns of RTV-2 silicone rubber and Single-Walled Carbon Nanotubes (SWCNT). A novel mold with two cavities and two different micropatterns was designed and tested as a proof-of-concept using Low-Force Stereolithography-based additive manufacturing (AM). The effect SWCNT concentrations (3 wt.%, 4 wt.%, and 5 wt.%) on the mechanical properties were characterized by quasi-static axial deformation tests, which allowed them to stretch up to ~160%. The elastomeric soft material's hysteresis energy (Mullin's effect) was fitted using the Ogden-Roxburgh model and the Nelder-Mead algorithm. The assessment showed that the resulting multilayer material exhibits high flexibility and high conductivity (surface resistivity ~7.97 × 104 Ω/sq) and that robust soft tooling can be used for other devices.
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In this paper, we characterized an assortment of photopolymers and stereolithography processes to produce 3D-printed molds and polydimethylsiloxane (PDMS) castings of micromixing devices. Once materials and processes were screened, the validation of the soft tooling approach in microfluidic devices was carried out through a case study. An asymmetric split-and-recombine device with different cross-sections was manufactured and tested under different regime conditions (10 < Re < 70). Mixing performances between 3% and 96% were obtained depending on the flow regime and the pitch-to-depth ratio. The study shows that 3D-printed soft tooling can provide other benefits such as multiple cross-sections and other potential layouts on a single mold.
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In this work we present a novel algorithm for generating in-silico biomimetic models of a cortical bone microstructure towards manufacturing biomimetic bone via additive manufacturing. The software provides a tool for physicians or biomedical engineers to develop models of cortical bone that include the inherent complexity of the microstructure. The correspondence of the produced virtual prototypes with natural bone tissue was assessed experimentally employing Digital Light Processing (DLP) of a thermoset polymer resin to recreate healthy and osteoporotic bone tissue microstructure. The proposed tool was successfully implemented to develop cortical bone structure based on osteon density, cement line thickness, and the Haversian and Volkmann channels to produce a user-designated bone porosity that matches within values reported from literature for these types of tissues. Characterization of the specimens using a Scanning Electron Microscopy with Focused Ion Beam (SEM/FIB) and Computer Tomography (CT) revealed that the manufacturability of intricated virtual prototype is possible for scaled-up versions of the tissue. Modeling based on the density, inclination and size range of the osteon and Haversian and Volkmann´s canals granted the development of a dynamic in-silico porosity (13.37â»21.49%) that matches with models of healthy and osteoporotic bone. Correspondence of the designed porosity with the manufactured assessment (5.79â»16.16%) shows that the introduced methodology is a step towards the development of more refined and lifelike porous structures such as cortical bone. Further research is required for validation of the proposed methodology model of the real bone tissue and as a patient-specific customization tool of synthetic bone.
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In this work, we assess the effects of sterilization in materials manufactured using additive manufacturing by employing a sterilization technique used in the food industry. To estimate the feasibility of the hydrostatic high-pressure (HHP) sterilization of biomedical devices, we have evaluated the mechanical properties of specimens produced by commercial 3D printers. Evaluations of the potential advantages and drawbacks of Fused Deposition Modeling (FDM), Digital Light Processing (DLP) technology, and Stereolithography (SLA) were considered for this study due to their widespread availability. Changes in mechanical properties due to the proposed sterilization technique were compared to values derived from the standardized autoclaving methodology. Enhancement of the mechanical properties of samples treated with Hydrostatic high-pressure processing enhanced mechanical properties, with a 30.30% increase in the tensile modulus and a 26.36% increase in the ultimate tensile strength. While traditional autoclaving was shown to systematically reduce the mechanical properties of the materials employed and damages and deformation on the surfaces were observed, HHP offered an alternative for sterilization without employing heat. These results suggest that while forgoing high-temperature for sanitization, HHP processing can be employed to take advantage of the flexibility of additive manufacturing technologies for manufacturing implants, instruments, and other devices.