Modern neuroscience increasingly relies on 3D models to study neural circuitry, nerve regeneration, and neural disease. Several different biofabrication approaches have been explored to create 3D neural tissue model structures. Among them, 3D bioprinting has shown to have great potential to emerge as a high-throughput/high precision biofabrication strategy that can address the growing need for 3D neural models. Here, we have reviewed the design principles for neural tissue engineering. The main challenge to adapt printing technologies for biofabrication of neural tissue models is the development of neural bioink, i.e., a biomaterial with printability and gelation properties and also suitable for neural tissue culture. This review shines light on a vast range of biomaterials as well as the fundamentals of 3D neural tissue printing. Also, advances in 3D bioprinting technologies are reviewed especially for bioprinted neural models. Finally, the techniques used to evaluate the fabricated 2D and 3D neural models are discussed and compared in terms of feasibility and functionality.

Volatile organic compounds (VOCs) are major environmental pollutants. Exposure to VOCs has been associated with adverse health outcomes. The monitoring of hazardous VOCs is a vital step towards identifying their presence and preventing the risk of acute or chronic exposure and polluting the environment. One of the challenges associated with monitoring VOCs is selectivity of the sensor. Microfluidic gas sensors offer selective and sensitive detection capabilities that have been recently applied for detection of VOCs. In this study, we achieve improved selectivity for detection of a range of VOCs by adding micro- and nanofeatures to the microchannel of microfluidic gas sensors. First, microfeatures are embedded into the microchannel and their geometries are optimized using Taguchi design of experiment method. In the next step the microfeatures embedded microchannel is coated with graphene oxide, to increase the surface to volume ratio by introducing nanofeatures to the surfaces. The nano- and microfeatures are characterized by SEM, XPS, and water contact angle measurement. Finally, the changes in the sensor response are compared to plain microfluidic gas sensor, the results show an average of 64.4% and 120.9% improvement in the selectivity of the sensor with microfeatures and both nano- and microfeatures, respectively

In this study, we analyzed the application of potentiodynamic electrochemical impedance spectroscopy (PDEIS) for a selective in situ recognition of biological trace elements, i.e., Cr (III), Cu (II), and Fe (III). The electrochemical sensor was developed using the electropolymerization of aniline (Ani) on the surface of the homemade pencil graphite electrodes (PGE) using cyclic voltammetry (CV). The film was overoxidized to diminish the background current. A wide range of potential (V = −0.2 V to 1.0 V) was investigated to study the impedimetric and capacitive behaviour of the PAni/modified PGE. The impedance behaviors of the films were recorded at optimum potentials through electrochemical impedance spectroscopy (EIS) and scrutinized by means of an appropriate equivalent circuit at different voltages and at their corresponding oxidative potentials. The values of the equivalent circuit were used to identify features (charge transfer-resistant and double layer capacitance) that can selectivity distinguish different trace elements with the concentration of 10 μM. The PDEIS spectra represented the highest electron transfer for Cu (II) and Cr (III) in a broad potential range between +0.1 and +0.4 V while the potential V = +0.2 V showed the lowest charge transfer resistance for Fe (III). The results of this paper showed the capability of PDEIS as a complementary tool for conventional CV and EIS measurement for metallic ion sensing.

Modern neuroscience increasingly relies on 3D models to study neural circuitry, nerve regeneration, and neural disease. Several different biofabrication approaches have been explored to create 3D neural tissue model structures. Among them, 3D bioprinting has shown to have great potential to emerge as a high-throughput/high precision biofabrication strategy that can address the growing need for 3D neural models. Here, we have reviewed the design principles for neural tissue engineering. The main challenge to adapt printing technologies for biofabrication of neural tissue models is the development of neural bioink, i.e., a biomaterial with printability and gelation properties and also suitable for neural tissue culture. This review shines light on a vast range of biomaterials as well as the fundamentals of 3D neural tissue printing. Also, advances in 3D bioprinting technologies are reviewed especially for bioprinted neural models. Finally, the techniques used to evaluate the fabricated 2D and 3D neural models are discussed and compared in terms of feasibility and functionality.

Neural tissue engineering holds a great promise for the treatment of neurodegenerative diseases and peripheral nerve injuries. However, the anisotropic mechanical and electrical properties of the highly aligned neural cells have hindered the development of a faithful in vitro disease models. In this study, a core-shell microfluidic extrusion method is implemented to fabricate a cell-laden composite hydrogel fiber with tunable mechanical properties. The hybrid hydrogel was formed using a core of GelMA mixed with gelatin seeded with human neuroblastoma cell (SH-SY5Y) and an alginate shell. The composition of the core hydrogel was optimized to support cellular growth and differentiation, yet allow a feasible fabrication of cell-laden fibers. The engineered fibers were remarkably biocompatible and enabled the formation of highly aligned cellular morphology.

Recent developments in nanofluids have led to a renewed interest in the geometrical effects of nanoparticles on the effective thermal conductivity (ETC) of nanofluids. Although the experimental data for the effect of nanoparticles shapes (the aspect ratio) on ETC are quite consistent, there are still controversial results regarding the effect of the nanoparticles size. Theoretical approaches have been proposed to shed light on the experimental observations in both macroscopic and microscopic scales. However, these approaches cannot be generalized due to the collective interrelated behaviours of nanoparticles. In this paper, a mesoscale numerical simulation, Lattice Boltzmann method (LBM), is implemented to study the effect of nanoparticle geometry on the nanofluids thermal conductivity. The results demonstrate that the nanofluids thermal conductivity and the nanoparticles volume fraction can be linearly correlated. In essence, increasing the nanoparticles aspect ratio can improve ETC of nanofluids. However, the nanoparticle size is not statistically significant (P-value>0.05) to be considered as an influencing parameter (as observed in the controversial results of the experiments) when the interfacial phenomena are not taken into account. The surface to volume ratio of the particle is the parameter that should be studied as all the solid/liquid interfacial interactions depends on this ratio.

An impedance-transducer sensor was developed for in situ detection of hydrogen sulfide (H2S) and ammonia (NH3) in aqueous media. Using cyclic voltammetry (CV), polypyrrole (PPy) was deposited on the surface of the microfabricated interdigitated gold electrode. Due to the proton acid doping effect of H2S on PPy and ionic conduction of the film, the sensor showed a decreasing impedance response to H2S unlike other reducing chemicals, i.e., ammonia (NH3). The recorded faradaic data was then associated with an equivalent circuit and compared with that of NH3 to examine the selectivity of the sensor. An electrochemical impedance spectroscopy (EIS) analysis was applied to the mixture of H2S and NH3 prepared at different ratios for the concentrations ranging from 2 ppm to 20 ppm (below 2-ppm, no response was observed due to the formation of NH4HS, not sensible with PPy). The principal component analysis (PCA) was used to train a real-time prediction model for both classification (for the type of the analyte) and regression (the concentration of the analyte). The results showed the high performance of the sensor in determining individual analytes while the model was able to accurately predict the amount of H2S and NH3 in the mixture.

Traditional cell/particle isolation methods are time-consuming and expensive and can lead to morphology disruptions due to high induced shear stress. To address these problems, novel lab-on-a-chip-based purification methods have been employed. Among various methods introduced for the separation and purification of cells and synthetics particles, acoustofluidics has been one of the most effective methods. Unlike traditional separation techniques carried out in clinical laboratories based on chemical properties, the acoustofluidic process relies on the physical properties of the sample. Using acoustofluidics, manipulating cells and particles can be achieved in a label-free, contact-free, and highly biocompatible manner. To optimize the functionality of the platform, the numerical study should be taken into account before conducting experimental tests to save time and reduce fabrication expenses. Most current numerical studies have only considered one-dimensional harmonic standing waves to simulate the acoustic pressure distribution. However, one-dimensional simulations cannot calculate the actual acoustic pressure distribution inside the microchannel due to its limitation in considering longitudinal waves. To address this limitation, a two-dimensional numerical simulation was conducted in this study. Our numerical simulation investigates the effects of the platform geometrical and operational conditions on the separation efficiency. Next, the optimal values are tested in an experimental setting to validate these optimal parameters and conditions. This work provides a guideline for future acoustofluidic chip designs with a high degree of reproducibility and efficiency.

Among the gas sensing technologies, microfluidic gas sensors have garnered attention because of their sensitivity, compact size, and low cost. In this study, we demonstrate improved selectivity of microfluidic gas sensors toward volatile organic compounds by increasing the effect of adsorption of analytes on the surface of the sensor’s microchannel through increasing the ratio of the surface area (in contact with analyte) to the volume of the microchannel. First, the effect of microchannel geometry modification (reduction of width) is studied through a computational parametric approach (which is validated experimentally). The results show an average improvement of 93.44 % and 60.1 % in selectivity toward polar and nonpolar VOCs, respectively. In the next step, the surface of the microchannel is modified with graphene quantum dots, which has a two-fold effect on VOCs adsorption: (i) increasing the surface area, and (ii) adding functional groups. The experimental results of this step show an average improvement of 101.45 % and 98.82 % in the sensor’s selectivity for the smallest widths toward polar and nonpolar VOCs, respectively. These results indicate that increasing the ratio of surface area (in contact with analyte) to the volume of the microchannel and adding functionalized nanofeatures to the microchannel surface area are promising ways to enhance the selectivity of microfluidic gas sensors.

Advances in lab-on-a-chip (LOC) devices have led to significant improvements in the on-chip manipulation, separation, sorting, and isolation of particles and cells. Among various LOC-based approaches such as inertia-based methods, acoustophoresis, and magnetophoresis, the planar-slanted-electrode dielectrophoresis (DEP) method has demonstrated great potential as a label-free, cost-effective, and user-friendly approach. However, the devices built based on this method suffer from low flow throughput compared to devices functioning based on other LOC-based manipulation approaches. In order to overcome this obstacle, the geometrical parameters of these types of DEP-based devices must be studied to increase the effectiveness of DEP manipulation. With the consideration of both numerical and experimental studies, this paper studies the geometrical factors of a LOC platform consisting of tilted planar electrodes with the goal of achieving higher throughput in continuous manipulation of polystyrene particles. COMSOL Multiphysics software was used to study the effect of the electrodes geometry on the induced electric field. The simulation results show that by increasing the electrode's width and decreasing the electrode's spacing, higher DEP force is generated. Furthermore, the experimental outcomes indicated that lower channel height, higher voltage, and larger particle size resulted in the most improvement to DEP manipulation. Additionally, the experimental results demonstrated that slanted electrodes with an angle of 8° with respect to the direction of flow provide a more effective configuration.

The standard enhancement in nanofluids thermal conductivity due to the addition of nanoparticles is well understood. Despite this, the reason behind observed anomalous increases is still controversial. Limitations in nano-scale experimental observations would make it even harder to approach into this topic. To address this issue, researchers have proposed many different macroscopic (continuum-based)/microscopic (molecular scale) numerical schemes as an alternative for experimental investigations. However, the overall thermal effect of suspended nano-scale particles cannot be observed in neither macroscopic nor microscopic scale due to collective interrelated behaviors such as nanoparticles aggregation. In this paper, a mesoscopic approach, Lattice Boltzmann method (LBM), aims to consider microscopic phenomena in a broader context (mesoscopic scale), been implemented to investigate the nanoparticles aggregation as a probable working mechanism behind the anomalous increase in nanofluids thermal conductivity. The stochastic and dynamic nature of nanoparticles aggregation is captured through generation of fractal random microstructures. The effects of size, shape and distribution regime of aggregates are studied and optimum values are calculated. The results indicate that the aggregation can anomalously enhance nanofluids effective thermal conductivity (ETC). The LBM results are found to be in great agreement with the available numerical/experimental data in the literature.

Legalization of cannabis for recreational use has compelled governments to seek new tools to accurately monitor Δ9-tetrahydrocannabinol (Δ9-THC) and understand its effect on impairment. Various methods have been employed to measure Δ9-THC, and its respective metabolites, in different biological matrices. Recently, breath analysis has gained interest as a non-invasive method for the detection of chemicals that are either produced as part of biological processes or are absorbed from the environment. Existing breath analyzers function by analyzing previously collected samples or by direct real-time analysis. Portable hand-held devices are of particular interest for law enforcement and personal use. This paper reviews and compares both commercially available and prototype devices that proclaim Δ9-THC detection in exhaled breath using methods such as Field Asymmetric Ion Mobility Spectrometry, Semiconductor-Enriched Single-Walled Carbon Nanotube chemiresistors, Liquid Chromatography Tandem-mass Spectrometry, microfluidic-based artificial olfaction, and optical-based gas sensing.

In this paper, an impedance-transduced sensor is developed based on a nanostructured graphene (GN) and poly (methyl methacrylate) (PMMA) sensing film for the detection of individual volatile organic compounds (VOCs) in aqueous media. Benefiting from a porous and high surface area, the nanostructured nanofiber is characterized by scanning electron microscopy (SEM) and optimized by the electrochemical impedance spectroscopy (EIS) technique. The recorded EIS data indicate the selective recognition of four VOCs of interest at a constant pH while there is no redox probe. The non-faradaic responses to each analyte at different concentrations are correlated with a three-element equivalent circuit (resistances of the solution and the film, and a pseudo-capacitance). To analyze the ability of the sensing film in distinguishing between VOCs with similar average boiling points, the values of the individual equivalent circuit elements are used as features and clustered in three-dimensional (3D) plots. Among the features, the two representing the maximum differences between the VOCs are represented in a two-dimensional (2D) plot to show the selectivity of the sensor. The feature extraction analysis demonstrates that the constant phase element (CPE) of the equivalent circuit is a more accurate predictor of VOCs than the interfacial capacitance. These results show high selectivity of the sensorial platform due to the synergistic pairing of nanostructured GN and PMMA.

Hydrogen sulfide (H2S) is a corrosive, flammable, and extremely toxic chemical compound which is typically formed under septic conditions in wastewater collection systems, and the root cause of odor from and corrosion of liquid waste service infrastructure. To control and mitigate H2S buildup in sewer pipelines, it is necessary to detect low concentrations of dissolved H2S. In this paper, a chemiresistive sensor, based on graphene and polymethyl methacrylate (PMMA) is developed, which is able to electrochemically measure various concentrations of H2S in aqueous media. Several tests using electrochemical impedance spectroscopy (EIS) have been conducted to characterize the sensor. The synergetic properties of graphene as the conductive component and PMMA as the linker depicted excellent performance of the thin film sensor to H2S in the range of 10 -2 %v/v. The results demonstrated the potential of the sensor for detection of H2S in aqueous media with high selectivity and sensitivity.

The primary working mechanism behind nanofluids thermal conductivity can be distinguished as one of the most controversial issues of nanofluids. Between several theories proposed in literature, nanopartides' clustering is the sole mechanism that can be observed even at the micro-scale level. Several parameters including nanofluid preparation method, particles concentration, particles morphology, temperature, and elapsed time are experimentally altered to investigate the role of nanoparticles micro-clusters in nanofluids thermal conductivity. It is observed that a minor variation in the altering parameters, would lead a remarkable change in the micro-clusters configuration and subsequently, in the thermal conductivity of nanofluids. In this paper, it is focused on this phenomenon by introducing a new application of image processing as a tool to study the morphological characteristics of micron sized clusters. Through this approach, it is demonstrated that higher thermal conductivity is obtained when particles micro-clusters are well percolated all over the base fluid. As a result, well-diffused percolating micro-structures are known to have a significant role in heat conduction of nanofluids. (C) 2016 Elsevier Ltd. All rights reserved.

In this study, the temperature and viscosity-dependent methods were used to identify the main heat conduction mechanism in nanofluids. Three sets of experiments were conducted to investigate the effects of Brownian motion and aggregation. Image processing approach was used to identify detailed configurations of different nanofluids microstructures. The thermal conductivity of the nanofluids was measured with respect to the dynamic viscosity in the temperature range between 0 and 55 degrees C. The results clearly indicated that the nanoparticle Brownian motion did not play a significant role in heat conduction of nanofluids, which was also supported by the observation that a more viscous sample rendered a higher thermal conductivity. Moreover, the microscopic pictures and the differences in the viscosity between theoretical and experimental values suggested the major role of particle aggregation and clustering. (C) 2017 Elsevier Ltd. All rights reserved.