Navigating viscosity of ferrofluid using response surface methodology and artificial neural network

Abstract The main purpose of this study is to investigate the capabilities of artificial neural network (ANN) and response surface methodology (RSM) in estimating the viscosity of Fe3O4 nanofluid. Nanoparticles increase the resistance to motion and thus boost the viscosity. Initially, the rheological behavior of the base fluid and nanofluid was investigated and it was found that both fluids are not particularly sensitive to the shear rate, which indicates the Newtonian behavior. Input parameters of temperature and volume fraction and output parameter, nanofluid viscosity were introduced to both techniques to find the best correlation in which the viscosity can be predictable. Comparison of R-square in ANN (0.999) and RSM (0.996) techniques showed that both techniques can navigate the viscosity well. Also the margin of deviation (MOD) and mean square error (MSE) for ANN were 4.22% and 0.0000741 which were lower than the corresponding values in RSM one (MOD = 5.52%, MSE = 0.00027)

Performing regression-based methods on viscosity of nano-enhanced PCM - Using ANN and RSM

Abstract Evaluation of the use of linear and nonlinear regression-based methods in estimating the viscosity of MWCNT/liquid paraffin nanofluid was investigated in this study. At temperature range of 5–65 °C, the viscosity of samples containing MWCNT nanoparticles at 0.005–5 wt.% which is measured by a Brookfield apparatus, was first evaluated to determine the response to the shear rate. The decrease in viscosity due to the increase in shear rate indicated that the rheological behavior of the nanofluid was non-Newtonian and therefore, in addition to temperature and mass fraction, the shear rate should be considered as an effective input parameter. Linear regression was performed by response surface methodology (RSM) and it was observed that the R-square for the best polynomial was 0.988. The results of nonlinear regression also showed that the neural network consisting of 3 and 13 neurons in the input and hidden layers was able to estimate the viscosity of the nanofluid more accurately so that the R-square value was calculated to be 0.998

Thermal Conductivity Enhancement via Synthesis Produces a New Hybrid Mixture Composed of Copper Oxide and Multi-walled Carbon Nanotube Dispersed in Water: Experimental Characterization and Artificial Neural Network Modeling

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Nanofluid is a solid–fluid mixture. By using one solid nanoparticle or one fluid, mono-nanofluid (MN) forms, and by using two solid nanoparticles (NPs) or two fluids, hybrid-nanofluid (HN) forms. For this study, for MN, copper oxide (CuO) and for HN, two solids, which are CuO and multi-walled carbon nanotube (MWCNT) were dispersed in base fluid which is water. After nanofluid preparation, thermal conductivity was measured, and the achievements were numerically modeled. After that, XRD–EDX were performed for the phase-structural analysis. Then, FESEM was examined for NPs-microstructural study. Thermal conductivity (TC) of MN and HN were investigated at 0.2 % to 1.0 % volume fractions (Vf) in 25 °C to 50 °C temperature (T) ranges. Thermal conductivity enhancements of 19.16 % and 37.05 % were seen at the utmost Vf and T for mono-nanofluid and hybrid-nanofluid, respectively. New correlations have been presented with R2 = 0.9, and also Artificial Neural Network (ANN) has been done with R2 = 0.999. For the presented correlation, 0.86 %, and 0.51 % deviations, and for the trained model, 0.41 % and 0.51 % deviations were estimated for mono-nanofluid and hybrid-nanofluid, respectively. As a final result, by adding MWCNT to CuO–H2O mixture, thermal conductivity is raised by 17.89 %, and the hybrid-nanofluid has acceptable heat-transfer capability

Real-time fluorescence assay for monitoring transglutaminase activity

Transglutaminases (TGs) form a family of enzymes that catalyze various posttranslational protein modifications such as crosslinking, esterification and deamidation in a Ca2+-dependent manner.(1) Their main function is the formation of covalent Nε-(γ-glutamyl)lysine bonds within or between polypeptides to stabilize protein assemblies. The activity of these enzymes is crucial for tissue homeostasis and function in a number of organ systems, and the lack of or the excessive crosslinking activity have been linked to human disease processes(1,2). Here we perform kinetic measurements using recombinant TG2 and a fluorescent peptide model substrate on a FLUOstar OPTIMA and FLUOstar Omega in a format suitable for high-throughput analysis. This assay principle can be applied to kinetic studies on closely related enzymes including TG6(3) and can be optimised by modification of the backbone peptide sequence

Molecular dynamics performance for coronavirus simulation by C, N, O, and S atoms implementation dreiding force field: drug delivery atomic interaction in contact with metallic Fe, Al, and steel

Coronavirus causes some illnesses to include cold, COVID-19, MERS, and SARS. This virus can be transmitted through contact with different atomic matrix between humans. So, this atomic is essential in medical cases. In this work, we describe the atomic manner of this virus in contact with various metallic matrix such as Fe, Al, and steel with equilibrium molecular dynamic method. For this purpose, we reported physical properties such as temperature, total energy, distance and angle of structures, mutual energy, and volume variation of coronavirus. In this approach, coronavirus is precisely simulated by O, C, S, and N atoms and they are implemented dreiding force field. Our simulation shows that virus interaction with steel matrix causes the maximum removing of the virus from the surfaces. After 1 ns, the atomic distance between these two structures increases from 45 to 75 Å. Furthermore, the volume of coronavirus 14.62% increases after interaction with steel matrix. This atomic manner shows that coronavirus removes and destroyed with steel surface, and this metallic structure can be a promising material for use in medical applications

Thermomechanical properties of coated PLA-3D-printed orthopedic plate with PCL/Akermanite nano-fibers: Experimental procedure and AI optimization

Nowadays, 3D printing has become a popular method among surgeons due to its merits in orthopedic treatments. In this method, polymeric biomaterials are deposited in a layer-by-layer manner to fabricate 3D objects that can be used as orthopedic implants and plates; however, 3D-printed implants or plates may lack properties required to bond with host tissue. Coating surface of plates with nano-fibers is an appropriate way to modify plates to overcome this challenge. In this study, first, an orthopedic plate was 3D printed with Polylactic acid (PLA) and coated with polycaprolactone (PCL)/Akermanite (AKT) nano-fibers. The composition included 8 wt% of PCL and 3 wt% of nAKT, while diameter of the PCL/AKT nano-fibers was approximately 253 nm ± 33 nm. Thermomechanical properties such as pressure, three-point bending flexural, and thermal conductivity of coated and non-coated specimens were examined and compared. In the next step, the bioactivity of the coated samples was evaluated following a 28-day immersion in simulated body fluid (SBF). Further, scanning electron microscope (SEM) images were taken to assess morphology of nanofibers and apatite formation on samples. By adding PCL to PLA, the maximum pressure force is enhanced by 16.83%. Further by adding nAKT to PLA + PCL sample, the maximum pressure force is enhanced by 4.72%. Further, by adding PCL to PLA, the maximum bending flexural force is enhanced by 21.06%. Further by adding nAKT to PLA + PCL sample, the maximum bending flexural force is enhanced by 21.39%. The results of this study are used to improve modeling of the orthopedic plates