Flow-induced shear stress and deformation of a core–shell-structured microcapsule in a microchannel

A numerical model was developed and validated to investigate the fluid–structure interactions between fully developed pipe flow and core–shell-structured microcapsule in a microchannel. Different flow rates and microcapsule shell thicknesses were considered. A sixth-order rotational symmetric distribution of von Mises stress over the microcapsule shell can be observed on the microcapsule with a thinner shell configuration, especially at higher flow rate conditions. It is also observed that when being carried along in a fully developed pipe flow, the microcapsule with a thinner shell tends to accumulate stress at a higher rate compared to that with a thicker shell. In general, for the same microcapsule configuration, higher flow velocity would induce a higher stress level over the microcapsule shell. The deformation gradient was used to capture the microcapsule's deformation in the present study. The effect of Young's modulus on the microcapsule shell on the microcapsule deformation was investigated as well. Our findings will shed light on the understanding of the stability of core–shell-structured microcapsule when subjected to flow-induced shear stress in a microfluidic system, enabling a more exquisite control over the breakup dynamics of drug-loaded microcapsule for biomedical applications.     

Design consideration and modelling studies of ultrasound and ultraviolet combined approach for shelf-life enhancement of pine apple juice

Although both ultraviolet (UV) radiation and ultrasound (US) treatment have their capabilities in microbial inactivation, applying any one method alone may require a high dose for complete inactivation, which may affect the sensory and nutritional properties of pineapple juice. Hence, this study was intended to analyse and optimise the effect of combined US and UV treatments on microbial inactivation without affecting the selected quality parameters of pineapple juice. US treatment (33 kHz) was done at three different time intervals, viz. 10 min, 20 min and 30 min., after which, juice samples were subjected to UV treatment for 10 min at three UV dosage levels, viz. 1 J/cm², 1.3 J/cm², and 1.6 J/cm². The samples were evaluated for total colour difference, pH, total soluble solids (TSS), titrable acidity (TA), and ascorbic acid content; total bacterial count and total yeast count; and the standardization of process parameters was done using Response Surface Methodology and Artificial Neural Network. The results showed that the individual, as well as combined treatments, did not significantly impact the physicochemical properties while retaining the quality characteristics. It was observed that combined treatment resulted in 5 log cycle reduction in bacterial and yeast populations while the individual treatment failed. From the optimization studies, it was found that combined US and UV treatments with 22.95 min and1.577 J/cm² ensured a microbiologically safe product while retaining organoleptic quality close to that of fresh juice.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Scattering coefficients for a sphere in a visco-acoustic medium for arbitrary partial wave order

Analytical solutions are reported for the scattering coefficients of a solid elastic sphere suspended in a viscous fluid for arbitrary partial wave order. Expressions are derived for incident compressional and shear wave modes, taking into account the viscosity of the surrounding fluid and resultant wave mode conversion. The long compressional wavelength limit is employed to simplify the derivation, whereas no restriction is placed on the shear wavelength in the fluid compared to the particle dimension. The analytical approximations are compared with numerical results obtained from matrix inversion of the boundary equations and agree within the validity domain of the solutions.     

A modified SPH method to model the coalescence of colliding non-Newtonian liquid droplets

This paper develops a modified smoothed particle hydrodynamics (SPH) method to model the coalescence of colliding non-Newtonian liquid droplets. In the present SPH, a van der Waals (vdW) equation of state is particularly used to represent the gas-to-liquid phase transition similar to that of a real fluid. To remove the unphysical behavior of the particle clustering, also known as tensile instability, an optimized particle shifting technique is implemented in the simulations. To validate the numerical method, the formation of a Newtonian vdW droplet is first tested, and it clearly demonstrates that the tensile instability can be effectively removed. The method is then extended to simulate the head-on binary collision of vdW liquid droplets. Both Newtonian and non-Newtonian fluid flows are considered. The effect of Reynolds number on the coalescence process of droplets is analyzed. It is observed that the time up to the completion of the first oscillation period does not always increase as the Reynolds number increases. Results for the off-center binary collision of non-Newtonian vdW liquid droplets are lastly presented. All the results enrich the simulations of the droplet dynamics and deepen understandings of flow physics. Also, the present SPH is able to model the coalescence of colliding non-Newtonian liquid droplets without tensile instability.     

喷流冷却复合陶瓷薄片激光器的热特性仿真分析及实验研究

为分析喷流冷却复合陶瓷薄片激光器的热特性,设计用于冷却复合陶瓷薄片的喷流冷却系统.利用湍流换热理论和计算流体动力学仿真方法建立喷流冷却复合陶瓷薄片激光器的流固耦合热仿真模型,定义评价其冷却能力和冷却均匀性的定量参数.根据该仿真模型得到喷流冷却系统的最优设计参数,并进行实验验证.使用163孔喷板,流量为0.2kg/s,入口温度为20℃,在1200 W泵浦时获得359 W激光输出功率,并测得复合陶瓷薄片上表面的最高温度为92℃.激光输出功率与复合陶瓷薄片上表面温度均与泵浦功率呈近似正线性关系,且温度的实验值与仿真值相符度较高.     

Sonoelectrochemical hydrogenation of safrole: A reactor design,statistical analysis and computational fluid dynamic approach

In this work, ultrasound-assisted electrocatalytic hydrogenation (US-ECHSA) of safrole was carried out in water medium, using sacrificial anode of nickel. The ultrasonic irradiation was carried out at frequency of 20 kHz ± 500 Hz with a titanium cylindrical horn (MS 73 microtip; Ti-6AI-4V alloy; 3.0 mm diameter). The optimal conditions were analyzed by statistical experimental design (fractional factorial). The influence of the sonoelectrochemical reactor design was also investigated by using computational fluid dynamics as simulation tool. Among the five parameters studied: catalyst type, use of β-cyclodextrin as inverse phase transfer catalyst, sonoelectrochemical reactor design, ultrasound mode and the temperature of the solution, only the last three were significant. The hydrogenation product, dihydrosafrole, reached 94% yield, depending on the experimental conditions applied. Data of computational fluid dynamics showed that a wing shape tube added to the sonoelectrochemical reactor can work as a cooling apparatus, during the electrochemical process. The reactional solution temperature diminishes 14 °C when compared to the four-way-type reactor. Cooper cathode, absence of β-cyclodextrin, four-way-type reactor, ultrasound continuous mode (14 W) and absence of temperature control were the most effective reaction parameters for the safrole hydrogenation using US-ECHSA method. The proposed approach represents an important contribution for understanding the hydrodynamic behavior of sonoelectrochemical reactors designs and, consequently, for the reducing of the experimental costs inherent to the sonoelectrochemical process.     

Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy

Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)—obtained from joints affected in ReA—holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of ¹H and ¹³C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients (n = 8) using high-resolution (800 MHz) ¹H and ¹H─¹³C NMR spectroscopy methods such as one-dimensional ¹H CPMG and two-dimensional J-resolved¹H NMR and homonuclear ¹H─¹H TOCSY and heteronuclear¹H─¹³C HSQC correlation spectra. Compared with normal SF samples, several distinctive ¹H NMR signals were identified and assigned to metabolites in the ¹H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF-based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.