Antimicrobial and antioxidant activities of natural extracts in vitro and in ground beef

Inhibition of Escherichia coli O157:H7, Salmonella Typhimurium, and Listeria monocytogenes by grape seed extract (ActiVin) and pine bark extract (Pycnogenol) and the effect of these natural extracts on the oxidative stability of raw ground beef were studied. In an agar dilution test, the MICs of ActiVin and Pycnogenol were determined to be 4.0 mg/ml for 4.43 log CFU per plate of E. coli O157:H7 and 4.0 mg/ml for 4.38 log CFU per plate of L. monocytogenes. In an inhibition curve test, populations of E. coli O157:H7, Salmonella Typhimurium, and L. monocytogenes fell to below the detection limit (10 CFU/ml) after 16 h of incubation. The numbers of E. coli O157:H7, L. monocytogenes, and Salmonella Typhimurium declined by 1.08, 1.24, and 1.33 log CFU/g, respectively, in raw ground beef treated with 1% Pycnogenol after 9 days of refrigerated storage. ActiVin (1%) and oleoresin rosemary (1%) resulted in an approximately 1-log CFU/g reduction in the populations of all three pathogens after 9 days. The addition of 1% ActiVin and Pycnogenol contributed to the maintenance of an acidic pH of 5.80 and 5.58, respectively, in raw ground beef. Compared to the control, all treatments increased in L* (lightness), with the exception of ActiVin. ActiVin and oleoresin rosemary had the highest a* (redness) and b* (yellowness) values, respectively. ActiVin most effectively retarded lipid oxidation, followed by Pycnogenol. The results suggest that these natural extracts have potential to be used with other preservative methods to reduce pathogenic numbers, lipid oxidation, and color degradation in ground beef.     

Engineered Nanoparticles as Potential Food Contaminants and Their Toxicity to Caco‐2 Cells

Engineered nanoparticles (ENPs), such as metallic or metallic oxide nanoparticles (NPs), have gained much attention in recent years. Increasing use of ENPs in various areas may lead to the release of ENPs into the environment and cause the contamination of agricultural and food products by ENPs. In this study, we selected two important ENPs (zinc oxide [ZnO] and silver [Ag] NPs) as potential food contaminants and investigated their toxicity via an in vitro model using Caco‐2 cells. The physical properties of ENPs and their effects on Caco‐2 cells were characterized by electron microscopy and energy dispersive X‐ray spectroscopic (EDS) techniques. Results demonstrate that a significant inhibition of cell viability was observed after a 24‐h of exposure of Caco‐2 cells to 3‐, 6‐, and 12‐mM ZnO NPs or 0.5‐, 1.5‐, and 3‐mM Ag NPs. The noticeable changes of cells include the alteration in cell shape, abnormal nuclear structure, membrane blebbing, and cytoplasmic deterioration. The toxicity of ZnO NPs, but not that of Ag NPs after exposure to simulated gastric fluid, significantly decreased. Scanning transmission electron microscopy shows that ZnO and Ag NPs penetrated the membrane of Caco‐2 cells. EDS results also confirm the presence of NPs in the cytoplasm of the cells. This study demonstrates that ZnO and Ag NPs have cytotoxic effects and can inhibit the growth of Caco‐2 cells.     

Application of response surface methodology (RSM) for optimization of cassava starch grafted polyDADMAC synthesis for cationic properties

Response surface methodology was used to study the process optimization of cationic percentage through grafting diallyldimethylammonium chloride (DADMAC) into cassava starch. Based on Box–Behken design, quadratic models were developed to correlate the synthesis variables: concentration of DADMAC (A), concentration of initiator (B), and polymerization temperature (C), to the responses of cationic percentage (y). Analysis of variance (ANOVA) revealed that the concentration of initiator was the most significant variable for the response. The optimum preparation conditions for starch grafted polyDADMAC were 14% cationic using 31 g DADMAC/10 g dry starch of concentration DADMAC, a polymerization temperature of 74°C, and 4.92 g cerric ammonium nitrate/10 mL of initiator concentration.     

Dielectric Constant,Metallization Criterion and Optical Properties of CuO Doped TeO2–B2O3 Glasses

Journal of Inorganic and Organometallic Polymers and Materials - Copper oxide doped TeO2–B2O3 glass system with empirical formula;...     

Dynamic modeling of gas phase propylene homopolymerization in fluidized bed reactors

A new model with comprehensive kinetics for propylene homopolymerization in fluidized bed reactors was developed to investigate the effect of mixing, operating conditions, kinetic and hydrodynamic parameters on the reactor performance as well as polymer properties. Presence of the particles in the bubbles and the excess gas in the emulsion phase was considered to improve the two-phase model, thus, considering the polymerization reaction to take place in both the bubble and emulsion phases. It was shown that in the practical range of superficial gas velocity and catalyst feed rate, the ratio of produced polymer in the bubble phase to the total production rate is roughly between 10% and 13%, which is a substantial amount and cannot be ignored. Simulation studies were carried out to compare the results of the improved two-phase, conventional well-mixed and constant bubble size models. The improved two-phase and well mixed models predicted a narrower and safer window at the same running conditions compared with the constant bubble size model. The improved two-phase model showed close dynamic behavior to the conventional models at the beginning of polymerization, but starts to diverge with the evolution of time.     

Microwave-assisted preparation of mesoporous-activated carbon from coconut (Cocos nucifera) leaf by H3PO4 activation for methylene blue adsorption

Mesoporous-activated carbon was prepared from fallen coconut (Cocos nucifera) leaf, an agricultural waste through a microwave-induced H₃PO₄ activation process. The characterization of the coconut leaf–activated carbon (CAC) was evaluated through the iodine number, ash content, bulk density, and moisture content. Fourier transform infrared spectroscopy, scanning electron microscope, Brunauer–Emmett–Teller (BET) surface area, X-ray diffraction, and pH_PZC. CAC has a mesopore content of 84% with an average pore size of 36.5?Å and a large BET surface area of 632?m²/g. The uptake properties of the CAC with methylene blue was evaluated at different CAC dosage levels (0.2–10?g/L), initial pH (3–10), methylene blue concentration (50–350?mg/L), and time (0–360?min) using batch mode operation. The kinetic profiles were described by the pseudo-second-order kinetics. The equilibrium data were well fitted to the Langmuir model with a maximum monolayer adsorption capacity of 250?mg/g at 30°C. Thermodynamic functions indicate a spontaneous and exothermic nature of the adsorption process. This study indicates that coconut leaves are a promising renewable precursor that can be utilized to develop an efficient mesoporous-activated carbon.     

Model predictive control for the reactant concentration control of a reactor

The reactant concentration control of a reactor using Model Predictive Control (MPC) is presented in this paper. Two major difficulties in the control of reactant concentration are that the measurement of concentration is not available for the control point of view and it is not possible to control the concentration without considering the reactor temperature. Therefore, MIMO control techniques and state and parameter estimation are needed. One of the MIMO control techniques widely studied recently is MPC. The basic concept of MPC is that it computes a control trajectory for a whole horizon time minimising a cost function of a plant subject to a dynamic plant model and an end point constraint. However, only the initial value of controls is then applied. Feedback is incorporated by using the measurements/estimates to reconstruct the calculation for the next time step. Since MPC is a model based controller, it requires the measurement of the states of an appropriate process model. However, in most industrial processes, the state variables are not all measurable. Therefore, an extended Kalman filter (EKF), one of estimation techniques, is also utilised to estimate unknown/uncertain parameters of the system. Simulation results have demonstrated that without the reactor temperature constraint, the MPC with EKF can control the reactant concentration at a desired set point but the reactor temperator is raised over a maximum allowable value. On the other hand, when the maximun allowable value is added as a constraint, the MPC with EKF can control the reactant concentration at the desired set point with less drastic control action and within the reactor temperature constraint. This shows that the MPC with EKF is applicable to control the reactant concentration of chemical reactors.     

气相石蜡聚合在流化床催化反应器中的数学模型和先进控制

In this study, the developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors （FBR） using Ziegler-Natta catalyst is presented. The modified mathematical model to account for mass and heat transfer between the solid particles and the surrounding gas in the emulsion phase is developed in this work to include site activation reaction. This model developed in the present study is subsequently compared with well-known models, namely, the bubble-growth, well-mixed and the constant bubble size models for porous and non porous catalyst. The results we obtained from the model was very close to the constant bubble size model, well-mixed model and bubble growth model at the beginning of the reaction but its overall behavior changed and is closer to the well-mixed model compared with the bubble growth model and constant bubble size model after half an hour of operation. Neural-network based predictive controller are implemented to control the system and compared with the conventional PID controller, giving acceptable results.     

Hybrid neural network for prediction of CO<Subscript>2</Subscript> solubility in monoethanolamine and diethanolamine solutions

The solubility of CO₂ in single monoethanolamine (MEA) and diethanolamine (DEA) solutions was predicted by a model developed based on the Kent-Eisenberg model in combination with a neural network. The combination forms a hybrid neural network (HNN) model. Activation functions used in this work were purelin, logsig and tansig. After training, testing and validation utilizing different numbers of hidden nodes, it was found that a neural network with a 3-15-1 configuration provided the best model to predict the deviation value of the loading input. The accuracy of data predicted by the HNN model was determined over a wide range of temperatures (0 to 120 °C), equilibrium CO₂ partial pressures (0.01 to 6,895 kPa) and solution concentrations (0.5 to 5.0M). The HNN model could be used to accurately predict CO₂ solubility in alkanolamine solutions since the predicted CO₂ loading values from the model were in good agreement with experimental data.