Hamid Reza Fard Masoumi

Formulation optimization of palm kernel oil esters nanoemulsion-loaded with chloramphenicol suitable for meningitis treatment

Palm kernel oil esters nanoemulsion-loaded with chloramphenicol was optimized using response surface methodology (RSM), a multivariate statistical technique. Effect of independent variables (oil amount, lecithin amount and glycerol amount) toward response variables (particle size, polydispersity index, zeta potential and osmolality) were studied using central composite design (CCD). RSM analysis showed that the experimental data could be fitted into a second-order polynomial model. Chloramphenicol-loaded nanoemulsion was formulated by using high pressure homogenizer. The optimized chloramphenicol-loaded nanoemulsion response values for particle size, PDI, zeta potential and osmolality were 95.33nm, 0.238, -36.91mV, and 200mOsm/kg, respectively. The actual values of the formulated nanoemulsion were in good agreement with the predicted values obtained from RSM. The results showed that the optimized compositions have the potential to be used as a parenteral emulsion to cross blood-brain barrier (BBB) for meningitis treatment.

Rapid Adsorption of Heavy Metals by Fe3O4/Talc Nanocomposite and Optimization Study Using Response Surface Methodology

Fe3O4/talc nanocomposite was used for removal of Cu(II), Ni(II), and Pb(II) ions from aqueous solutions. Experiments were designed by response surface methodology (RSM) and a quadratic model was used to predict the variables. The adsorption parameters such as adsorbent dosage, removal time, and initial ion concentration were used as the independent variables and their effects on heavy metal ion removal were investigated. Analysis of variance was incorporated to judge the adequacy of the models. Optimal conditions with initial heavy metal ion concentration of 100, 92 and 270 mg/L, 120 s of removal time and 0.12 g of adsorbent amount resulted in 72.15%, 50.23%, and 91.35% removal efficiency for Cu(II), Ni(II), and Pb(II), respectively. The predictions of the model were in good agreement with experimental results and the Fe3O4/talc nanocomposite was successfully used to remove heavy metals from aqueous solutions.

Multivariate Optimization in the Biosynthesis of a Triethanolamine (TEA)-Based Esterquat Cationic Surfactant Using an Artificial Neural Network

An Artificial Neural Network (ANN) based on the Quick Propagation (QP) algorithm was used in conjunction with an experimental design to optimize the lipase-catalyzed reaction conditions for the preparation of a triethanolamine (TEA)-based esterquat cationic surfactant. Using the best performing ANN, the optimum conditions predicted were an enzyme amount of 4.77 w/w%, reaction time of 24 h, reaction temperature of 61.9 °C, substrate (oleic acid: triethanolamine) molar ratio of 1:1 mole and agitation speed of 480 r.p.m. The relative deviation percentage under these conditions was less than 4%. The optimized method was successfully applied to the synthesis of the TEA-based esterquat cationic surfactant at a 2,000 mL scale. This method represents a more flexible and convenient means for optimizing enzymatic reaction using ANN than has been previously reported by conventional methods.

Enhanced biocatalytic esterification with lipase-immobilized chitosan/graphene oxide beads.

In this work, lipase from Candida rugosa was immobilized onto chitosan/graphene oxide beads. This was to provide an enzyme-immobilizing carrier with excellent enzyme immobilization activity for an enzyme group requiring hydrophilicity on the immobilizing carrier. In addition, this work involved a process for the preparation of an enzymatically active product insoluble in a reaction medium consisting of lauric acid and oleyl alcohol as reactants and hexane as a solvent. This product enabled the stability of the enzyme under the working conditions and allowed the enzyme to be readily isolated from the support. In particular, this meant that an enzymatic reaction could be stopped by the simple mechanical separation of the “insoluble” enzyme from the reaction medium. Chitosan was incorporated with graphene oxide because the latter was able to enhance the physical strength of the chitosan beads by its superior mechanical integrity and low thermal conductivity. The X-ray diffraction pattern showed that the graphene oxide was successfully embedded within the structure of the chitosan. Further, the lipase incorporation on the beads was confirmed by a thermo-gravimetric analysis. The lipase immobilization on the beads involved the functionalization with coupling agents, N-hydroxysulfosuccinimide sodium (NHS) and 1-ethyl-(3-dimethylaminopropyl) carbodiimide (EDC), and it possessed a high enzyme activity of 64 U. The overall esterification conversion of the prepared product was 78% at 60°C, and it attained conversions of 98% and 88% with commercially available lipozyme and novozyme, respectively, under similar experimental conditions.

Artificial neural network modeling of p -cresol photodegradation

BackgroundThe complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation.ResultsThe comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97.ConclusionArtificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.

Determining optimum conditions for lipase-catalyzed synthesis of triethanolamine (TEA)-based esterquat cationic surfactant by a Taguchi Robust design method.

A Taguchi robust design method with an L9 orthogonal array was implemented to optimize experimental conditions for the biosynthesis of triethanolamine (TEA)-based esterquat cationic surfactants using an enzymatic reaction method. The esterification reaction conversion% was considered as the response. Enzyme amount, reaction time, reaction temperature and molar ratio of substrates, [oleic acid: triethanolamine (OA:TEA)] were chosen as main parameters. As a result of the Taguchi analysis in this study, the molar ratio of substrates was found to be the most influential parameter on the esterification reaction conversion%. The amount of enzyme in the reaction had also a significant effect on reaction conversion%.

Statistical Optimization of Process Parameters for Lipase-Catalyzed Synthesis of Triethanolamine-Based Esterquats Using Response Surface Methodology in 2-Liter Bioreactor

Lipase-catalyzed production of triethanolamine-based esterquat by esterification of oleic acid (OA) with triethanolamine (TEA) in n-hexane was performed in 2 L stirred-tank reactor. A set of experiments was designed by central composite design to process modeling and statistically evaluate the findings. Five independent process variables, including enzyme amount, reaction time, reaction temperature, substrates molar ratio of OA to TEA, and agitation speed, were studied under the given conditions designed by Design Expert software. Experimental data were examined for normality test before data processing stage and skewness and kurtosis indices were determined. The mathematical model developed was found to be adequate and statistically accurate to predict the optimum conversion of product. Response surface methodology with central composite design gave the best performance in this study, and the methodology as a whole has been proven to be adequate for the design and optimization of the enzymatic process.

Rapid adsorption of copper(II) and lead(II) by rice straw/Fe3O4 nanocomposite: Optimization, equilibrium isotherms, and adsorption kinetics study

Rice straw/magnetic nanocomposites (RS/Fe3O4-NCs) were prepared via co-precipitation method for removal of Pb(II) and Cu(II) from aqueous solutions. Response surface methodology (RSM) was utilized to find the optimum conditions for removal of ions. The effects of three independent variables including initial ion concentration, removal time, and adsorbent dosage were investigated on the maximum adsorption of Pb (II) and Cu (II). The optimum conditions for the adsorption of Pb(II) and Cu(II) were obtained (100 and 60 mg/L) of initial ion concentration, (41.96 and 59.35 s) of removal time and 0.13 g of adsorbent for both ions, respectively. The maximum removal efficiencies of Pb(II) and Cu(II) were obtained 96.25% and 75.54%, respectively. In the equilibrium isotherm study, the adsorption data fitted well with the Langmuir isotherm model. The adsorption kinetics was best depicted by the pseudo-second order model. Desorption experiments showed adsorbent can be reused successfully for three adsorption-desorption cycles.

Semi-empirical study of ortho-cresol photo degradation in manganese-doped zinc oxide nanoparticles suspensions.

The optimization processes of photo degradation are complicated and expensive when it is performed with traditional methods such as one variable at a time. In this research, the condition of ortho-cresol (o-cresol) photo degradation was optimized by using a semi empirical method. First of all, the experiments were designed with four effective factors including irradiation time, pH, photo catalyst’s amount, o-cresol concentration and photo degradation % as response by response surface methodology (RSM). The RSM used central composite design (CCD) method consists of 30 runs to obtain the actual responses. The actual responses were fitted with the second order algebraic polynomial equation to select a model (suggested model). The suggested model was validated by a few numbers of excellent statistical evidences in analysis of variance (ANOVA). The used evidences include high F-value (143.12), very low P-value (<0.0001), non-significant lack of fit, the determination coefficient (R2 = 0.99) and the adequate precision (47.067). To visualize the optimum, the validated model simulated the condition of variables and response (photo degradation %) be using a few number of three dimensional plots (3D). To confirm the model, the optimums were performed in laboratory. The results of performed experiments were quite close to the predicted values. In conclusion, the study indicated that the model is successful to simulate the optimum condition of o-cresol photo degradation under visible-light irradiation by manganese doped ZnO nanoparticles.

Ultrasonic emulsification of parenteral valproic acid-loaded nanoemulsion with response surface methodology and evaluation of its stability

Response surface methodology (RSM) was used to optimize the formulation of a nanoemulsion for central delivery following parenteral administration. A mixture of medium-chain triglyceride (MCT) and safflower seed oil (SSO) was determined as a sole phase from the emulsification properties. Similarly, a natural surfactant (lecithin) and non-ionic surfactant (Tween 80) (ratio 1:2) were used in the formulation. A central composite design (CCD) with three-factor at five-levels was used to optimize the processing method of high energy ultrasonicator. Effects of pre-sonication ultrasonic intensity (A), sonication time (B), and temperature (C) were studied on the preparation of nanoemulsion loaded with valproic acid. Influence of the aforementioned specifically the effects of the ultrasonic processing parameters on droplet size and polydispersity index were investigated. From the analysis, it was found that the interaction between ultrasonic intensity and sonication time was the most influential factor on the droplet size of nanoemulsion formulated. Ultrasonic intensity (A) significantly affects the polydispersity index value. With this optimization method, a favorable droplet size of a nanoemulsion with reasonable polydispersity index was able to be formulated within a short sonication time. A valproic acid loaded nanoemulsion can be obtained with 60% power intensity for 15 min at 60 °C. Droplet size of 43.21±0.11 nm with polydispersity index of 0.211 were produced. The drug content was then increased to 1.5%. Stability study of nanoemulsion containing 1.5% of valproic acid had a good stability as there are no significant changes in physicochemical aspects such as droplet size and polydispersity index. With the characteristisation study of pH, viscosity, transmission electron microscope (TEM) and stability assessment study the formulated nanoemulsion has the potential to penetrate blood-brain barrier in the treatment of epilepsy.