Extraction of ingredients from tea leaves using oxidative enzymatic reaction and optimization of extraction conditions.

Peroxidase (POD) and polyphenol oxidase (PPO) are used as biocatalyst in many processes such as oxidization reactions, wastewater treatment, phenol synthesis and so on. The purpose of current study is enzymes extraction from biomass (tea leaves) as well as evaluation of their activation. Different parameters including temperature, buffer concentration, buffer type, buffer/tea leaves ratio, addition of high molecular weight polymers and emulsifiers, and pH were optimized in order to obtain the highest enzymes activity. Response Surface Methodology (RSM) procedure is employed for statistical analysis of enzymes extraction. It is understood from the result that PPO and POD possess the highest activity at temperatures of 25 °C and 50 °C, pH 7 and 5, buffer molarity of 0.1, and 0.05, buffer/tea leaves ratio = 5 for both, contact time = 20 min and 10 min, and presence of 6% and 3% PVP, 5% and 0% Tween 80 for PPO and POD, respectively. Amounts of highest activity for PPO and POD biocatalysts were calculated 0.42 U/mL and 0.025493 U/mL, respectively. Moreover, the entire inactivation of PPO took place after 30 min at 40 °C and 60 °C and 20 min at 80 °C. However, POD lost 35% of its activity after 30 min at 40 °C and 60 °C. The amount of 6% POD activity was kept after 45 min at 80 °C. Generally, it was indicated that POD was more resistant to thermal treatment than PPO.

Synthesis of multi-organo-functionalized fibrous silica KCC-1 for highly efficient adsorption of acid fuchsine and acid orange II from aqueous solution.

Multi-functionalized fibrous silica KCC-1 (MF-KCC-1) bearing amine, tetrasulfide, and thiol groups was synthesized via a post-functionalization method and fully characterized by several methods such as FTIR, FESEM, EDX-Mapping, TEM, and N2 adsorption-desorption techniques. Due to abundant surface functional groups, accessible active adsorption sites, high surface area (572 m2 g-1), large pore volume (0.98 cm3 g-1), and unique fibrous structure, mesoporous MF-KCC-1 was used as a potential adsorbent for the uptake of acid fuchsine (AF) and acid orange II (AO) from water. Different adsorption factors such as pH of the dye solution, the amount of adsorbent, initial dye concentration, and contact time, affecting the uptake process were optimized and isotherm and kinetic studies were conducted to find the possible mechanism involved in the process. For both AF and AO dyes, the Langmuir isotherm model and the PFO kinetic model show the most agreement with the experimental data. According to the Langmuir isotherm, the calculated maximum adsorption capacity for AF and AO were found to be 574.5 mg g-1 and 605.9 mg g-1, respectively, surpassing most adsorption capacities reported until now which is indicative of the high potential of mesoporous MF-KCC-1 as an adsorbent for removal applications.

Synthesis, molecular dynamics simulation and adsorption study of different pollutants on functionalized mesosilica.

Experimental and computational works were carried out on a new type of mesoporous silica. In the experimental section, functionalized hollow mesosilica spheres were prepared via a facile technique and then evaluated using some analytical techniques (FESEM, TEM, L-XRD, FTIR, BET-BJH, and TGA). The obtained results revealed that the synthesized material had hollow structure with a diamino-grafted porous shell. The molecular separation of crystal Violet (CV) and neutral Red (NR) dyes from water were investigated by adsorption process using the synthesized powder. Influence of adsorbent loading was evaluated as adsorption ability and dyes removal efficiency. Also, the obtained modeling results revealed appropriate fitting of data with non-linear Langmuir model. The theoretical studies were employed to study the adsorption and removal mechanism of cationic (CV and NR) and anionic (orange II (OII)) dyes using molecular dynamics calculations. Moreover, the simulation outcomes provided valuable information about quantum chemical properties including the HOMO-LUMO maps, chemical reactivity, global softness (σ) and hardness (η) for silica-linker-water-dyes components.

Influence of machine learning membership functions and degree of membership function on each input parameter for simulation of reactors.

To understand impact of input and output parameters during optimization and degree of complexity, in the current study we numerically designed a bubble column reactor with a single sparger in the middle of the reactor. After that, some input and output parameters were selected in the post-processing of the numerical method, and then the machine learning observation started to investigate the level of complexity and impact of each input on output parameters. The adaptive neuro-fuzzy inference system (ANFIS) method was exploited as a machine learning approach to analyze the gas-liquid flow in the reactor. The ANFIS method was used as a machine learning approach to simulate the flow of a 3D (three-dimensional) bubble column reactor. This model was also used to analyze the influence of input and output parameters together. More specifically, by analyzing the degree of membership functions as a function of each input, the level of complexity of gas fraction was investigated as a function of computing nodes (X, Y, and Z directions). The results showed that a higher number of membership functions results in a better understanding of the process and higher model accuracy and prediction capability. X and Y computing nodes have a similar impact on the gas fraction, while Z computing points (height of reactor) have a uniform distribution of membership function across the column. Four membership functions (MFs) in each input parameter are insufficient to predict the gas fraction in the 3D bubble column reactor. However, by adding two membership functions, all features of gas fraction in the 3D reactor can be captured by the machine learning algorithm. Indeed, the degree of MFs was considered as a function of each input parameter and the effective parameter was found based on the impact of MFs on the output.

A novel and facile green synthesis method to prepare LDH/MOF nanocomposite for removal of Cd(II) and Pb(II).

To date, many nanoadsorbents have been developed and used to eliminate heavy metal contamination, however, one of the challenges ahead is the preparation of adsorbents from processes in which toxic organic solvents are used in the least possible amount. Herein, we have developed a new carboxylic acid-functionalized layered double hydroxide/metal-organic framework nanocomposite (LDH/MOF NC) using a simple, effective, and green in situ method. UiO-66-(Zr)-(COOH)2 MOF nanocrystals were grown uniformly over the whole surface of COOH-functionalized Ni50Co50-LDH ultrathin nanosheets in a green water system under a normal solvothermal condition at 100 °C. The synthesized LDH/MOF NC was used as a potential adsorbent for removal of toxic Cd(II) and Pb(II) from water and the influence of important factors on the adsorption process was monitored. Various non-linear isotherm and kinetic models were used to find plausible mechanisms involved in the adsorption, and it was found that the Langmuir and pseudo-first-order models show the best agreement with isotherm and kinetic data, respectively. The calculated maximum adsorption capacities of Cd(II) and Pb(II) by the LDH/MOF NC were found to be 415.3 and 301.4 mg g-1, respectively, based on the Langmuir model (pH = 5.0, adsorbent dose = 0.02 g, solution volume = 20 mL, contact time = 120 min, temperature = 25 â„ƒ, shaking speed 200 rpm).

Efficient oxidation/mineralization of pharmaceutical pollutants using a novel Iron (III) oxyhydroxide nanostructure prepared via plasma technology: Experimental, modeling and DFT studies.

High-performance novel iron oxyhydroxide (limonite) nanostructure, with improved surface reactive sites, was prepared via one-pot, eco-friendly, free precursor and cold glow discharge N2-plasma technique. Natural and plasma treated (PTNL/N2) limonite samples were characterized by FESEM, XPS, XRD, FTIR, AAS, EDX, BET/BJH and pHpzc to confirm the successful synthesis. Central composite design (CCD) and artificial neural network (ANN, topology of 4:8:1) methods were utilized to study the oxidation/mineralization of phenazopyridine (PhP) as a hazardous contaminant by heterogeneous catalytic ozonation process (HCOP). The obtained results indicated that PTNL/N2 had the highest catalytic performance in PhP degradation (98.6% in 40 min) and mineralization (80.4% in 120 min). The degradation mechanism in different processes was investigated by dissolved ozone concentration, various organic scavengers (BQ and TBA) and inorganic salts (NaNO3, NaCl, Na2CO3 and NaH2PO4). Moreover, reusability-stability, Fe and nitrogen (NO3- and NH4+) ions release were assessed during different AOPs. Furthermore, toxicity tests indicated that the HCOP using PTNL/N2 was able to detoxify the PhP solutions efficiently. Finally, Density Functional Theory (DFT) studies were employed to introduce the most plausible contaminant degradation pathway, reactive sites and byproducts. This research provided a new insight into the improvement of wastewater treatment studies by a combination of experiment and computer simulation.

High performance ozone based advanced oxidation processes catalyzed with novel argon plasma treated iron oxyhydroxide hydrate for phenazopyridine degradation.

The present study has focused on the degradation of phenazopyridine (PhP) as an emerging contaminant through catalytic ozonation by novel plasma treated natural limonite (FeOOH·xH2O, NL) under argon atmosphere (PTL/Ar). The physical and chemical characteristics of samples were evaluated with different analyses. The obtained results demonstrated higher surface area for PTL/Ar and negligible change in crystal structure, compared to NL. It was found that the synergistic effect between ozone and PTL/Ar nanocatalyst was led to highest PhP degradation efficiency. The kinetic study confirmed the pseudo-first-order reaction for the PhP degradation processes included adsorption, peroxone and ozonation, catalytic ozonation with NL and PTL/Ar. Long term application (6 cycles) confirmed the high stability of the PTL/Ar. Moreover, different organic and inorganic salts as well as the dissolved ozone concentration demonstrated the predominant role of hydroxyl radicals and superoxide radicals in PhP degradation by catalytic Ozonation using PTL/Ar. The main produced intermediates during PhP oxidation by PTL/Ar catalytic ozonation were identified using LC-(+ESI)-MS technique. Finally, the negligible iron leaching, higher mineralization rate, lower electrical energy consumption and excellent catalytic activity of PTL/Ar samples demonstrate the superior application of non-thermal plasma for treatment of NL.

Using static method to measure tolmetin solubility at different pressures and temperatures in supercritical carbon dioxide.

Tolmetin is a non-steroidal anti-inflammatory drug being used to decrease the level of hormones which are the reasons for pain, swelling, tiredness, and stiffness for osteoarthritis and rheumatoid arthritis cases. We evaluated its solubility in supercritical carbon dioxide (SC-CO2) with the aim of drug nanonization, considering temperature and pressure variations between 120 and 400 bar and 308-338 K, in the experiments. In this way, a PVT solubility cell based on static solubility approach coupled with a simple gravimetric procedure was utilized to evaluate the solubility of tolmetin. The solubility values between 5.00 × 10-5 and 2.59 × 10-3 mol fraction were obtained for tolmetin depending on the pressure and temperature of the cell. The measured data demonstrated a direct correlation between pressure and solubility of tolmetin, while the effect of temperature was a dual effect depending on the crossover pressure (160 bar). The calculated solubility data were modeled using several semi-empirical correlations, and the fitting parameters were calculated using the experimental data via appropriate optimization method. The correlated solubility data revealed that the KJ model was the most accurate one with an average absolute relative deviation percent (AARD%) of 6.9. Moreover, the carried out self-consistency analysis utilizing these correlations illustrated great potential of these models to extrapolate the solubility of tolmetin beyond the measured conditions.

Degradation of sulfonamide antibiotics using ozone-based advanced oxidation process: Experimental, modeling, transformation mechanism and DFT study.

In this research, degradation of three sulfonamide antibiotic compounds (SNAs) such as Sulfasalazine (SSZ), Sulfamethoxazole (SMX) and Sulfamethazine (SMT) as well as Metronidazole (MNZ) were investigated for the first time using experimental, modeling and simulation data under O3, H2O2, and O3/H2O2 systems. The kinetic and synergistic study confirmed the pseudo-first-order reaction and highest performance of the O3/H2O2 process for the SNAs degradation process. Two modeling approach, central composite design (CCD) based on response surface methodology (RSM) and artificial neural network (ANN) were utilized to investigate the optimization and modeling of SSZ degradation as the response of O3/H2O2 system and results were compared. The individual and interactive effects of main operational parameters were also possessed by the main effect graphs, contour and response surface plots. The experimental results showed maximum degradation efficiency at the optimum condition for SSZ, SMX, SMT and MNZ were 98.10%, 89.34%, 86.29% and 58.70%, respectively in O3/H2O2 process. For proposed reaction mechanism of SNAs in O3/H2O2 process the influence of inorganic salts including Na2SO4, NaH2PO4, Na2CO3, NaCl and tert butanol (TBA) as organic OH scavenger was studied. Besides that, LC-MS/MS analysis and DFT calculation were employed to identify the intermediate molecules produced (31 species) during the SSZ degradation (as a SNAs model) and a probable degradation pathway was proposed. The results provided a new strategy by combination of experiment and computer simulation to evaluate the O3/H2O2 system for optimization of SNAs removal from wastewater.

Enhanced heterogeneous catalytic ozonation of pharmaceutical pollutants using a novel nanostructure of iron-based mineral prepared via plasma technology: A comparative study.

Plasma-treated goethite nanoparticles with high surface area and improved density of surface hydroxyl groups were synthesized from natural goethite (NG) using Argon (PTG-Ar) and Nitrogen (PTG-N2) as plasma environment to enhance the performance of heterogeneous catalytic ozonation process. Synthesized samples were characterized by FESEM, EDX, TEM, XRD, XPS, BET-BJH, FTIR, AAS and pHPZC. Results indicated a significantly different morphology for the prepared samples with negligible change in crystal structure. Furthermore, the catalytic activity and synergy factor of the NG and PTG nanocatalysts were evaluated for degradation and mineralization of Sulfasalazine antibiotic (SSZ) as an environmental hazardous contaminant. The highest removal efficiency was achieved 96.05 % under the optimal operating conditions. The kinetic study confirmed the pseudo-first-order reaction for the degradation process. Moreover, the dissolved ozone concentration and effect of organic and inorganic salts were studied in order to assess the reactive oxidant species (ROSs) and catalyst active sites in the process. The mechanism investigation showed the catalytic ozonation of SSZ was mainly performed by successive attacks of hydroxyl radicals (•OH), superoxide radicals (O2-) and direct ozone molecules. Environmentally-friendly modification of the NG, negligible iron leaching, successive reusability and superior catalytic activity are the major benefits of the PTG nanoparticles.