In ZnO-based low voltage varistor, the two essential features of microstructure determining its nonlinear response are the formation Bi-enriched active grain boundaries as well as a controlled ZnO grain size by secondary spinel-type phases. Besides, the microstructure and phase composition are strongly affected by the dopant concentration during sintering process. In this study, the optimal dopant levels of Bi2O3, TiO2, and Sb2O3 to achieve maximized nonlinear electrical property (alpha) were quantified by the response surface methodology (RSM). RSM was also used to understand the significance and interaction of the factors affecting the response. Variables were determined as the molar ratio of Bi2O3, TiO2, and Sb2O3. The alpha was chosen as response in the study. The 5-level-3-factor central composite design, with 20 runs, was used to conduct the experiments by ball milling method. A quadratic model was established as a functional relationship between three independent variables and alpha. According to the results, the optimum values of Bi2O3, TiO2, and Sb2O3 were obtained 0.52, 0.50, and 0.30, respectively. Under optimal conditions the predicted alpha (9.47) was calculated using optimal coded values from the model and the theoretical value is in good agreement with the value (9.43) obtained by confirmation experiment.
Photocatalytic degradation of p-cresol was carried out using ZnO under UV irradiation. The amount of photocatalyst, concentration of p-cresol and pH were studied as variables. The residual concentration and mineralization of p-cresol was monitored using a UV-visible spectrophotometer and total organic carbon (TOC) analyzer, respectively. The intermediates were detected by ultra high pressure liquid chromatography (UPLC). The highest photodegradation of p-cresol was observed at 2.5 g/L of ZnO and 100 ppm of p-cresol. P-cresol photocatalytic degradation was favorable in the pH range of 6-9. The detected intermediates were 4-hydroxy-benzaldehyde and 4-methyl-1,2-benzodiol. TOC studies show that 93% of total organic carbon was removed from solution during irradiation time. Reusability shows no significant reduction in photocatalytic performance in photodegrading p-cresol.
The effective removal of o-cresol is currently both an environmental and economic challenge. ZnO is not only an efficient photocatalyst but is also cost effective, as its photoabsorption can extend from the ultraviolet (UV) to the visible range thereby allowing the use of inexpensive visible light sources, such as sunlight. The principal objective of the present work is to investigate the visible light-driven removal of o-cresol from aqueous solution in the presence of 1.0 wt% Mn-doped ZnO. To measure the efficiency ofphotodegradation, the variables studied included the amount ofphotocatalyst, concentration of o-cresol, pH and irradiation time. The concentration ofo-cresol and residual organic carbon was monitored using a UV-visible spectrophotometer, ultra high-pressure liquid chromatography and a total organic carbon analyser. The optimum conditions under which the photodegradation of o-cresol was most favourable corresponded to 1.5 g/l ZnO, 35 ppm o-cresol and pH 9. The ZnO-1 wt% Mn photoprocess has demonstrated reusability for more than three times, which warrants its scale-up from laboratory- to in industrial-scale application.
The global attention has been focused on degradation of the environmental organic pollutants through green methods such as advanced oxidation processes (AOPs) under sunlight. However, AOPs have not yet been efficient in function of the photocatalyst that has been used. In this work, firstly, CaCu3Ti4O12 nanocomposite was simultaneously synthesized and decorated in different amounts of graphene oxide to enhance photodegradation of the organics. The result of the photocatalyst characterization showed that the sample with 8% graphene presented optimum photo-electrical properties such as low band gap energy and a great surface area. Secondly, the photocatalyst was applied for photodegradation of an organic model in a batch photoreactor. Thirdly, to scale up the process and optimize the efficiency, the photodegradation was modeled by multivariate semi-empirical methods. As the optimized condition showed, 45 mg/L of the methyl-orange has been removed at pH 5.8 by 0.96 g/L of the photocatalyst during 288 min of the light irradiation. Moreover, the photodegradation has been scaled up for industrial applications by determining the importance of the input effective variables according to the following organics order > photocatalyst > pH > irradiation time.
The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software's option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.
The introduction of low detection limit ion selective electrodes (ISEs) may well pave the way for the determination of trace targets of cationic compounds. This research focuses on the detection of titanium (III) cation using a new PVC-membrane sensor based on synthesized tris(2pyridyl) methylamine (tpm) ionophore. The application and validation of the proposed sensor was done using potentiometric titration, inductively coupled plasma atomic emission spectrometry (ICP-AES), and atomic absorption spectrometry (AAS). The membrane sensor exhibited a Nernstian response to the titanium (III) cation over a concentration range of 1.0 × 10(-6)-1.0 × 10(-2) M and pH range from 1-2.5. The Nernstian slope, the lower of detection (LOD), and the response time (t95%) of the proposed sensor were 29.17 ± 0.24 mV/dec, 7.9 × 10-7 M, and 20 s, respectively. The direct determination of 4-39 μg/ml of titanium (III) standard solution showed an average recovery of 94.60 and a mean relative standard deviation of 1.8 at 100.0 μg/ml. Finally, the utilization of the electrodes as end-point indicators for potentiometric titration with EDTA solutions for titanium (III) sensor was successfully carried out.
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.
The 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.
Novel ionophores comprising various hydroxide and amine structures were immobilized onto poly(vinyl chloride) (PVC) matrices, and these were examined to determine Ti(III) selectivity. To predict the selectivity of Ti(III), a PVC membrane was used to investigate the binding of Ti(III) to c-methylcalix[4]resorcinarene (CMCR). The study showed that the chelating ligand, CMCR, was coordinated selectively to Ti(III) at eight coordination sites involving the oxygen atoms at the interface of the membrane/solution. The membrane was prepared, based on CMCR as an ionophore, sodium tetrakis(4-fluorophenyl) borate (NaTFPB) as a lipophilic ionic additive, and dioctylphthalate (DOP) as a plasticizer. The immobilization of the ionophore and surface characterization studies revealed that the performance of CMCR-immobilized PVC was equivalent to that of mobile ionophores in supported liquid membranes (SLMs). The strengths of the ion-ionophore (CMCR-Ti(OH)(OH(2))(5) (2+)) interactions and the role of ionophores on membranes were studied via UV-Vis, Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM) and and X-ray diffraction (XRD).
The interactions of p-cresol photocatalytic degradation components were studied by response surface methodology. The study was designed by central composite design using the irradiation time, pH, the amount of photocatalyst and the p-cresol concentration as variables. The design was performed to obtain photodegradation % as actual responses. The actual responses were fitted with linear, two factor interactions, cubic and quadratic model to select an appropriate model. The selected model was validated by analysis of variance which provided evidences such as high F-value (845.09), very low P-value (
Due to the increasing industrial use of titanium compounds, its determination is the subject of considerable efforts. The ionophore or membrane active recognition is the most important component of any polymeric membrane sensor. The sensor's response depends on the ionophore and bonding between the ionophore and the target ion. Ionophores with molecule-sized dimensions containing cavities or semi-cavities can surround the target ion. The bond between the ionophore and target ion gives different selectivity and sensitivity toward the other ions. Therefore, ionophores with different binding strengths can be used in the sensor.
The roles of green chemistry in nanotechnology and nanoscience fields are very significant in the synthesis of diverse nanomaterials. Herein, we report a green chemistry method for synthesized colloidal silver nanoparticles (Ag NPs) in polymeric media. The colloidal Ag NPs were synthesized in an aqueous solution using silver nitrate, polyethylene glycol (PEG), and β-D-glucose as a silver precursor, stabilizer, and reducing agent, respectively. The properties of synthesized colloidal Ag NPs were studied at different reaction times. The ultraviolet-visible spectra were in excellent agreement with the obtained nanostructure studies performed by transmission electron microscopy (TEM) and their size distributions. The Ag NPs were characterized by utilizing X-ray diffraction (XRD), zeta potential measurements and Fourier transform infrared (FT-IR). The use of green chemistry reagents, such as glucose, provides green and economic features to this work.
Green synthesis of noble metal nanoparticles is a vastly developing area of research. Metallic nanoparticles have received great attention from chemists, physicists, biologists, and engineers who wish to use them for the development of a new-generation of nanodevices. In this study, silver nanoparticles were biosynthesized from aqueous silver nitrate through a simple and eco-friendly route using Curcuma longa tuber-powder extracts, which acted as a reductant and stabilizer simultaneously. Characterizations of nanoparticles were done using different methods, which included ultraviolet-visible spectroscopy, powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy-dispersive X-ray fluorescence spectrometry, and Fourier-transform infrared spectroscopy. The ultraviolet-visible spectrum of the aqueous medium containing silver nanoparticles showed an absorption peak at around 415 nm. Transmission electron microscopy showed that mean diameter and standard deviation for the formation of silver nanoparticles was 6.30 ± 2.64 nm. Powder X-ray diffraction showed that the particles are crystalline in nature, with a face-centered cubic structure. The most needed outcome of this work will be the development of value-added products from C. longa for biomedical and nanotechnology-based industries.
High level of phosphotungstic acid (PWA) was self-immobilized on electrospun nylon nanofiberous sheet to fabricate highly selective methanol barrier layer for sandwich structured proton conducting membranes. Simple tuning for the assembly conditions of central layer and thickness of outer Nafion layers allowed obtaining different composite membranes with superior methanol barrier properties (namely, P=3.59×10(-8) cm2 s(-1)) coupled with proton conductivities reaching 58.6 mS cm(-1) at 30 °C. Comparable activation energy for proton transport and more than 20 times higher selectivity than Nafion 115 confirm the effectiveness of the central layer and resulting membranes for application in direct methanol fuel cells (DMFCs). When tested in DMFC single cell, the performance of hybrid membrane was far better than Nafion 115 especially at higher methanol concentrations.
This review discusses the impact of green and environmentally safe chemistry on the field of nanotechnology-driven drug delivery in a new field termed "green nanomedicine". Studies have shown that among many examples of green nanotechnology-driven drug delivery systems, those receiving the greatest amount of attention include nanometal particles, polymers, and biological materials. Furthermore, green nanodrug delivery systems based on environmentally safe chemical reactions or using natural biomaterials (such as plant extracts and microorganisms) are now producing innovative materials revolutionizing the field. In this review, the use of green chemistry design, synthesis, and application principles and eco-friendly synthesis techniques with low side effects are discussed. The review ends with a description of key future efforts that must ensue for this field to continue to grow.
In this study, silver nanoparticles (Ag-NPs) were synthesized using a green physical synthetic route into the lamellar space of montmorillonite (MMT)/chitosan (Cts) utilizing the ultraviolet (UV) irradiation reduction method in the absence of any reducing agent or heat treatment. Cts, MMT, and AgNO(3) were used as the natural polymeric stabilizer, solid support, and silver precursor, respectively. The properties of Ag/MMT/Cts bionanocomposites (BNCs) were studied as the function of UV irradiation times. UV irradiation disintegrated the Ag-NPs into smaller sizes until a relatively stable size and size distribution were achieved. Meanwhile, the crystalline structure and d-spacing of the MMT interlayer, average size and size distribution, surface morphology, elemental signal peaks, functional groups, and surface plasmon resonance of Ag/MMT/Cts BNCs were determined by powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy dispersive X-ray fluorescence, Fourier transform infrared, and UV-visible spectroscopy. The antibacterial activity of Ag-NPs in MMT/Cts was investigated against Gram-positive bacteria, ie, Staphylococcus aureus and methicillin-resistant S. aureus and Gram-negative bacteria (ie, Escherichia coli) by the disk diffusion method on Muller-Hinton Agar at different sizes of Ag-NPs. All of the synthesized Ag/MMT/Cts BNCs were found to have high antibacterial activity. These results show that Ag/MMT/Cts BNCs can be useful in different biologic research and biomedical applications, such as surgical devices and drug delivery vehicles.
In fabrication of ZnO-based low voltage varistor, Bi2O3 and TiO2 have been used as former and grain growth enhancer factors respectively. Therefore, the molar ratio of the factors is quit important in the fabrication. In this paper, modeling and optimization of Bi2O3 and TiO2 was carried out by response surface methodology to achieve maximized electrical properties. The fabrication was planned by central composite design using two variables and one response. To obtain actual responses, the design was performed in laboratory by the conventional methods of ceramics fabrication. The actual responses were fitted into a valid second order algebraic polynomial equation. Then the quadratic model was suggested by response surface methodology. The model was validated by analysis of variance which provided several evidences such as high F-value (153.6), very low P-value (<0.0001), adjusted R-squared (0.985) and predicted R-squared (0.947). Moreover, the lack of fit was not significant which means the model was significant.
Fatty hydroxamic acids derivatives based on palm kernel oil which are phenyl fatty hydroxamic acids (PFHAs), methyl fatty hydroxamic acids (MFHAs), isopropyl fatty hydroxamic acids (IPFHAs) and benzyl fatty hydroxamic acids (BFHAs) were applied as chelating agent for copper liquid-liquid extraction. The extraction of copper from aqueous solution by MFHAs, PFHAs, BFHAs or IPFHAs were carried out in hexane as an organic phase through the formation of copper methyl fatty hydroxamate (Cu-MFHs), copper phenyl fatty hydroxamate (Cu-PFHs), copper benzyl fatty hydroxamate (Cu-BFHs) and copper isopropyl fatty hydroxamate (Cu-IPFHs). The results showed that the fatty hydroxamic acid derivatives could extract copper at pH 6.2 effectively with high percentage of extraction (the percentages of copper extraction by MFHAs, PFHAs, IPFHs and BFHAs were found to be 99.3, 87.5, 82.3 and 90.2%, respectively). The extracted copper could be quantitatively stripped back into sulphuric acid (3M) aqueous solution. The obtained results showed that the copper recovery percentages from Cu-MFHs, Cu-PFHs, Cu-BFHs and Cu-IPFHs are 99.1, 99.4, 99.6 and 99.9 respectively. The copper extraction was not affected by the presence of a large amount of Mg (II), Ni (II), Al (III), Mn (II) and Co (II) ions in the aqueous solution.
Liquid-liquid iron(III) extraction was investigated using benzyl fatty hydroxamic acids (BFHAs) and methyl fatty hydroxamic acids (MFHAs) as chelating agents through the formation of iron(III) methyl fatty hydroxamate (Fe-MFHs) or iron(III) benzyl fatty hydroxamate (Fe-BFHs) in the organic phase. The results obtained under optimized conditions, showed that the chelating agents in hexane extract iron(III) at pH 1.9 were realized effectively with a high percentage of extraction (97.2% and 98.1% for MFHAs and BFHAs, respectively). The presence of a large amount of Mg(II), Ni(II), Al(III), Mn(II) and Co(II) ions did affect the iron(III) extraction. Finally stripping studies for recovering iron(III) from organic phase (Fe-MFHs or Fe-BFHs dissolved in hexane) were carried out at various concentrations of HCl, HNO(3) and H(2)SO(4). The results showed that the desired acid for recovery of iron(III) was 5 M HCl and quantitative recovery of iron(III) was achieved from Fe(III)-MFHs and Fe(III)-BFHs solutions in hexane containing 5 mg/L of Fe(III).
It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up.