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Application of least squares support vector regression and linear multiple regression for modeling removal of methyl orange onto tin oxide nanoparticles loaded on activated carbon and activated carbon prepared from Pistacia Atlantica wood
Abstract
Two novel and eco friendly adsorbents namely tin oxide nanoparticles loaded on activated carbon (SnO2-NP-AC) and activated carbon prepared from wood tree Pistacia atlantica (AC-PAW) were used for the rapid removal and fast adsorption of methyl orange (MO) from the aqueous phase. The dependency of MO removal with various adsorption influential parameters was well modeled and optimized using multiple linear regressions (MLR) and least squares support vector regression (LSSVR). The optimal parameters for the LSSVR model were found based on γ value of 0.76 and σ(2) of 0.15. For testing the data set, the mean square error (MSE) values of 0.0010 and the coefficient of determination (R(2)) values of 0.976 were obtained for LSSVR model, and the MSE value of 0.0037 and the R(2) value of 0.897 were obtained for the MLR model. The adsorption equilibrium and kinetic data was found to be well fitted and in good agreement with Langmuir isotherm model and second-order equation and intra-particle diffusion models respectively. The small amount of the proposed SnO2-NP-AC and AC-PAW (0.015g and 0.08g) is applicable for successful rapid removal of methyl orange (>95%). The maximum adsorption capacity for SnO2-NP-AC and AC-PAW was 250mgg(-1) and 125mgg(-1) respectively.
... The robust optimization of SVR model parameters directly influences prediction accuracy and the generalization capability of the model. SVR has been applied in modelling dye removal processes [13], [24], [25], [26], [27], [28], [29], [26], [30], with various studies employing different optimization techniques for SVR hyperparameter tuning. While some studies utilize the trial-and-error method [24], [26], others employ the grid research method [25], [27], [28], or genetic algorithms [29], [31], [30]. ...
... The robust optimization of SVR model parameters directly influences prediction accuracy and the generalization capability of the model. SVR has been applied in modelling dye removal processes [13], [24], [25], [26], [27], [28], [29], [26], [30], with various studies employing different optimization techniques for SVR hyperparameter tuning. While some studies utilize the trial-and-error method [24], [26], others employ the grid research method [25], [27], [28], or genetic algorithms [29], [31], [30]. ...
... SVR has been applied in modelling dye removal processes [13], [24], [25], [26], [27], [28], [29], [26], [30], with various studies employing different optimization techniques for SVR hyperparameter tuning. While some studies utilize the trial-and-error method [24], [26], others employ the grid research method [25], [27], [28], or genetic algorithms [29], [31], [30]. The trial-and-error method and the grid research method are very time-consuming [17]. ...
Artificial intelligence (AI) has gathered significant attention and acceptance across various domains, including water treatment and desalination, where it has proven to be a valuable tool for enhancing process efficiency and addressing water pollution and scarcity. AI techniques offer optimized chemical usage, reduced operational costs, and effective solutions. In this paper, a hybrid method, SVR-PSO is proposed for modeling the dye removal process. First, Particle Swarm Optimization (PSO) tunes the best hyperparameters for Support Vector Regression (SVR). Subsequently, Support Vector Regression (SVR) is built using the obtained best hyperparameters and then fitted. The proposed method is tested and evaluated for modeling the removal of The Malachite Green dye using Helianthus Annuus seed shells as an eco-friendly adsorbent. The proposed method is compared with SVR tuned with Genetic Algorithm (SVR-GA) and SVR tuned with the Grid Search method (SVR-GS), and the obtained results demonstrate the efficiency of the proposed method.
... Results indicate that the pH value of solution exerted a significant influence on the MB adsorption onto CS-H 2 SO 4 + NaOH-HTC. At lower pH, cationic MB resists to combine with acidified adsorbent surface because of the electrostatic repulsive force that weakens mass transfer and leads to a reduction in MB removal percentages (Ghaedi et al. 2016;Zhang et al. Fig. 7 Effect of initial concentration (a), temperature (b), solution pH (b) and reusability on remove of MB onto CS-H 2 SO4 + NaOH-HTC. ...
... The maximum MB adsorption was achieved at a pH value of 7.0, which can be explained by the fact that the absented positive charge on H 2 SO 4 + NaOH-HTC surface can enhance MB mass transfer and adsorption. An alkali environment was not investigated in this study because there is an agreement that the competitive MB adsorption onto reactive sites with abundant OH − in solution will lead to the MB adsorption reduced (Ghaedi et al. 2016). In fact, aniline and malachite green oxalate salt were also tested to evaluate their adsorption capacity onto H 2 SO 4 + NaOH-HTC at the same conditions (adsorbent dosage is 70 mg, 30 °C and 120 min). ...
Conversion of the abundant agricultural residual cotton stalk (CS) into useful chemicals or functional materials could alleviate the fossil fuels caused energy shortages and environmental crises. Although some advances have been achieved, less attention has been paid to the plant tissues effect. In this study, the plant tissue of CS was changed by part degradation of some components (hemicelluloses and lignin, for example) with the aid of acid/base (or both). The pretreated CS was transformed into hydrochar by hydrothermal carbonization (HTC) method. Morphological and chemical compositions of CS hydrochar were analyzed by various techniques, including elemental analysis, Fourier transform infrared (FTIR), BET analysis, X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). Methylene blue (MB) removal of prepared CS hydrochar was used to evaluate CS hydrochar pollutions adsorption capacity. Results reveal acid/base (or both) pretreatment is beneficial for CS raw material to prepare high-quality CS hydrochar. The effects of some parameters, such as initial MB concentration, temperature, pH value and recyclability on the adsorption of MB onto both acid and base-pretreated CS hydrochar (CS-H 2 SO 4 + NaOH-HTC) were studied. The present work exhibits the importance of agricultural waste biomass material plant tissues on its derived materials, which will have a positive effect on the direct utilization of waste biomass.
... Energy consumption problem is a complex nonlinear problem. So far, scholars have proposed various forecasting models, including grey prediction theory [7][8][9], multiple regression [10][11][12], input-output method [13,14], and time-series forecasting models [15,16]. ...
... Hsu et al. [7] applied artificial neural network and an improved gray model to predict electricity consumption of Taiwan, and the examples showed that the improved grey prediction model has higher prediction accuracy. Sehgal, V et al. [10] proposed the wavelet-bootstrap-multiple linear regression (WBMLR) predictive model to forecast India Mahathir Power Load Nadi River Basin, and the examples showed that the model owns higher accuracy than the artificial neural networks (ANN), wide area network (WAN), machine learning in R (MLR) model. Erdogdu E [16] applied co-integration analysis and auto-regressive integrated moving average (ARIMA) model to predict power load of the Republic of Turkey, and proved the high power load forecasting officially. ...
China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.
... The ideal setting for the SVM Model with RBF kernel had an R 2 of 0.9843 and a mean sum square error (MSSE) of 1.444. Likewise, in another study [89], the methyl orange adsorption-SVR model achieved R 2 values of 0.976. The adsorption system of methylene blue dye [90] reported an R 2 value of [80] also produced an R 2 of 0.998 and an RMSE of 1.144 comparable with the present work. ...
In this work, machine learning (ML) models were formulated to predict the batch and column adsorption of Congo red (CR) dye on high-performance polyethyleneimine (PEI) based macroporous monolithic sponge (S100) adsorbent, which was designed using the ice-templating technique. Compared to previous PEI-based adsorbents, the fundamental innovation of presently developed sponges is their cost-effective, single-step production technique, which resulted in excellent selectivity and remarkable adsorption ability against anionic contaminants. The dye adsorption percentage for the batch process was predicted as a function of adsorbent dose, contact time, and concentration of dye, whereas the relative concentration for column experiments was forecast as a function of flow rate, concentration of dye, adsorbent dose, and time of contact. The competency of the ML models namely, support vector regression (SVR) and artificial neural network (ANN) was examined against the frequently used conventional multiple regression (MR) model. For batch SVR, ANN, and MR adsorption models, the correlation coefficient (R) values of 0.9955, 0.9972 and 0.8857 were achieved. The R values for continuous SVR, ANN, and MR adsorption models were 0.9991, 0.9994, and 0.9541, whereas the root mean square error (RMSE) values were found as 0.0141, 0.0118, and 0.1034, respectively. The generalized machine learning models best matched the breakthrough experimental data. However, it can be concluded that these ML techniques can help users to estimate how the adsorption system will react to variations in the experimental conditions, giving them a tool to improve the design and operation of adsorption processes, especially employed for wastewater treatment.
... Application of the SVM to solve the regression problems is called SVR which has been very successful in building nonlinear data driven models, also problems characterized by small samples, nonlinearity, high dimension space and local minima. It tries to find an optimal hyper-plane function in a high dimensional space [19,20] . ...
In this study, for the first time the activated carbon has been produced from medlar seed (Mespilus ger- manica) via chemical activation with KOH. The carbonization process was carried out at different tem- peratures of 450, 550, 650 and 750 °C. The Nitrogen adsorption-desorption, Fourier transform infrared spectroscopy (FTIR) and Field Emission Scanning Electron Microscope (FESEM) analyses were carried out on the adsorbents. The effect of operating parameters, such as pH, initial concentration of Cr(VI), adsor- bent dosage and contact time were investigated. The experimental data showed better agreement with the Langmuir model and the maximum adsorption capacity was evaluated to be 200 mg/g. Kinetic studies indicated that the adsorption process follows the pseudo second-order model and the chemical reaction is the rate-limiting step. Thermodynamic parameters showed that the adsorption process could be con- sidered a spontaneous ( G < 0), endothermic ( H > 0) process which leads to an increase in entropy ( S > 0). The application of support vector machine combined with genetic algorithm (SVM-GA) and ar- tificial neural network (ANN) was investigated to predict the percentage of chromium removal from aque- ous solution using synthesized activated carbon. The comparison of correlation coefficient (R 2 ) related to ANN and the SVR-GA models with experimental data proved that both models were able to predict the percentage of chromium removal, by synthetic activated carbon while the SVR-GA model prediction was more accurate.
... The MO dye absorption kinetic study performed in this research indicated that the pseudo-second-order adsorption kinetics model fitted well (as the interparticle diffusion model did) with the corresponding observations of this study. Ghaedi et al. also (Ghaedi et al., 2016b) studied the remediation of MO dye by a tin-oxide-nanoparticles-loaded AC. This study's synthesized tin-oxide-nanoparticles-loaded AC adsorbent had a high MO-dye-adsorption-capacity even with this adsorbent's low dosage. ...
Polluted water resources, particularly those polluted with industrial effluents' dyes, are carcinogenic and hence pose a severe threat to sustainable and longstanding worldwide development. Meanwhile, adsorption is a promising process for polluted/wastewater treatment. In particular, activated carbon (AC) is popular among various wastewater treatment adsorbents, especially in the organic contaminants' remediation in wastewater. Hence, the AC's synthesis from degradable and non-degradable resources, the carbon activation involved in the AC synthesis, and the AC's modification to cutting-edge and effective materials have been modern-research targets in recent years. Likewise, the main research focuses worldwide have been the salient AC characteristics, such as its surface chemistry, porosity, and enhanced surface area. Notably, various modified-AC synthesis methods have been employed to enhance the AC's potential for improved contaminants-removal. Hence, we critically analyze the different modified ACs (with enhanced (surface) functional groups and textural properties) of their capacity to remove different-natured anionic dyes in wastewater. We also discuss the corresponding AC modification techniques, the factors affecting the AC properties, and the modifying agents' influence on the AC's morphological/adsorptive properties. Finally, the AC research of future interest has been proposed by identifying the current AC research gaps, especially related to the AC's application in wastewater treatment.
... Tin oxide is abundant chemically stable and has high electrical conductivity. SnO 2 nanoparticles can be used as catalyst for the removal of dyes [5,6], as gas sensors [7,8], as anodes in lithium ion batteries [9,10], etc. Synthetic techniques such as sol gel method [11], solvothermal method [12], spray pyrolysis [13], hydrothermal [14], pulsed laser deposition [15], precipitation method [16], wet chemical method [17] and microwave assisted method [18] were adopted by researchers for the synthesis of tin oxide nanoparticles. Zhang et al. [19] adopted the spark plasma technique for the preparation of antimony doped tin oxide nanoceramics. ...
In present work, a simple and facile microwave irradiated solvothermal method was applied for the synthesis of undoped and Cu2+ doped tin oxide nanoparticles. Thermal characterization was performed to study the thermal stability and to fix the annealing temperature. All the prepared samples were annealed at 500 ºC and then characterized. Structural, morphological and optical characterizations were done using PXRD, TEM, SEM and UV-Vis NIR analysis. The PXRD measurement shows the tetragonal phase of tin oxide. Agglomerated and spherical morphology was observed in SEM and TEM micrographs. Presence of blue shift was observed in the absorption spectra of the prepared samples. Elemental compositions of the samples were analyzed using EDX patterns. Magnetic characterization was performed using vibrating sample magnetometer and the analysis revealed that the prepared samples were diamagnetic in nature. Antibacterial studies showed the efficient activity against the Gram-positive and Gram-negative bacterial stains.
... However, this method is unable to adequately correlate the effects of the different factors on adsorption process also as their mutual interactions (Tahereh et al. 2014). To rise above this difficulty when studying HA adsorption, statistical tools such as support vector regression (Ghaedi et al. 2016), artificial neural networks (Mohammad Hadi et al. 2018), response surface methodology (RSM) (Jafari et al. 2017) (Mahmood et al. 2019), and principal component analysis (PCA) can be useful. ...
The adsorption of humic acid (HA) onto granular-activated carbon (GAC) was studied. The effect of four experimental parameters (GAC dose, pH, ionic strength, and temperature) on HA adsorption and evaluation of optimum levels of process variables were evaluated in a batch system using central composite design (CCD). The principal component analysis (PCA) and dendrogram were performed to study the possible correlation between different parameters. Pseudo-first-order, pseudo-second-order (Types 1 and 2), Elovich, Weber–Morris intraparticle diffusion, and Boyd models were used to find out the kinetic data and mechanism of adsorption process. The pseudo-second-order model type 2 described well the kinetic data, while the rate limiting the process in the beginning was external mass transfer and intra-particle diffusion is not the only rate limiting step. The statistical analysis proves the suitability of the model in representing the adsorption process with an R2 value of 0.9898. Operating conditions of GAC weight: 3 g L−1, pH 6, NaCl concentration: 0.57 g L−1, and temperature 40 °C allow reaching an adsorption of 98.10%. Temkin isotherm model and pseudo-second-order kinetic model type 2 were found to be statically in accordance with the adsorption data. In conclusion, understanding of HA removal is well described by the optimization of the GAC dose, pH, ionic strength, and temperature parameters. Comparison of different GACs in full-scale studies is also important due to differences in the TOC removals of GACs with similar product specifications.
... At present, the effective methods for removal of heavy metal ions and organic dyes from water mainly include membrane separation [22], ion exchange [23], bioremediation [24], photocatalysis [25,26], chemical precipitation [27], and adsorption [28,29]. Among the adsorption has a wide range of applications because of its simplicity, effectiveness, and strong selectivity in dealing with water pollution [13,[30][31][32][33][34]. However, at present, most of the adsorbents are prepared via simple surface modification or core-shell structure, and the limitation lies in the relatively simple action with pollutants [35][36][37][38]. ...
Heavy metal ions and organic dyes are the main harmful substances in natural water bodies. At the same time, single-component treatment can be challenging to cope with the increasingly severe water pollution circumstances. In this work, Janus phenol–formaldehyde resin (PF) and periodic mesoporous organic silica (PMO) nanoparticles with an asymmetric structure are prepared, using PF microspheres as seeds and PMO for branch growth. The hydroxyl groups on the PF surface and the large specific surface area of PMO facilitate the efficient adsorption of heavy metal ions and organic dyes by utilizing chemical and physical properties, respectively. The maximum adsorption of Janus PF&PMO for Cd²⁺ in water reaches 17.07 mg/g. The adsorption behavior fits well with the pseudo-second-order kinetic model and Langmuir adsorption isotherm, suggesting monolayer and chemical adsorption as main mechanisms. Moreover, the adsorption of Janus PF&PMO for the organic dye methyl orange (MO) fits the pseudo-second-order kinetic model and Freundlich adsorption isotherm model with a maximum adsorption of 98.21 mg/g, suggesting monolayer and physical adsorption as main mechanisms. Therefore, Janus PF&PMO nano-adsorbent is expected to be used effectively in sewage treatment systems.
Graphical abstract
The synthesized Janus-shaped nano-adsorbent demonstrated outstanding potentials for removing heavy metal ions and organic dye from polluted water.
... performance of the SLR and ANN models [48,49]: ...
Thermal plasma gasification is considered as an attractive technology to produce high quality syngas from municipal solid waste (MSW). It is imperative to study the effect of operating parameters on syngas quality and find a practical way to predict syngas characteristics. This paper compiled 112 research cases to develop quantitative models for 8 kinds of syngas characteristics and explored the simultaneous effects of input parameters during the plasma gasification by applying stepwise linear regression (SLR) and artificial neural network (ANN) methods. The SLR model has a higher predictive accuracy than the ANN model for gas yield, volume fraction of CH4 and CO2, as well as mechanical gasification efficiency (MGE), with Rtesting² = 0.659–0.916. The ANN model demonstrates better performance than the SLR model for low heating value (LHV), dry gas ratio, volume fraction of H2 and CO, with Rtesting² = 0.807–0.939. According to the importance analysis, flow rates of the work gas-N2, feedstock type, flow rates of the work gas-steam, and input power are the most critical parameters for LHV, gas yield, and volume fraction of CH4 and H2, respectively. Input power and specific energy requirements (SER) are the most important factors affecting volume fractions of H2 (25.7–57.3 vol%) and input power plays a dominant role. The models developed in this study could enhance understanding of plasma gasification and are unique to considering multiple input parameters together.
... According to the result, the adsorption capacities of the adsorbents increase rapidly with increasing initial MB concentration. This result illustrates the improvement in the driving force at the solid−liquid interface with increasing initial MB concentration, before the adsorption sites are saturated with MB. 48 An adsorption capacity equilibrium can be observed at MB concentrations higher than 200 mg/L for UAPT and SA-APT, which indicates the saturation of the available adsorption sites by MB. To study the adsorption mechanism of adsorbents on dye molecules, Freundlich and Langmuir isotherm models have been frequently used. ...
Attapulgite (denoted as APT, also called palygorskite) has been regarded as the green material in the “21st century world” and has attracted widespread attention due to its advantages of low cost, natural abundance, nontoxic nature, and environmental friendliness. However, the limited adsorption sites and surface charges of natural APT greatly hinder its application as an adsorbent in industrial fields. In this work, natural APT was modified with sodium humate (SA) via a facile one-step hydrothermal process to improve its adsorption capacity and systematically studied its ability to remove methylene blue (MB) from aqueous solutions. The effect of hydrothermal modification in the presence of SA on the microscopic structure, morphology, and physicochemical properties of APT was studied by field-emission scanning electron microscopy, Fourier transform infrared spectrometry, X-ray diffraction, and Brunauer–Emmett–Teller analyses. The adsorption properties of the modified APT toward MB were evaluated systematically. The results demonstrated that the modified APT has a high adsorption capacity of 227.27 mg/g and also shows a high removal rate up to 99.7% toward MB in a dye solution with an initial concentration of 150 mg/L, which was a 64.7% increase as compared to that of raw APT. The adsorption kinetics could be fitted to the pseudo-second-order model, while the adsorption isotherm could be well-described with the Langmuir model. It was concluded that electrostatic attraction, hydrogen-bonding interaction, and chemical association are the main driving force during the adsorption process.
... Mahmoodian et al. synthesised pHEMA-CS-MWCNTs nanocomposite [27] and Robati et al. reported graphene oxide [28] for the adsorption of methyl orange from water. Ghaedi et al. fabricated SnO 2 @AC nanoparticles [29] and Shahzad et al. reported PMOS composites with Bismuth and Cerium [30] for the removal of methyl orange from water. Researchers are gaining more attention to produce advanced adsorbent materials having excellent adsorption ability to clean the environment [31]. ...
Zeolitic imidazole frameworks (ZIFs) and their derivatives, being an emergent category of adsorbents, are anticipated to adsorb organic dyes from aqueous mediums. Herein, ZIF-67 and Ni-doped ZIF-67 were designated due to their high stability in aqueous mediums, porosity and straightforward synthesis. Porous Ni-doped ZIF-67 nanocomposite was produced by doping secondary metal (Ni) to ZIF-67. The fabricated Ni@ZIF-67 showed excellent adsorption efficiency to adsorb methyl orange from water. The performance of Ni-doped ZIF-67 was analysed by varying pH, contact time with adsorbent and concentration of dye. Results exposed that the Ni@ZIF-67 nanocomposite adsorbs more dye in mild acidic condition (qe = 24.24 mg. g⁻¹ pH = 6) as compared to acidic (qe = 17.69 mg. g⁻¹ pH = 2) and basic medium (qe = 15.74 mg. g⁻¹ pH = 10). Langmuir, Freundlich and Dubinin-Radushkevich adsorption isotherm models were applied and results showed that Langmuir model proved to be best fitted, suggesting adsorption process as monolayer. The maximum adsorption capacity attained was 151.74 mg. g⁻¹ with excellent recycling performance up to fifth cycle. Also with 180 min contact time, 68.5 and 82.9% of MO were adsorbed by ZIF-67 and Ni-doped ZIF-67 nanocomposite, respectively. Consequently, the as-synthesised Ni@ZIF-67 could be a prospective adsorbent material for methyl orange elimination from polluted water.
... Mahmoodian et al. synthesised pHEMA-CS-MWCNTs nanocomposite [27] and Robati et al. reported graphene oxide [28] for the adsorption of methyl orange from water. Ghaedi et al. fabricated SnO 2 @AC nanoparticles [29] and Shahzad et al. reported PMOS composites with Bismuth and Cerium [30] for the removal of methyl orange from water. Researchers are gaining more attention to produce advanced adsorbent materials having excellent adsorption ability to clean the environment [31]. ...
... In order to investigate the kinetic adsorption of of dye on as-prepared samples, the pseudo-first-order model [13] and pseudo-second-order kinetic model [14] were used and described in equation of (4) and (5), respectively. ...
Flower‐like Cu2O/ZnO was prepared by hydrothermal method with the reducing agent of glucose for removal of dyes in aqueous medium. As‐prepared samples were characterized by FE‐SEM, XRD, and TEM. Flower‐like Cu2O/ZnO could both photodegrade Tartrazine (TA) under UV and visible lights with the removal efficiencies of 85.3 and 96.1 %, respectively, and adsorbed TA with the removal efficiency of 77.5 %. The effects of adsorbent dosage, dye concentration, and pH solution on adsorption of dyes were investigated. The kinetic adsorption of TA on flower‐like Cu2O/ZnO was studied by pseudo‐first‐order and pseudo‐second‐order models. In addition, the performance of as‐prepared material was evaluated by cyclic test and adsorption of other dyes such as methylene blue, Janus green B and Congo red.
... The study of chemical kinetics can provide important information on adsorption rate and the factors affecting the sorption rate 25 . In order to investigate the mechanism of dye adsorption on MgO, the following models were use. ...
... The study of chemical kinetics can provide important information on adsorption rate and the factors affecting the sorption rate 25 . In order to investigate the mechanism of dye adsorption on MgO, the following models were use. ...
Objectives: The current study was focused on preparation of MgO with the different morphologies for enhanced removals
of dyes and heavy metal from wastewaters. Methods/Statistical Analysis: The MgO samples were synthesized by a facile
precipitation method with the calcination temperature of 500°, its morphology was controlled by altering the concentration of K2
CO3
and Mg(NO3
)2
. As-prepared samples were characterized by TGA, XRD, N2
adsorption/desorption isotherm,
FT-IR, FE-SEM and TEM. The performance of as-prepared MgO samples was evaluated by adsorptions of dyes and leads
(II) and compared to those of commercial MgO (CM-MgO) and Activated Carbon (AC). Findings: Flower-like MgO (F-MgO)
had the high surface area and pore volume (232 m2
/g and 0.575 cm3
/g, respectively) as compared to rod-like MgO (R-MgO)
and CM-MgO. The adsorption rate and adsorption capacity of tartrazine on F-MgO were faster and larger, respectively, than
those of R-MgO, CM-MgO and AC. The adsorption of F-MgO was achieved equilibrium in 15 min and the adsorption capacity
was 65.8 mg/g at the MgO dosage of 0.12 g, initial tartrazine concentration of 50 mg/L, solution pH of 7.0, and temperature of 30°C. The adsorption rate was followed the pseudo second equation and the rate constant was 0.02 g.mg–1.min–1
at 30 °C. The heat of adsorption (∆H° = 19.48 kJ/mol) confirmed the exothermic adsorption of tartrazine and the negative
values of G°indicatedthe spontaneous adsorption. The removal efficiency of tartrazine was gradually decreased, but it still
remained at 95.2% for the fifth cyclic experiment. In addition, the removal efficiency of Pb (II) on MgO/tartrazine (95%)
was much larger than that of F-MgO (about 10%). Application/Improvements: This study gives promising results in
concern with treatment of industrial wastewaters before they are charged into the environment.
... One of the major pollutants found in wastewater is organic dyes, resulting from textile production and other industrial processes [1]. Different wastewater treatment methods are available, ranging from physical and chemical to biological procedures [2,3]. Photocatalytic decomposition of organic pollutants by using sunlight is an environmentally friendly approach developed for wastewater purification. ...
Nanocomposite films of rGO/MFeO3 (M = Bi, La) nanofibers were grown by matrix-assisted pulsed laser evaporation of frozen target dispersions containing GO platelets and MFeO3 nanofibers. Electron microscopy investigations confirmed the successful fabrication of MFeO3 nanofibers by electrospinning Part of nanofibers were broken into shorter units, and spherical nanoparticles were formed during laser processing. Numerical simulations were performed in order to estimate the maximum temperature values reached by the nanofibers during laser irradiation. X-ray diffraction analyses revealed the formation of perovskite MFeO3 phase, whereas secondary phases of BiFeO3 could not be completely avoided, due to the high volatility of bismuth. XPS measurements disclosed the presence of metallic bismuth and Fe2+ for BiFeO3, whereas La2(CO3)3 and Fe2+ were observed in case of LaFeO3 nanofibers. High photocatalytic efficiencies for the degradation of methyl orange were achieved for nanocomposite films, both under UV and visible light irradiation conditions. Degradation values of up to 70% after 400 min irradiation were obtained for rGO/LaFeO3 nanocomposite thin layers, with weights below 10 µg, rGO platelets acting as reservoirs for photoelectrons generated at the surface of MFeO3.
... The pseudo-first-order rate equation [13] is given by: ...
Mesoporous SiO2 was prepared from rice husk for removal of methylene blue (MB) from aqueous solution. As‐prepared SiO2 was characterized by FE‐SEM, TEM, XRD, FT‐IR and N2 adsorption/desorption isotherm. The effects of SiO2 dosage and contact time on adsorption of dye were investigated. The optimal dosage of SiO2 for removing methylene blue was 0.05 g and the removal efficiency of methylene blue reached to 91.9 % after 50 min of adsorption. The adsorption kinetic of methylene blue was studied to understand the mechanism of adsorption process. The kinetic data was analyzed by pseudo‐first‐order, pseudo‐second‐order, and intra‐particle diffusion models. The results showed that rate of adsorption dye followed pseudo‐second‐model with a good regression correlation (R2 = 0.999), the value of rate constant for adsorption process was calculated as 0.017 (g.mg‐1.min‐1) and film diffusion was the rate limiting step. In addition, the cyclic test for as‐prepared SiO2 was investigated and the removal efficiency was remained at 80.2% at 3rd cyclic experiment.
... whereȳ is mean of observed rice yield data. R 2 is a statistical measure of how close the data are to the fitted regression line. The R 2 is always between 0 and 1 which the higher the R 2 , the better the model fits the observed data [13] [14]. Table 2 summarises the estimation of the model coefficients for OLS Regression using the inverse method and CG methods with RMSE values and R 2 . ...
Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.
... SVR is a machine learning where it is called support vector machine (SVM) to use the useful computational load [9]. The dots results would constantly utilize to figure the variables in the unique space with portion work chosen by the SVM with gatherings made the mapping of the problem [10]. SVR will develop degeneration strategy that formed from SVM, the place might have been taken those hyperplanes closest to a measure of information the place it turns into a purpose. ...
Support Vector Regression (SVR) is a part of Data Mining (DM) techniques where it can be used for forecasting the instant noodle. The cycle of the product demand is hard to predict. It will influence the resistant of the product quality where the product be expired easily and the other thing is the market demand. The objective of this research is approaching the predictive models with their performance measured with Mean Square Error (MSE) of SVR The data was collected from the determinant of instant noodle demand dataset. The random normal generated data was explored to get the amount of specific data. Then, it used SVR to forecast the demand. The result of this study the MSE of standard is 1.612 and the SVR is 1.436, means it increases around 11% better the performance than the original dataset. Since, we conclude that the SVR method would be promising to be one of a forecast demand method.
... High decolorization of MB and MO by eco-friendly and low-cost chitin and CS-g-PAM adsorbents was due to the presence of chelating groups on chitin and chitosan derivative structures that were responsible for this total removal of two dyes. Data regarding removal of MB and MO by other adsorbents reported in previous study are presented in Table 2 [50][51][52][53][54][55][56][57][58][59]. As shown in Table 2, various materials are used for MB and MO removal: chitin-based materials such as MnO 2 -chitin hybrid, Chitosan/Al 2 O 3 /magnetite nanoparticle composite materials and other different material composites. ...
Dyes are classified as one of the major pollutants of water. They have negative impacts not only on environment but also on human health. In fact, wastewater that contains these harmful substances requires many types of treatments. Therefore, alternative methods and adsorption agents are needed. Herein, we propose to evaluate the decolorization of methylene blue (MB) and methyl orange (MO) as two models of soluble dyes from water using chitin and chitosan-graft-polyacrylamide. Furthermore, the applicability of these biomacromolecules as alternative adsorption agents, their sticking probability and desorption were also examined. Experimental parameters such as dye concentration, contact time, pH solution, adsorbent dosage and temperature were thoroughly examined for the grafted chitosan and chitin. The activation energy ( E a ) and the thermodynamic variables (i.e., standard Gibb’s free energy ( Δ G 0 ), standard enthalpy ( Δ H 0 ), and standard entropy ( Δ S 0 )) were determined using the Van’t Hoff and Arrhenius equations. The sticking probability ( S *) model for MB and MO removal by chitin and the chitosan derivative demonstrated that both dyes were successfully removed under the proposed conditions. Desorption studies of MB and MO showed the reusability of both materials, suggesting their application for removing dyes from aqueous solution.
... whereȳ is mean of observed rice yield data. R 2 is a statistical measure of how close the data are to the fitted regression line. The R 2 is always between 0 and 1 which the higher the R 2 , the better the model fits the observed data [13] [14]. Table 2 summarises the estimation of the model coefficients for OLS Regression using the inverse method and CG methods with RMSE values and R 2 . ...
Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data are collected from the year 1980 until 2014 from the website Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved by using least square method. Since least square problem is an unconstrained optimization, the Conjugate Gradient (CG) is chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables. Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.
... In order to reflect the user's personalization, the paper presents the user's preference by setting the weight in each dimension of the project, and uses the support vector regression machine (SVR) to realize the relationship learning between the total score and each dimension score. This paper assumes that in the original space, the sample set S is linearly inseparable, so a non-linear mapping is used to map the data into a high-dimensional space, so that there is a very good linear regression feature in the feature space H, Linear regression, and then returned to the original space [15]. Given the training sample data sets ( ) ...
Traditional collaborative filtering recommendation algorithm uses single dimensional data to calculate the similarity between users or items, ignoring the user’s preference, thus affect the recommendation accuracy. To this end, an averaging forecasting based multi-dimensional aggregation recommendation algorithm was proposed in this paper, which constructs the relationship aggregation function by user’s total score and dimension scores firstly, then apply the aggregation function to the initial multi-dimensional score that calculated by the modified averaging forecasting algorithm. The experiment result shows that compared with the previous collaborative filtering based recommendation algorithm, it has higher recommendation accuracy.
An earthquake is one of the deadliest natural disasters. Forecasting an earthquake is a challenging task since natural causes such as rainfall or volcanic eruptions disrupt data. Earthquakes can also be caused by human beings, such as mining or dams. Solar activity has also been suggested as a possible cause of earthquakes. Solar activity and earthquakes occur in different parts of the solar system, separated by a huge distance. However, scientists have been trying to figure out if there are any links between these two seemingly unrelated occurrences since the 19th century. In this chapter, the authors explored the methods of how machine learning algorithms including k-nearest neighbour, support vector regression, random forest regression, and long short-term memory neural networks can be applied to predict earthquakes and to understand if there is a relationship between solar activity and earthquakes. The authors investigated three types of solar activity: sunspots number, solar wind, and solar flares, as well as worldwide earthquake frequencies that ranged in magnitude and depth.
A wide range of dyes are being disposed in water bodies from several industrial runoff and the quantity is rapidly increasing over the years. From an environmental safety point of view, it is urgent to improve the removal process of dyes. It is important to understand the stochastic and highly redundant behavior of the process of dye removal (DR) in wastewater treatment. This leads to better utilization of Machine Learning (ML) models for both optimization as well as prediction of the DR process efficiency. In this review, 200 papers (Years, 2006–2021) have been systematically reviewed from the Web of Science and Scopus-indexed journals, covering a total of 84 journals. All applied ML models have been thoroughly studied in the review and analyzed in terms of their architecture setup, hyper-parameters selection, performance, advantages, and disadvantages. A wide range of optimization methods for hyper-parameters tuning were analyzed and discussed scientifically. Explicit information about the data sizes, splitting structure for training-validation-testing along with input and output selection approaches have been logically reviewed and discussed. Data availability, transparency, and reusability have been reported adequately. Various software for data-driven modeling have been discussed with their pros and cons. Trends in statistical evaluators (among about 60 types) have been discussed with their pros and cons including their sensitivity with the data fluctuations. Moreover, the most popular performance metrics have reported. In addition, the DR mechanism has reviewed and discussed inclusively. Extensive media used for the decolorization were discussed thoroughly, including their physical and chemical characteristics, along with feasibility and equilibrium data based on Langmuir model. The cost of the applied media in the decolorization process reported adequately. Finally, the research gap and future road map of the next 5 years, which bridge the gap of the domain are scientifically drafted along with the limitations. This critical review not only provides the appraisal of growth of DR research integrated with ML in the last couple of decades but also scouts the potential studies where all experimental, chemical and modeling processes should be taken under consideration.
The contamination of water has become a major concern, where the source of contamination could either be geogenic or anthropogenic. The contaminants discussed here are broadly grouped as inorganic contaminants, organic contaminants, and emerging contaminants. Several nanoparticles (NPs) and nanocomposites (NCs) materials are explored for removal of different types of contaminants in recent times. Numerous studies have reported on performance evaluation of those NPs and NCs in removing different contaminants. In this chapter, first, a comprehensive review of efficiency and suitability of different NPs and NCs in removing various organic pollutants such as As, Cd, Cr, Cu, Mn, Ni, Pb and Zn, organic contaminants such as dyes, halogenated hydrocarbons, PAHs, etc., and emerging contaminants are discussed. Secondly, the process involved in removing contaminants in the fixed bed column filter and the performance of NPs and NCs as the filtering material in the column filter has been discussed. The parameters affecting the performance of the fixed bed column in removing the contaminants from water are also emphasized.
Deterioration of water quality is a desperate threat worldwide. Industrial development regularly added a vast amount of organic and inorganic contaminants to water. Therefore, eradicating these contaminants is highly essential for the well-being of biotic components. Adsorption-assisted technologies are among the most benign and principally used due to their higher efficiencies at lower costs and independence of complex technological supports. Carbon nanomaterials, such as activated carbons, carbon nanotubes, graphene-based materials, and carbon dots have been broadly adopted as adsorbents because of their outstanding surface characteristics. Researchers worked a lot on activation procedures and chemicals to achieve an ultrahigh surface area (above 3000 m²/g) to increase the adsorption efficiency. Further, cross-linked carbon nanotubes exhibited much-selected adsorption to organics. Carbon dots, a new type of carbon-based nanomaterials emerged as excellent adsorbents when introduced into the polymers matrix and provided promising platforms for the removal of metal ions. Also, graphene oxide and other oxidized forms of carbon being rich in functional groups are reported to exhibit excellent adsorption capacities as high as 2564 mg/g at 25 °C for basic and cationic compounds.
Here, we reviewed the various types of pollutants found in aquatic biota with some basics on fundamentals and mechanistic features of adsorption, adsorption efficiency regulating factors. The article emphasized the performances of carbon nanomaterials and associated nanocomposites for the adsorption of various pollutants. The review also addresses the essential issues for the technical development and commercial application of carbon-assisted materials as nano adsorbents for water decontamination.
The discharge of industrial effluents containing harmful synthetic dyes has gone up significantly since the last few decades and poses a severe environmental threat. Among a wide variety of treatment techniques available for the remediation of dye-laden wastewater, the adsorption process has been reported to be an effective method. Nanoscale adsorbents derived from metal oxides and their composites have recently attracted attention due to their enhanced ability to adsorb contaminants. The removal of congo red (CR) dye from dye solution was studied using a new adsorbent Fe–Mn-Zr trimetal oxide nanocomposite synthesized with polyaniline (PANI). The structural characterization of the Fe–Mn-Zr/PANI was investigated by X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM) analysis and energy dispersive X-ray (EDX) analysis. It was observed that the adsorption process was substantially dependent on sonication time, the dose of adsorbent and the initial concentration of CR dye. Optimum solution pH was obtained as 5.0 from the effect of the pH study that has been performed for different initial concentrations of CR dye. The maximum percentage of adsorption efficiency for CR dye obtained was 89.25% at the initial concentration of 20 mg/L, with 0.2 g/L adsorbent dose and pH 5.0 within 15 min. In this work, kinetic analysis was performed utilizing pseudo-first-order, pseudo-second-order and intra-particle diffusion kinetic models, with the second-order kinetics model being shown to be the best fit. In comparison to pseudo-first-order kinetics, the experimental and calculated values of Qe for the pseudo-second-order kinetics were in good agreement. The isotherm study was performed by varying the initial CR concentration from 5 to 70 mg/L with 0.1–0.4 g/L adsorbent dose. The adsorption experiments are best fitted with the Langmuir isotherm, giving a CR dye adsorption capacity of 111.111 mg/g at pH 5.0. The correlation coefficient value (0.991) indicates an accurate fit for the Langmuir model. The primary adsorption mechanism is the electrostatic interaction between the sulfonated group (–SO3Na) of negatively charged CR dye molecules and the positively charged amine group (–NH2) of PANI surface. The response surface methodology (RSM) was studied to optimize the removal of the CR dye used with different experimental parameters like initial CR concentrations (5, 10, 15, 20 and 25 mg/L), with adsorbent dose (0.1–0.5 g/L), at reaction time (2 min, 4 min, 6 min, 8 min and 10 min) and solution pH: 5.0. Maximum CR dye removal of 97.78% was obtained at an optimum contact time of 15 min, with an initial CR dye concentration of 20 mg/L and adsorbent dose at 0.2 g/L.KeywordsMetal oxide nanoparticlesPolyanilineNanocompositeCongo redDye adsorptionResponse surface methodologyAdsorption mechanismKinetic studyIsotherm modellingCentral composite designOptimization
The recovery of levulinic acid (LA) and formic acid (FA) from aqueous solutions and actual biomass hydrolysates by using the stepwise adsorption method was investigated for the first time. The carbon molecular sieve was used as the adsorbent for FA and microporous activated carbon was used as the adsorbent for LA according to the steric hindrance effect of the pore channels and the difference in molecular diameter of FA and LA. The adsorption data of LA and FA onto their respective adsorbents were measured and numerically analyzed by adopting batch adsorption experiment and fixed-bed column methods. In the study of static adsorption equilibrium experiment, the Langmuir equation fitted the data of LA adsorption on activated carbon, while the Freundlich equation fitted well the adsorption data of FA adsorption on carbon molecular sieve. In the kinetic study, the LA adsorption on activated carbon and FA adsorption on carbon molecular sieve confirmed the PSO model. Moreover, the effect of various conditions on fixed-bed column adsorption progress was investigated, which indicated that the adsorption property improved with feed flow rate, column length, temperature decreasing and initial concentration increasing. The Yoon-Nelson model was adopted to predict and analyze the whole process of adsorption breakthrough and consistent well with the experimental data. The separation of LA and FA was realized by continuous fractional adsorption.
The effect of external mass transfer on the high-rate adsorption system was evaluated in the present study. In the experimental section, the removal of methyl orange (MO) was carried out by using surfactant modified chitosan-clinoptilolite at specific conditions such as the optimum adsorbent dosage of 11 g L⁻¹, solution pH of 3.5, contact time of about 3 min and constant temperature of 30 °C. The characterization of adsorbent showed the porosity value of 0.064 and the BET surface area of 10.5 m² g⁻¹. These features of adsorbent established the high-rate adsorption process in which the shape of decay curves is not smooth and the concentration of adsorbate falls quickly. In high-rate adsorption processes, the external mass transfer mechanism has a significant effect on total mass transfer at early time of adsorption. The pore volume and surface diffusion model (PVSDM) were applied for predicting MO concentration decay curves. This model and its parameters could not estimate properly the concentration of MO at the early time of the adsorption process. To overcome this problem, in addition to optimization of surface diffusion coefficient (Ds), the external mass transfer coefficient (ke) was optimized and the effect of those parameters was studied in three dimensions. The optimization provided a better agreement between the model and experimental data in such a way to reduce the error rate. To show better the effect of external mass transfer, Biot number (between (2.15-24.05) × 10⁻³) was applied and it revealed that the adsorption mechanism was controlled by external mass transport. The high external surface area illustrated that in all values of ke and initial concentration of MO, surface diffusion is the dominant mechanism in intraparticle diffusion and the low porosity of adsorbent limited the penetration of adsorbate species into the adsorbent cavities, whereby the pore volume diffusion could be considered negligible.
This study describes the removal of methyl orange (MO) and methyl red (MR) dyes from water samples using morpholinomethylcalix[4]arene immobilised silica (MIS) resin. The silica surface has been modified by p-morpholinomethylcalix[4]arene moiety and was characterised by FT-IR spectroscopy and SEM techniques. The adsorption capacity of MIS-resin was checked through batch adsorption experiments under the optimised conditions of pH, MIS-resin dose, time, and temperature. Results show that adsorption of MO and MR dyes are highly affected by the change in pH; thus, the higher adsorption percentages were achieved at pH 5.3 and 6.6 respectively. The adsorbent dosage has been optimised and it was noticed that the maximum adsorption was achieved by using 40 mg.L⁻¹ of MIS-resin dose. The adsorption rate of dyes was investigated by applying the pseudo-first and second-order kinetic models and it has been observed that the experimental data shows a better correlation coefficient with the pseudo-second-order kinetic model. The feasibility of adsorption was analysed by thermodynamic parameters such as ∆H°, ∆G°, and ∆S° values indicate that the adsorption of dyes is exothermic and spontaneous. The equilibrium data have been validated using Langmuir and Freundlich models and the Langmuir model has a good correlation coefficient (R² 0.99). The MIS-resin was applied onto industrial effluents and it has been observed that the prepared resin is a very efficient adsorbent for the treatment of dyes contaminated wastewater. The adsorption of MO and MR dyes onto MIS-resin was well defined by computational chemical modelling at the B3LYP/LANL2DZ/6-311++G (d,p) level using G09W software.
The existence of contaminants in metal alloys products is the main problem affecting the product quality, which is an important requirement for competitiveness in industries. This paper proposes the application of a relevance vector machine for regression (RVR) and a support vector machine for regression (SVR) optimized by a self-adaptive differential evolution algorithm for regression to model the phosphorus concentration levels in a steelmaking process based on actual data. In general, the appropriate choice of learning hyperparameters is a crucial step in obtaining a well-tuned RVM and SVM. To address this issue, we apply a self-adaptive differential evolution algorithm, which is an evolutionary algorithm for global optimization. We compare the performance of the RVR and SVR models with the ridge regression, multiple linear regression, model trees, artificial neural network, and random vector functional link neural network models. RVR and SVR models have smaller RMSE values and better performance than the other models. Our study indicates that the RVR and SVR models are adequate tools for predicting the phosphorus concentration levels in the steelmaking process.
PurposeDyes are highly toxic coloured compounds in nature that are largely applied in paper, food, textile and printing industries. Here, the adsorption technique was performed to remove methyl orange (MO) dye from water by polyethylene glycol (PEG) modified iron oxide nanoparticles (Fe3O4 NPs).Methods
The method used for Fe3O4 NPs synthesis was chemical precipitation. The particles were analyzed by transmission electron microscope, magnetometer, BET analyzer, fourier-transform infrared spectroscopy, X-ray powder diffraction, zetasizer and particle size analyzer. The influence of pH (4.0 to 10.0), NaCl concentration (0.01 mM to 2 M), adsorbent dosage (1 to 10 mg), and the role of surface charge on adsorptive removal were investigated.ResultsThe NPs size, zeta potential and surface area was found to be 26 ± 1.26 nm, 33.12 ± 1.01 mV and 119 m2/g respectively. The adsorption of MO on Fe3O4 NPs agreed best to Freundlich model (R2 = 0.965) when compared with Langmuir model (R2 = 0.249). By comparing pseudo-first-order kinetic model (R2 = 0.937), kinetic adsorption study was better followed by pseudo-second-order kinetic model (R2 = 1). The adsorption rate decreased with increasing NaCl concentration. At pH 4, maximum adsorption was noted. The particles were also exhibited excellent antibacterial and antibiofilm activities. The ROS formation, lipid peroxidation and oxidative stress were increased with increase in NPs concentration. The NPs precoated slides exhibited more than 50% growth inhibition.Conclusion
The investigation denotes the versatile applications of the prepared particles for removing the dye stuffs from industrial effluents and as antibacterial and antibiofilm agent.
Cognitive impairments are typical in PD and are indicated by mild cognitive impairment (PD-MCI) in the early stages or dementia (PDD) in higher stages. The Montreal Cognitive Assessment (MoCA) is an instrument commonly used for ascertaining cognitive impairments in PD. This research uses the clinical, neuroimaging, and CSF data as a predictor variable and the MoCA score as the target variable representing cognitive impairments. Machine learning approaches through support vector machine (SVM) and random forest (RF) methods were applied for modeling. The mean absolute error (MAE) and the root mean square error (RMSE) are used to compare the predicted performance values of the method's application. The experimental results showed that both SVM and RF performed well in predicting cognitive impairments in PD patients, indicated by the relatively small MAE value at 0.076 and RMSE at 0.542. This research also discovers that SVM is better than RF in predicting cognitive impairments. Meanwhile, RF presents an apparent and explicable outcome, which is beneficial for determining important variables that correspond to cognitive impairments. The five measurements with the highest mean decrease accuracy (%IncMSE) are age of onset, phosphorylated tau, α-synuclein (aSyn), mean putamen, and total tau.
The conventional water and wastewater treatment methods are unable to provide up-to-data organized standards for drinking water and discharging effluents into natural ecosystems. Therefore, developing advanced and cost-effective methods to achieve published standards for water and wastewater and population needs are nowadays necessity. The important parts of this article are providing literature information about dyes and their effects on the environment and human health, adsorption properties and mechanism, adsorbent characteristics, and recent information on various aspects of modified activated carbons with nanosized metal oxides (AC- NMOs) in the removal of dyes. This review also summarized the effect of main environmental and operational parameters such as adsorbent dosage, pH, initial dye concentration, contact time, and temperature on the dye adsorption using AC-NMOs. Furthermore, the applied isotherm and kinetic models have been discussed.
New host‐guest supramolecular coordination polymer catalyst 3∞[Co(μ2CN)5(CN)(μ2‐Me3Sn)2(Me3Sn)(H2O)(qox)], SCP1 (qox = quinoxaline) has been synthesized and characterized by single crystal X‐ray diffraction, FT‐IR, UV/Visible and fluorescence spectra, thermal and elemental analyses. The tin atoms act as bridges connecting the Co (CN)6 building blocks. The structure of SCP1 exhibits an unusual self‐coordinated host‐guest 3D network with qox as guest molecule. Also, the nanosized1\ was prepared under ultrasonic irradiation while the morphological features of both were examined by TEM and SEM. SCP1 and nanosized1\ are used as heterogeneous catalysts for removal of toxic dyes under UV and ultrasonic irradiation. The results show high effectively decolorized of indigo carmine dye (IC) without generation of any hazardous wastes or byproducts. The reaction is first order with respect to IC, while the factors affecting the rate constant of the degradation reaction are investigated. Mineralization of IC was investigated by IR and UV spectra. The trapping experiments were carried out to determine the role of active species used for degradation of the dye. The activation parameters of the reaction have been estimated and a possible mechanism of degradation was proposed and discussed in detail. New host‐guest supramolecular coordination polymer catalyst 3∞[Co(µ2CN)5(CN)(μ2‐Me3Sn)2(Me3Sn)(H2O)(qox)], SCP1 (qox = quinoxaline) hasbeen synthesized and characterized. The nanosized1\ are prepared under ultrasonicirradiation and their morphological features were examined. SCP1 and nanosized1\ are used as heterogeneouscatalysts for removal of toxic dyes under UV and ultrasonic irradiation.
In this research, Response Surface Methodology (RSM) was applied by utilizing Central Composite Design (CCD) in order to optimize the ultrasound-assisted adsorption of methylene blue (MB) (which is a cationic dye) and Acid Blue 25 (AB 25) (which is an anionic dye), and their binary dye solutions onto tobacco residues activated carbon (TRAC) from water. In order to know the characteristics of the adsorbent, UV–visible spectrophotometry, FTIR, SEM, XRD and BET were applied. The effect of various parameters such as initial concentrations of AB25 and MB, adsorbent mass, pH, sonication time and adsorbent regeneration were studied on the adsorption. In order to make a quadratic model, the experiments needed to be performed 32 times. The adsorption efficiency was measured after all the above parameters were set on their optimized values. The optimized values of initial concentration were achieved 200 and 100 mg/l for MB and AB 25, respectively. For adsorption mass, it was 0.6 g/l, for sonication time it was 8 min and the pH was 10. Under these conditions, the highest efficiency in adsorption belonged to TRAC with the value of 98.94% for MB and 98.65% for AB 25. The responses which were selected showed that one of the parameters which had an outstanding impact statistically, was the Sonication time. The Amount of R² for the Temkin model is 0.955 and 0.879 mg/g for MB and AB 25 in a single system. This value is equal to 0.978 and 0.996 mg/g in systems that are binary and they all show that the model is applicable. Pseudo-first order, pseudo-second order, Weber–Morris intraparticle diffusion rate and Elovich kinetic models were applied and analyzed for their quality to fit the data. Elovich kinetic model was the best in fitting the adsorption data. Thermodynamics studies affirmed the nature of adsorption is spontaneous and the process is endothermic. The endothermic nature of the processes was determined because of the positive amounts of both enthalpy (ΔH) and entropy (ΔS).
Rapid, high-efficient adsorbents with efficient solid-liquid separation ability has broad application prospects in the treatment of dye wastewater. In this study, polydopamine and methacryloyloxyethyltrimethyl ammonium chloride were grafted onto hollow glass microspheres to prepare the enhanced polydopamine shell structure adsorbent with self-floating ability. Furthermore, the adsorbent achieves high-efficiency surface solid-liquid separation, which overcomes the disadvantages of the traditional separation methods. Characterizations of the as-synthesized microspheres were performed using various techniques such as SEM, EDS, BET, XPS, FT-IR, TGA and XRD. The adsorbent achieved rapid and high-efficient adsorption of alizarin cyanine green F via the interactions of electrostatic attracting and π-π stacking, and under the optimal experimental conditions, the removal efficiency of 0.10 m mol L-1 dye solution reaches 94.51%, and the adsorption process reaches equilibrium within 60 min. Adsorption isotherms and kinetics data of the adsorbent were well fitted by Langmuir isotherm and pseudo-second-order kinetic models, respectively. The as-synthesized adsorbent has excellent recyclability, and its adsorption performance can be maintained after 5 cycles of reuse. The self-floating adsorbent has great potential for the removal of dissolved contaminants and cost-effective separation.
This paper presents a novel method to determine the optimal Multi-layer Perceptron structure using Linear Regression. Starting from clustering the dataset used to train a neural network it is possible to define Multiple Linear Regression models to determine the architecture of a neural network. This method work unsupervised unlike other methods and more flexible with different datasets types. The proposed method adapt to the complexity of training datasets to provide the best results regardless of the size and type of dataset. Clustering algorithm used to impose a specific analysis of data used to train the network such us determining the distance measure, normalization and clustering technique suitable with the type of training dataset used.
Inpresent paper the recovery of Mn-Zn ferrite nano-particles from waste batteries by using acid dissolution and ferrite process were studied. The recovered Mn-Zn ferrite nano-particles were decorated on rGO by using facile solvothermal method. The prepared material were characterized by X-ray powder diffraction (XRD) to study particle size and crystallinity, the morphology of the particles were analyzed by using Scanning Electron Microscopy (SEM), functional group were observed using Fourier Transform Infrared Spectrometer (FTIR) and band gap energy were examined by UV-Visible spectrometer. Graphene oxide used toeliminatethe pollutantspresent in the wastewater. The developed material used as an adsorbent for the removal of ionic dye in acid orange 88. The Mn-Zn ferrite nano-particles decorated with rGO showed a superior photocatalytic activity compared to that of Mn-Zn ferrite nano-particles.
The fabrication of novel functionalized composite materials as adsorbent is considered to be the core research area in adsorption technology for environmental applications. Indiscriminate disposal of industrial effluents containing toxic dyes has become a serious environmental issue across the globe since last few decades. In view of above, this study focused on the performance evaluation of ZnO/polyaniline nanocomposite (ZnO-PANI-NC) for quick ultrasonic assisted adsorptive remediation of methyl orange dye from aqua matrix. ZnO nanoparticles were fabricated by a simple co-precipitation method and ZnO-PANI-NC was synthesized by in situ oxidative polymerization of aniline monomer in presence of ZnO nanoparticles. The nanocomposite was extensively characterized for its crystalline nature, morphological characteristics, surface chemical bonding, specific surface area and pore volume by employing XRD, SEM, TEM, FTIR, and BET analysis. The ZnO-PANI-NC has shown superior adsorptive performance as compared to pure PANI as well as ZnO nanoparticles and the maximum monolayer adsorption capacity of 240.84 mg/g was obtained for the ZnO-PANI-NC. Under ultrasonic environment the adsorption reaction reached to equilibrium (more than 98% MO dye removal) within 15 min of reaction. Adsorption process followed Langmuir isotherm model and second order kinetic model strictly and contribution of intra-particle diffusion was also observed. The ZnO-PANI-NC has shown its high regeneration ability (more than 86%) even after 5th consecutive cycles of adsorption-desorption. Response surface methodology based optimization was used to optimize the adsorption experimental data and maximum MO removal of 99.12% was observed at optimum sonication time 13 min, adsorbent dose 0.38 g/L and initial MO concentration at 28 mg/L.
Activated carbon was successfully generated from the mixture of corn cob and petai hull which served as adsorbents for Pb (II) ion removal at an aqueous solution. The activation was done using KOH at 800°C carbonization temperature. Synthetic waste used in the various concentration of 100, 200, 300, 400 and 500 ppm. The adsorption process was carried out at 30 minutes, the ratio of adsorbent mass was grouped as B (1:3 corn cob native activated carbon /petai hull native activated carbon ), C (1:1 corn cob native activated carbon /petai hull native activated carbon ), D (3:1, corn cob native activated carbon /petai hull native activated carbon ), and H (1:1, corn cob modifiedactivated carbon /petai hull modifiedactivated carbon ). The results showed that the highest adsorption capacity was found in H adsorbent that was 2,368 mg/g at concentration 300 ppm. Dubinin model fit the adsorption isotherms of B, C, D, and H.
This study evaluated the feasibility of Rhodamine-B dye (Rh B) removal from aqueous solution, using Lead-Iron Oxide nanoparticles Loaded Activated Carbon (FePbO@AC). The parameters like pH, contact time, adsorbent/adsorbate dosage and temperature on adsorption was studied. Optimized conditions are pH of 7.0, 25 min contact time, 50 ppm of dye concentration and 200 mg of adsorbent concentration. The kinetics of adsorption was calculated using pseudo-first-order, pseudo-second-order, and intra-particle diffusion models. The calculations revealed that the pseudo-second-order kinetic equation best-fit the adsorption data. The Langmuir isotherm model best fit the equilibrium data. The maximum sorption capacity (Qmax) for dye is 1000 mg Rh B/g FePbO@AC. Change in entropy (ΔS), Gibb’s free energy change (ΔG), and enthalpy (ΔH) were calculated for the adsorption of Rh B dye.
An Ag-MnFe2O4-bentonite composite was synthesized by a chemical co-precipitation method and used for adsorption removal of Pb(II), Cd(II) and disinfection. The result of X-ray diffraction indicate that the diffraction peaks of MnFe2O4 and Ag can be perfectly indexed to the cubic spinel MnFe2O4(JCPDS No.88-1965) and metallic Ag(JCPDS No.41-1402), respectively. The results of scanning electron microscopy and energy dispersive X-ray spectroscopy manifest the deposition of MnFe2O4 and Ag on the bentonite surface and the presence of Mn, Fe and Ag. The result of X-ray photoelectron spectroscopy displayed that the composition of Ag-MnFe2O4-bentonite was Mn(II), Fe(III) and metallic Ag. The analysis of Brunauer-Emmett-Teller showed that the specific surface area of Ag-MnFe2O4-bentonite was the largest compared with that of bentonite, MnFe2O4 and MnFe2O4-bentonite. Thermo-dynamic studies revealed that the adsorption of Pb(II) and Cd(II) ions was spontaneous and endothermic. Langmuir model showed an adsorption capacity of 129.87 mg/g for Pb(II) and 48.31 mg/g for Cd(II) ions. The adsorption ki-netics of Pb(II) and Cd(II) ions onto Ag-MnFe2O4-bentonite can be best described by a pseudo-second-order model. The adsorption rate constant of the pseudo-second-order model was 0.0019 g·mg‒1·min‒1 for Pb(II) and 0.0065 g·mg‒1·min‒1 for Cd(II) ions. In addition to the adsorption experiment, the antibacterial properties of Ag-MnFe2O4-bentonite were studied through plate count method. Gram-negative(G‒) bacteria Escherichia coli and Gram-positive(G+) bacteria Lactobacillus plantarum were used to test the antibacterial properties. The results showed that the composite demonstrated excellent antibacterial activity. Thus, Ag-MnFe2O4-bentonite can be em-ployed as an adsorbent as well as an antimicrobial agent.
Activated carbon from oak tree is used as adsorbent for the removal of noxious anionic dye sunset yellow. The prepared adsorbent is characterized using X-ray diffraction, Scanning Electron microscopy equipped with Energy-Dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy. In addition to this, parameters like initial concentration, adsorbent dosage, contact time, pH, and particle size on the uptake of SY dye from wastewater is well investigated and optimized. For maximum adsorption, the initial concentration of 10 mg/L; adsorbent dose of 0.25 g; pH =1; contact time = 35 min and particle size = 150–250 μm is found to be optimal value. The adsorption isotherm data at different adsorbent dosage of 0.05–0.25 g is in agreement with the Langmuir isotherm having Qmax = 5.8377–30.1205 mg/g. On the other hand, models like, Group Method of Data Handling and multiple linear regression were used to forecast the removal efficiency of noxious anionic dye sunset yellow and from results, it is specified that the GMDH model possess a high performance than MLR model for forecasting removal percentage of SY dye. Hence, activated carbon from oak tree can be efficiently used as adsorbent for the removal of SY dye from wastewater.
Laccase, a type of multicopper oxidase, is capable of efficiently degrading lignin. Until now, molecular basis of laccase interacting with lignin is still poorly known. Here, five lignin model compounds (2,6-dimethoxyphenol, ferulic acid, guaiacol, sinapic acid and vanillyl alcohol) were selected to demonstrate the key binding mechanisms between Trametes versicolor laccase and lignin. The results showed that the interaction energies of lignin model compounds with laccase varied widely, which suggested different molecular efficiency of laccase in degrading various components of lignin. This was in full agreement with experimental reports. Hydrophobic interactions seemed to be necessary to the interaction of lignin/lignin model compounds with laccase, while H-bonds were alternative. Molecular basis produced by this study was helpful in designing highly efficient laccases again lignin waste to achieve the environmental protection.
Chromium is one of the hazardous pollutants in industrial effluents. The aim of this research is to investigate feasibility of using waste eggshells for the removal of Cr(VI) ions from its aqueous solutions. Characterization of crushed and sieved eggshell have been carried out using scanning electron microscope, Fourier transform infrared spectroscopy, X-ray diffraction, X-ray fluorescence, etc. analysis. The effect of pH, Cr(VI) ions concentration, amount of eggshell, contact time, temperature, etc. parameters have been investigated on the adsorption and it has been found that the maximum removal (about 93%) of Cr(VI) onto eggshells can be achieved at 25°C and pH 5 in 90 min. Freundlich and Langmuir adsorption isotherm models e have been verified using experimental data. Results also include calculation of thermodynamic parameters like, change in enthalpy (ΔH 0), change in entropy (ΔS 0), and change in free energy (ΔG 0) of the ongoing adsorption process. Chromium sorption kinetics is also found to be fitted in pseudo-first-order kinetic model. Results clearly indicate that the waste material eggshell, a solid waste from the food industry, can be very effectively used as a sorbent for the removal of chromium ions from its aqueous solutions.
In this study, Congo red (CR) was removed from aqueous solution by multiwalled carbon
nanotube (MWCNT) oxidized and treated with HNO3. In this regard, the influence of variables
such as size and amount of MWCNT, pH, stirring time, and initial dye concentration
on the procedure efficiency was examined and optimized. At optimum value of all variables,
high adsorption capacity (357.14 mg g−1) indicates the efficiency and suitability of
MWCNT for achievement a safe and clean environment. The initial pH has significant role
on the chemistry of dye molecules and adsorbent, and the best pH was 1.0. The removal
rates follow the pseudo-second order in cooperate with interparticle diffusion models. The
correlation coefficients were in the range of 0.9987–0.9998. The high adsorption capacities
make MWCNT superior to activated carbon and admit to design safe, economic, and nontoxic
wastewater treatment technology. The pH of zero charge of MWCNT was around 1.0.
The higher the fraction of mesopores with size between 10 and 50 nm lead to enhance in
the rate and amount of dyes adsorbed on its surface. Fitting the equilibrium adsorption data
by Langmuir and Freundlich models shows that experimental data well explained by the
by the Langmuir equation. The treated MWCNT has surface area, pore volume, and average
pore diameter of 217.022 m2/g, 0.757 cm3/g, and 276.482 A ° . The increase in the adsorption
of the CR with rising temperature indicates its endothermic nature.
Removal and recovery of a hazardous halogen-containing dye Eosin Yellow were investigated using Bottom Ash as adsorbent. During the studies various essential factors influencing the adsorption, like sieve size of adsorbent, adsorbate concentration, amount of adsorbent, pH of the solution, contact time, and temperature have been monitored. Attempts have also been made to verify Langmuir, Freundlich, Tempkin, and D-R adsorption isotherm models. The feasibility of the ongoing adsorption has been ascertained on the basis of Langmuir adsorption isotherm. The free energy, entropy, and enthalpy of the ongoing adsorption process have been evaluated as about 26 kJ mol−1, 22 kJ mol−1, and 20 kJ mol−1, respectively. Contact time studies reveal that the ongoing adsorption equilibrate within 3 h of contact. It is found that the adsorption of Eosin Yellow over Bottom Ash follows a pseudo-second-order kinetics. At all the temperatures rate constant of the process was calculated around 5 × 10−8 sec g mol−1. During bulk removal through column operation about 97% percentage saturation of the dye is obtained. Desorption studies exhibit that the percentage recovery of Eosin Yellow dye on eluenting NaOH solution through exhausted column is about 90%.
Palladium nanoparticles (Pd-NPs) and nickel oxide nanoparticles (NiO-NPs) were synthesized and loaded on activated carbon (AC). This novel material successfully used for the removal of methylene blue (MB) dye from aqueous medium. Full characterization of both material using X-ray diffraction, transmission electron microscopy, scanning electron microscopy and Brunauer-Emmet-Teller analyses for Pd-NP show their high surface area (>1340 m(2)/g) and low pore size (<20 Å) and average particle size lower than 45 Å and for NiO-NP show their high surface area (>1316.1554 m(2)/g) and low pore size (<20 Å) and average particle size lower than 46 Å in addition to high reactive atom and presence of various functional groups. These unique properties make them possible for efficient removal of MB. In batch experimental set-up, optimum conditions for maximum removal of MB by both adsorbents were attained following searching effect of variables such as central composite design. The Langmuir isotherm was found to be highly recommended for fitting the experimental equilibrium data. The kinetic of adsorption of MB on both adsorbents strongly can be fitted by a combination of pseudo-second order and intraparticle diffusion pathway. The experimental result achieved in this article shows the superiority of Pd-NP-AC for MB removal than NiO-NP-AC, so the maximum adsorption capacities of Pd-NP-AC and NiO-NP-AC were 555.5 mg/g and 588.2 mg/g, respectively.
A pseudo-second order rate equation describing the kinetics of sorption of divalent metal ions on to sphagnum moss peat at various initial metal ion concentrations and peat doses has been developed. The sorption kinetics were followed based on the concentrations of metal sorbed at various time intervals. Results show that chemical sorption processes might be rate-limiting for the sorption of divalent metal ions on to peat during agitated batch contact time experiments. The rate constant, the equilibrium sorption capacity and the initial sorption rate were calculated. From these parameters, an empirical model for predicting the concentrations of metal ions sorbed was derived.
The discharge of heavy metal pollutants, particularly Cr(VI) ions emerging out from the industries into water, has become a serious concern. In the present study, feasibility of eggshell membrane has been tested for the removal of Cr(VI) ions from its aqueous solutions. The membrane was separated from the eggshells, dried at 40°C for about 3 h and then crushed into powder. The characterization of powdered eggshell membrane was carried out using scanning electron microscopy, FTIR, and XRF analysis. Batch adsorption experiments have been performed for the investigation of the ability of powdered egg membrane to remove Cr(VI) ions from its solution. The effect of pH, Cr(VI) ion concentration, sorbent dosage, contact time, and temperature has been evaluated. The theoretical treatment from the experimentally gathered data has been carried out to measure the effect of these variables using central composite design (CCD). The results of the CCD show that the maximum removal (81.47%) was achieved at temperature 20°C, pH 3.54, Cr(VI) ion concentration 5.0 mg/L, time 117.52 min, and dosage 3.78 g.
A simple hydrothermal process is proposed for synthesizing SnO2 quantum dots (QDs), in which hydrazine hydrate is used as the mineralizer instead of NaOH. X-ray diffraction, high-resolution transmission electron microscopy (HRTEM) and UV–vis diffuse reflectance spectroscopy (DRS) were employed to characterize the product. The HRTEM image shows that the diameters of the SnO2 nanoparticles fall into a small range of 2.3–3.1 nm, with the majority being less than the exciton Bohr radius of SnO2 (~2.7 nm). Analysis of the DRS spectrum showed the band gap of the SnO2 QDs to be 3.88 eV, exhibiting a 0.28 eV blue shift from that of the bulk SnO2 (3.6 eV). The mechanism for hydrothermal synthesis of the SnO2 QDs using hydrazine hydrate as the mineralizer is discussed.
Textile wastewater presents a challenge to conventional physico-chemical and biological treatment methods. Liquid-phase adsorption has been shown to be highly efficient for the removal of dyes and other organic matters from process or waste effluent. Many different types of adsorbent are used to remove colour from wastewater among which the most widely used material is activated carbon. Since activated carbon is expensive and necessitates regeneration, attempts have been made to substitute alternatives that are biodegradable, low cost and/or waste materials. This article presents the investigations carried out by numerous researchers on the use of different kinds of adsorbents and their adsorption capacities for the removal of specific dyes from textile wastewater.
In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM''s. The approach is illustrated on a two-spiral benchmark classification problem.
In this research, activated carbon (AC) simply was prepared from a local, abundant tree in south of Iran.
The AC with low cost and toxicity is a good candidate for bromophenol blue (BPB) removal from aqueous
media. The AC with nano scale pore diameter is applicable for this dye removal following optimization of
the influence of various parameters including contact time, pH, initial dye concentration and amount of
adsorbent. Subsequently, experimental data was analyzed by four kinetic models including pseudo first
and second-order, Elovich and the intraparticle diffusion equations and subsequently their respective
parameters such as rate constants, equilibrium adsorption capacities and correlation coefficients was
investigated and based on well known criterion their applicability was judged. The result shows that
adsorption of BPB onto proposed adsorbent at all conditions such as versatile adsorbent dosages and
initial BPB concentrations sufficiently described by the combination of the pseudo second-order
equation and interparticle diffusion model. It was found that equilibrium rate of the BPB adsorption at
various adsorbent dosage well fitted by Langmuir. Investigation of experimental result by two
approaches (multiple linear regressions (MLR) and random forest (RF)) models show that RF is a
powerful tool for prediction of BPB adsorption by activated carbon obtained from Astragalus bisulcatus
tree. The optimal tuning parameters for RF model are obtained based on the ntree = 100, mtry = 2. For the
training data set, the MSE values of 0.0006 and the coefficient of determination (R2) values of 0.9895 for
RF model and the MSE value of 0.0104 and the R2 value of 0.823 for MLR model are obtained.
The present research is focused on the synthesis and characterization of cobalt (III) oxide (Co2O3) nanoparticle loaded on activated carbon to prepare an outstanding sorbent for the removal of eosin Y (EY) as hazardous dye from aqueous solution. The sorbent was identified by SEM and XRD analysis. The effect of solution pH, adsorbent dosage (0.005–0.02 g), contact time (0.5–30 min) and initial eosin Y concentration (30–80 mg L−1) on the adsorption process was investigated and modeled by artificial neural network. Following optimization of variables, the experimental equilibrium data was analysis by Langmuir, Freundlich, Tempkin and D–R isothermal models and explored that the data well presented by Langmuir model with a maximum adsorption capacity of 555.56 mg g−1 at 25 °C. Kinetic studies at various adsorbent dosage and initial EY concentrations show that high removal percentage (>90%) was achieved within 15 min of the start of every experiment at most conditions. The adsorption of EY follows the pseudo-second-order rate equation in addition to intraparticle diffusion model. The experimental data were applied to train the multilayer feed forward neural network with three inputs and one output with different algorithms and different numbers of neurons in the hidden layer. The minimum mean squared error (MSE) of 1.49e − 04 and determination coefficient of (R2) 0.9991.
The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R(2)) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015g and 0.75g) is applicable for successful removal of methyl orange (>98%) in short time (20min for Au-NP-AC and 45min for Tamarisk-AC) with high adsorption capacity 161mgg(-1) for Au-NP-AC and 3.84mgg(-1) for Tamarisk-AC.
In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way.
In this paper, we propose a LSSVR-based time series method (LTSM) to predict dynamic lateral deformation of retaining structure and ground surface settlement in deep foundation pit engineering. After reconstructing phase space, time-varying lateral displacement of each observation point on retaining structure can be predicted using its historic unary time series data. And then, the ground settlement nearby the deep foundation pit can be predicted using all the observed values of lateral deformation which were collected at the same time from different depths on the retaining structure. The experimental results show that LTSM achieved a high accuracy when predicting the lateral deformation and ground settlement.
This work devoted to the investigation of adsorption of reactive orange 12 (RO-12) by gold nanoparticles loaded with activated carbon (Au-NP-AC), which in high efficiency by routine manner was synthesized in our laboratory. Generally, in batch adsorption procedure, the effect of variables, including adsorbent mass, initial RO-12 concentration, and contact time on its removal percentage was optimized by the application of artificial neural networks and based on an imperialist competitive algorithm. This novel adsorbent by small amount (0.02 g) really is applicable to the removal of the high amount of dye (RO 12) in a short time (<20 min). The optimum variables for adsorption of RO 12 onto gold nanoparticle-activated carbon were 0.02 g adsorbent mass, 10 mg L-1 initial RO-12 concentration, 20 min contact time and pH 1. The kinetic of proposed adsorption processes efficiently followed, pseudo-second-order and intra-particle diffusion kinetic models. The equilibrium data of the removal process strongly follow the Langmuir monolayer adsorption with high adsorption capacity. The adsorption capacity of Au-NP-AC for the removal of RO-12 was found to be 714.3 mg g(-1). The comparison of the results obtained using the proposed models showed that the ANN model is better than the MLR model for the prediction of reactive orange 12 adsorption onto gold nanoparticles loaded on activated carbon. The coefficient of determination (R-2) and mean squared error (MSE) for the optimal ANN model with 9 neurons at hidden layer were obtained to be 0.9720 and 0.0007, respectively.
In this work Tin oxide nanoparticles were synthesized and loaded on activated carbon (SnO2–NP–AC). Prepared SnO2–NP–AC was used as an adsorbent for the removal of Malachite green-oxalate from aqueous medium. The morphological properties of the prepared adsorbent were investigated by using X-ray diffraction (XRD), scanning electron microscope (SEM) and BET analysis. The removal percentage in batch mode was investigated at various operating parameters like; initial pH, contact time, amount of adsorbents and initial dye concentration. The experimental equilibrium data were analyzed by using various models and it was seen that Langmuir isotherm model fitted well with maximum monolayer adsorption capacity of 142.87 mg g− 1. The adsorption kinetic data followed both pseudo second-order kinetics and intraparticle diffusion mechanism.
An agricultural waste-orange peel powder (OPP) was successfully modified into a novel magnetic nano-adsorbent (MNP–OPP) by co-precipitating it with Fe3O4 nanoparticles (MNP) for cadmium ion removal from aqueous solutions. Characterization of MNP–OPP by FTIR, SEM, XRD, TEM and VSM revealed the covalent binding of hydroxyl groups of MNP with the carboxyl groups of OPP, and further confirmed its physico-chemical properties favorable for metal binding. The cadmium adsorption onto MNP–OPP, MNP and OPP was tested under different pH, ionic strength, natural organic matter, adsorbate concentration, contact time and temperature conditions. Results revealed a faster kinetics and efficiency of MNP–OPP in comparison to those of MNP and OPP and further confirmed a complexation and ion exchange mechanism to be operative in metal binding. The adsorption equilibrium data obeyed the Langmuir model and the kinetic data were well described by the pseudo-second-order model. Thermodynamic studies revealed the feasibility and endothermic nature of the system. Breakthrough capacity from column experiments, adequate desorption as well as reusability without significant loss of efficiency established the practicality of the developed system. Cadmium removal was achieved at 82% from a simulated electroplating industry wastewater. The experimental results reveal the technical feasibility of MNP–OPP, its easy synthesis, recovery, economic, eco-friendly and a promising advanced adsorbent in environmental pollution cleanup.
The CdSe nanoneedles were successfully grown by a green room temperature solution method. The preparation conditions induce a unilateral growth from base to tip of nanoneedle-like structures, despite the absence of any template. X-ray diffraction and scanning electron microscopy show that the CdSe nanoneedles crystallize in the wurtzite-structure with the growth direction along . A photoluminescence peak with full width at half maximum of around 30nm could be obtained, which indicates a homogeneous and narrow size distribution for the CdSe nanoneedles.
The voltammetric oxidation of paracetamol on single-walled carbon nanotubes (SWNT) modified edge plane pyrolytic graphite electrode (EPPGE) was explored in phosphate buffer solution by using square wave voltammetry. Cyclic and square wave voltammetry studies indicated the oxidation of paracetamol at the electrode surface through a two-electron reversible step and fundamentally controlled by adsorption. Besides semi-infinite planar diffusion, the role of thin layer diffusion at nanotube modified electrodes is also suggested. The sensitivity at SWNT modified EPPGE is ∼2 times more than that at MWNT modified EPPGE. Paracetamol gave a sensitive oxidation peak at ∼187mV at pH 7.2 (μ=0.5M) which was used to quantitate the drug in the range of 5–1000nM with a detection limit of 2.9×10−9M at SWNT modified EPPGE. The interfering effect of physiologically common interferents on the current response of paracetamol has been reported. The procedure was successfully applied for the assay of paracetamol in pharmaceutical formulations. The applicability of the developed method to determine the drug in human urine samples obtained after 4h of administration of paracetamol is illustrated.
The effluent from dye manufacturing plant is generally recalcitrant to be decolourized visibly because of versatile composition resulting environmental problem. Hence, the UV/H2O2 process conducted using the recirculated batch reactor system with four annular UV lamps was proposed to treat the dye plant effluent in this work, while identifying the effects of hydrogen peroxide dosage, UV power input and wastewater strength on the decolouration and COD removal. From the experimental results, substantial decolouration and COD removal was increased significantly by supplementing hydrogen peroxide dosage, UV power input. Moreover, the pseudo-first order model was developed to describe the decolouration behaviour that the kinetic rates were calculated by linear regression obtaining the decolouration rate constant of 0.0993mins−1 while 72.0Wl−1 of UV power and 116.35mM of H2O2 dosage for 10% diluted plant wastewater with PtCo colour of 4550 units and COD of 1065mgl−1. Ultimately, the proposed recirculated four-lamp annular UV/H2O2 reactor conducted profitably to not only decolourize but also mineralize the dye plant effluent at the same time.
5,7,12,14-Tetramethyldibenzotetraazaannulene (Me4Bzo2TAA) has been explored as an electroactive material for preparing poly(vinyl chloride) (PVC)-based membrane electrodes selective to Ni2+. The membrane having the constituents Me4Bzo2TAA, sodium tetraphenyl borate (NaTPB) and PVC in the optimum ratio 2:1:97 (w/w) gave the best working concentration range (7.9×10−6–1.0×10−1M) with a Nernstian slope (30.0+−1.0mV/decade of activity) in the pH range 2.7–7.6. The sensor exhibits a fast response time of 15s. The electrode shows good selectivity for nickel(II) over a number of mono-, bi- and tri-valent cations. Analytical application of the electrode has been investigated for the quantitative determination of Ni2+ in chocolates and the sensor has been successfully used as an indicator electrode in the potentiometric titration of Ni2+ against EDTA.
writes that the use of neonicotinoid insecticides has been tl y restricted because of their fects on pollinators. Neonicotinoid to v er tebrates due to their high to xicity , en vironmental persistence, w ater solubility , and poten tial
Polyamine flocculants were synthesized and applied for the removal of color, turbidity, and organic compounds from dye wastewater. The effect of polyamine on color removal was investigated by comparing 2 treatments: 1) alum alone and 2) alum/polyamine in combination. The effects of polyamine flocculant, concentration, types, and pH on the removal efficiency of colored materials were investigated. Polyamine flocculants were highly efficient in the removal of color and turbidity from dye wastewater. Compared with alum alone treatment, an addition of 25 mg/L of polyamine could reduce alum dosage by more than 50% and improve the color and turbidity removal efficiency. Highly efficient color removal was obtained by adding polyamine as a flocculant at widely different pH ranges. Results indicate that the use of polyamine flocculant is cost effective in dye wastewater treatment because it minimizes the amount of sludge produced as the dosage of inorganic coagulant is highly reduced. Effects of zeta potential and pH are also discussed in the paper.
An overview of potentiometric sensors that are capable of detecting toxic heavy metal ions in environmental samples is presented and discussed. Notwithstanding the tremendous work performed so far, it is obvious that still several limitations do exist in terms of selectivity, limits of detection, dynamic ranges, applicability to specific problems, and reversibility. A survey on important advances in potentiometric sensors with regard to high selectivity, lower detection limit, fast response time, and on-line environmental analysis is presented in this review article.[Supplemental materials are available for this article. Go to the publisher's online edition of Critical Reviews in Analytical Chemistry to view the free supplemental file.]
The removal of the dye—tartrazine by photodegradation has been investigated using titanium dioxide surface as photocatalyst under UV light. The process was carried out at different pH, catalyst dose, dye concentration and effects of the electron acceptor H2O2. It was found that under the influence of TiO2 as catalyst, the colored solution of the dye became colorless and the process followed a pseudo first order kinetics. The optimum conditions for the degradation of dye were 6 × 10− 5 M dye concentration, pH of 11, and 0.18 mg/L of catalyst dose. In order to evaluate the effect of electron acceptor, the effect of H2O2 on the degradation process was also monitored and it was found that the hydroxyl radical formation and retardation of electron–hole recombination took place simultaneously. The adsorption studies of tartrazine at various dose of TiO2 followed the Langmuir isotherm trend. In order to determine the quality of waste water, Chemical Oxygen Demand (COD) measurements were carried out both before and after the treatment and a significant decrease in the values was observed, implying good potential of this technique to remove tartrazine dye from aqueous solutions.Graphical abstractResearch highlights►Degradation efficiency increases with increase in catalyst concentration. ►Adsorption of tartrazine on TiO2 followed the Langmuir isotherm. ►The photocatalytic kinetics follows first order.
Activated carbon, developed from fertilizer waste, has been used for the removal of Hg2+, Cr6+, Pb2+, and Cu2+. Mass transfer kinetic approach has been successfully applied for the determination of various parameters necessary for designing a fixed-bed absorber. Parameters selected are the length of the (PAZ) primary adsorption zone (δ), total time involved for the establishment of primary adsorption zone (tx), mass rate of flow to the absorber (Fm), time for primary adsorption zone to move down its length (tδ), amount of adsorbate adsorbed in PAZ from breakpoint to exhaustion (Ms), fractional capacity (f), time of initial formation of PAZ (tf) and per cent saturation of column at break point. Chemical regeneration has been achieved with 1 M HNO3.
The sorption of three disperse dyes, namely, Disperse Blue 56 (DB56), Disperse Red 74 (DR74) and Disperse Yellow 119 (DY119), onto alunite has been studied in terms of pseudo-first- and second-order sorptions and intraparticle diffusion processes thus comparing chemical sorption and diffusion sorption processes. The pseudo-second-order model provided a high degree of correlation with the experimental data for the sorption processes. There was a small discrepancy at the beginning of the experiments (5–30 min) which suggested that intraparticle diffusion may be involved up to 30 min of the sorption process. The kinetics of sorption, based on the sorption capacities of disperse dyes on alunite, were followed at various time intervals. Results show that the intraparticle diffusion may be rate-limiting, followed by the pseudo-second-order kinetic model in the sorption of disperse dyes onto alunite during agitated batch contact time experiments. The rate constant, the equilibrium sorption capacity and the initial sorption rate were calculated as a function of the effect of alunite particle size, alunite dose, initial dye concentration and pH of the solution.
This work concerns the treatment of textile plant effluent after conventional biological processing. The objective was a feasibility study of the combination of physicochemical treatment with nanofiltration (NF) and/or reverse osmosis (RO) for water reuse. In fact, dead-end filtration by microfiltration (MF), ultrafiltration (UF), NF and RO tests showed that a primary physicochemical treatment (coagulation/flocculation) was necessary to limit membrane fouling. Two coagulants (organic polyelectrolyte and/or ferric chloride) were tested and compared by carrying out jar-tests using different chemical concentrations at pH 6.8. Then, NF and/or RO experiments were performed and investigated at different operating pressures. Results showed that NF allowed the higher flow rate, 90 L.h−1.m−2 at 18.5 bar transmembrane pressure. Moreover, the permeate quality obtained in this condition was similar to the RO. Conductivity, absorbance at 490 nm and the dissolved organic carbon value of the NF permeates were lower than 390 μS.cm−1, 0 and 2 mg.L−1 of C, respectively. The percent production rate increased with the transmembrane pressure. NF performed at 18.5 bar transmembrane pressure allowed a higher yield (22.6%) than RO (18.3%).
The filtration of wastewaters generated in the cork industrial process is investigated by using three membranes in tangential filtration laboratory equipment. The three membranes used were two microfiltration membranes with pores sizes of 0.65 and 0.1 μm (DUR-0.65 and DUR-0.1 membranes), and a ultrafiltration membrane with a molecular weight cut-off of 300 kDa (BIO-300K membrane). The water hydraulic permeability was determined for each membrane (values of 860, 248 and 769 L h−1 m−2 bar−1 were found), and the influence on the permeate flux of the main operating variables, such as transmembrane pressure, feed flow rate, temperature and nature of the membranes, was established. The effectiveness of the different membranes and operating conditions was evaluated by determining the removal obtained for several parameters which measure the global pollutant content of the effluent: COD, absorbance at 254 nm, tannic content, color and ellagic acid, which is selected as a major model pollutant among the different organic compounds present in this wastewater. The values of the corresponding retention coefficients depended on the operating conditions, but in all cases were in the sequence: ellagic acid and color > absorbance at 254 nm > tannic content > COD. Globally, the higher removals were obtained for the BIO-300K membrane at 20 °C, with QF = 5.3 L h−1 and TMP = 1.8 bar. Finally, the fouling of the membranes was assessed, and the corresponding mechanism for each membrane was established by fitting the experimental data to various filtration fouling models reported in the literature.
Textile dyeing processes are among the most environmentally unfriendly industrial processes, because they produce coloured wastewaters that are heavily polluted with dyes, textile auxiliaries and chemicals. The coagulation/flocculation method was studied as a wastewater treatment technique for the decolourization of residual dyebath effluents after dyeing cotton/polyamide blends using reactive and acid dyes. It was discovered that a combination of aluminium sulphate and a cationic organic flocculant yields an effective treatment for residual dyebath wastewaters since almost complete decolourization was achieved, TOC, COD, AOX, BOD and the anionic surfactants were reduced and the biodegradability was increased.
The high rate of electron/hole pair recombination reduces the quantum yield of the processes with TiO(2) and represents its major drawback. Adding a co-adsorbent increases the photocatalytic efficiency of TiO(2). In order to hybridize the photocatalytic activity of TiO(2) with the adsorptivity of carbon nanotube, a composite of multi-walled carbon nanotubes and titanium dioxide (MWCNT/TiO(2)) has been synthesized. The composite was characterized by means of X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Fourier transform infrared absorption spectroscopy (FTIR), and diffuse reflectance UV-vis spectroscopy. The catalytic activity of this composite material was investigated by application of the composite for the degradation of methyl orange. It was observed that the composite exhibits enhanced photocatalytic activity compared with TiO(2). The enhancement in photocatalytic performance of the MWCNT/TiO(2) composite is explained in terms of recombination of photogenerated electron-hole pairs. In addition, MWCNT acts as a dispersing agent preventing TiO(2) from agglomerating activity during the catalytic process, providing a high catalytically active surface area. This work adds to the global discussion of how CNTs can enhance the efficiency of catalysts.
In this study, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average Index, and New York Stock Exchange Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting
In this study, a zirconium nanoparticle sorbent for significantly enhanced adsorption of arsenate (As(V)) was successfully synthesized. The characterization of the zirconium nanoparticle sorbent and its adsorption behavior for arsenate were investigated. The HRTEM micrographs showed that the sorbent was nanoscale with particle sizes ranging from 60 to 90nm. The thermal gravimetric and elemental analyses indicated that the sorbent had a molecular formula of Zr(2)(OH)(6)SO(4)·3H(2)O. The X-ray diffraction study revealed that the sorbent was amorphous. The potentiometric titration study demonstrated the surface charge density of the sorbent decreased with an increase in solution pH, and the pH of zero point charge of the sorbent was around 2.85. The kinetics study showed that most of the uptake took place in the first 6h, and the adsorption equilibrium was obtained within 12h. The optimal pH for As(V) adsorption was between 2.5 and 3.5. The Langmuir equation well described the adsorption isotherm; the maximum adsorption capacity of 256.4mg As/g was found at the optimal pH, better than most of sorbents available in the market. The presence of fluoride or nitrate did not obviously affect the adsorption of As(V) onto the sorbent; however, the existence of humic acid, phosphate or silicate in aqueous solution significantly reduced the uptake of As(V). The humic acid did not cause the reduction of the As(V). The FTIR and XPS spectroscopic analyses revealed that surface hydroxyl and sulfur-containing groups played important roles in the adsorption.
In this article a super selectivity potentiometric methodology, using an ion-selective electrode, for determination of mercury ion(II) in aqueous solution was investigated. For modification of the electrode a room temperature ionic liquid, 1-n-butyl-3-methylimidazolium tetrafluoroborate (BMIM·BF(4)), was applied as a super conductive binder, and Multi-walled carbon nanotubes (MWCNTs) was used in the composition of the carbon paste to improve conductivity and transduction of chemical signal to electrical signal. Moreover, incorporation of 1-(2-ethoxyphenyl)-3-(3-nitrophenyl)triazene (ENTZ) as an ionophore to this composition caused to significantly enhanced selectivity toward Hg(II) ions over a wide concentration range of 1.0×10(-4) to 5.0×10(-9) M with a lower detection limit of 2.5×10(-9) M (0.5 ppb) and a Nernstian slope of 29.3±(0.2) mV decade(-1) of Hg(II) activity. The electrode has a short response time (∼5s) and can be used for at least 55 days without any considerable divergence in potentials, and the working pH range was 2.0-4.3. Finally, the proposed electrode was successfully used as an indicator for potentiometric determination of Hg(II) in dental amalgam and water samples.
Al(3+) selective sensor has been fabricated from poly(vinyl chloride) (PVC) matrix membranes containing neutral carrier morin as ionophore. Best performance was exhibited by the membrane having composition as morin:PVC:sodium tetraphenyl borate:tri-n-butylphosphate in the ratio 5:150:5:150 (w/w, mg). This membrane worked well over a wide activity range of 5.0x10(-7) to 1.0x10(-1)M of Al(3+) with a Nernstian slope of 19.7+/-0.1mV/decade of Al(3+) activity and a limit of detection 3.2x10(-7)M. The response time of the sensor is approximately 5s and membrane could be used over a period of 2 months with good reproducibility. The proposed sensor works well over a pH range (3.5-5.0) and demonstrates good discriminating power over a number of mono-, di- and trivalent cations. The sensor can also be used in partially non-aqueous media having up to 20% (v/v) methanol, ethanol or acetone content with no significant change in the value of slope or working activity range. The sensor has also been used in the potentiometric titration of Al(3+) with EDTA and for its determination in zinc plating mud and red mud.
A mu-bis(tridentate) ligand named 2-phenyl-1,3-bis[3'-aza-4'-(2'-hydroxyphenyl)-prop-4-en-1'-yl]-1,3-imidazolidine (I) has been synthesized and scrutinized to develop iron(III)-selective sensors. The addition of sodium tetraphenyl borate and various plasticizers, viz., chloronaphthalene, dioctylphthalate, o-nitrophenyl octyl ether and dibutylphthalate has been used to substantially improve the performance of the sensors. The membranes of various compositions of the ligand were investigated and it was found that the best performance was obtained for the membrane of composition (I) (10mg):PVC (150mg):chloronaphthalene (200mg):sodium tetraphenyl borate (9mg). The sensor showed a linear potential response to iron(III) over wide concentration range 6.3x10(-6) to 1.0x10(-1)M (detection limit 5.0x10(-6)M) with Nernstian slope (20.0mV/decade of activity) between pH 3.5 and 5.5 with a quick response time of 15s. The potentiometric selectivity coefficient values as determined by match potential method (MPM) indicate excellent selectivity for Fe(3+) ions over interfering cations. The sensor exhibits adequate life of 2 months with good reproducibility. The sensor could be used in direct potentiometry.
SnO2 quantum dots (QDs) and ultrathin nanowires (NWs) with diameters of approximately 0.5-2.5 and approximately 1.5-4.5 nm, respectively, were controllably synthesized in a simple solution system. They are supposed to be ideal models for studying the continuous evolution of the quantum-confinement effect in SnO2 1D --> 0D systems. The observed transition from strong to weak quantum confinement in SnO2 QDs and ultrathin NWs is interpreted through the use of the Brus effective-mass approximation and the Nosaka finite-depth well model. Photoluminescence properties that were coinfluenced by size effects, defects (oxygen vacancies), and surface capping are discussed in detail. With the SnO2 QDs as building blocks, various 2D porous structures with ordered hexagonal, distorted hexagonal, and square patterns were prepared on silicon-wafer surfaces and exhibited optical features of 2D photonic crystals and enhanced gas sensitivity.
This paper describes the use of photo-fenton process for color removal from textile wastewater stream. The wastewater sample to be treated was simulated by using colorless polyvinyl alcohol (PVA) and reactive dyestuff of R94H. As a result, the hydroxyl radical (HO*) oxidation can effectively remove color, but the chemical oxygen demand (COD) was removed in a slight degree. The color removal is markedly related with the amount of HO* formed. The optimum pH for both the OH* formation and color removal occurs at pH 3-5. Up to 96% of color can be removed within 30 min under the studied conditions. Due to the photoreduction of ferric ion into ferrous ion, color resurgence was observed after 30 min. The ferrous dosage and UV power affect the color removal in a positive way, however, the marginal benefit is less significant in the higher range of both. PVA as the major background COD of a textile wastewater stream inhibits the color removal insignificantly as its concentration increases.