In this study, different sludge-based adsorbents were synthesised using ZnCl2 (Zn-SBACs) and NaOH (Na-SBACs) under varying experimental conditions and subsequently employed for simultaneous aqueous uptake of phenol, resorcinol, and catechol. Response surface modelling (RSM) technique was employed to evaluate and optimise the collective influences of the SBAC production operating conditions based on adsorptive performances of the produced SBACs. The adsorbent-adsorbate interactive mechanisms were explored through several structural and surface characterisation techniques such as Scanning Electronic Microscopy (SEM), Thermogravimetric Analysis (TGA), Brunauer, Emmett and Teller machine (BET), Fourier Transformed Infrared spectroscopy (FTIR), pKa pHpzc and zeta potentials. These techniques depicted the SBACs as finely granular, thermally stable and mesoporous adsorbents enriched with oxygen functionalities. There existed a higher affinity of the Zn-SBACs towards catechol, then followed by resorcinol with the Na-SBACs yielding lower performance. Respectively, the maximum adsorption capacities of (45.57 mg/g and 42.99 mg/g); (14.19 mg/g and 29.28 mg/g); (20.95 mg/g and 17.82 mg/g) were obtained with higher pronounced effects of chemical–sludge ratio and activation temperature. For the Zn-SBAC, the best operating conditions for combined optimisation of the phenolic compounds' removal were obtained at 615°C, 1.06 ZnCl2-sludge ratio and 86.74-min activation time that yielded 100%, 57% and 80% for catechol, phenol, and resorcinol, respectively. The adsorption mechanism insight taken into cognisance FTIR, pKa, isoelectric point, zeta potential, and the phenolics-SBAC-adsorbent surface charge interactions suggest the interplay of chemisorption involving strong surface electrostatic attraction. The high performance of the SBACs under three cycles of regeneration affirmed the economic potentials of the usability of the SBACs for the treatment of phenolic wastewater. This study establishes the aptness of creating optimal conditions for SBACs production through the evaluation of the performance of simultaneous removal of multi-solute pollutants from an aqueous phase.
The quasi-solid-state electrolytes for flexible energy storage devices have indicated a great advancement during the last decade. However, further progress is still required to resolve non-flammability issues, high ionic conductivity, as well as electrochemical stability. Herein, we designed a new complex gel electrolyte with glycerol (Gly)/boric acid (BA) to address the non-flammability and maintained high conductivity by doping with potassium hydroxide (KOH). The Gly/3KOH/3BA combination was the optimum composition in terms of stability as well as hierarchical array for improved ionic conductivity to 2.9 × 10–3 S cm–1. Flexible electrochemical double-layer supercapacitors were assembled by using carbon composite electrodes, and the device provided a specific capacitance of 327 F g–1 at 1 A g–1. A remarkable cyclic stability of 93.4% capacitive performance is maintained after 10,000 cycles. The device indicated a specific energy of 45.4 W h kg–1 at a power of 920 W kg–1. Highly flexible devices constructed by using boron-incorporated gel electrolytes can provide a new strategy to assemble flexible devices for wearable electronics.
In this study, date-palm biochar MgAl-augmented double-layered hydroxide (biochar–MgAl–LDH) nanocomposite was synthesized, characterized, and used for enhancing the removal of phosphate and nitrate pollutants from wastewater. The biochar–MgAl–LDH had higher selectivity and adsorption affinity towards phosphate compared to nitrate. The adsorption kinetics of both anions were better explained by the pseudo-first-order model with a faster removal rate to attain equilibrium in a shorter time, especially at lower initial phosphate-nitrate concentration. The maximum monolayer adsorption capacities of phosphate and nitrate by the non-linear Langmuir model were 177.97 mg/g and 28.06 mg/g, respectively. The coexistence of anions (Cl−, SO42−, NO3−, CO32− and HCO3−) negligibly affected the removal of phosphate due to its stronger bond on the nano-composites, while the presence of Cl− and PO43− reduced the nitrate removal attributed to the ions’ participation in the active adsorption sites on the surface of biochar–MgAl–LDH. The excellent adsorptive performance is the main synergetic influence of the MgAl–LDH incorporation into the biochar. The regeneration tests confirmed that the biochar–MgAl composite can be restored effortlessly and has the prospective to be reused after several subsequent adsorption-desorption cycles. The biochar-LDH further demonstrated capabilities for higher removal of phosphate and nitrate from real wastewater.
This experimental work focused on the synthesis, characterization, and testing of a unique, magnetically separable, and eco-friendly adsorbent composite material for the advanced treatment and efficient removal of nitrate and phosphate pollutants from wastewater. The MgAl-augmented double-layered hydroxide (Mg-Fe/LDH) intercalated with sludge-based activated carbon (SBAC-MgFe) composites were characterized by FT-IR, XRD, BET, VSM, SEM, and TEM techniques, revealing homogeneous and efficient dispersion of MgFe/LDH within the activated carbon (AC) matrix, a highly mesoporous structure, and superparamagnetic characteristics. The initial solution pH, adsorbent dose, contact time, and temperature parameters were optimized in order to reach the best removal performance for both pollutants. The maximum adsorption capacities of phosphate and nitrate were found to be 110 and 54.5 mg/g, respectively. The competition between phosphate and coexisting ions (Cl-, CO32-, and SO42-) was studied and found to be remarkably lower in comparison with the nitrate adsorption. The adsorption mechanisms were elucidated by kinetic, isotherm, thermodynamic modeling, and post-adsorption characterizations of the composite. Modeling and mechanistic studies demonstrated that physisorption processes such as electrostatic attraction and ion exchange mainly governed the nitrate and phosphate adsorption. The composite indicated an outstanding regeneration performance even after five sequences of adsorption/desorption cycles. The fabricated composite with magnetically separable characteristics can be used as a promising adsorbent for the removal of phosphate and nitrate pollutants from wastewater.
The anhydrous electrolytes have become an important part of supercapacitors, which provide temperature-tolerant applications in various electronic devices. This work reports on the fabrication of a wide-temperature-range supercapacitor using 3-amino-1H-1,2,4-triazole (Atri)/1,4-butanediol diglycidyl ether (BG) and imidazole (Imi)/BG–based electrolytes in active carbon-based electrodes. The triazole-terminated BG (BG(Atri)2) and Imi-terminated BG (BG(Imi)2) were initially synthesized, and then anhydrous electrolytes were produced by doping BG(Atri)2 and BG(Imi)2 with phosphoric acid (H3PO4) and ionic liquid (IL) at different molar fractions. The supercapacitors constructed with the anhydrous BG(Atri)2/H3PO4/0.1IL and BG(Imi)2/H3PO4/0.1IL electrolytes provided maximum specific capacitances (Cs) of 114 and 191 F g−1 at 1 A g−1, respectively. The corresponding electrolytes yielded the highest energy densities of 15.8 and 26.7 Wh kg−1 at the power densities of 1150 and 1225 W kg−1, respectively. The Imi-terminated electrolyte-based supercapacitor indicated superior performance and efficiency even after 2300 charge-discharge cycles by holding 20% of its original capacitance. The temperature dependence of the supercapacitors' capacitances was studied, and they increased from 191 to 266 F g−1 for BG(Imi)2/H3PO4/0.1IL and from 114 to 148 F g−1 for BG(Tri)2/H3PO4/0.1IL as the temperature increased from 25°C to 75°C.
A method for air pollution evaluation and control, based on one of the most popular neural networks – the backpropagation algorithm, is proposed. After the backpropagation training, the neural network, based on weather forecasting data, determines the future concentration of critical air pollution indicators. Depending on these concentrations, relevant episode warnings and actions are activated. A case study is carried out to illustrate and validate the method proposed, based on Istanbul air pollution data. Sulphur dioxide and inhalable particulate matter are selected as air pollution indicators (neural network outputs). Relevant episode measures are proposed. Among ten backpropagation algorithms, the BFGS algorithm (Quasi-Newton algorithms) is adopted since it showed the lowest training error. A comparison of NN-AirPol method against regression and perceptron models showed significantly better performance.
Abstract In this work, the effect of meteorological parameters and local topography on mass concentrations of fine (PM2.5) and coarse (PM2.5–10) particles and their seasonal behavior was investigated. A total of 236 pairs of samplers were collected using an Anderson Dichotomous sampler between December 2004 and October 2005. The average mass concentrations of PM2.5, PM2.5–10, and particulate matter less than 10 μm in aerodynamic diameter (PM10) were found to be 29.38, 23.85, and 53.23 μ/m3, respectively. The concentrations of PM2.5 and PM10 were found to be higher in heating seasons (December to May) than in summer The increase of relative humidity, cloudiness, and lower temperature was found to be highly related to the increase of particulate matter (PM) episodic events. During non-rainy days, the episodic events for PM2.5 and PM10 were increased by 30 and 10.7%, respectively. This is a result of the extensive use of fuel during winter for heating purposes and also because of stagnant air masses formed because of low temperature and low wind speed over the study area.
The main aim of this study is to statistically investigate the correlation between TOMS/Aerosol Index (AI) remote sensing particulate data and Ground Level (GL) aerosol concentrations. GL fine and coarse particles are collected from watershed area of Buyukcekmece at Marmara region in Istanbul, Turkey (41°0.04′ N; 28°0.59′ E). Randomly collected, 24 hr GL samples are statistically compared with the two years (2002-2003) TOMS/AI data. It is found that there is a significant relationship (R² = 0.47, p < 0.001) between TOMS/AI data and wintertime GL aerosols. The relation of TOMS/AI data and GL aerosol data are modelled using curvilinear models. Very good agreements between the data sets are obtained. The obtained models are first level exponential model and third level polynominal model for TOMS/AI versus fine and TOMS/AI versus coarse data. R-square values of the models are calculated as 0.92 and 0.67, respectively.
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