Data-driven models for atmospheric air temperature forecasting at a continental climate region.

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

Synthesis, Characterization, and Analysis of Hybrid Carbon Nanotubes by Chemical Vapor Deposition: Application for Aluminum Removal.

Hybrid carbon nanotubes (CNTs) are grown on biomass powder-activated carbon (bio-PAC) by loading iron nanoparticles (Fe) as catalyst templates using chemical vapor deposition (CVD) and using acetylene as carbon source, under specific conditions as reaction temperature, time, and gas ratio that are 550 °C, 47 min, and 1, respectively. Specifications of hybrid CNTs were analyzed and characterized using field emission scanning electron microscope (FESEM) with energy-dispersive X-ray spectroscopy (EDX), transmission electron microscopic (TEM), Fourier-transform infrared (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), surface area Brunauer-Emmett-Teller (BET), and zeta potential. The results revealed the high quality and unique morphologies of hybrid CNTs. Furthermore, removal and capacity of Al3+ were optimized by response surface methodology (RSM). However, the results revealed that the pseudo-second-order model well represented adsorption kinetic data, while the isotherm data were effectively fitted using a Freundlich model. The maximum adsorption capacity was 347.88 mg/g. It could be concluded that synthesized hybrid CNTs are a new cost-effective and promising adsorbent for removing Al3+ ion from wastewater.

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs.

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.

Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent.

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

A clean approach for functionalized carbon nanotubes by deep eutectic solvents and their performance in the adsorption of methyl orange from aqueous solution.

In this study two deep eutectic solvents (DESs) were prepared using ethylene glycol (EG) and two different ammonium-based salts. The potential of these DESs as novel agents for CNTs functionalization was examined by performing a comprehensive characterization study to identify the changes developing after the functionalization process. The impact of DESs was obvious by increasing the surface area of CNTs to reach 197.8 (m2/g), and by adding new functional groups to CNTs surface without causing any damage to the unique structure of CNTs. Moreover, CNTs functionalized with DESs were applied as new adsorbents for the removal of methyl orange (MO) from water. The adsorption conditions were optimized using RSM-CCD experimental design. The kinetics and the equilibrium adsorption data were analyzed using different kinetic and isotherm models. According to the regression results, adsorption kinetics data were well described by pseudo-second order model, whereas adsorption isotherm data were best represented by Langmuir isotherm model. The highest recorded maximum adsorption capacity (qmax) value was found to be 310.2 mg/g.

Growth and optimization of carbon nanotubes in powder activated carbon for an efficient removal of methylene blue from aqueous solution.

This work demonstrated the synthesis of carbon nanotubes (CNTs) on powder activated carbon (PAC) impregnated with Ni-catalyst through chemical vapour deposition. The optimized effects of reaction temperature, time and feedstock flow rates on CNT growth were examined. Potassium permanganate (KMnO4) and potassium permanganate in acidic solution (KMnO4/H2SO4) were used to functionalize CNTs samples. A primary screening of methylene blue (MB) adsorption was conducted. The chemical, physical and morphological properties of the adsorbent with the highest removal efficiency were investigated using FESEM, EDX, TEM, BET surface area, RAMAN, TGA, FTIR, and zeta potential. The resulting carbon nanotube-loaded activated carbons possessed abundant pore structure and large surface area. The MB removal by the as-synthesized CNTs was more remarkable than that by the modified samples. Adsorption studies were carried out to evaluate the optimum conditions, kinetics and isotherms for MB adsorption process. The response surface methodology-central composite design (RSM-CCD) was used to optimize the adsorption process parameters, including pH, adsorbent dosage and contact time. The investigation of the adsorption behaviour demonstrated that the adsorption was well fitted with the pseudo-second-order model and Langmuir isotherm with the maximum monolayer adsorption capacity of 174.5 mg/g. Meanwhile, the adsorption of MB onto adsorbent was driven by the electrostatic attraction and π-π interaction. Moreover, the as-obtained CNT-PAC exhibited good reusability after four repeated operations. In view of these empirical findings, the low-cost CNT-PAC has potential for removal of MB from aqueous solution.

The formation of hybrid carbon nanomaterial by chemical vapor deposition: an efficient adsorbent for enhanced removal of methylene blue from aqueous solution.

In this study, carbon species were grown on the surface of Ni-impregnated powder activated carbon to form a novel hybrid carbon nanomaterial by chemical vapor deposition. The carbon nanomaterial was obtained by the precipitation of the methane elemental carbon atoms on the surface of the Ni catalyst. The physiochemical properties of the hybrid material were characterized to illustrate the successful growth of carbon species on the carbon substrate. The response surface methodology was used for the evaluation of adsorption parameters effect such as pH, adsorbent dose and contact time on the percentage removal of MB dye from aqueous solution. The optimum conditions were found to be pH = 11, adsorbent dose = 15 mg and contact time of 120 min. The material we prepared showed excellent removal efficiency of 96% for initial MB concentration of 50 mg/L. The adsorption of MB was described accurately by the pseudo-second-order model with R2 of 0.998 and qe of 163.93 (mg/g). The adsorption system showed the best agreement with Langmuir model with R2 of 0.989 and maximum adsorption capacity (Qm) of 250 mg/g.

Optimization of the Synthesis of Superhydrophobic Carbon Nanomaterials by Chemical Vapor Deposition.

Demand is increasing for superhydrophobic materials in many applications, such as membrane distillation, separation and special coating technologies. In this study, we report a chemical vapor deposition (CVD) process to fabricate superhydrophobic carbon nanomaterials (CNM) on nickel (Ni)-doped powder activated carbon (PAC). The reaction temperature, reaction time and H2/C2H2 gas ratio were optimized to achieve the optimum contact angle (CA) and carbon yield (CY). For the highest CY (380%) and CA (177°), the optimal reaction temperatures were 702 °C and 687 °C, respectively. However, both the reaction time (40 min) and gas ratio (1.0) were found to have similar effects on CY and CA. Based on the Field emission scanning electron microscopy and transmission electron microscopy images, the CNM could be categorized into two main groups: a) carbon spheres (CS) free carbon nanofibers (CNFs) and b) CS mixed with CNFs, which were formed at 650 and 750 °C, respectively. Raman spectroscopy and thermogravimetric analysis also support this finding. The hydrophobicity of the CNM, expressed by the CA, follows the trend of CS-mixed CNFs (CA: 177°) > CS-free CNFs (CA: 167°) > PAC/Ni (CA: 65°). This paves the way for future applications of synthesized CNM to fabricate water-repellent industrial-grade technologies.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Novel deep eutectic solvent-functionalized carbon nanotubes adsorbent for mercury removal from water.

Due to the interestingly tolerated physicochemical properties of deep eutectic solvents (DESs), they are currently in the process of becoming widely used in many fields of science. Herein, we present a novel Hg2+ adsorbent that is based on carbon nanotubes (CNTs) functionalized by DESs. A DES formed from tetra-n-butyl ammonium bromide (TBAB) and glycerol (Gly) was used as a functionalization agent for CNTs. This novel adsorbent was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, XRD, FESEM, EDX, BET surface area, and Zeta potential. Later, Hg2+ adsorption conditions were optimized using response surface methodology (RSM). A pseudo-second order model accurately described the adsorption of Hg2+. The Langmuir and Freundlich isotherm models described the absorption of Hg2+ on the novel adsorbent with acceptable accuracy. The maximum adsorption capacity was found to be 177.76mg/g.

Allyl triphenyl phosphonium bromide based DES-functionalized carbon nanotubes for the removal of mercury from water.

Recently, deep eutectic solvents (DESs) have shown their new and interesting ability for chemistry through their involvement in variety of applications. This study introduces carbon nanotubes (CNTs) functionalized with DES as a novel adsorbent for Hg2+ from water. Allyl triphenyl phosphonium bromide (ATPB) was combined with glycerol as the hydrogen bond donor (HBD) to form DES, which can act as a novel CNTs functionalization agent. The novel adsorbent was characterized using Raman, FTIR, XRD, FESEM, EDX, BET surface area, TGA, TEM and Zeta potential. Response surface methodology was used to optimize the removal conditions for Hg2+. The optimum removal conditions were found to be pH 5.5, contact time 28 min, and an adsorbent dosage of 5 mg. Freundlich isotherm model described the adsorption isotherm of the novel adsorbent, and the maximum adsorption capacity obtained from the experimental data was 186.97 mg g-1. Pseudo-second order kinetics describes the adsorption rate order.

Environmental application of nanotechnology: air, soil, and water.

Global deterioration of water, soil, and atmosphere by the release of toxic chemicals from the ongoing anthropogenic activities is becoming a serious problem throughout the world. This poses numerous issues relevant to ecosystem and human health that intensify the application challenges of conventional treatment technologies. Therefore, this review sheds the light on the recent progresses in nanotechnology and its vital role to encompass the imperative demand to monitor and treat the emerging hazardous wastes with lower cost, less energy, as well as higher efficiency. Essentially, the key aspects of this account are to briefly outline the advantages of nanotechnology over conventional treatment technologies and to relevantly highlight the treatment applications of some nanomaterials (e.g., carbon-based nanoparticles, antibacterial nanoparticles, and metal oxide nanoparticles) in the following environments: (1) air (treatment of greenhouse gases, volatile organic compounds, and bioaerosols via adsorption, photocatalytic degradation, thermal decomposition, and air filtration processes), (2) soil (application of nanomaterials as amendment agents for phytoremediation processes and utilization of stabilizers to enhance their performance), and (3) water (removal of organic pollutants, heavy metals, pathogens through adsorption, membrane processes, photocatalysis, and disinfection processes).

Functionalization of graphene using deep eutectic solvents.

Deep eutectic solvents (DESs) have received attention in various applications because of their distinctive properties. In this work, DESs were used as functionalizing agents for graphene due to their potential to introduce new functional groups and cause other surface modifications. Eighteen different types of ammonium- and phosphonium-salt-based DESs were prepared and characterized by FTIR. The graphene was characterized by FTIR, STA, Raman spectroscopy, XRD, SEM, and TEM. Additional experiments were performed to study the dispersion behavior of the functionalized graphene in different solvents. The DESs exhibited both reduction and functionalization effects on DES-treated graphene. Dispersion stability was investigated and then characterized by UV-vis spectroscopy and zeta potential. DES-modified graphene can be used in many applications, such as drug delivery, wastewater treatment, catalysts, composite materials, nanofluids, and biosensors. To the best of our knowledge, this is the first investigation on the use of DESs for graphene functionalization.