Modeling caffeine adsorption by multi-walled carbon nanotubes using multiple polynomial regression with interaction effects

Evaluation of hydrochar from peach stones for caffeine removal from aqueous medium and treatment of a synthetic mixture

The presence of micropollutants, such as caffeine (CAF), has been detected throughout the world, since conventional treatment plants are not able to properly degrade them. CAF is a widely consumed stimulant, and has been demanding the development of efficient methodologies for its removal. Aiming at the agriculture waste valorization, a new hydrochar was developed based on chemical and thermal modification of peach stones (mod-PS) for CAF removal from water and from a synthetic mixture. The morphology, functional groups and surface electrical charge of the adsorbent were characterized by SEM, FTIR and zeta potential, respectively. Regarding CAF adsorption performance, the equilibrium time was reached at 480 min and the pseudo-second-order model presented the best fit for the experimental data. The maximum adsorption capacity was 68.39 mg g-1 (298 K) and the Langmuir model exhibited a better fit for the isothermal data. The thermodynamic properties confirmed that the process was exothermic, spontaneous and reversible. The main adsorption mechanisms were hydrogen bonds and π-interactions. The global removal efficiency was satisfactory in the synthetic mixture simulating real wastewater (67%). Therefore, the proposed new hydrochar has potential application as a low-cost adsorbent for CAF removal.

Development of a Multicomponent Adsorption Isotherm Equation and Its Validation by Modeling

Researchers have made significant efforts over the past few decades to understand adsorption by developing various simple adsorption isotherm models. However, though many contaminants usually occur as multicomponent mixtures in nature, multicomponent adsorption isotherms have received limited attention and remain an area of inadequate research. We have presented here in a new multicomponent adsorption isotherm model, named the Jeppu Amrutha Manipal Multicomponent (JAMM) isotherm, that can alleviate this problem. We first developed the JAMM multicomponent isotherm using our experimental data sets of arsenic and fluoride competitive adsorption on activated carbon. We then tested the JAMM multicomponent isotherm for a case study of cadmium and zinc competitive adsorption. Next, we further assessed the JAMM isotherm using another competitive adsorption case study of copper and chromium. Through extensive validation studies and error analysis, the JAMM isotherm was able to demonstrate its efficacy in predicting the adsorption behavior in several multicomponent adsorption systems accurately. The main advantage of JAMM isotherm over other multicomponent isotherms is that it utilizes and leverages the single-component adsorption parameters to simulate multicomponent isotherms. The proposed JAMM analytical isotherm model furthermore incorporates the interaction between the components, a mole fraction parameter, and a heterogeneity index, providing a more comprehensive modeling framework for multicomponent adsorption. The mole fraction term was introduced for the distribution of adsorption sites based on the relative number of molecules of each component. An additional term for interaction coefficient was introduced for the representation of interactions. During the validation of JAMM with three experimental case studies with negligible, small, and high competition systems of adsorbates, impressive predictions were exhibited, with the average normalized absolute percentage error as 6.05% and average R2 as 0.86, highlighting the model's robustness, versatility, and reliability. We propose that the new JAMM isotherm modeling framework might profoundly help in chemical engineering, environmental engineering, and materials science applications by providing a potent tool for analyzing and predicting multicomponent adsorption systems.

Rule-Based Arabic Sentiment Analysis using Binary Equilibrium Optimization Algorithm

With the development of websites and social networks, Internet users generate a massive amount of comments and information on the Web. Sentiment analysis, also called opinion mining, offers an opportunity to mine the people's sentiments and emotions from the textual comments. In the last decade, sentiment analysis has been applied in research areas such as recommendation and support systems and has become an area of interest for many researchers. Therefore, many studies have been carried out on English, while other languages, such as Arabic, received less attention. Increasingly, sentiment analysis researchers use machine learning due to its excellent performance. However, the generated models are black boxes and non-interpretable by the users. The rule-based classification is a promising approach for generating interpretable models. This work proposes a classification rule-based Arabic sentiment analysis approach together with a new binary equilibrium optimization metaheuristic algorithm as an optimization method for classification rule generation from Arabic documents. The proposed approach has been experimented on the Opinion Corpus for Arabic (OCA) and generates a classification model of thirteen rules. The comparison results with state-of-the-art methods show that the proposed approach outperforms all other white-box models regarding classification accuracy.

Mechanism and behavior of caffeine sorption: affecting factors

Caffeine is one of the emerging pollutants with a diverse chemical composition. It is mixed with the hydrobiota as a result of its high consumption, and when certain dose intervals are exceeded, it re-enters the human body through indirect routes such as plants, animals, soil, water, and the food chain, causing health problems that are difficult or impossible to treat, and irreversible environmental problems. This situation raises concerns about the presence of pollutants emerging in water resources, igniting interest in water treatment processes and the development of alternative methods. Although there are several methods for removing caffeine from aqueous media, adsorption is the most popular because it is less expensive than other methods and has the highest removal efficiency. Furthermore, it has the benefit of selectively attaching the molecules in solution. In this article, studies on the caffeine adsorption process have been examined, and the caffeine adsorption efficiency of various adsorbents has been summarized by compiling information such as pH, contact time, temperature, and concentration of adsorbent and adsorbate, which are considered as optimum processing conditions. The binding mechanism was investigated, and it was clearly stated how caffeine adheres to the adsorbent surface. Among the equilibrium adsorption isotherms, the isotherm model with the best agreement with the experimental data was attempted to be determined. Many studies clearly show that the process of developing environmentally friendly and high-capacity adsorbents in sustainable processes and in harmony with the circular economy is increasing day by day.

Synthesis and Analysis of Impregnation on Activated Carbon in Multiwalled Carbon Nanotube for Cu Adsorption from Wastewater

Industrial wastes contain more toxins that get dissolved in the rivers and lakes, which are means of freshwater reservoirs. The contamination of freshwater leads to various issues for microorganisms and humans. This paper proposes a novel method to remove excess copper from the water. The nanotubes are used as a powder in membrane form to remove the copper from the water. The multiwalled carbon nanotube is widely used as a membrane for filtration. It contains many graphene layers of nm size that easily adsorbs the copper when the water permeates through it. Activated carbon is the earliest and most economical method that also adsorbs copper to a certain extent. This paper proposes the methods of involving the activated carbon in the multiwalled carbon nanotube to improve the adsorption capability of the copper. Here, activated carbon is impregnated on the multiwalled carbon nanotube's defect and imperfect surface areas. It makes more adsorption sites on the surface, increasing the adsorption amount. The same method is applied to Hydroxyl functionalized multiwalled carbon nanotubes. Both the methods showed better results and increased the copper removal. The functionalized method removed 93.82% copper, whereas the nonfunctionalized method removed 80.62% copper from the water.

Ground coffee waste-derived carbon for adsorptive removal of caffeine: Effect of surface chemistry and porous structure

Carbon-based adsorbents show high adsorption capacity towards caffeine due to their porosity and surface functionality. However, the main limiting factor for high performance has not been addressed; furthermore, the adsorption interaction with different active sites needs to be explored. In this study, we synthesized a hierarchical porous nitrogen-doped carbon with unique surface functionality by single-step calcination of coffee waste with KOH under N₂. The porous structure, nitrogen content, and types are optimized by varying calcination temperature and KOH concentration. The result of the adsorption experiments shows that both the nitrogen type and the pore size distribution are the limiting factors to adsorption. In addition, the effect of acidic and basic functional groups is studied in detail. The adsorption of caffeine on CW-C is dominantly governed by EDA interaction between the resonance structure of pyridonic-N and the electron-withdrawing group of the caffeine, and the dispersive force caused by the oxidized-N and delocalized π electron of caffeine. Furthermore, we demonstrate that the surface of CW-C is not suitable for the formation of electrostatic and non-electrostatic interaction with caffeine. The maximum adsorption capacity of caffeine at 25 °C is 274.2 mg/g. Moreover, we demonstrate that the unique physio-chemical properties of CW-C are capable of adsorbing other emerging contaminants such as diclofenac, where maximum adsorption capacity of 242.3 mg/g diclofenac is recorded.

Factor analysis approach to classify COVID-19 datasets in several regions

The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships.

Caffeine removal from aqueous media by adsorption: An overview of adsorbents evolution and the kinetic, equilibrium and thermodynamic studies

Caffeine is an emerging pollutant and is considered the most representative pollutant of the Pharmaceutical Active due to its high consumption by the general population. It can be used to track pollution caused by humans. Different technologies have been employed to remove the caffeine from aqueous media, however the adsorption has been preferred due to its simplicity, high removal efficiency, operational and implementation facility and low cost. This paper provides a systematic review of the published peer-reviewed literature concerned with caffeine removal by the adsorption process. The Scopus and ScienceDirect databases were used to identify relevant articles researches on caffeine removal. Many authors have studied caffeine's adsorption equilibrium in aqueous media, different conditions, and different adsorbents. This paper aims to uncover the overall trend of adsorbent used, kinetic and thermodynamic studies. The impact of pH, temperature, adsorbent dosage and competitive effect were presented and analyzed. It was observed that the adsorption capacities ranged between 10 and 1000 mg g^-1, according to the nature and properties of the adsorbent. The pseudo-second order (kinetic model) and the Langmuir isotherm model showed the best adjustment of the experimental data from caffeine adsorption in most studies. The mechanistic understanding of adsorption and the development of new adsorbents are still a matter of future research, as well as the use of other kinetic models based on statistical factors and the thermodynamic studies should be considered.

How temperature can alter the combined effects of carbon nanotubes and caffeine in the clam Ruditapes decussatus?

Nowadays, multi-walled carbon nanotubes are considered to be emerging contaminants and their impact in ecosystem has drawn special research attention, while other contaminants, such as caffeine, have more coverage in literature. Despite this, the effects of a combination of the two has yet to be evaluated, especially considering predicted temperature rise. In the present study a typical bioindicator species for marine environment, the clam Ruditapes decussatus, and classical tools, such as biomarkers and histopathological indices, were used to shed light on the species' response to these contaminants, under actual and predicted warming scenarios. The results obtained showed that both contaminants have a harmful effect at tissue level, as shown by higher histopathological index, especially in digestive tubules. Temperatures seemed to induce greater biochemical impacts than caffeine (CAF) and -COOH functionalized multi-walled carbon nanotubes (f-MWCNTs) when acting alone, namely in terms of antioxidant defences and energy reserves content, which were exacerbated when both contaminants were acting in combination (MIX treatment). Overall, the present findings highlight the complex response of clams to both pollutants, evidencing the role of temperature on clams' sensitivity, especially to mixture of pollutants.

Investigations on characteristics of polyurethane foam impregnated with nanochitosan and nanosilver/silver oxide and its effectiveness in phosphate removal

A novel potential adsorbent, produced with chitosan nanoparticles and silver/silver oxide nanoparticles impregnated on polyurethane foam (PFCA), is developed for phosphate removal in aqueous solutions. The ultraviolet-visible (UV-Vis) spectroscopy uncovered the emergence of nanoparticles. The field emission scanning electron microscopy (FESEM) provided the mean size of chitosan nanoparticles between 56 and 112 nm and that of silver-silver oxide nanoparticles between 44 and 75 nm. Energy dispersive X-ray (EDX) spectroscopy determined the presence of specific elements (C, O, P and Ag) in the adsorbent before and after treatment. Fourier transform infrared (FTIR) spectroscopy revealed the interplay between the N-H bond of amino group in PFCA and phosphate ions during adsorption. X-ray diffraction (XRD) analysis of PFCA showed nearly the same pattern before and after treatment, indicating the stability of PFCA. The silver ion concentration in the effluent from inductively coupled plasma optical emission spectroscopy (ICP-OES) analysis was found to be very less and below the drinking water limits. The surface area estimated by Brunauer-Emmett-Teller (BET) studies was found to be 2.17 m²/g. The experimental studies showed that PFCA can remove 61.24% of phosphate from an influent phosphate phosphorus concentration of 50 mg P/L, at its propitious condition. Even after 7 cycles of reuse, PFCA proved to be effective in removing 20.58% of phosphate. Hence, PFCA can be considered to be a potential sorbent for removing phosphate from surface water. Graphical abstract.

Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries

In this research, the number of patients with Covid-19 and the number of deaths due to this disease in France, Germany, Iran, Italy, Spain, United Kingdom, and Unites States America are considered. First, the relations between the considered countries are studied using Pearson’s correlation. Then, based on the spread rate of Covid-19, these countries are categorized using principal component analysis.

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.

A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent

The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R² of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.

Clustering the Adsorbents of Horizontal Series Filtration in Greywater Treatment

One of the important alternative water resources for non-potable purposes is greywater (GW), which must be cleaned of contaminants. In this regard, the clustering analysis of materials consisting of sand (S), zeolite (Z), peat (P) and granular activated carbon (GAC) within a horizontal series filter (HSF) was used for removal of chemical oxygen demand (COD), biochemical oxygen demand (BOD), total dissolved solids (TDS), and turbidity in GW taken from the Fasa University Student Hostel, Iran. The hierarchical clustering technique was applied to classify the adsorbents. The findings indicated that there were significant differences (more than 95%) between these materials. According to the similarity of level 95%, for COD, BOD, TDS, and turbidity removal, these adsorbents could be separately clustered in three, three, two, and three clusters, respectively. In addition, by considering the simultaneous changes of COD, BOD, TDS, and turbidity together, these adsorbents could be clustered in three different clusters. This paper proposed an efficient method to select the best combination of adsorbents for eliminating of COD, BOD, TDS, and turbidity from GW. Generally, based on the quality of treated greywater and literature, reusing greywater can be implemented for agriculture, artificial recharge of aquifers, desertification, and preventing the dust creation in arid areas such as southern Iran.

A response surface methodology for optimization of 2,4-dichlorophenoxyacetic acid removal from synthetic and drainage water: a comparative study

The potential of a granular activated carbon (GAC), a rice husk biochar (BRH), and multi-walled carbon nanotubes (MWCNTs) for removing 2,4-dichlorophenoxyacetic acid (2,4-D) from simulated wastewater and drainage water has been evaluated. In this regard, a response surface methodology (RSM) with a central composite design (CCD) (CCD-RSM design) was used to optimize the removal of 2,4-D from simulated wastewater under different operational parameters. The maximum adsorption capacities followed the order GAC > BRH > MWCNTs, whereas the equilibrium time increased in the order MWCNTs < GAC < BRH. In the case of GAC and BRH, the 2,4-D removal percentage increased significantly upon increasing the adsorbent dosage and temperature and decreased upon increasing the initial 2,4-D concentration and pH. The results showed that the contact time and temperature were not important as regards the adsorption efficiency of 2,4-D by MWCNTs, whereas rapid removal of 2,4-D from simulated wastewater was achieved within the first 5 min of contact with the MWCNTs. The results confirmed that the Freundlich isotherm model with the highest coefficient of determination (R²) and the lowest standard error of the estimate (SEE) satisfactorily fitted the 2,4-D experimental data. In addition, successful usage of the three adsorbents investigated was observed for removal of 2,4-D from drainage water from an agricultural drainage system. An economic analysis with a rate of return (ROR) method indicated that BRH could be used as an eco-friendly, low-cost, versatile, and high adsorption capacity alternative to GAC and MWCNTs for the removal of 2,4-D.

Adsorption of 2,4-dichlorophenoxyacetic acid using rice husk biochar, granular activated carbon, and multi-walled carbon nanotubes in a fixed bed column system

The 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide, as an aromatic hydrocarbon, is a dangerous and toxic organic pollutant among the agricultural pesticides. In this research, the performance of the biochar made from rice husk (BRH), granular activated carbon (GAC), and multi-walled carbon nanotubes (MWCNTs) was investigated for adsorption of 2,4-D in a fixed-bed column system. The influence of pH (2, 5, 7, 9), flow rate (0.5, 1, 1.5 mL min^-1), bed depth (3, 6, 9 cm), and influent 2,4-D concentration (50, 100, 150, 300 mg L^-1) on the adsorption process was evaluated. The resulting breakthrough curves indicated that the higher removal efficiency of 2,4-D took place at the lower flow rate, lower influent 2,4-D concentration, higher bed depth, and lower pH. While in most cases the removal ability of GAC was better than other adsorbents, generally, this study confirmed that the BRH, as a cheap and sustainable material, can be a viable alternative to GAC and MWCNTs for remediation and treatment scenarios, particularly in developing countries.