Adsorption is an important phenomenon widely used for the removal of contaminants. Several drinking water contaminants such as arsenic and fluoride, vanadium and chromium, nickel, cadmium and cobalt are found to coexist in nature as multi-component mixtures in water. Hence, the modeling of multi-component adsorption isotherms for designing water treatment systems has gained importance recently. However, review studies of multi-component adsorption and competitive adsorption modeling are limited. The current review paper summarizes twenty-six multi-component adsorption isotherm models. Also, case studies of several common multi-component adsorption systems and the mechanisms of multi-component adsorption are discussed. Furthermore, a modeling analysis of four multi-component isotherms models for three commonly found two-component adsorption systems, i.e., cadmium-nickel, nickel–cobalt, and cadmium-cobalt, is reported. The Extended Langmuir isotherm, Competitive Langmuir isotherm, Extended Langmuir–Freundlich isotherm and Extended Freundlich isotherm models were applied in the modeling study for the competitive adsorption of Cd, Ni, and Co. The goodness of fit parameters and adsorption isotherm constants were estimated for these models. The factors influencing competitive adsorption, mechanisms of adsorption, various single and multi-component isotherm models, their significance, and limitations are also discussed in this review article. Highlights: • Twenty-six multi-component and ten single-component isotherm models are compiled • Factors affecting multi-component competitive adsorption isotherms are discussed in this review paper • The applications of four multi-component isotherm models for three binary contaminant systems are presented. Graphical

Greenleaf extracts have been used as reducing agents for the synthesis of various nanoparticles because of their high antioxidant capacity and environmentally benign reducing properties. Five different plant species were chosen for this comparative study of the synthesis of iron oxide nanoparticles for arsenic adsorption. Based on the excellent reducing properties reported in previous studies, the following plant leaves were selected: black tea leaves (Camellia sinensis), oak tree leaves (Quercus virginiana), green tea leaves (C. sinensis), pomegranate leaves (Punica granatum), and eucalyptus leaves (Eucalyptus globulus). Iron nanoparticles were synthesized using the green synthesis method with the above leaves. The adsorption capacity of the nanoparticles was determined by carrying out kinetic and adsorption isotherm studies. Eucalyptus leaf nanoparticles were determined to be having the highest arsenic adsorption capacity of 39.84 mg/g, followed by oaktree leaf nanoparticles of adsorption capacity 32.05 mg/g. This indicates that locally available and nonagricultural trees are better suited for green synthesis of iron nanoparticle for arsenic remediation compared to green tea, or back tea leaves. The experiments revealed that the adsorption kinetics followed the pseudo-second-order rate equation and that the Langmuir equation could best describe adsorption isotherm data. The nanoparticles were characterized using SEM coupled with EDS, XRD, BET surface area, and UV Spectroscopy. The SEM images indicated that the iron oxide nanoparticles had spherical morphology with particle diameter around 10–100 nm and were amorphous in structure. The elemental analysis done by Energy dispersive spectroscopy (EDS) showed their weight percentage of C, O, Fe, S to be 44.70%, 32.80%, 20.56%, and 0.65%, respectively.

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.

The elevated co-occurrence of arsenic and fluoride in surface and groundwater poses risks to human health in many parts of the world. Using single and competitive batch equilibrium adsorption studies, this research focuses on As(V) and F adsorption by activated carbon and its modeling. BET, XRD, FESEM, EDS, and FTIR analysis were used to discern the structural characteristics of activated carbon. The influence of dosage, pH, and contact time were also investigated in single and simultaneous adsorption systems. The maximum adsorption capacity of activated carbon for arsenic and fluoride were found to be 3.58 mg/g and 2.32 mg/g, respectively. Kinetics studies indicated that pseudo-second-order kinetic model fit better than pseudo-first-order, Elovich, and intraparticle diffusion kinetic models. The non-linear regression analysis of Langmuir, Freundlich, Toth, Redlich Petersons, and Modified Langmuir Freundlich models was used to determine single-component asorption model parameters. Additionally, the simultaneous adsorption was rigorously modeled and compared using the Extended Langmuir (EL), Extended Langmuir Freundlich (ELF), Modified Competitive Langmuir (MCL), and Jeppu Amrutha Manipal Multicomponent (JAMM) isotherm models, and competitive mechanisms were interpreted for the simultaneous adsorption system. Further, the model performances were evaluated by statistical error analysis using the normalized average percentage error (NAPE), root mean square errors (RMSE), and the correlation coefficient (R²). According to the modeling results, single equilibrium data fitted better with the Modified Langmuir Freundlich isotherm model, with a higher R² of 0.99 and lower NAPE values of 3.8 % and 1.28 % for As(V) and F, than other models. For the binary adsorption, the Extended Langmuir Freundlich isotherm model demonstrated excellent fit with lowest errors. All the competitive isotherm models fit the As(V) and F simultaneous sorption systems reasonably well. Furthermore, the research unveiled a nuanced hierarchy of isotherm fitting, with ELF > EL > MCL > JAMM in varying arsenic at a constant fluoride concentration, and ELF > JAMM > EL > MCL in varying fluoride at a constant arsenic concentrations. In addition, competitive studies divulged crucial insights into selective adsorption, as As(V) exhibits a pronounced adsorption selectivity over F on activated carbon. In essence, As(V) showed a more pronounced antagonistic behavior over F, whereas F exhibited a much lesser competitive behavior in the adsorption of arsenic.

Analytical isotherm models such as Langmuir isotherm, Freundlich isotherm, and other linear isotherms are commonly used for modeling adsorption datasets for a wide range of adsorption studies. Most of these studies consider pH to be fixed. However, pH is an important parameter that varies widely. Hence, the model parameters developed for one set of experiments cannot be used in another scenario where the pH is different. Surface complexation models that can simulate pH changes are complex, multi-parameter models that are difficult to use. The modified Langmuir–Freundlich (MLF) isotherm developed earlier by us could simulate pH-dependent adsorption on goethite-coated sands. However, it has only been tested for arsenic adsorption on goethite-coated sands. Therefore, chromium adsorption datasets were considered to extend this MLF isotherm for other metal ions. Two different adsorbents, viz. coconut root activated carbon (CoAC) and palm male flower activated carbon (PaAC), were selected for the adsorption modeling of Cr(VI) using the MLF isotherm model. An improved modeling strategy was developed for fitting the MLF isotherm, which required only a single pH versus adsorption dataset, instead of several isotherms at different pH values. The new methodology could simulate the pH-dependent adsorption satisfactorily for various experimental datasets. The maximum adsorption capacity was 88.64 (mg/g) and 100.1 (mg/g) for PaAC and CoAC, respectively. The affinity constant for this model (K_a) was found to be 0.007 (L/mg) for PaAC dataset and 0.0106(L/mg) and 0.004 (L/mg) for the CoAC dataset. The average R² values of fitting were calculated and found to be 0.98 for PaAC and 0.85 for CoAC. The average root mean square error (RSME) of the fitting of the model was 0.07 (less than 10%). This modeling strategy required less experimental data and did not require advanced characterization studies. Therefore, this study indicates that the MLF isotherm can be extended to other contaminants and for different adsorbents to model the pH-dependent adsorption.