Herein, we reviewed response surface methodology (RSM), a powerful statistical tool widely used in optimizing adsorption processes to remove synthetic dyes, toxic heavy metals, and phenols from wastewater. The widely used RSM models during optimization are the central composite design and Box-Behnken, which are second-order polynomial models. The models give a predictive insight into the number of experimental runs to be carried out. Furthermore, an in-depth overview of RSM and its application in optimizing various adsorption parameters, such as adsorbent dosage, initial pollutant concentration, contact time, and pH is discussed. In addition, RSM enables researchers to efficiently determine the optimal conditions for maximum pollutant removal. Lastly, the findings of this research highlight the potential of RSM as a valuable tool for optimizing adsorption processes and contributing to sustainable water treatment technologies.

Herein we reviewed Artificial intelligence (AI) and Machine learning (ML) models in the prediction and optimization of process parameters during the removal of toxic heavy metals and textile dyes. Parameters normally optimized include pH, contact time, initial concentration, adsorbent dosage, and temperature. This review focuses on common AI models such as Artificial Neural Networks (ANN), Particle Swarm Optimization, and Genetic Algorithms (GA). Furthermore, the review describes the common prediction statistical indicators such as coefficient of determination (R²), root mean square error (RMSE), mean squared error (MSE), absolute average deviation (AAD), etc. Lastly, this review highlights the significant potential of AI and ML in revolutionizing the field of wastewater treatment and mitigating the environmental impact of industrial pollution.

This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to predict and optimize the adsorptive removal of Rhodamine B (RhB) from water. The adsorption process was optimized using RSM with a Central Composite Design (CCD), which predicted a maximum removal efficiency of 95.91% under the following conditions: initial dye concentration (10 mg/L), adsorbent dosage (15 mg), pH (6), and temperature (25 °C). ANN was also optimized using similar conditions and the resulting predictive removal efficiency of 97.18% was obtained. Non-linear isotherm studies strongly correlated with the Freundlich (R² = 0.9987) and Sips (R² = 0.9928) models, indicating multilayer and monolayer adsorption. Non-linear Pseudo-first-order, Pseudo-second-order, and Elovich model correlation coefficients of 0.9644, 0.9998, and 0.952 suggested that the mechanisms were by chemisorption and physisorption on energetically stable heterogeneous surfaces. The findings of this study show a dual approach based on metal-organic framework and machine learning models as efficient alternatives to understanding the removal of RhB from water.

Addressing the global demand for cost-effective and sustainable energy sources, dye-sensitized solar cells (DSSCs) have emerged as a promising alternative to conventional silicon-based photovoltaics. However, the use of platinum which is a rare and expensive counter electrode (CE) hinders the widespread application of DSSCs, necessitating the use of cheap, abundant, and efficient materials. The review therefore focuses on carbon-based nanomaterials specifically carbon nanotubes (CNTs) and graphene as CEs in DSSCs. The CE plays a vital role in regenerating the redox couple, and its charge transfer resistance (Rct) should ideally be 1 Ω cm² for optimal performance. Carbon nanotubes comprising single-walled carbon nanotubes (SWCNTs), double-walled carbon nanotubes (DWCNTs), and multiwalled carbon nanotubes (MWCNTs) are mainly prepared by chemical vapor deposition (CVD). The SWCNTs have achieved an efficiency of 7.79%, comparable to platinum electrodes, and this was due to the morphology, which influenced the redox mediator regeneration but also reduced the R_ct. In addition, graphene with high transparency (97.7%), large specific surface area (2630 m² g^− 1), excellent thermal conductivity (3000 W m^− 1 K^− 1), and good carrier mobility properties (10,000 cm² V^− 1 S^− 1) have also been applied. In this, the Graphene nanosheets demonstrated a 6.81% efficiency, comparable to platinum (7.59%) due to a high open circuit voltage (V_oc), which accounts for the reduction of iodide/triiodide redox couple. Lastly, the Graphene nanoplatelets demonstrated a 9.3% efficiency comparable to that of Platinum 7.53% due to low charge transfer resistance, high electrocatalytic activity, and good fill factor.

A low-cost and efficient activated carbon was prepared from banana peels for the removal of alizarin. Non-linear kinetic studies, isotherm models, and predictive models were used to study the adsorption process. The choice of the kinetic studies and isotherm models was based on the error functions of R ² , Adj.R ² , chi-square, SSE and ARE. The kinetic studies using intraparticle diffusion, pseudo-first-order, Elovich, and pseudo-second-order models fit the data well. It was concluded that the pseudo-second-order model best fitted the data, thus the mechanism was by chemisorption. Isotherm studies indicated that the Langmuir, Temkin, Dubinin-Radushkevich, and Freundlich models all describe the adsorption process. The mode was best described by the Freundlich model, thus, adsorption occurred on multilayer surfaces. The quadratic model, with a high R ² value of 0.9740, accurately predicted the removal efficiency and identified dosage and concentration as the most significant factors. The optimized conditions were found to be 3.05 min, a pH of 5.55, a dosage of 0.014 g, and a concentration of 21.50 ppm, which resulted in a maximum removal efficiency of 92.18%. The artificial neural networks with a predictive capability (96.26%) and a correlation coefficient of 0.99999 for both training and validation sets was superior to the central composite design. This was confirmed through the comparison of the residual errors and the statistical error functions of SSE, MSE, RMSE and MAE. This study shows a dual approach of coupling activated carbon from banana peels with mathematical models to understand the adsorption process.

Herein, we reviewed the main components of dye-sensitized solar cells (DSSCs) which are an emerging cheap and environmentally benign alternative for solar energy capture and conversion to electricity. The role of individual parts such as the semiconductor electrode, counter electrode, photosensitizer, electrolyte, and substrate and their contribution to the overall efficiency (η) of DSSCs are discussed. In addition, parameters such as short circuit current, open circuit voltage, and fill factor used to quantify the efficiency of DSSCs are addressed. The highest solar-to-electric energy conversion efficiency of 13 % has been achieved using titanium dioxide as a semiconductor electrode, a triiodide system as a redox couple, and platinum counter electrodes. Semiconductors are made up of materials such as glass, carbon, conductive polymers and other metal oxides have lower efficiencies (< 8 %). In addition, synthetic photosensitizers especially ruthenium complexes have higher efficiencies (10-11 %) compared to natural dyes among which the highest efficiency (4.6 %) was achieved using chlorophyll. The performance of natural dyes based on efficiency of the DSSC is generally in the order: chlorophyll > anthocyanins > carotenoids that is highly attributed to their structure which not only dictates electron release and recombination but also attachment to other components. The DSSC performance is not fixed but rather tunable by variations in the components to achieve desired structural and electronic properties such as firm anchorage between the photosensitizer and the semiconductors, the reduction of the energy band gap by incorporation of other metal salts to extend the absorption range and use of additives that prevent electron recombination with the photosensitizer or any hindrances in the electrolyte redox reactions.