In this work, African maize cobs (AMC) were used as a rich biomass precursor to synthesize carbon material through a chemical activation process for application in electrochemical energy storage devices. The carbonization and activation were carried out with concentrated Sulphuric acid at three different temperatures of 600, 700 and 800 °C, respectively. The activated carbon exhibited excellent microporous and mesoporous structure with a specific surface area that ranges between 30 and 254 m²·g⁻¹ as measured by BET analysis. The morphology and structure of the produced materials are analyzed through Field Emission Scanning Electron Microscopy (FESEM), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), Boehm titration, X-ray Photoelectron Spectroscopy (XPS) and Raman Spectroscopy. X-ray photoelectron spectroscopy indicates that a considerable amount of oxygen is present in the materials. The functional groups in the activated carbon enhanced the electrochemical performance and improved the material’s double-layer capacitance. The carbonized composite activated at 700 °C exhibited excellent capacitance of 456 F g⁻¹ at a specific current of 0.25 A g⁻¹ in 6 M KOH electrolyte and showed excellent stability after 10,000 cycles. Besides being a low cost, the produced materials offer good stability and electrochemical properties, making them suitable for supercapacitor applications.

Poly(ethylene terephthalate) (PET) has a wide range of applications that generate a lot of waste globally; thus, upcycling PET is important because it offers several industrial and economic advantages. This study describes a sustainable, emissions-free process for converting PET plastics into carbon nanomaterials (CNMs) named PT-nano powder. The thermal-hydrothermal method has employed the production of PT-nano powder above the glass transition temperature (Tg) of PET plastics. Under optimal conditions, PET plastics were efficiently converted into PT-nano powder with 86.6% crystallinity and an average particle size of 6.5 nm. The PT-nano powder was characterized for physical and chemical properties using different techniques, including UV-Vis, FTIR, Raman spectroscopy, XRD, FESEM, TEM, and proton NMR analysis. The characterization confirms the complete conversion of PET to solid fractions of carbon nanomaterial. The PT-nano powder was tested in supercapacitor performance application with electrochemical characterization. The symmetric fabrication showed a specific capacitance of 250.8 F/g, energy density of 34.83Wh/kg, and power density of 999.9W/kg with a current density of 0.5A/g. The device fabrication exhibited high cycle stability and high capacitance retention of 96.8% with a current density of 1.5A/g after 10000 cycles. GRAPHICAL ABSTRACT

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.