Advanced Oxidation Processes (AOPs) are water treatment methods that use reactive oxygen species (ROS), especially hydroxyl radicals (˙OH), to degrade persistent organic pollutants effectively. This chapter reviews the role of metal-based catalysts in enhancing AOPs, focusing on their synthesis, mechanisms, and applications in degrading contaminants like dyes, pharmaceuticals, and industrial chemicals. Recent advances in nanotechnology and hybrid materials have improved catalyst efficiency, stability, and reusability. However, scaling AOPs from laboratory to industrial scale presents challenges, including catalyst deactivation, cost, and environmental concerns such as metal leaching. Strategies like catalyst immobilization, green synthesis, and integration with renewable energy sources offer promising solutions to these issues. Future research directions emphasize developing sustainable, cost-effective catalysts and expanding applications to emerging pollutants. Overall, metal-based catalysts are critical for advancing AOPs toward more efficient, scalable, and environmentally friendly wastewater treatment technologies.

Abstract The distribution of 24 potentially hazardous elements (PHEs) was examined in sediments gathered from several Mediterranean coastal regions in Egypt. Inductively coupled plasma-mass spectroscopy (ICP-MS) was used to analyze the samples. The lowest concentration of PHEs was recorded at 2492.95 µg/g at Salloum Station (Ia), whereas Sedi Krrir Station (X) recorded the maximum concentration at 5890.61 µg/g dry weight. The PHEs under investigation can be grouped as follows based on the highest average concentration: Ti > Fe > Al > Cu > Zn > Pb > Co > Mn > Ni > Cd > Cr. Several indicators were used to evaluate the contamination level for PHEs. Pollution indices, such as the enrichment factor (EF), geoaccumulation index (I geo), pollution load index (PLI), and contamination degree (Cd), are determined for the recorded PHE. Principal component analysis (PCA) was employed to analyze all sediment constituents and determine their sources. To assess the degree of PHE contamination in sediments and identify the overall and specific pollution levels of different components in the sediment, PCA was utilized in conjunction with other pollution indices. According to the human risk assessment results for the PHEs found in the sediments, some of the investigated metals may pose a danger. According to the USEPA’s SQGs (sediment quality guidelines), sediments were categorized as either non-contaminated, moderately polluted, or highly polluted. Furthermore, the studied sediment samples are not contaminated with Cu, Zn, or Ni; nonetheless, Cd levels exceeded the thresholds of both probable effective level (PEL) and effective range medium (ERM). According to the Hazard Quotient (HQ) for cutaneous exposure route data, children’s HQ is 3–4 times that of adults.

This study investigated, for the first time, the effect of combined hydrothermal and alkaline pretreatment on methane (CH4) generation from pine nut shells (PNS), a renewable substrate for anaerobic digestion (AD). The experiments were conducted under mesophilic conditions for 40 days in batch mode. With 5% NaOH following a hydrothermal (HT) pretreatment at 100 degrees C, lignin and hemicellulose removals of 48% and 37% were achieved, respectively. This resulted in a cumulative CH4 yield (CMY) of 228 mL CH4/g volatile solids (VS), which was approximately four times higher than the control (i.e., 60 mL CH4/g VS). Also, HT at 100 degrees C + 5% NaOH achieved a net energy balance of 1.302 MJ/kg VS. The enhanced methane yield and reduced digestate mass (i.e., 56% on a VS basis) effectively offset the high hydrothermal input, demonstrating that joint pretreatment can be energetically feasible when biogas benefits outweigh operating costs. In parallel, this study proposes a comparative modeling framework by evaluating time-series approaches, i.e., autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models for both CMY and daily CH4 yield (DMY). High predictive accuracy, with R2 values up to 0.9990 for CMY and 0.9365 for DMY, was achieved via ARIMA and LSTM, respectively. Integration of pretreatment and forecasting tools enables real-time AD optimization within a circular bioeconomy.

Microplastics (MPs) act as reactive surfaces and carriers in aquatic systems, influencing the mobility, bioavailability, and toxicity of heavy metals. Recent research has clarified how heavy metals adsorb onto MPs, focusing on adsorption pathways, physicochemical factors, analytical methods, toxicological impacts, and remediation strategies. Metal binding to MPs depends on factors such as polymer type, particle size, aging, surface properties, zeta potential, pH, salinity, dissolved organic matter, temperature, and the presence of biofilms. Adsorption occurs through mechanisms including electrostatic interactions, surface complexation, ion exchange, pore filling, hydrogen bonding, van der Waals forces, cation-π interactions, surface precipitation, and biofilm-mediated binding. MP-metal interactions vary with environmental conditions; for example, salinity and dissolved organic matter can enhance or reduce adsorption, depending on metal speciation, polymer characteristics, and experimental conditions. Toxicological studies show that MPs carrying heavy metals can increase bioaccumulation and combined toxicity by promoting transport and cellular uptake. However, strong adsorption and limited desorption may sometimes reduce the bioavailability of dissolved metals. The Mediterranean Sea is a key case study due to its semi-enclosed nature, high urbanization, maritime activity, wastewater discharge, and significant plastic pollution, all of which heighten the importance of MP-metal interactions. There is an urgent need for standardized adsorption protocols, thorough in situ and ex situ characterization, ecologically relevant toxicological studies, and integrated remediation strategies addressing both MPs and associated heavy metals.

Abstract This work describes the synthesis of various di- and tri-cellulose acetates from rice husk cellulose (RHC) and commercial microcrystalline cellulose (CMCC) using copper(II) perchlorate hexahydrate (Cu(ClO4)2·6H2O) as an effective catalyst at room temperature and 50 °C. This investigation was conducted using different amounts of Cu(ClO4)2·6H2O (100, 200, 300 mg) and 2 g of cellulose for various times (0.5–6 h) in the presence of a constant volume of acetic anhydride (15 mL). Multiple reactions have led to the formation of di- and tri-cellulose acetates. CMCC was remarkably converted into cellulose acetate at room temperature and 50 °C, with yields of 98.10% and 96.10%, respectively. The extracted cellulose from rice husk produced di- and tri-cellulose acetate at room temperature and 50 °C, yielding 86.93% and 92.85%, respectively. Critical expected results were obtained in this work: a strong relationship was found between the degree of substitution (DS) and acetyl percentage (AP%) of the products, the catalyst level in the reaction mixture, the presence or absence of temperature, and the reaction time. The DS and AP% were characterized using FTIR, 1H-NMR, XRD, and the thermal stability was evaluated by TGA and DTA. This work presents a new catalyst that can produce varying degrees of cellulose acetylation by adjusting reaction temperature, catalyst amount, and reaction duration.

This study developed a novel method to synthesize self-doped CaTiO3 (SD-CaTiO3) from Triesta Marble (TM) waste. HNO3 treatment of the marble powder yielded a sustainable calcium nitrate precursor, Ca(NO3)2 & sdot;(H2O)x. In situ Mg2+ doping from TM impurities during perovskite synthesis was made possible by the subsequent addition of TiO2 and carefully regulated calcination. Chemical interactions during synthesis are essential to creating a stable and effective photocatalyst. Acidic treatment of TM facilitates Ca2+ leaching and complexation with nitrate ions. A strong perovskite lattice is formed during calcination by solid-state interactions involving Ca2+, Ti2+, and Mg2+. Lattice deformation and oxygen vacancies are created when Mg2+ partially replaces Ti4+. These flaws improve charge carrier separation and stabilize the perovskite structure. Mg2+ ions from TM impurities integrate into the CaTiO3 lattice during perovskite crystallization (at 850 degrees C), as demonstrated by X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), and Ultraviolet-Visible Spectroscopy (UV-Vis). Using Fourier Transform Infrared Spectroscopy, UV-Vis, Brunauer-Emmett-Teller Surface Area Analysis, Scanning Electron Microscopy, XRD, and XPS, the physicochemical characteristics of the generated SD-CaTiO3 were comprehensively described. The crystallite phase was orthorhombic (COD 9002801), and most of the nanoparticles were between 52.43 and 126.52 nm in size, according to the XRD test. The visible-light response of SD-CaTiO3 with a band gap of 2.393 eV was confirmed by the redshifted absorption edge towards longer wavelengths produced by adding TiO2 to TM nitrate. Using 900 mg/L of SD-CaTiO3 at a pH of 10 and a 1200-W metal halide lamp as the visible light source without any sacrificial agents, a maximum H2 generation rate of 11,778 mu mol/L was attained under visible light. Using XRD and XPS, the suggested mechanism of H2 generation was validated. Using artificial neural networks and the Box-Behnken architecture, H2 productions were effectively predicted. According to the energy balance study, high-energy inputs for synthesis and calcination result in a considerable net energy deficit (-128,246 kJ). This emphasizes the necessity of optimization to increase the process's economic and energy efficiency.

The urgent demand for sustainable biofuels that do not compromise food security has intensified efforts to optimize Chlorella salina (C. salina) for biomass and lipid production. This study employed a sequential experimental-modelling approach, utilizing Response Surface Methodology (RSM) in conjunction with Artificial Neural Network (ANN) validation, to optimize growth parameters that influence biomass, lipid productivity, and biodiesel attributes. When cultivated in F/2 medium, C. salina entered the stationary phase on day 9, achieving a maximum cell density of 5.39 x 10(6) cells/mL after 12 days, with a peak biomass yield of 312 mg L-1. The highest lipid content (26.11 %) and productivity (6.79 mg L-1 d(-1)) were recorded under the same conditions after 12 days. Gas chromatography revealed saturated fatty acids (SFA) at 44.04 %, while Basal SAG medium yielded maximum monounsaturated (MUFA, 36.73 %) and polyunsaturated fatty acids (PUFA, 18.23 %). RSM models exhibited excellent predictive power (R-2 > 0.97), corroborated by the ANN, which showed strong alignment with the experimental data. Multi-response optimization via desirability function identified optimal conditions: 27 days of cultivation, N-2 at 800 ppm, NaHCO3 at 100 ppm, and CO2 at 13 ppm. This validated sequential strategy provides a robust framework for optimizing complex biological systems, enhancing the economic feasibility of algal biofuels.

Abstract This work presents a novel hydrogel produced from a newly manufactured aminobiochar with a high adsorption capacity for the removal of various pollutants from water. This study investigates the adsorption of Cr (VI) ions and methylene blue (MB) dye using a fabricated aminobiochar hydrogel (ABHG) synthesized from orange peels via microwave-assisted sulfuric acid activation, followed by oxidation, amination, and hydrogel formation. The ABHG exhibited a high surface area and diverse functional groups, confirmed by BET, SEM, FTIR, and TGA analyses. The FTIR analysis shows the presence of C–H, C≡C, C=C, C=O, and SO3 groups on the surface of the prepared hydrogel adsorbent. Under optimal conditions (initial concentration of 25 mg/L for both MB dye and Cr (VI) ions, contact time of 90 min for MB dye and 180 min for Cr (VI), pH 7.2 for MB dye and 1.04 for Cr (VI), and temperature 25 °C), maximum removal efficiency of both contaminants was achieved. Optimal adsorption experiments demonstrated maximum capacities of 476.19 mg/g for MB dye and 1250.00 mg/g for Cr (VI) ions based on the Langmuir isotherm model. Kinetic analysis showed MB dye adsorption followed a pseudo-second-order model, whereas Cr (VI) adsorption fitted a pseudo-first-order model. Using response surface methodology (RSM), the highest removal efficiencies were achieved with 0.958 g ABHG for 59.78 mg/L Cr (VI) and 1.91 g ABHG for 27.41 mg/L MB dye. Artificial neural network (ANN) modeling further validated these findings, confirming the potential of ABHG as an effective adsorbent for wastewater treatment.

A magnetic amino polyacrylonitrile nanocomposite (MAPA) was prepared and applied as an adsorbent to eliminate Cu2+ ions from aqueous media through batch experiments. Its physicochemical properties were examined using most known characterization methods. The optimal removal efficiency was obtained at pH 5.5. Adsorption studies were performed under different experimental conditions, considering initial copper concentration, solution pH, and temperature. The maximum removal efficiency attained was 78.29%, while the corresponding maximum adsorption capacity ($$\:{q}_{m}$$) was calculated to be 5.65 mg/g. The equilibrium adsorption data were analyzed using different isotherm models, among which the Langmuir (LIM) exhibited the best correlation with the experimental results. Kinetic behavior was also assessed through various known models. Among these models, the pseudo-second-order (PSO) demonstrated the strongest correlation (R² = 1.0), indicating that it most precisely represents the adsorption behavior. In summary, the fabricated MAPA demonstrated strong potential for efficiently eliminating Cu2+ ions from aqueous solutions. A maximum removal percentage of 51.51 mg/L of Cu2+ ions and 5.12 g of MAPA could be attained by using a Response Surface Methodology (RSM) optimization of the adsorption parameters. The optimized BPNN of ANN model stopped after 4 epochs with the best validation of 5.474, with an overall R2 of 0.996.

Abstract This paper examined the removal of Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) dyes using a novel Ammonia-decorated Red Algae Biochar (RAB-A) synthesized from red algae (Pterocladia capillacea) via a reflux technique in the presence of 25% ammonium hydroxide (NH4OH). The physicochemical properties of the synthesized RAB-A, including its surface area, morphology, functional groups, elemental composition, and thermal stability, were comprehensively characterized through Brunauer–Emmett–Teller (BET) analysis, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) integrated with energy-dispersive X-ray (EDX) analysis, and thermogravimetric analysis (TGA). RAB-A demonstrated a low specific surface area (3.262 m2/g) and a monolayer adsorption capacity of 0.7495 cm3 (STP)/g. The adsorbent demonstrated an overall pore volume of 0.011 cm³/g, accompanied by an average pore diameter of 13.648 nm. Thermogravimetric analysis revealed an overall mass loss of 40.84% for RAB-A, demonstrating enhanced thermal stability relative to RAB, which showed a weight loss of 51.05%. FTIR analysis confirmed the presence of diverse functional moieties on the surface of RAB-A. Adsorption experiments targeting Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) were conducted in batch mode by independently adjusting the initial dye concentration (100–200 mg/L), contact time (5–180 min), solution pH (2–12), and adsorbent dosage (0.5–1.5 g/L). The adsorption equilibrium behavior was best described by the Langmuir isotherm, which indicated maximum uptake capacities of 222.22 mg/g for AY36, 192.31 mg/g for MR dye, and 833.33 mg/g for MB dye. Kinetic analyses revealed that the adsorption of all examined dyes was best described by a pseudo-second-order model, thereby demonstrating the high suitability of the synthesized RAB-A for the efficient elimination of dyes from aqueous solutions. Additionally, adsorption was predicted and adjusted utilizing artificial neural networks (ANN).

Abstract The increasing discharge of recalcitrant azo dyes from textile and industrial effluents poses serious ecological and human health risks, necessitating the development of sustainable and cost-effective treatment strategies. The objective of the present study is to examine the adsorption performance of ethylenediamine-functionalized Delonix regia pod biochar (DRPB-ED) for the efficient removal of Acid Brown (AB14) 14 dye from aqueous solutions. The approach integrates the valorization of agricultural waste with surface amine functionalization to enhance adsorption performance. Batch experiments demonstrated a maximum removal efficiency of 98.6% under optimized conditions (pH 4.0, 1.0 g/L dosage, 100 mg/L initial concentration, 60 min). Equilibrium data were best described by the Langmuir model, with a maximum monolayer adsorption capacity ( q max ) of 60.61 mg/g, indicating favorable monolayer adsorption. Kinetic analysis followed a pseudo-second-order model (R 2 = 0.99), suggesting dominant chemisorption interactions facilitated by amine functional groups. Process optimization using response surface methodology (RSM) achieved high desirability (D = 0.97), while artificial neural network (ANN) modeling demonstrated strong predictive capability ( R 2 = 0.98). Unlike many conventional biochar-based adsorbents that rely solely on physical adsorption, the present study introduces targeted amine functionalization combined with integrated statistical and machine learning modeling, providing both enhanced adsorption performance and robust process predictability. The findings highlight the dual environmental and technological significance of transforming low-cost biomass into high-efficiency adsorbents for sustainable wastewater treatment applications.

Cobalt-doped zinc oxide nanoparticles were fabricated and examined in this study as a potential photocatalyst for the antibiotic ciprofloxacin (CIPF) degradation when exposed to visible LED light. The Co-precipitation technique created Cobalt-doped zinc oxide nanoparticles that were 5, 10, and 15% Co-loaded. Different known techniques have been used to characterize the synthesized ZnO and cobalt-doped ZnO nanoparticles. Compared to ZnO and other Cobalt-doped ZnO nanoparticles, the experiments showed that 10% Cobalt-doped ZnO nanoparticles were a very effective catalyst for CIPF photodegradation. According to XRD, these NPs have a hexagonal Wurtzite structure with an average size of between 38.47 and 48.06 nm. Tauc plot displayed that the optical energy band-gap of ZnO NPs (3.21) slowly declines with Co doping (2.75 eV). The enhanced photocatalytic activity of Cobalt-doped ZnO nanoparticles, which avoids electron-hole recombination, is brought on by the implantation of Co. Within 90 min, a 30 mg/L solution of ciprofloxacin was destroyed (> 99%). The kinetics studies demonstrated that the first-order model, with R2 = 0.9703, is appropriate for illuminating the pace of reaction and quantity of CIPF elimination. The recycled Cobalt-doped zinc oxide nanoparticles enhanced photocatalytic performance toward CIPF for 3 cycles with the same efficiency. Furthermore, optimization of the 10% Cobalt-doped zinc oxide nanoparticles using a Central composite design (CCD) was also studied. The optimal parameters of pH 6.486, 134.39 rpm shaking speed, 54.071 mg catalyst dose, and 31.04 ppm CIPF initial concentration resulted in the highest CIPF degradation efficiency (93.99%). Artificial neural networks (ANN) were used to simulate the experimental data. The backpropagation technique was used to train the networks with 152 input-output patterns. After experimenting with various configurations, the best results with a correlation value (R2) of 0.9780 for data validation were obtained using a three-hidden layered network that included five, five, and eight neurons, respectively.