Sustainable wastewater purification with crab shell-derived biochar: Advanced machine learning modeling & experimental analysis

From pollutant to purifier: Leveraging plastic waste-derived activated carbon for sustainable water remediation solutions

The ubiquitous presence of plastic waste presents a significant environmental challenge, characterized by its persistence and detrimental impacts on ecosystems. The valorization of plastic waste through conversion into high-value carbon materials offers a promising circular economy approach. This review critically examines the potential of plastic waste-derived activated carbon (PAC) as a sustainable and effective adsorbent for water remediation. The manuscript commences with a concise overview of the multifaceted nature of plastic pollution, highlighting its classification, environmental implications, and the limitations of existing waste management frameworks. Subsequently, it delves into the intricacies of PAC production, critically analyzing various preparation methods and their associated challenges. A comprehensive exploration of modification strategies, including chemical activation and surface functionalization, is undertaken to elucidate their role in enhancing PAC's adsorption selectivity and capacity for diverse pollutants. The effectiveness of PAC in removing a diverse array of pollutants, including emerging contaminants and recalcitrant organic compounds, is thoroughly examined. While acknowledging the influence of key factors such as pollutant characteristics and solution chemistry on adsorption efficiency, the review also identifies critical challenges, including the high production costs associated with PAC synthesis, variability of plastic waste composition, the potential for leaching of residual monomers, and the complexities of multi-pollutant adsorption. Future research directions are outlined, emphasizing the need for advanced characterization techniques, computational modeling to optimize adsorbent design, and rigorous life cycle assessments to evaluate the environmental sustainability of PAC production. By addressing these challenges, PAC offers a promising pathway towards a circular economy, mitigating plastic pollution while providing a sustainable and effective solution for water remediation.

Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation

In response to escalating global wastewater issues, particularly from dye contaminants, many studies have begun using hydrochar to adsorb dye from wastewater. However, the relationship between the preparation conditions of hydrochar, the properties of hydrochar, experimental conditions, types of dyes, and equilibrium adsorption capacity (Q) has not yet been fully explored. This study conducted a comprehensive assessment using twelve distinct ML models. The Gradient Boosting Regressor (GBR) model exhibited superior performance with R² (0.9629) and RMSE (0.1166) in the test dataset, marking it as the most effective among the evaluated models. Moreover, this study also proved the feasibility of the GBR model through stability testing and residual analysis. A feature importance analysis prioritized the variables as follows: experimental conditions (41.5 %), properties of hydrochar (26.0 %), preparation conditions (18.1 %), and type of dye (14.4 %). Meanwhile, experimental conditions (C0 > 30 mmol/g, pH > 8, and higher solvent temperatures) and hydrochar properties (the BET surface area > 2000 m²/g, an (O+N)/C molar ratio < 0.6, and an H/C molar ratio of approximately 0.06) show higher Q for dyes. Experimental validation of the GBR model confirmed its practical utility with a suitable predictive accuracy (R² = 0.8704). Moreover, the study developed a Python-based GUI that has integrated the best GBR models to facilitate researchers' ongoing application and improvement of this predictive model. This study not only underscores the efficacy of ML in enhancing the understanding of dye adsorption by hydrochar but also sets a precedent for future research on sustainable contaminants removal through bio-based adsorbents.

Comparison of surface adsorption efficacies of eco-sustainable agro/animal biomass-derived activated carbon for the removal of rhodamine B and hexavalent chromium

Effective management and remediation strategies are crucial to minimize the impacts of both organic and inorganic contaminants on environmental quality and human health. This study investigates a novel approach utilizing cotton shell activated carbon (CSAC), rice husk activated carbon (RHAC), and wasp hive activated carbon (WHAC), produced through alkali treatment and carbonization under N2 atmosphere at 600 °C. The adsorption capacities of biomass-derived mesoporous activated carbons (CSAC, RHAC, WHAC) alongside macroporous commercial activated carbons (CAC) were evaluated for removing rhodamine B (Rh B) and hexavalent chromium (Cr6+). The CSAC exhibits remarkable adsorption efficiency (255.4 mg.g-1) for Cr(VI) removal, while RHAC demonstrates superior efficacy (174.2 mg.g-1) for Rh B adsorption. Investigating various optimal parameters including initial pH (pH 3 for Cr and pH 7 for Rh B), catalyst dosage (200 mg.L-1), and initial concentration (20 mg.L-1), the Redlich-Peterson isotherm model is applied to reveal a hybrid adsorption mechanism encompassing monolayer (chemisorption) and multilayer (van der Waals adsorption) processes. Kinetic analysis highlights the pseudo-second-order and Elovich models as the most suitable, suggesting physiochemisorption mechanisms. Thermodynamic analysis indicates the endothermic nature of the adsorption process, with increased randomness at the solid-solution interface. Isosteric heat investigations using Clausius-Clapeyron, Arrhenius, and Eyring equations reveal a heterogeneous surface nature across all activated carbons. Further confirmation of Rh B and Cr(VI) adsorption onto activated carbons is provided through FTIR, FESEM, and EDAX analysis. This study highlights the innovation and promise of utilizing biomass-derived activated carbons for effective pollutant removal.

Seafood waste derived carbon nanomaterials for removal and detection of food safety hazards

Nanobiotechnology and the engineering of nanomaterials are currently the main focus of many researches. Seafood waste carbon nanomaterials (SWCNs) are a renewable resource with large surface area, porous structure, high reactivity, and abundant active sites. They efficiently adsorb food contaminants through π-π conjugated, ion exchange, and electrostatic interaction. Furthermore, SWCNs prepared from seafood waste are rich in N and O functional groups. They have high quantum yield (QY) and excellent fluorescence properties, making them promising materials for the removal and detection of pollutants. It provides an opportunity by which solutions to the long-term challenges of the food industry in assessing food safety, maintaining food quality, detecting contaminants and pretreating samples can be found. In addition, carbon nanomaterials can be used as adsorbents to reduce environmental pollutants and prevent food safety problems from the source. In this paper, the types of SWCNs are reviewed; the synthesis, properties and applications of SWCNs are reviewed and the raw material selection, preparation methods, reaction conditions and formation mechanisms of biomass-based carbon materials are studied in depth. Finally, the advantages of seafood waste carbon and its composite materials in pollutant removal and detection were discussed, and existing problems were pointed out, which provided ideas for the future development and research directions of this interesting and versatile material. Based on the concept of waste pricing and a recycling economy, the aim of this paper is to outline current trends and the future potential to transform residues from the seafood waste sector into valuable biological (nano) materials, and to apply them to food safety.

Integrating DFT and machine learning for the design and optimization of sodium alginate-based hydrogel adsorbents: Efficient removal of pollutants from wastewater

This study presents a novel approach to design and optimize a sodium alginate-based hydrogel (SAH) for efficient adsorption of the model water pollutant methylene blue (MB) dye. Utilizing density functional theory (DFT) calculations, sodium alginate-g-poly (acrylamide-co-itaconic acid) was identified with the lowest adsorption energy (Eads) for MB dye among 14 different clusters. SAHs were prepared using selected monomers and sodium alginate combinations through graft co-polymerization, and swelling studies were conducted to optimize grafting conditions. Advanced characterization techniques, including FTIR, XRD, XPS, SEM, EDS, and TGA, were employed, and the process was optimized using statistical and machine learning tools. Screening tests demonstrated that Eads serves as an effective predicting indicator for adsorption capacity (qe) and MB removal efficiency (RRMB,%), with reasonable agreement between Eads and both responses under given conditions. Process modeling and optimization revealed that 5 mg of selected SAH achieves a maximum qe of 3244 mg g-1 at 84.4% RRMB under pH 8.05, 98.8 min, and MB concentration of 383.3 mg L-1, as identified by the desirability function approach. Moreover, SAH effectively eliminated various contaminants from aqueous solutions, including sulfasalazine (SFZ) and dibenzothiophene (DBT). MB adsorption onto selected SAH was exothermic, spontaneous, and followed the pseudo-first-order and Langmuir-Freundlich isotherm models. The remarkable ability of SAH to adsorb MB is attributed to its well-designed structure predicted through DFT and optimal operational conditions achieved by AI-based parametric optimization. By integrating DFT-based computations and machine-learning tools, this study contributes to the efficient design of adsorbent materials and optimization of adsorption processes, also showcasing the potential of SAH as an efficient adsorbent for the abatement of aqueous pollution.

Chitin/calcite composite extracted from shell waste as a low-cost adsorbent for removal of tetracycline and ciprofloxacin: Effects and mechanisms

Recently, water contamination caused by the misuse of antibiotics has become a growing concern. In this study, an economical chitin/calcite composite (CCA) was extracted from crab shell waste, and the effects and mechanisms of its removal of ciprofloxacin (CIP) and tetracycline (TC) from aqueous solution were investigated. The functional groups of chitin and the metal phase of calcite gave CCA the ability to remove antibiotics. Experiments on kinetics, isothermal adsorption, thermodynamics, co-removal, and reusability were conducted to systematically explore the adsorption performances of CCA toward antibiotics. The pseudo-second-order (FSO) and Langmuir models suited the data obtained from experiments best and displayed a good fit for the chemisorption and a certain homogeneity of adsorption sites. At 25 °C, the maximum adsorption capacities (Qmax) toward CIP and TC were 228.86 and 150.76 mg g-1, respectively. The adsorption mechanisms of CCA with TC and CIP are pH dependent since pH can affect the surface charge of CCA and the form in which CIP and TC are existing. The X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FTIR) demonstrated that the keto-O and carboxyl groups of CIP and the carbonyl, hydroxyl, and amido groups of TC could be responsible for the binding with the calcite and the functional groups of chitin through surface complexation, cation bridge and hydrogen bonding.