Insights into the highly efficient treatment of dyeing wastewater using algal bloom derived activated carbon with wide-range adaptability to solution pH and temperature

Enhanced peroxymonosulfate activation for organic decontamination by Ni-doped δ-FeOOH under visible-light assistance

There is an urgent need for efficient and cost-effective methods without secondary pollution to decompose pollutants from contaminated water in the face of severe water pollution caused by the extensive use of synthetic dye in industry. In this work, Ni-doped δ-FeOOH was synthesized using co-precipitation method at room temperature and was applied as the catalyst in a visible-light-assisted peroxymonosulfate system for assessing its catalytic performance in RhB removal. An optimum RhB degradation of 99.5% was achieved after 30 min with the addition of 20 mg of PMS and 60 mg of isomorphic substituted δ-FeOOH at an initial pH value of 7. Through various characterization, degradation experiments and DFT calculation, the enhanced photocatalytic PMS performance of Ni-20 wt% doped δ-FeOOH compared to pure δ-FeOOH can be attributed to higher specific surface area and faster electron transfer, which resulted in the generation of more reactive oxygen species (ROSs), including SO4•-, •OH, O2•- and 1O2, all involved in the abatement of RhB. The possible RhB degradation pathways were inferred based on intermediates detected by liquid chromatograph-mass spectrometer (LC-MS), combined with Fukui indices and the analysis of bond lengths. The toxicity of the intermediates was evaluated using T.E.S.T. software, which proved that the system could effectively reduce the biotoxicity of the parent pollutant RhB.

Molecular simulation-based insights into dye pollutant adsorption: A perspective review

Growing concerns about environmental pollution have highlighted the need for efficient and sustainable methods to remove dye contamination from various ecosystems. In this context, computational methods such as molecular dynamics (MD), Monte Carlo (MC) simulations, quantum mechanics (QM) calculations, and machine learning (ML) methods are powerful tools used to study and predict the adsorption processes of dyes on various adsorbents. These methods provide detailed insights into the molecular interactions and mechanisms involved, which can be crucial for designing efficient adsorption systems. MD simulations, detailing molecular arrangements, predict dyes' adsorption behaviour and interaction energies with adsorbents. They simulate the entire adsorption process, including surface diffusion, solvent layer penetration, and physisorption. QM calculations, especially density functional theory (DFT), determine molecular structures and reactivity descriptors, aiding in understanding adsorption mechanisms. They identify stable adsorption configurations and interactions like hydrogen bonding and electrostatic forces. MC simulations predict equilibrium properties and adsorption energies by sampling molecular configurations. ML methods have proven highly effective in predicting and optimizing dye adsorption processes. These models offer significant advantages over traditional methods, including higher accuracy and the ability to handle complex datasets. These methods optimize adsorption conditions, clarify adsorbent functionalization roles, and predict dye removal efficiency under various conditions. This research explores MD, MC, QM, and ML approaches to connect molecular interactions with macroscopic adsorption phenomena. Probing these techniques provides insights into the dynamics and energetics of dye pollutants on adsorption surfaces. The findings will aid in developing and optimizing new materials for dye removal. This review has significant implications for environmental remediation, offering a comprehensive understanding of adsorption at various scales. Merging microscopic data with macroscopic observations enhances knowledge of dye pollutant adsorption, laying the groundwork for efficient, sustainable removal technologies. Addressing the growing challenges of ecosystem protection, this study contributes to a cleaner, more sustainable future.

Multiporous ZIF-8 carbon/cellulose composite beads: Highly efficient and scalable adsorbents for water treatment

Metal-organic framework (MOF) particles are one of the most promising adsorbents for removing organic contaminants from wastewater. However, powder-type MOF particles face challenges in terms of utilization and recovery. In this study, a novel bead-type adsorbent was prepared using activated carbon based on the zeolitic imidazolate framework-8 (AC-ZIF-8) and a regenerated cellulose hydrogel for dye removal. AC-ZIF-8 particles with a large surface area were obtained by carbonization and chemical activation with KOH. The AC-ZIF-8 powders were efficiently immobilized in hydrophilic cellulose hydrogel beads via cellulose dissolution/regeneration. The prepared AC-ZIF-8/cellulose hydrogel (AC-ZIF-8/CH) composite beads exhibit a large specific surface area of 1412.8 m2/g and an excellent maximum adsorption capacity of 565.13 mg/g for Rhodamine B (RhB). Moreover, the AC-ZIF-8/CH beads were effective over a wide range of pH, temperatures and for different types of dyes. These composite beads also offer economic benefits through desorption of dyes for recycling. The AC-ZIF-8/CH beads can be produced in substantial amounts and used as fillers in a fixed-bed column system, which can purify the continuous inflow of dye solutions. These findings suggest that our simple approach for preparing high-performance adsorbent beads will broaden the application of dye adsorbents, oil-water separation, and catalysts.

Construction of Cu-N coordination into natural biopolymer lignin backbone for highly efficient and selective removal of cationic dyes

Here, a Cu²⁺-doped lignin-based adsorbent (Cu-AL) was fabricated via the amination and Cu²⁺-doping of industrial alkali lignin for massive and selective adsorption of cationic dyes azure B (AB) and saffron T (ST). The Cu-N coordination structures endowed Cu-AL with stronger electronegativity and higher dispersity. Through the electrostatic attraction, π-π interaction, H-bonding, and Cu²⁺ coordination, the adsorption capacities of AB and ST reached up to 1168 and 1420 mg g^-1, respectively. The pseudo-second-order model and Langmuir isotherm model were more relevant to the AB and ST adsorption on Cu-AL. Based on the thermodynamic study, the adsorption progresses were endothermic, spontaneous, and feasible. The Cu-AL maintained high removal efficiency to dyes after 4 reuses (>80%). Importantly, the Cu-AL could efficiently remove and separate AB and ST from dye mixtures even in real time. All the above characteristics demonstrated that Cu-AL was an excellent adsorbent for fast wastewater treatment.