Green materials are emerging as sustainable alternatives in water and wastewater treatment. Due to their biodegradability, renewable origin and low toxicity characteristics, green materials are an alternative to conventional synthetic materials. Green materials include nanomaterials of natural origin, biopolymers and composites that optimize the adsorption and removal of contaminants. The applications of cellulose nanofibers, alginates, chitosan and lignin stand out, as well as functionalized hydrogels and aerogels for the removal of heavy metals, dyes and organic contaminants. The analysis of the mechanisms and processes of contaminant removal and modeling and optimization techniques are included as key emerging tools for the design and optimization of these materials, allowing one to predict properties, simulate interactions and customize solutions. Despite the sustainability benefits of green materials, they face technical and economic challenges, such as scalability, synthesis costs and experimental validation. This work concluded that green materials, combined with modeling and optimization tools, are essential to move towards more sustainable, efficient and environmentally friendly water treatment technologies, aligned with global objectives of sustainable development and climate change mitigation.

In this work, we report a nonaqueous one-step method to synthesize polystyrene macroporous magnetic nanocomposites through high internal phase emulsions (HIPEs) formulated with the deep eutectic solvent (DES) composed of urea:choline chloride (U:ChCl, in a 2:1 molar ratio) as the internal phase and co-stabilized with mixtures of Span 60 surfactant and non-functionalized magnetite nanoparticles (Fe₃O₄ NPs). The porous structure and the magnetic and lipophilic properties of the nanocomposite materials were easily tailored by varying the amount of Fe₃O₄ NPs (0, 2, 5 and 10 wt %) and the surfactant Span 60 (0, 5, 10, and 20 wt %) used in the precursor emulsion. The resultant nanocomposite polyHIPEs exhibit high sorption capacity toward different oils (hexane, gasoline, and vegetable oil) due to their high porosity, interconnectivity, and hydrophobic surface. It was observed that the oil sorption capacity was improved when the amount of surfactant decreased and Fe₃O₄ NPs increased in HIPE formulation. Therefore, polyHIPE formulated with 5 and 10 wt % Span 60 and Fe₃O₄ NPs, respectively, showed the highest oil sorption capacities of 4.151, 3.556, and 3.266 g g^-1 for gasoline, hexane, and vegetable oil, respectively. In addition, the magnetic monoliths were reused for more than ten sorption/desorption cycles without losing their oil sorption capacity.

This study investigates the drying kinetics of green tomatoes under controlled conditions, comparing 14 mathematical models including Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Two-Term, Wang and Singh, Thomson, Approximate Diffusion, Verma, Modified Henderson and Pabis, Two-Term Exponential, Simplified Fick’s Diffusion, and Midilli et al. The Verma, Two-Term Exponential, and Modified Page models demonstrated superior accuracy (R² > 0.998, RMSE 0.01268 - 0.01345). A novel approach integrating a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) optimized drying process prediction, significantly enhancing moisture content prediction over time. This hybrid CNN-LSTM model, coupled with the accurate mathematical models (especially the Two-Term Exponential), offers a robust framework for predicting and optimizing food dehydration.

Green-synthesized nanomaterials have emerged as promising candidates for environmentally sustainable remediation; however, experimental evidence describing their synthesis routes, physicochemical properties, remediation performance, and sustainability-relevant attributes remains fragmented, inconsistently reported, and difficult to integrate across studies. This work addresses this challenge by proposing an ontology-based semantic framework for the interoperable integration of green-synthesized nanomaterials, contaminants, and remediation processes, incorporating explicit provenance metadata and structured sustainability descriptors. The ontology was developed using the Linked Open Terms (LOT) methodology and implemented in OWL 2 DL, with selective alignment to established vocabularies including eNanoMapper, ChEBI, ENVO, and PROV-O. Adsorption and photocatalysis were instantiated as representative remediation mechanisms to evaluate the framework’s capacity to accommodate structurally distinct processes. Logical reasoning and SHACL-based validation were applied to assess semantic consistency, provenance traceability, and data completeness. The results demonstrate that the proposed ontology effectively integrates heterogeneous experimental data within a unified, FAIR-compliant semantic framework, supports conservative and provenance-aware inference, and enables comparative analysis across mechanistically diverse remediation systems without structural modification. This ontology-based approach provides a robust foundation for sustainability-aware knowledge integration in environmental nanotechnology and establishes the basis for future extensions involving data quality assessment and explainable AI-driven analysis.