Khaled Chawraba

Persistent organic pollutants (POPs) remain a major environmental challenge because of their persistence, long-range transport, bioaccumulation, and resistance to degradation. Conventional monitoring and remediation systems provide reliable analytical performance but are largely fragmented, laboratory dependent, and unable to deliver the continuous, predictive, and adaptive management required for complex, dynamic contamination scenarios. Recent literature reports substantial advances in biosensors, Internet of Things (IoT)-enabled sensing, machine learning (ML), and artificial intelligence (AI) for pollutant detection, forecasting, source identification, and remediation optimization. However, these developments often operate separately, with limited coordination between environmental sensing, data analytics, treatment processes, and decision-making. The literature further highlights persistent barriers to field deployment, including limited and geographically heterogeneous datasets, inconsistent sensor performance and calibration, restricted model transferability, insufficient independent validation, uncertainty in AI predictions, and the lack of mature regulatory frameworks for AI-assisted environmental assessment. From our perspective, the next advance in POP management should move beyond isolated AI applications toward integrated, closed-loop AI–IoT architectures that continuously connect sensing, prediction, remediation, and feedback control. Such systems could transform POP management from reactive assessment to proactive and adaptive environmental governance. Future progress will depend on standardized, Findable, Accessible, Interoperable, and Reusable (FAIR) environmental datasets, explainable and uncertainty-aware AI, robust field validation, and interoperable sensing infrastructures.

Graphical abstract showing citric-acid modification of loquat seed powder and its application as a biosorbent for methylene blue removal. The material achieved a maximum adsorption capacity of 47.53 mg/g within 9 min, followed by the Freundlich and pseudo-second-order models, which were accurately predicted by a random forest model, and retained more than 85% removal efficiency after five reuse cycles. Synthetic dye contamination poses a major environmental challenge, requiring sustainable, high-performance remediation technologies. Herein, an eco-friendly biosorbent, citric acid-modified loquat powder (LP-CA), was developed for efficient removal of methylene blue (MB) from aqueous solutions. FTIR, FE-SEM-EDS, and pHpzc analyses confirmed successful surface functionalization by introducing oxygen-containing groups, enhancing surface reactivity and yielding a pHpzc of 4.0. LP-CA reached adsorption equilibrium within 9 min and achieved a maximum adsorption capacity of 47.53 mg/g, representing a 19% improvement over the pristine biomass. Equilibrium and kinetic data were best described by the Freundlich isotherm (R2 = 0.98) and the pseudo-second-order model. Thermodynamic and activation energy analyses revealed a spontaneous, exothermic process predominantly governed by physisorption, with electrostatic attraction, hydrogen bonding, and π-interactions contributing to adsorption. A Random Forest model accurately predicted MB removal (R2 = 0.970; RMSE = 3.57 %), and feature importance analysis identified solution pH and pHpzc as the dominant variables, quantitatively validating the proposed adsorption mechanism. LP-CA retained >85% removal efficiency through the first five adsorption-desorption cycles and demonstrated practical applicability in a bag-assisted adsorption configuration. Machine learning provides a powerful framework for predictive adsorption modeling, mechanistic interpretation, and future process optimization.

A comprehensive thermodynamic and molecular-level investigation of adsorption on MgY and NH₄Y zeolites is presented using inverse gas chromatography at infinite dilution combined with a Hamaker-based formalism and an extended five-parameter Lewis acid–base model. The study establishes a unified framework that integrates dispersive, polar, and donor–acceptor interactions while explicitly accounting for temperature-dependent intermolecular geometry. The results demonstrate that adsorption is governed by a dynamic interplay between London dispersion forces, specific acid–base interactions, and thermal effects, rather than by static or purely additive contributions. The London dispersive surface energy decreases linearly with temperature, reflecting the progressive weakening of electronic correlation forces, while the inter-molecular separation distance exhibits a well-defined linear expansion, enabling the determination of intrinsic contact distances at 0 K. A major finding of this work is that the molecular surface area of adsorbed probes is not a constant geometric property but a thermodynamic quantity that follows a quadratic temperature dependence, revealing the adaptive nature of adsorption geometry. The comparison between MgY and NH₄Y highlights two distinct adsorption regimes: MgY is characterized by a structured and strongly dispersive interaction field associated with Mg²⁺ cations, whereas NH₄Y exhibits enhanced polarity, stronger specific interactions, and increased molecular flexibility driven by hydrogen bonding and protonic effects. The thermodynamic analysis of Lewis acid–base interactions shows that classical linear models are insufficient to describe adsorption on zeolite surfaces. A rigorous statistical evaluation demonstrates that the Hamieh five-parameter model provides the most accurate and physically meaningful description, capturing nonlinear donor–acceptor interactions and amphoteric coupling effects. Overall, this work introduces a novel thermodynamic methodology that links macroscopic surface energetics to microscopic interaction parameters, providing new insights into adsorption mechanisms and offering a powerful tool for the rational design of porous materials in catalysis, separation, and energy-related applications.

The commercialization of direct methanol fuel cells (DMFCs) is hindered by the high cost, limited durability, and sluggish kinetics of platinum‐based catalysts. To address these challenges, NiO/FTO, Mn ₂ O ₃ /FTO, and NiO–Mn ₂ O ₃ /FTO thin films were fabricated using a simple and low‐cost dip‐coating method. Structural and compositional analyses using SEM and EDS confirmed the formation of uniform and porous morphologies, while XRD and Raman spectroscopy verified the crystalline nature and phase formation of the materials. Among the prepared electrodes, the NiO– Mn ₂ O ₃ /FTO composite exhibited superior electrocatalytic activity for methanol oxidation, delivering a prominent anodic peak at 0.99 V and a maximum current density of 5.8 mA cm ^{− 2} . At 0.65 V, it achieved a current density of 2.5 mA cm ^{− 2} in 1.4 M methanol at a scan rate of 100 mV s ^{− 1} . Chronoamperometric measurements demonstrated good electrochemical stability, with 86% current retention after 2000 s. Electrochemical impedance spectroscopy further confirmed the enhanced charge‐transfer capability, showing a significantly lower charge‐transfer resistance (R _ct ) of 71 Ω compared with 349 Ω for NiO and 517 Ω for Mn ₂ O ₃ electrodes. Overall, the NiO–Mn ₂ O ₃ /FTO thin film offers a cost‐effective, stable, and efficient catalytic system, demonstrating strong potential as an alternative anode material for DMFC applications.

Driven by growing environmental concerns and increasing regulatory pressures, bio‐based polyurethanes (Bio‐PUs) derived from renewable resources such as vegetable oils and lignocellulosic biomass are attracting significant interest as sustainable alternatives. This review critically examines their mechanical performance, with a particular emphasis on fundamental structure–property relationships. The inherent chemistry of bio‐derived polyols and isocyanates, including hydroxyl functionality, molecular weight, and aromatic or aliphatic nature, is a key determinant of tensile strength, elongation, modulus, and toughness. We highlight effective reinforcement strategies utilizing natural fibers, nanomaterials, and hybrid fillers to enhance stiffness, strength, and thermal stability without compromising sustainability. A comparative analysis with petrochemical PUs reveals that certain bio‐based formulations can achieve comparable elasticity, and toughness, and in some cases improved weathering resistance depending on the specific bio‐derived components and formulation strategies. However, challenges remain in consistently achieving the high rigidity, flame resistance, and long‐term durability typical of some petrochemical PU systems. Broader life‐cycle considerations are also discussed, including feedstock variability, cost competitiveness, and end‐of‐life management. Finally, emerging pathways such as non‐isocyanate polyurethanes (NIPUs), advanced nanocomposites, and digital manufacturing techniques are highlighted as promising strategies to address current limitations and expand the potential application scope of Bio‐PUs, including in structural and biomedical fields.

Hydrogels are widely used in biomedical applications because they are biocompatible, respond to external stimuli, and closely mimic the mechanical properties of natural tissues. However, conventional hydrogels often exhibit poor electrical conductivity, low mechanical strength, and limited functionality, hindering their use in advanced biomedical platforms. These challenges can be addressed by integrating 2D graphene and its derivatives into polymer‐based nanocomposite hydrogels (CHGs). Graphene‐enhanced CHGs offer superior mechanical strength, electrical conductivity, thermal stability, and optical properties, making them ideal for advanced biomedical applications. This review provides a comprehensive analysis of graphene's structural and functional properties and its incorporation into hydrogel matrices for CHG synthesis. It explores recent advancements in graphene‐based CHGs for drug delivery, tissue engineering, and photothermal therapy (PTT), highlighting their enhanced performance. Additionally, it examines the emerging role of machine learning (ML) in optimizing CHG properties, such as predictive modeling for wearable sensors in biomedical contexts. By bridging materials science and computational intelligence, this review outlines a roadmap for designing next‐generation smart hydrogels, emphasizing their transformative potential in addressing complex healthcare challenges.