Al-Ahliyya Amman University

This study addresses the challenge of accurately predicting oil–water interfacial tension through the integration of surfactant physicochemical descriptors and machine learning algorithms. The main objective was to establish quantitative relationships between surfactant structure, concentration, and oil characteristics to determine their collective effect on interfacial energy minimization. A dataset of 260 experimentally reported points was compiled from peer-reviewed studies, encompassing seven inputs (Surfactant MW, Charge, HLB value, CMC, Concentration, oil ZPC, and Oil API) against measured IFT as output. After ensuring dataset uniformity through leverage-based outlier detection, six algorithms (DT, AdaBoost, RF, KNN, CNN, and MLP-ANN) and one hybrid framework were trained and evaluated using 5-fold cross-validation with performance indices R2, MSE, and AARE%. The Ensemble Learning model achieved the highest accuracy (R2test = 0.986, MSEtest = 4.09), demonstrating superior generalization compared with single learners. SHAP analysis confirmed surfactant concentration as the dominant factor with a strong negative association to IFT, followed by Oil API and ZPC, consistent with Gibbs adsorption theory. The results emphasize that interfacial behavior is mainly dictated by surfactant molecular architecture and concentration rather than oil composition. This unified data-driven approach provides a reproducible framework for evaluating and optimizing surfactant formulations to minimize IFT effectively in industrial applications.

MXene quantum dots (MQDs) provide a chemically versatile platform for metal-ion sensing because their surface coordination environments, defect-associated states, and size-dependent electronic structure can couple molecular recognition to measurable optical responses. This review develops a mechanism-centered framework for understanding that coupling rather than treating sensing performance as a collection of isolated analytical metrics. It first examines how surface terminations, coordination-site accessibility, electronic-state organization, solvation, and interfacial binding thermodynamics define the recognition landscape of MQDs. It then analyzes how recognition events are translated into fluorescence or electrochemiluminescence responses through charge transfer, energy transfer, static or dynamic quenching, inner-filter effects, and related excited-state perturbations. Particular emphasis is placed on structure–property–signal relationships, mechanistic distinguishability, and trade-offs among sensitivity, selectivity, reversibility, and robustness. Evidence from reported metal-ion sensing systems is comparatively assessed to identify recurring design trends and limitations in mechanistic validation, cross-platform benchmarking, and real-matrix translation. The analysis indicates that future progress depends less on maximizing signal magnitude than on establishing reproducible coordination environments, experimentally resolved transduction pathways, and standardized performance criteria. This perspective positions MQD sensing as a recognition-to-signal engineering problem governed by coupled surface chemistry and excited-state dynamics.

A novel ionic liquid-functionalized graphitic carbon nitride/graphene oxide (g-C3N4-IL@GO) nanocomposite was successfully synthesized through a facile multi step strategy. Graphitic carbon nitride (g-C3N4) was prepared by thermal polymerization and then functionalized with a DABCO-based ionic liquid via the substitution of surface active sites with 1,4-dibromobutane and functionalized DABCO to afford g-C3N4-IL. The functionalized material was then combined with graphene oxide sheets by interfacial assembly. This approach facilitated the development of a hybrid layered nanostructure with enhanced physicochemical properties for energy storage applications. The successful functionalization and nanocomposite formation were verified by complementary structural and surface analysis methods such as (XRD), (FTIR), (SEM), (EDS), (XPS), (BET), and (TGA). The electrochemical behavior of the g-C3N4-IL@GO electrode material was comprehensively analyzed using CV, GCD, and EIS in a three electrode system. The CV curves exhibited redox features and a total charge-storage mechanism comprising of Faradaic and electric double-layer capacitance. The GCD curves showed a high specific capacitance value of 425F/g at 2 A/g with a high current processing ability and good utilization of electroactive sites with a favorable ion-accessibility in the electrode. The excellent cycling stability of the electrode with 93% of initial capacitance retained after 10,000 successive cycles demonstrated that the electrode possessed robust structural integrity and long-term electrochemical durability. The energy and power densities were calculated from the GCD curves of the three-electrode system and found to be as high as 35 Wh/kg and 549 W/kg, respectively. To sum up, these results demonstrate the synergistic effect of the g-C3N4 framework, ionic liquid group and graphene oxide layer structure, which leads to fast charge transport, high reversibility, and better electrochemical performance. Thus the composite could be considered a potential candidate for supercapacitor electrodes.

Persistent organic pollutants remain among the most challenging environmental contaminants due to their persistence, bioaccumulation, and long-range ecological toxicity. In recent years, nitrogen-doped graphene quantum dots (N-GQDs) have emerged as highly promising fluorescent nano-probes for ultrasensitive POP detection because of their tunable electronic structure, high surface activity, excellent aqueous dispersibility, and controllable photophysical behavior. This review discusses the structural engineering, dopant modulation, and morphological control of N-GQDs as key determinants of fluorescence quenching and enhancement performance. Particular emphasis is placed on the mechanistic foundations of photophysical transduction pathways, including photoinduced electron transfer, fluorescence resonance energy transfer, trap-state passivation, and competitive displacement mechanisms governing “turn-off” and “turn-on” sensing responses. The review further evaluates recent advances in analytical sensing architectures, ratiometric platforms, hybrid multimodal systems, and field-deployable devices for environmental monitoring applications. Importantly, current limitations involving structural heterogeneity, mechanistic ambiguity, matrix interference, and poor reproducibility are analyzed. Finally, future perspectives focusing on artificial intelligence-assisted nanomaterial design, hybrid sensing technologies, and standardized fabrication strategies are proposed to accelerate the transition of N-GQDs from laboratory-scale fluorescent materials to reliable environmental sensing platforms for next-generation pollutant surveillance.

This study investigates quasi-consensus in fractional-order multi-agent systems (FOMASs) with three time-varying delays, unknown nonlinear dynamics, bounded disturbances, and intermittent Denial-of-Service (DoS) attacks. A dual-channel adaptive Radial Basis Function Neural Network (RBFNN) is developed to estimate unknown nonlinearities online. An adaptive impulsive pinning strategy dynamically selects agents according to their consensus errors, concentrating control effort on agents with larger deviations. A Lyapunov-Razumikhin-based framework is established to derive sufficient conditions for quasi-consensus under delayed and attacked communication. A DoS-aware impulsive contraction factor is introduced to capture the effects of adaptive pinning and attack frequency-duration characteristics. An explicit quasi-consensus error bound is derived to quantify the effects of disturbances, neural approximation errors, fractional-order dynamics, and impulsive intervals. Numerical results demonstrate accurate leader-following tracking, rapid convergence, and strong resilience against intermittent DoS attacks, confirming the effectiveness of the proposed approach for secure coordination of uncertain FOMASs.