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.
Emotional Intelligence (EI) is progressively regarded as a key aspect in determining how well employees perform, especially in service-focused fields like banking. The paper explores how EI affects the Job Performance (JP) of personnel working in public sector banks in India. To investigate this, a descriptive research method is used and gathered primary data from 400 employees at branches of SBI and PNB in Himachal Pradesh, utilizing a structured questionnaire. Using SmartPLS 4 for PLS- SEM, measurement model and structural linkages are assessed. Results demonstrate that various dimensions of EI have a statistically significant and positive impact on JP. This highlights value of emotional skills in fostering employee adaptability, resilience, and productivity. The study expands present literature by presenting a multi-faceted view of linkage between EI and JP in realm of public banking. Additionally, it presents practical recommendations for HR management, highlighting need to incorporate EI into training, performance management, and organizational policies to maximize efficiency.
MXenes, a rapidly expanding family of two-dimensional transition metal carbides and nitrides, have emerged as promising materials for hospital-on-chip (HoC) diagnostics because of their high electrical conductivity, chemically tunable surfaces, and versatile biofunctionalization. Despite substantial advances in device performance, the molecular origins of signal generation, selectivity, and long-term stability remain poorly defined. This review establishes coordination chemistry as a mechanistic framework for interpreting MXene-based biosensing, highlighting how metal–ligand interactions at the biointerface can govern the analytical performance alongside intrinsic electronic conductivity. Ligand-field effects, hard–soft acid–base (HSAB) principles, redox-active coordination environments, and coordination-mediated charge transfer were examined in relation to biomolecular recognition, interfacial electron transfer, and signal transduction. Particular emphasis is placed on the influence of surface terminations (–O, –OH, –F, and –Cl), defect-associated metal sites, and dynamic ligand exchange on sensitivity, selectivity, antifouling behavior, signal fidelity, and operational stability. Coordination-engineered architectures, including MXene–metal nanoparticle hybrids, metalloporphyrin- and phthalocyanine-functionalized MXenes, MXene–metal–organic framework heterostructures, and assemblies incorporating metalloenzymes, aptamers, and antibodies, have been evaluated across electrochemical, optical, field-effect transistor, photoelectrochemical, and piezoelectric sensing platforms. Coordination-controlled nanozyme catalysis, oxidative degradation, biofouling, and interfacial electron transfer pathways are further considered in the context of device reliability and clinical translation. Oxidative instability, heterogeneous surface chemistry, limited clinical validation, and the absence of standardized manufacturing processes remain major barriers to implementation. Emerging strategies for next-generation MXene-enabled HoC diagnostics include ligand-programmable interfaces, single-atom coordination sites, metalloprotein-inspired biointerfaces, computational ligand field engineering, and artificial intelligence-assisted materials discovery.
