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.
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.
This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561–1739 rpm), primary air flow (98.87–351.13 m3/h), and secondary air flow (6–74 m3/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15–18 μm and 18–21 μm. The Pareto results revealed a clear trade-off: CAI decreased from ∼2.30 to ∼1.65 as CS increased slightly in the fine separation regime and stabilized at ∼1.58–1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate–high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.
Neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and multiple sclerosis, remain leading causes of disability and premature death. Although they present with distinct clinical phenotypes, they converge on several pathogenic processes. Among these, mitochondrial dysfunction has emerged as a key driver of neurodegeneration, encompassing impaired bioenergetic capacity, disturbed calcium handling, altered mitochondrial dynamics, insufficient mitophagy, and excessive production of reactive oxygen species (ROS). This review provides a focused synthesis of the ways in which mitochondrial pathology contributes to neurodegeneration across major neurodegenerative disorders and summarizes therapeutic strategies designed to target mitochondria. We outline disease-relevant mitochondrial abnormalities and connect them to neuronal loss, synaptic failure, and neuroinflammatory cascades, with particular attention to mitochondrial ROS and inflammatory signaling linked to mitochondrial DNA. The manuscript further evaluates current and emerging interventions, including mitochondria-targeted antioxidants, mitochondrial transfer/transplantation, exercise, dietary approaches, and nanotechnology-enabled delivery systems. For each strategy, we consider the mechanistic rationale, key preclinical findings, and barriers to translation. Across experimental models, many of these approaches confer measurable neuroprotection—often reflected by lower oxidative burden, stabilization of mitochondrial membrane potential, and partial restoration of ATP production. However, clinical findings have been inconsistent, suggesting that efficacy depends strongly on disease stage, patient heterogeneity, and the specific mitochondrial defect being targeted. By integrating mechanistic insights with therapeutic evidence, this review offers a structured perspective on shared and disease-specific features of mitochondrial dysfunction and highlights priorities for advancing mitochondria-centered interventions toward meaningful clinical benefit.
Nutrition, encompassing vital macro- and micronutrients (including carbs, proteins, fats, vitamins such as D and C, and minerals like iron and calcium), is fundamental to supporting life, fostering healthy ageing, and averting disease. The information on the combined impact of dietary patterns, nutritional content, and lifestyle factors on metabolic health, disease risk, and healthy ageing is summarised in this publication. According to the discussion, both macronutrient and micronutrient surpluses and deficiencies upset metabolic homeostasis, which leads to obesity, insulin resistance, cardiovascular disease, and hepatic steatosis. Dietary habits are one of the three cornerstones of health and longevity, according to biogerontological theories of ageing. The free radical theory emphasises oxidative stress in cellular damage, and the disposable soma hypothesis emphasises the trade-off between reproduction and somatic upkeep. Clinical and epidemiological data indicate that cardiometabolic outcomes, including elevated insulin sensitivity, lipid profiles, and inflammatory markers, are strongly influenced by diet quality, dietary timing (such as time-restricted eating), and overall energy balance. The review also demonstrates how nutrients can control gene expression, signalling pathways, and epigenetic alterations in addition to serving as energy sources. Nutrient availability and disease susceptibility are largely determined by interactions between the gut microbiota, food, and human metabolism. Furthermore, individual reactions to diet are very variable due to genetic, metabolic, and environmental factors, which explains why customised nutrition plans are becoming more and more popular. The findings also show that dietary habits during childhood and adolescence have an impact on long-term disease prevention, and that combining lifestyle treatments (diet and exercise) can consistently reduce the risk of chronic illness. In order to optimise health and wellness over the course of a lifetime, the data generally supports switching from reductionist, single-nutrient dietary methods to integrated, systems-oriented strategies that take into account molecular, clinical, and sociocultural factors.
