Dimensionally and morphologically driven effects of nanomaterials on the enhancement of electroanalytical sensing performance: A review

Electrochemical sensors are widely used in environmental monitoring, clinical diagnostics, and food-safety assessment because they can support rapid analysis, relatively low-cost instrumentation, and sensitive detection when sensing interfaces and recognition strategies are appropriately designed. Bare electrodes, however, may show slow electron-transfer kinetics, limited electroactive area, or insufficient stability. Incorporating nanomaterials can address these constraints by modifying charge transport, active-site accessibility, analyte accumulation, and interfacial chemistry. This review uses dimensionality and morphology as operational descriptors to examine how zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D)/hierarchical architectures influence structure-dependent electroanalytical behavior. 0D materials provide discrete catalytic or redox-active sites and, in selected nanoclusters and quantum dots, size-dependent electronic effects. 1D architectures can support directional or percolated charge transport, subject to orientation, connectivity, junction resistance, and electrode adhesion. 2D materials provide accessible interfaces, exposed basal, edge, or defect sites, and tunable surface chemistry, including selected ultrathin nanosheets and nanofilms. 3D/hierarchical architectures regulate analyte-accessible reaction volumes and spatially partition functions: hollow and nanoporous structures organize internal transport, core–shell structures provide radial partitioning, and Janus interfaces provide lateral or anisotropic partitioning. Representative studies are compared using unified analytical and structure-related criteria, while experimentally supported mechanisms are distinguished from plausible structure-based interpretations. The resulting trends are discussed across environmental, food, and biomedical applications without treating dimensionality as a universal performance ranking. Despite substantial advances in nanoscience, challenges remain in colloidal and structural stability, reproducible fabrication, complex-matrix interference, scalability, and device integration. Future progress will require mechanistically supported interface design, greener and more reproducible manufacturing, and experimentally validated artificial intelligence (AI)- or machine learning (ML)-assisted workflows for materials optimization and signal interpretation. Future progress will require mechanistically supported interface design, greener, more reproducible manufacturing, and clearer integration of artificial intelligence/machine learning (AI/ML) approaches, in which transferable materials-design precedents are distinguished from experimentally validated electrochemical sensing and signal-interpretation workflows.