Mapping Synthesis–Structure-Performance Relationships: A Data-Driven Framework for Optimizing Ni Electrodeposition in Non-Enzymatic Glucose Sensors

Although nonenzymatic glucose sensors based on nickel (Ni) nanostructures offer high performance and stability for wearable electronic platforms, a comprehensive understanding linking specific electrochemical synthesis parameters to the resulting nanostructure morphology and final electronic performance remains elusive. The objective of this work is to address this critical gap by performing a precision-guided statistical investigation that quantitatively correlates the synthesis conditions with sensor function. Three different electrochemical deposition methods (cyclic voltammetry, galvanostatic, and potentiostatic) were applied to deposit Ni nanostructures onto three different carbon substrates (glassy carbon, CNTs, and rGO). The analytical performance of 27 unique sensor configurations was rigorously evaluated using a weighted normalized Composite Performance Index (CPI), enabling a data-driven ranking and identification of the optimal electrode design. This statistical ranking converged directly with the FESEM morphological analysis, confirming that the nucleation-growth balance is the key determinant of superior electrocatalytic activity. The top-ranked electrode configuration was then successfully integrated onto a flexible textile substrate, demonstrating a stable performance suitable for practical wearable applications. This methodology establishes a transferable, rational fabrication framework for electroactive electronic materials, minimizing empirical trial-and-error and accelerating the development of next-generation sensing devices.