Lab-created conductive filament based on nickel and graphite particles: An attractive material for the additive manufacture of enhanced electrochemical sensors for non-enzymatic and selective glucose sensing.

Developing tailor-made conductive filaments has emerged as a promising niche for producing affordable and high-performance 3D-printed electrochemical sensors. In this context, we propose a novel conductive filament based on graphite, nickel, and polylactic acid (G/Ni/PLA) for the fabrication of non-enzymatic electrochemical sensors aimed at glucose (GLU) determination, a key biomarker in diabetes diagnosis. The materials were thoroughly characterized using morphological, structural, elemental, and electrochemical techniques, which confirmed the effective incorporation of G and Ni into the thermoplastic matrix. Special emphasis was placed on the electrochemical conversion of Ni2⁺ in an alkaline medium (0.1 mol L⁻1 NaOH) into redox-active species (Ni(OH)₂ and NiOOH), which mediate the electrocatalytic oxidation of GLU. Additionally, the influence of varying nickel contents (7.5 %, 10 %, and 12.5 % wt.) on the electrochemical response towards GLU was systematically investigated, with the best performance observed at the highest nickel loading. The innovative 3D-printed G/Ni/PLA sensor was integrated with a batch injection analysis (BIA) system for rapid and sensitive amperometric detection of GLU in artificial biological fluids. The sensor demonstrated a wide linear range (50-1500 μmol L⁻1), a low detection limit (2.6 μmol L⁻1), excellent repeatability (RSD < 9.0 %), and high selectivity, even in the presence of potential interferents such as urea, uric acid, and ascorbic acid. Furthermore, the method was successfully applied to analyze synthetic saliva (a non-invasive sample matrix) and blood plasma under normal and abnormal GLU levels, achieving satisfactory recovery rates ranging from 93 % to 100 %. Therefore, the proposed analytical approach is simple, selective, precise, and accurate, making it highly suitable for non-enzymatic GLU sensing in clinical samples, contributing to the effective diagnosis of diabetes.