Statistical Framework for Multiresponse Analyses: Comparative Assessment of Surface Activation Strategies for 3D-Printed Carbon Black/Polylactic Acid Electrodes toward Electrochemical (Bio)Sensing Applications

To address this gap, the present work introduces a statistical framework for multiresponse comparison, applicable to any experimental system requiring simultaneous evaluation of multiple variables, and demonstrates its use through a case study on 3D-printed carbon black/polylactic acid (CB/PLA) electrodes fabricated via fused deposition modeling (FDM). Electrodes subjected to chemical, electrochemical, and combined activations were evaluated using the [Fe(CN)₆]^3–/4– redox probe.

A multiresponse approach was employed, using a weighted performance function (<i>L</i>) to aggregate normalized electrochemical variables (anodic peak current, peak current ratio, peak-to-peak separation, and charge transfer resistance). This function was further converted into a risk-adjusted score (<i>L</i>_99%), penalizing variability and enabling robust statistical analysis. Alkaline treatments proved the most effective activation route.

The best condition combined 3.0 mol L^–1 NaOH for 30 min with subsequent electrochemical activation in 0.1 mol L^–1 NaOH, although the simpler chemical treatment alone offered similar performance, making it more practical for future optimization. Beyond identifying effective activation protocols for 3D-printed electrodes, this work provides a versatile statistical framework for multiresponse analyses that can serve as a practical guide for the objective comparison and optimization of diverse systems, including filament development, aging assessment, nanomaterial integration, bioreceptor immobilization, and any application involving the simultaneous evaluation of multiple variables.