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

Abstract Additive manufacturing using conductive polymeric filaments offers a powerful strategy for fabricating electrochemical (bio)sensors. However, the insulating polymer matrix often limits electron transfer, demanding surface activation to expose conductive fillers and enhance electrochemical performance. Even electrodes made from bespoke filaments may demand activation. While some reagent-free treatments may require automation or careful process control to minimize operator-dependent variability, and biological treatments may involve longer processing times, chemical and electrochemical activations have provided promising results. Yet, the absence of standardized, statistically validated protocols can lead to unnecessarily complex or costly procedures, as optimal treatments are often selected based on isolated electrochemical metrics rather than on a comprehensive statistical assessment. 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 (L) 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 (L99%), 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.