STATISTICAL CHARACTERIZATION AND MULTIVARIATE MODELING OF ELECTROCHEMICAL POLARIZATION RESPONSE VARIABILITY
DOI:
https://doi.org/10.69980/y9gqaa47Keywords:
electrochemical polarization, statistical modeling, numerical characterization, principal component analysis, response variabilityAbstract
Electrochemical polarization curves encode quantitative data about response size, response dispersion, integrated response behavior and gradient characteristics, but these attributes are most often studied individually instead of in an integrated statistical framework. This study aimed to characterize polarization-response variability across experimental conditions and identify the dominant multivariate structure of curve-derived numerical features. A total of 955 polarization curves across five NaCl concentration and scan-rate conditions were analyzed. Descriptive statistics, Kruskal-Wallis test with epsilon-squared effect sizes, log-transformation regression modeling, principal component analysis, and sensitivity analysis (with ≥95% curve completeness) were analyzed. All principal characteristics differed significantly across experimental conditions (p<0.001), with epsilon-squared values ranging from 0.257 to 0.489. Mean absolute slope showed the strongest between-condition separation and the highest regression fit (R2=0.509). PC1 explained 90.583% of the total variance, while PC1 and PC2 jointly accounted for 97.274%. Sensitivity analysis produced comparable statistical and multivariate patterns. The results show that numerical feature extraction combined with inferential, regression, and multivariate methods is a concise and powerful approach to characterise the variability in electrochemical polarization-response.
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