Authors: Jon Ustarroz | Daniel Torres | Souheil Mourtada | A. P. Leontiev
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Chemistry — Electrochemistry

<i>(Invited)</i> Data-Driven Approaches and Analytical Modelling to Study Nucleation and Growth by Local Electrochemistry and Correlative Electron Microscopy Electrochemical nucleation and growth (EN&amp;G) are essential to optimize nanostructure electrodeposition methods, specifically these where a precise control of the size, composition and structure is paramount [1]. Despite extensive studies, a comprehensive mechanistic understanding remains elusive. This is due to the random nature of initiation events (nucleation), the heterogeneity of surfaces and (very) fast kinetics across several length scales. Consequently, establishing precise correlations between local surface properties, electrochemical driving force, and nucleation events remains challenging [2]. This process is typically studied using electrochemical characterization combined with ex-situ (post-mortem) analysis of the resulting deposits. When performed on TEM-compatible substrates, this standard approach has provided valuable insights into non-classical growth pathways—key to controlling the size, shape, and composition of nanostructured materials [3]. However, it falls short in capturing the effects of surface heterogeneity, as well as the dynamic processes occurring before, during, and after nucleation. In recent years, our group has addressed these challenges by integrating high-throughput local electrochemistry via SECCM with ex-situ/in-situ electron microscopy and data-centric analysis to study metal nucleation on carbon surfaces [4–5]. This method allows the measurement of electrochemical data corresponding to the growth of a limited number of nanoparticles (NPs) and a direct 1-to-1 correlation with the properties of the deposit. In this contribution, we first show how both overpotential and surface state significantly influence the probability of nucleation. The dispersion in nucleation kinetics evolves with overpotential and surface preparation. Our derived analytical model successfully fits experimental current transients, revealing an excellent correlation between the number of active sites inferred from the model and the number of nanoparticles observed by FESEM. [6] This approach allows us to introduce a distribution-based framework to quantify the variability of nucleation and growth rates across the surface. The latter allows linking local activity with macroscopic responses, offering a pathway to integrate stochastic and deterministic aspects of EN&amp;G. Second, we extend this approach by automatically electrodepositing arrays of alloy NPs onto TEM-compatible working electrodes. To extract the maximum information from these large datasets, automated data analysis is used based on (unsupervised) machine learning approaches [7]. This includes dimensionality reduction by principal component analysis, dynamical time warping or K-means clustering of electrochemical signals (e.g. cyclic voltammetry and chronoamperometry curves), particle detection, as well as correlation between the electrochemical and surface datasets. Furthermore, we demonstrate here that an automated and unbiased approach is a valuable tool to differentiate between intrinsic (potential, scan rate) and external/uncontrolled experimental factors influencing NPs nucleation and growth. References: [1] Bernal, M. et al. Current Opinion in Electrochemistry 27, 100688 (2021). [2] Ustarroz, J. Current Opinion in Electrochemistry 19, 144–152 (2020). [3] Ustarroz, J et al. Journal of the American Chemical Society 135, 11550–11561 (2013). [4] Bernal, M. et al. Electrochimica Acta 445, 20, 142023 (2023). [5] Torres, D. et al. Journal of the Electrochemical Society 169, 102513 (2022). [6] Torres, D, et al. Small Methods 2401029 (2024). [7] Mourtada, S et al. Manuscript in Preparation Nucleation ; Overpotential ; Nanostructure ; Electrochemistry ; Characterization (materials science) ; Dispersion (optics) ; Nanoparticle Electrochemical Analysis and Applications ; Electrodeposition and Electroless Coatings ; Advanced Electron Microscopy Techniques and Applications