Spatiotemporal and Statistical Mapping of Transition Metal Equilibria in Alkaline Media
Source abstract
Abstract
Transition metal dissolution and redeposition (D/R) kinetics in alkaline media play a critical role in various chemical and electrochemical processes. Competitive reaction kinetics between different transition metals can modulate individual metal behavior in these processes. To date, these phenomena have remained largely unmeasured, and even when captured, they are difficult to statistically characterize due to their dynamic nature, simultaneous occurrence, and spatially heterogeneous nature. Here, we develop a statistical analysis framework based on in situ and operando X-ray fluorescence microscopy (XFM) to investigate the relative D/R kinetics of multiple transition metals in alkaline media. By employing statistical analysis, we quantify the spatial distribution of D/R species and assess the rate at which the system reaches equilibrium under varying reaction conditions. We show that pH does not simply change the rate of dissolution and redeposition, but reorganizes the cross-element kinetic correlations among Ni, Fe, and Mn and accelerates the spatial equilibration of D/R events, as quantified through correlation analysis, reaction-rate estimation, probability function distributions, and texture-based monitoring statistics. Additionally, we demonstrate how modifying the solvent environment can influence D/R kinetics, providing a pathway for tuning materials synthesis and process optimization. Our study offers valuable insights into the complex interplay between different transition metals and provides a reliable statistical framework for spatial analysis of diverse imaging data sets, enabling deeper extraction of latent information across multiple modalities.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Metal Extraction and Bioleaching · Electrochemical Analysis and Applications · X-ray Spectroscopy and Fluorescence Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemistry
Virginia Tech
Department of Statistics
School of Engineering
Brown University
National Synchrotron Light Source II
Brookhaven National Laboratory
X-ray Science Division
Argonne National Laboratory
Stanford Synchrotron Radiation Lightsource
SLAC National Accelerator Laboratory
Department of Geosciences
Funding
Basic Energy Sciences · National Science Foundation · Virginia Tech College of Science Academy of Data Science Discovery Fund
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