EPFL - École Polytechnique Fédérale de Lausanne

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Published August 5, 2025 | Version v1
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Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties

Authors/Creators

  • 1. ROR icon École Polytechnique Fédérale de Lausanne
  • 2. ROR icon Tongji University

Description

This dataset supports the findings reported in the manuscript titled:
Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties.

Understanding the kinetics of manganese oxide reduction is critical for redox processes in soils and sediments. In this study, we developed a thermodynamic framework to predict manganese oxide reactivity.

This dataset includes:

  • Raw UV–Vis spectra collected from manganese oxide reduction experiments

  • Deconvoluted absorbance profiles used to calculate reduction rates

  • Analyzed reduction rates and thermodynamic parameters

  • MATLAB and R scripts for spectral deconvolution and kinetic modeling

The data were used to determine initial reduction rates and correlate them with thermodynamic driving forces and structural properties of manganese oxides. Users can reuse the provided scripts to reproduce the deconvolution results and model fits reported in the manuscript.

Files

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