Published 2026 | Version v3
Dataset Open

Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties

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

Description

Data for: 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

README.txt

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