Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Datasets and Code
  4. Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties
 
dataset

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

Xinru, Liu
•
Pothanamkandathil, Vineeth
•
Schwab, Lorenz  
Show more
August 5, 2025
Zenodo

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

  • Details
  • Metrics
Type
dataset
DOI
10.5281/zenodo.16746936
ACOUA ID

803ea149-7789-4d6c-ad9f-cc2885dad909

Author(s)
Xinru, Liu
Pothanamkandathil, Vineeth

École Polytechnique Fédérale de Lausanne

Schwab, Lorenz  

École Polytechnique Fédérale de Lausanne

Mao, Shun

Tongji University

Aeppli, Meret  

EPFL

Date Issued

2025-08-05

Version

v1

Publisher

Zenodo

License

CC BY

Subjects

redox kinetics

•

thermodynamics

•

free energy relationship

•

manganese oxide

•

surface energy

•

extracellular electron shuttle

•

reductive dissolution

•

anaerobic respiration

EPFL units
SOIL  
FunderFunding(s)Grant NO

China Scholarship Council

RelationRelated workURL/DOI

IsVersionOf

https://doi.org/10.5281/zenodo.16746935

IsSupplementTo

https://doi.org/10.26434/chemrxiv-2025-5w8sg
Available on Infoscience
August 8, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/252839
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés