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PourPy - A python package to generate potential-pH diagrams

Authors: Korber, Anja; Furcas, Fabio E.; Pundir, Mohit; Kammer, David S.; Angst, Ueli M.;

PourPy - A python package to generate potential-pH diagrams

Abstract

PourPy is an open-source Python package for generating thermodynamic stability diagrams of solid phases and complexes in aqueous electrolytes. These so-called Pourbaix diagrams provide valuable information about the reactivity of chemical elements and compounds as a function of the electrochemical potential and the pH. In the context of corrosion science, environmental and process engineering, Pourbaix diagrams are useful to predict the reactivity of aqueous complexes, the passivation behaviour of metals, and the electrochemical stability of the aqueous electrolyte. PourPy is a tool enabling users to inspect the reactivity of aqueous systems under full control of all chemical species considered. Users can define custom reactive systems containing multiple solid, aqueous and gaseous species thereof and build all (electro)chemical reactions to be considered. The package provides additional functionality to perform basic manipulations on the thermodynamic parameters associated with each chemical component, change the system's reference electrode as well as calculate the number of phases stable across a given potential-pH space or at discrete values.

Related Organizations
Keywords

pH, Thermodynamics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average