CHNOSZ: Thermodynamic calculations and diagrams for geochemistry

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Authors JanniceA. M. Dick
Journal/Conference Name Front. Earth Sci.
Paper Category
Paper Abstract Thermodynamic calculations are an essential tool for many areas of geochemistry. Thermodynamics provides a framework for the quantitative description and prediction of the solubilities and relative stabilities of different minerals, metal transport in hydrothermal fluids, and geobiochemical reactions that drive microbial metabolism and contribute to the compositional variation of proteins. Accessible and up-to-date software and databases are important for the development and reproducible application of thermodynamic models. CHNOSZ is a free package for R that has been frequently updated since its first release in 2008. The package provides an integrated set of functions to calculate the standard molal thermodynamic properties and chemical affinities of reactions. It uses the graphical capabilities of R to produce high-quality chemical activity diagrams for aqueous species and predominance diagrams including Eh-pH (Pourbaix) and logfO2-T diagrams. The extensive database utilizes the well-known revised Helgeson-Kirkham-Flowers (HKF) equations for aqueous species. Recent additions to the database include the Berman equations for minerals, the Deep Earth Water model, which extends the applicability of the HKF equations to higher pressures, and the Akinfiev-Diamond model for aqueous nonelectrolytes. The package comes with many types of documentation, including technical help pages with short code examples, longer code demos, and in-depth vignettes combining code, text and graphics, giving users a wide array of starting points for their own research. This paper provides a concise overview of the package and illustrates the new features using examples selected from the literature. Although the package does not provide a complete chemical speciation model, numerous examples from the package demonstrate the ease of reproducing selected published calculations and sometimes identifying issues with existing datasets and models.
Date of publication 2019
Code Programming Language R

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