Abstract
The LLNL Automized Surface Titration Model (L-ASTM) is a community data-driven surface complexation
modeling workflow for simulating potentiometric titration of mineral surfaces. The model accepts raw
experimental potentiometric titration data formatted in a findable, accessible, interoperable, and reusable
(FAIR) structure. The workflow was coded in Python and coupled to PHREEQC for surface complexation
modeling and PEST for data fitting and parameter estimation.
- Developers:
-
Solchan, Han [1] ; Chang, Elliot [1] ; Zavarin, Mavrik [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2023-09-29
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 1.0
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 115054
- Site Accession Number:
- LLNL-CODE-856359
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Solchan, Han, Chang, Elliot S., and Zavarin, Mavrik.
LLNL Automized Surface Titration Model.
Computer Software.
https://github.com/LLNL/LLNL-ASTM.
USDOE National Nuclear Security Administration (NNSA).
29 Sep. 2023.
Web.
doi:10.11578/dc.20231030.1.
Solchan, Han, Chang, Elliot S., & Zavarin, Mavrik.
(2023, September 29).
LLNL Automized Surface Titration Model.
[Computer software].
https://github.com/LLNL/LLNL-ASTM.
https://doi.org/10.11578/dc.20231030.1.
Solchan, Han, Chang, Elliot S., and Zavarin, Mavrik.
"LLNL Automized Surface Titration Model." Computer software.
September 29, 2023.
https://github.com/LLNL/LLNL-ASTM.
https://doi.org/10.11578/dc.20231030.1.
@misc{
doecode_115054,
title = {LLNL Automized Surface Titration Model},
author = {Solchan, Han and Chang, Elliot S. and Zavarin, Mavrik},
abstractNote = {The LLNL Automized Surface Titration Model (L-ASTM) is a community data-driven surface complexation
modeling workflow for simulating potentiometric titration of mineral surfaces. The model accepts raw
experimental potentiometric titration data formatted in a findable, accessible, interoperable, and reusable
(FAIR) structure. The workflow was coded in Python and coupled to PHREEQC for surface complexation
modeling and PEST for data fitting and parameter estimation.},
doi = {10.11578/dc.20231030.1},
url = {https://doi.org/10.11578/dc.20231030.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20231030.1}},
year = {2023},
month = {sep}
}