We have established two databases, one for organic solvent nanofiltration (OSN) and another one for nuclear magnetic resonance (NMR) impurities.
The OSN Database is the first repository for membrane separation data obtained in organic solvents published in September 2021. We envision a future where membrane process and material development relies on data-driven design methodologies. The process design part of any membrane separation are optimized and monitored using machine learning models and the new membrane materials are developed based on the collected data. The database offers several predictive tools for free to use: rejection prediction, enantioseparation prediction, and similarity search. Read the corresponding, open-access article introducing the original database and our opinion on data science in the membrane field.
The NMR Impurities Database contains solvents, acids and basis, including the emerging sustainable alternatives, and was established in March 2023. Besides obtaining new data, we compiled the current and previous works as well. The searchable database facilitates impurity identification. This database forms the basis of an online interface through which users can browse solvent spectra and search for signals of unknown origins to easily identify residual impurities in NMR spectra. In the online Python-based application, the NMR database connects to the website using Flask. A search on the main site triggers a query from the database managed by Pandas. Read the corresponding, open-access article introducing the original database in ACS-SCE.
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