This is a collection of deep eutectic solvent (DES) experimental and simulation data that is stored in CML format and analysed using gradient boosting decision trees.
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Created: 20th Aug 2019 at 14:42
Last updated: 23rd Aug 2019 at 10:20
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Projects: Simulation Foundries, CML for thermophysical properties of mixtures, Test Project (dummy), Towards Reproducible Enzyme Modeling
Institutions: University of Stuttgart, Karlsruhe Institute of Technology (KIT)
https://orcid.org/0000-0001-9119-1778Expertise: enzyme kinetics, enzymes, Enzymatic reactions, biochemical enzyme characterization, Biochemistry, molecular simulation, molecular modeling, Programming, Bioinformatics, Computational Biology
Tools: Gromacs, Python, Molecular Dynamics, bash, Biochemistry, Bioinformatics, Biochemistry and protein analysis, Enzyme assay, enzyme kinetics, isothermal titration calorimetry, dynamic light scattering, Spectrophotometry
Polyglot European Scientist. I thrive working in interdisciplinary environments combining the study of enzyme reactions and mechanisms with bioinformatics, molecular modelling, automated data analysis and data stewardship.
A meta-analysis of the impact of water content and temperature on the viscosities of four deep eutectic solvents (glyceline, reline, DEAG, DEACG), their components (choline chloride, urea, glycerol, ethylene glycol), methanol, and pure water was performed. We analyzed the viscosity data by an automated workflow, using Arrhenius and Vogel–Fulcher–Tammann-Hesse models.
Snapshots: Snapshot 1
This is a collection of data involving choline chloride:glycerol:water mixtures stored in CML.
Snapshots: Snapshot 1, Snapshot 2, Snapshot 3, Snapshot 4
This is a collection of data that have been used to analyse data on deep eutectic solvent mixtures of choline chloride:glycerol:water.
Submitter: Gudrun Gygli
Biological problem addressed: Model Analysis Type
Investigation: Deep Eutectic Solvents
Organisms: No organisms
Models: No Models
SOPs: No SOPs
Data files: CML files, CSV data, Python scripts for CML
Snapshots: No snapshots
Submitter: Gudrun Gygli
Biological problem addressed: Model Analysis Type
Investigation: Deep Eutectic Solvents
Organisms: No organisms
Models: No Models
SOPs: Instructions for using the workflow
Data files: Defining names, Modelling Workflow, Results of modelling analysis, Viscosity data input, Wrapper script
Snapshots: No snapshots
Python script wrapping up (executing) all the steps of the workflow the user enters in it. Together with "names.py" this script requires input files to be located in a folder called "input" in the same directory.
Creators: Gudrun Gygli, Juergen Pleiss, Xinmeng Xu
Submitter: Gudrun Gygli
Investigations: Deep Eutectic Solvents
Studies: Meta-analysis of viscosity of aqueous deep eute...
Assays: VFT and Arrhenius Modelling
Input data (viscosities, in .csv format) for the modelling workflow, collected from literature, see associated publication for details.
Creators: Gudrun Gygli, Xinmeng Xu, Juergen Pleiss
Submitter: Gudrun Gygli
Investigations: Deep Eutectic Solvents
Studies: Meta-analysis of viscosity of aqueous deep eute...
Assays: VFT and Arrhenius Modelling
Resulting data from the modelling workflow, see associated publication.
Creators: Gudrun Gygli, Xinmeng Xu, Juergen Pleiss
Submitter: Gudrun Gygli
Investigations: Deep Eutectic Solvents
Studies: Meta-analysis of viscosity of aqueous deep eute...
Assays: VFT and Arrhenius Modelling
The modelling workflow used on the input data, which leads to the results, see associated publication.
Creators: Gudrun Gygli, Juergen Pleiss, Xinmeng Xu
Submitter: Gudrun Gygli
Investigations: Deep Eutectic Solvents
Studies: Meta-analysis of viscosity of aqueous deep eute...
Assays: VFT and Arrhenius Modelling
Python script allowing the user to define the names of the input files to be used. Together with "wrapper.py" this script requires input files to be located in a folder called "input" in the same directory.
Creators: Gudrun Gygli, Juergen Pleiss, Xinmeng Xu
Submitter: Gudrun Gygli
Investigations: Deep Eutectic Solvents
Studies: Meta-analysis of viscosity of aqueous deep eute...
Assays: VFT and Arrhenius Modelling
A collection of python scripts used to generate CML from csv (CSV_to_CML.py), apply machine learning (Gradient Boosting using decision trees, prediction_viscosity.py), model based on eq 6 in the associated publication, and to plot the generated data (plot*.py).
Creators: Gudrun Gygli, Xinmeng Xu, Juergen Pleiss
Submitters: Gudrun Gygli, Xinmeng Xu
Investigations: Deep Eutectic Solvents
Studies: Choline chloride:glycerol:water mixtures in CML
Assays: CML processing and analysis
Contains CML dictionaries created to store deep eutectic solvent data with CML.
Contains CML created for experimental, simulation and predicted data. Predictions were made based on experimental data using a gradient boosting decision tree.
Creators: Gudrun Gygli, Xinmeng Xu, Juergen Pleiss
Submitters: Gudrun Gygli, Xinmeng Xu
Investigations: Deep Eutectic Solvents
Studies: Choline chloride:glycerol:water mixtures in CML
Assays: CML processing and analysis
Contains exp.csv, a collection of experimental data of CholineChloride:Glycerol:Water mixtures. Contains sim.csv, a collection of molecular dynamics simulation data of CholineChloride:Glycerol:Water mixtures. Contains Modelling_exp_Figure3.csv, a collection of modelled Eeta (energy activation of viscous flow), lnEta0 (viscosity at infinite temperature) values of CholineChloride:Glycerol:Water mixtures, based on experimental data, see the associated publication for details. Contains ...
Creator: Xinmeng Xu
Submitters: Gudrun Gygli, Xinmeng Xu
Investigations: Deep Eutectic Solvents
Studies: Choline chloride:glycerol:water mixtures in CML
Assays: CML processing and analysis
Instructions and details on the data analysis workflow.
Creators: Gudrun Gygli, Juergen Pleiss, Xinmeng Xu
Submitter: Gudrun Gygli
Investigations: Deep Eutectic Solvents
Studies: Meta-analysis of viscosity of aqueous deep eute...
Assays: VFT and Arrhenius Modelling
Abstract
Authors: Xinmeng Xu, Jan Range, Gudrun Gygli, Jürgen Pleiss
Date Published: 12th Mar 2020
Publication Type: Journal
Citation: J. Chem. Eng. Data 65(3):1172-1179