![viscosity chemistry viscosity chemistry](http://www.compoundchem.com/wp-content/uploads/2015/07/Ice-Cream-Chemistry-1024x724.png)
Alba, Daniel Bahamon, Fèlix Llovell, Ana B. Industrial & Engineering Chemistry Research 2021, 60 Data-Driven Ionic Liquid Design for CO2 Capture: Molecular Structure Optimization and DFT Verification.
![viscosity chemistry viscosity chemistry](https://i.pinimg.com/originals/90/df/54/90df54561a6404d01730d4c84a3a4dc2.jpg)
Jingwen Wang, Xudong Tang, Zhiwen Qi, Tao Xu, Ting Zou, Yu Bie, Dengjia Wang, Yanfeng Liu.
![viscosity chemistry viscosity chemistry](https://cdn1.byjus.com/chemistry/2015/12/24120813/Viscocity.jpg)
Industrial & Engineering Chemistry Research 2022, 61 Rational Design and Screening of Ionic Liquid Absorbents for Simultaneous and Stepwise Separations of SO2 and CO2 from Flue Gas.
![viscosity chemistry viscosity chemistry](https://i.pinimg.com/originals/26/23/c7/2623c7328bcdea5e7f5a30cfa9ccc65f.jpg)
#Viscosity chemistry free#
This material is available free of charge via the Internet at.
#Viscosity chemistry full#
Table S1 presenting a detailed summary of the viscosity database, including ranges of P–η– T data, references to literature sources for each data set, and the decision on acceptance/rejection of a given data set in GC-FFANN calculations Table S2 listing functional groups defined within the model and their molecular structures Table S3 containing a full list of weights and biases of the optimized FFANN Table S4 presenting detailed results of calculations using the developed GC model, including values of all the statistical measures given in eqs 13a to 13e for each data set Table S5 showing the comparison between calculations obtained with the GC-FFANN model and those obtained with the Gardas–Coutinho GC model an extensive catalog of ions, where abbreviations, IUPAC preferred names, and chemical formulas are shown separately for almost 900 ions under study and a simple code allowing one to perform calculations using the MATLAB/GNU Octave platform. The results calculated in this work were shown be more accurate than those obtained with the best current GC model for viscosity of ILs described in the literature. The neural network training, validation, and testing processes, involving 90, 5, and 5% of the whole data pool, respectively, gave mean square errors of 0.0334, 0.0595, and 0.0603 log units, corresponding to squared correlation coefficients of 0.986, 0.973, and 0.972 and overall relative deviations at the level of 11.1, 13.8, and 14.7%, respectively. In total, the resulting GC-FFANN model employs 242 GC-type molecular descriptors that are capable of accurately representing the viscosity behavior of ILs composed of 901 distinct ions. The model employs a two-layer feed-forward artificial neural network (FFANN) strategy to represent the relationship between the viscosity and the input variables: temperature, pressure, and group contributions (GCs). The data were critically revised and then used to develop and test a new model allowing in silico predictions of the viscosities of ILs on the basis of the chemical structures of their cations and anions. In this work, over 13 000 data points of temperature- and pressure-dependent viscosity of 1484 ILs were retrieved from more than 450 research papers published in the open literature in the last three decades. A knowledge of various thermophysical (in particular transport) properties of ionic liquids (ILs) is crucial from the point of view of potential applications of these fluids in chemical and related industries.