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Processes
Special Issues
Ionic Liquids: Modeling, Design and Applications
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Ionic Liquids: Modeling, Design and Applications

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 1772

Special Issue Editors

Dr. Yuqiu Chen

E-Mail Website
Guest Editor
Chemical and Biomolecular Engineering Department, University of Delaware, Newark, DE 19716, USA
Interests: ionic liquid; process systems engineering; thermodynamics data-driven approaches; life-cycle assessment
Prof. Dr. Xiaoyan Ji

E-Mail Website
Guest Editor
Division of Energy Science, Energy Engineering, Luleå University of Technology, Luleå, Sweden
Interests: CO2 capture/separation; CO2 electrochemical conversion; theoretical modeling; process simulation and evaluation

Special Issue Information

Dear Colleagues,

The distinctive characteristics and diverse applications of ionic liquids position them as an appealing subject for research and development across various scientific and industrial domains. Despite their potential, the ionic liquid community faces several challenges hindering the exploration and development of these unique solvents for diverse applications. To address these challenges and promote the utilization of ionic liquids in innovative and sustainable manners, collaborative endeavors involving researchers, industries, and regulatory bodies are imperative. This Special Issue serves as a platform for researchers and scientists to share innovative research ideas, fostering the realization of the complete potential of ionic liquids in modeling, design and applications. The topics covered include, but are not limited to, the following:

  1. Thermodynamic modeling of ionic liquids;
  2. Molecular dynamics simulation of ionic liquids;
  3. Ionic liquid solvent design;
  4. Ionic liquid-based process design;
  5. Ionic liquids in CO2 capture;
  6. Ionic liquids in electrolyte chemistry.

Dr. Yuqiu Chen
Prof. Dr. Xiaoyan Ji
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • thermodynamic modeling of ionic liquids
  • molecular dynamics simulation of ionic liquids
  • ionic liquid solvent design
  • ionic liquid-based process design
  • ionic liquids in CO2 capture
  • ionic liquids in electrolyte chemistry

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Published Papers (1 paper)

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Research

26 pages, 5128 KiB  
Article
Modeling Study on Heat Capacity, Viscosity, and Density of Ionic Liquid–Organic Solvent–Organic Solvent Ternary Mixtures via Machine Learning
by You Shu, Lei Du, Yang Lei, Shaobin Hu, Yongchao Kuang, Hongming Fang, Xinyan Liu and Yuqiu Chen
Processes 2024, 12(7), 1420; https://doi.org/10.3390/pr12071420 - 7 Jul 2024
Cited by 2 | Viewed by 1285
Abstract
Physicochemical properties of ionic liquids (ILs) are essential in solvent screening and process design. However, due to their vast diversity, acquiring IL properties through experimentation alone is both time-consuming and costly. For this reason, the creation of prediction models that can accurately forecast [...] Read more.
Physicochemical properties of ionic liquids (ILs) are essential in solvent screening and process design. However, due to their vast diversity, acquiring IL properties through experimentation alone is both time-consuming and costly. For this reason, the creation of prediction models that can accurately forecast the characteristics of IL and its mixtures is crucial to their application. This study proposes a model for predicting the three important parameters of the IL-organic solvent–organic solvent ternary system: density, viscosity, and heat capacity. The model incorporates group contribution (GC) and machine learning (ML) methods. A link between variables such as temperature, pressure, and molecular structure is established by the model. We gathered 2775 viscosity, 6515 density, and 1057 heat capacity data points to compare the prediction accuracy of three machine learning methods, namely, artificial neural networks (ANNs), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). As can be observed from the findings, the ANN model produced the best results out of the three GC-based ML methods, even though all three produced dependable predictions. For heat capacity, the mean absolute error (MAE) of the ANN model is 1.7320 and the squared correlation coefficient (R2) is 0.9929. Regarding viscosity, the MAE of the ANN model is 0.0225 and the R2 is 0.9973. For density, the MAE of the ANN model is 7.3760 and the R2 is 0.9943. The Shapley additive explanatory (SHAP) approach was applied to the study to comprehend the significance of each feature in the prediction findings. The analysis results indicated that the R-CH3 group of the ILs, followed by the imidazolium (Im) group, had the highest impact on the heat capacity property of the ternary system. On the other hand, the Im group and the R-H group of ILs had the most effects on viscosity. In terms of density, the Im group of the ILs had the greatest effect on the ternary system, followed by the molar fraction of the organic solvent. Full article
(This article belongs to the Special Issue Ionic Liquids: Modeling, Design and Applications)
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