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Related Concept Videos

Ion-Exchange Chromatography01:09

Ion-Exchange Chromatography

Ion-exchange chromatography, or IEC, is a technique for separating ions based on their affinity for the stationary phase. The stationary phase is a cross-linked polymer resin with covalently attached ionic functional groups. The functional groups can be either positively charged (cation exchangers) or negatively charged (anion exchangers). A cation exchanger consists of a polymeric anion and active cations, while an anion exchanger is a polymeric cation with active anions. The choice of...
Gas Chromatography: Introduction01:13

Gas Chromatography: Introduction

Gas chromatography (GC) is a technique for separating and analyzing volatile compounds in a sample. Its primary purpose is to identify and quantify components in complex mixtures, making it essential in fields such as environmental analysis, pharmaceuticals, and petrochemicals. GC is also called vapor-phase chromatography (VPC) or gas-liquid partition chromatography (GLPC).
In GC,  a sample is vaporized and mixed with an inert carrier gas (the mobile phase), which transports it through a column.
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
Gas Solubility01:31

Gas Solubility

Gas solubility in liquids forms liquid-gas solutions, such as soft drinks, where carbon dioxide is dissolved in water, and the ocean, where the solubility of oxygen and carbon dioxide supports marine life. The ability of oceans to dissolve gases impacts weather conditions in the troposphere.However, gas-liquid interactions vary. For instance, hydrogen chloride gas is highly soluble in water, while oxygen's solubility is much lower. Because these solutions are non-ideal, Raoult’s law, which...

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Related Experiment Video

Updated: Jul 16, 2026

Development, Characterization, and Evaluation of CAGE-based Ionic Liquid Systems for Transdermal Delivery
09:44

Development, Characterization, and Evaluation of CAGE-based Ionic Liquid Systems for Transdermal Delivery

Published on: September 26, 2025

Machine Learning for Gas Capture in Ionic Liquids: Current Status and Future Trends.

Guocai Tian1, Zhiqiang Hu1, Ranran Geng1

  • 1State Key Laboratory of Complex Non-Ferrous Metal Resource Clean Utilization, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Molecules (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Machine learning accelerates the prediction of gas solubility in ionic liquids, crucial for developing greener carbon capture technologies. This review details ML models for predicting solubility of gases like CO2, aiding ionic liquid design.

Keywords:
QSARgas capturegreen solventsionic liquidsmachine learningsolubility

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Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature
11:04

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

Published on: December 20, 2016

Related Experiment Videos

Last Updated: Jul 16, 2026

Development, Characterization, and Evaluation of CAGE-based Ionic Liquid Systems for Transdermal Delivery
09:44

Development, Characterization, and Evaluation of CAGE-based Ionic Liquid Systems for Transdermal Delivery

Published on: September 26, 2025

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature
11:04

Synthesis of Ionic Liquid Based Electrolytes, Assembly of Li-ion Batteries, and Measurements of Performance at High Temperature

Published on: December 20, 2016

Area of Science:

  • Green Chemistry and Materials Science
  • Computational Chemistry and Data Science

Background:

  • Ionic liquids are promising green solvents for gas solubility applications, including carbon capture and industrial gas purification.
  • The vast number of potential ionic liquid combinations poses a significant challenge for experimental screening and traditional simulation methods.
  • Predicting gas solubility in ionic liquids is critical for optimizing their performance in various industrial applications.

Purpose of the Study:

  • To systematically review the advancements in applying machine learning (ML) for predicting gas solubility in ionic liquids.
  • To analyze the classification, modeling processes, and performance of ML models for various gases (CO2, H2S, NH3, SO2, N2O) in ionic liquids.
  • To discuss current challenges and future directions for ML in ionic liquid-based gas capture.

Main Methods:

  • Review and analysis of existing research on machine learning models for gas solubility prediction in ionic liquids.
  • Classification of machine learning approaches used in this domain.
  • Evaluation of model construction and performance metrics for predicting solubility of key industrial gases.

Main Results:

  • Machine learning offers a high-throughput approach to overcome the limitations of traditional methods in screening ionic liquids for gas solubility.
  • Various machine learning models have been developed and show promise in accurately predicting the solubility of gases like CO2, H2S, and NH3.
  • The review summarizes the state-of-the-art in ML-based gas solubility prediction for ionic liquids.

Conclusions:

  • Machine learning is a powerful tool for accelerating the discovery and design of ionic liquids for efficient gas capture.
  • Addressing existing challenges in ML model development and data integration is key for industrial application.
  • Further research is needed to refine ML models and provide theoretical guidance for the directional design of ionic liquids.