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Related Concept Videos

Factors Affecting Solubility04:01

Factors Affecting Solubility

Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
Solubility Equilibria: Overview01:09

Solubility Equilibria: Overview

When a substance such as sodium chloride is added to water, it dissolves, forming an aqueous solution. The extent of dissolution is called solubility. The process of dissolution can exist in equilibrium, just like other chemical processes. Solubility equilibria are also called precipitation equilibria because the process of solubility can be reversible. The reverse of the solubility process is called precipitation.
Solubility is important in biological and environmental processes. A notable...
Solubility Equilibria03:07

Solubility Equilibria

Solubility equilibria are established when the dissolution and precipitation of a solute species occur at equal rates. These equilibria underlie many natural and technological processes, ranging from tooth decay to water purification. An understanding of the factors affecting compound solubility is, therefore, essential to the effective management of these processes. This section applies previously introduced equilibrium concepts and tools to systems involving dissolution and precipitation.
The...
Entropy and Solvation02:05

Entropy and Solvation

The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ ≥ 15); an...
Chemical and Solubility Equilibria02:21

Chemical and Solubility Equilibria

The free energy change associated with dissolving a solute in a liter of solvent is called the free energy of a solution, ΔGsolution. The overall ΔGsolution is expressed as the balance of ΔGinteraction against the always-favorable free-energy of mixing, ΔGmixing. Solution formation is favorable if  ΔGsolution is less than zero, whereas it is unfavorable if ΔGsolution is greater than zero. In short, for a solution to form and complete dissolution to take place, the Gibbs energy change must be...
Solubility03:00

Solubility

Solution, Solubility, and Solubility Equilibrium
A solution is a homogeneous mixture composed of a solvent, the major component, and a solute, the minor component. The physical state of a solution—solid, liquid, or gas—is typically the same as that of the solvent. Solute concentrations are often described with qualitative terms such as dilute (of relatively low concentration) and concentrated (of relatively high concentration).
In a solution, the solute particles (molecules, atoms, and/or ions)...

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Robust modelling of solubility in supercritical carbon dioxide using Bayesian methods.

Anna Tarasova1, Frank Burden, Johann Gasteiger

  • 1CSIRO Molecular & Health Technologies, Private Bag 10, Clayton South MDC, Clayton, Victoria 3168, Australia.

Journal of Molecular Graphics & Modelling
|January 12, 2010
PubMed
Summary

Predicting organic compound solubility in supercritical carbon dioxide (scCO2) was improved using a non-linear Bayesian method. This approach offers better predictive models than linear methods for solubility in scCO2.

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Area of Science:

  • Computational chemistry
  • Physical chemistry
  • Chemical engineering

Background:

  • Solubility prediction is crucial for designing separation processes.
  • Supercritical carbon dioxide (scCO2) is an environmentally friendly solvent.
  • Accurate predictive models are needed for solubility in scCO2.

Purpose of the Study:

  • To develop and compare predictive models for solubility of organic compounds in scCO2.
  • To evaluate sparse Bayesian methods for quantitative structure-property relationship (QSPR) modeling.
  • To assess the performance of linear and non-linear descriptor selection.

Main Methods:

  • Applied two sparse Bayesian methods: Multiple Linear Regression Expectation Maximization (MLREM) and Bayesian Regularized Artificial Neural Network with Laplacian Prior (BRANNLP).
  • Utilized a dataset of organic dyes and polycyclic aromatic compounds.
  • Validated models using a randomly selected test set.

Main Results:

  • The non-linear BRANNLP method significantly outperformed MLREM and Multiple Linear Regression (MLR) in predictive accuracy.
  • MLREM showed similar predictivity to the less sparse MLR method.
  • BRANNLP simultaneously identified relevant descriptors and built a robust non-linear QSPR model.

Conclusions:

  • Non-linear Bayesian approaches, like BRANNLP, are superior for modeling solubility in scCO2.
  • BRANNLP offers a powerful tool for QSPR development, integrating descriptor selection and robust modeling.
  • Understanding the differences between linear and non-linear descriptor selection is key for accurate solubility predictions.