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Development of a quantitative structure-property relationship model for predicting the electrophoretic mobilities.
Qianfeng Li1, Lijun Dong, Runping Jia
1Department of Chemistry, Lanzhou University, PR China.
Computers & Chemistry
|March 1, 2002
Summary
A new artificial neural network model accurately predicts electrophoretic mobility (mu0) for aliphatic carboxylates and amines. This model utilizes molecular weight, volume, charge code, and pK values for precise separation analysis in capillary electrophoresis.
Area of Science:
- Analytical Chemistry
- Physical Chemistry
Background:
- Electrophoretic mobility (mu0) is crucial for solute separation in capillary zone electrophoresis.
- Accurate prediction of mu0 is essential for optimizing separation techniques.
Purpose of the Study:
- To develop a novel model for estimating the electrophoretic mobility of aliphatic carboxylates and amines.
- To utilize simpler experimental properties as input for predicting complex electrophoretic behavior.
Main Methods:
- A multilayer neural network model was constructed using the extended delta-bar-delta (EDBD) algorithm.
- Input parameters included molecular weight (W), molecular volume (V), acid/base charge code (+1 or -1), and pK value.
- Neural network architecture and learning times were optimized for performance.
Main Results:
- The optimized artificial neural networks (ANNs) demonstrated excellent prediction capabilities for electrophoretic mobility.
- The model successfully correlated simpler molecular properties with complex electrophoretic behavior.
Conclusions:
- The developed ANN model provides a robust and accurate method for predicting electrophoretic mobility.
- This approach can enhance the efficiency and precision of capillary zone electrophoresis separations.