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Related Experiment Video

Updated: Jun 28, 2026

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Optimization of Crystal Growth for Neutron Macromolecular Crystallography

Published on: March 13, 2021

An improved computer model of struvite solution chemistry.

T J Wrigley¹, W D Scott, K M Webb

¹Environmental Science, School of Biological and Environmental Sciences, Murdoch University, Murdoch 6150, Western Australia Australia.

|December 1, 1992

Summary

This study enhanced a computer model for struvite solution chemistry, improving calculation speed and accuracy. The refined model better predicts struvite formation, crucial for understanding its environmental and industrial applications.

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Automated Protocols for Macromolecular Crystallization at the MRC Laboratory of Molecular Biology

Automated Protocols for Macromolecular Crystallization at the MRC Laboratory of Molecular Biology

Published on: January 24, 2018

Area of Science:

Environmental Chemistry
Computational Chemistry
Materials Science

Background:

Struvite (magnesium ammonium phosphate) precipitation is significant in various environmental and industrial processes.
Accurate modeling of struvite solution chemistry is essential for predicting and controlling its formation.
Previous models had limitations in speed, scope, and predictive accuracy.

Purpose of the Study:

To improve the existing computer model for struvite solution chemistry.
To enhance the model's efficiency and incorporate additional chemical species.
To achieve a more precise fit with experimental data.

Main Methods:

Utilized ammonia as the primary calculation point in the liquid phase for a smaller, faster algorithm.
Incorporated distilled magnesium hydrogen phosphate to increase solution concentrations.
Included estimates of activity coefficients and association constants.
Employed the symbolic manipulator Maple for flexible modeling and inclusion of all possible species, including associated ammonium phosphates.

Main Results:

Achieved a marginal 5-10% improvement in the overall fit of the model.
Significantly improved the relative standard deviation of the fit from 0.5 to 0.2 for both Taylor's and Webb's data.
Demonstrated the model's enhanced ability to predict solution concentrations and species interactions.

Conclusions:

The enhanced computer model provides a more accurate and efficient representation of struvite solution chemistry.
The flexible modeling approach allows for easy inclusion of various chemical species, increasing predictive power.
The improved fit with experimental data validates the model's enhanced capabilities for scientific and industrial applications.