A software demonstration will be held each day. Access will be available through our virtual booth.
Digital Strategy for Compositional Identity Profiling
Characterizing a material’s chemical composition is imperative for a variety of industries. Analytical chemists conduct a variety of chromatographic and spectroscopic experiments to detect, identify, and quantify components present in such products. This presentation will describe the identity lifecycle of components—from initial chromatographic detection, to chemical identification, to identity verification—while accounting for use of systematic naming conventions. Moreover, assuring that the component identity lifecycle can be managed digitally will be discussed; from initial analytical data acquisition, to associating interpretation, to applying interpretation to incident materials.Learn More
More efficient method development with chromatographic simulation and 3D optimization
Developing quality methods requires an understanding of the entire chromatographic design space. How do controllable factors, such as gradient, temperature, and pH, affect desired measures, such as resolution? While repeated experiments supply that knowledge, they also consume significant time, manpower, and resources.
Chromatographic simulation can help chromatographers explore the design space thoroughly and select points of optimal performance and robustness. Here, we present a software tool, ACD/Method Selection Suite, that uses chromatographic simulation to assist with method development.
The simulator was used to optimize the separation of three compounds in 3D mode. A 2x2x3 matrix of experimental conditions was designed to vary gradient, temperature, and pH, and a 3D rotating-cube model was produced to visualize predicted resolution at each point. The 3D map also marked areas of poor robustness. While a few of the initial conditions failed to resolve all three compounds, the simulator suggested several conditions that produced good resolution.
The user retains control over the simulator and can adjust settings based upon their chromatographic knowledge. For example, modelling equations can be customized to improve model accuracy, and the success criteria can be modified.
This work demonstrates the simultaneous optimization of 3 chromatographic parameters with only 12 experiments. The use of such software tools can help the chromatographer work with minimal time and resources to develop a method in which they have full confidence. Such tools could be further extended by combining them with other useful chromatographic algorithms like physical-property prediction, in order to simulate ideal conditions for new compounds.Learn More