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Advanced Fluid Characterization - Laboratory Information Management System


The Fluid Characterization Group is developing the advanced fluid characterization laboratory information management system (AFC-LIMS) to support several ongoing projects. The system is designed to harvest, index, and archive research data generated by a variety of analytical instruments across multiple laboratories. By utilizing custom software tools, raw experimental data and equipment metadata will be easily accessible to researchers and can also be utilized for machine learning and data mining.


diagram of lab information management system
In our LIMS data flow, data collected from each instrument will be harvested and aggregated in a network-accessible location. The stored data can be accessed by each user and a visualization tool will be used for data analysis.

Breath sampling of cannabis users. In July 2019, the National Institute of Justice awarded our team funding for a three-year project to provide the chemical foundation for industry to develop a cannabis breathalyzer. We are developing new experimental methods to enhance the clinical, thermodynamic, pharmacokinetic, and materials knowledge of the scientific community.  One phase of the research focuses on the collection of human subject breath samples before and after cannabis consumption. The AFC-LIMS will be used to aggregate the breath sample data and may be expanded to other aspects of the project as analytical instruments are incorporated into the system.

Fuel characterization. Our group has developed state of the art characterization techniques that have been applied to a variety of fuels. Decades of research and extensive testing have resulted in a significant amount of data spread across multiple systems. The AFC-LIMS will be used to process the existing data and to make it accessible to researchers. New tools will be developed to migrate old data formats to current standards. By aggregating the data, machine learning and data mining approaches can be utilized to discover new patterns and to guide future research.

Created May 27, 2020, Updated July 10, 2020