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Eigenvector Awarded SBIR Contract

Jul 8, 2009

We got some good news last week when we found out we won an SBIR (Small Business Innovation Research) award to work with NIST (National Institute of Standards and Technology). The project will focus on continued advancement of the Temperature-Programmed Sensing (TPS) system developed at NIST by Steve Semancik and co-workers.

The TPS system is a multi-sensor array, typically with different materials on the individual sensing elements. But there is a twist: the TPS system allows for temperature control of the individual chemical sensor elements. TPS sensors are also known as “micro-hotplate sensors.” The ability to do temperature programming, i.e. vary the sensing element temperature as a function of time in a prescribed way, provides many opportunities for optimization. In addition, proper utilization of the sensor’s time/temperature response may lead to the realization of the “second order advantage” typically found in much larger and more complex analytical systems such as GS-MS.

We’re excited about working on this project and trust that our collective experience (100+ man-years of chemometrics consulting!) will lead to some improvements in the system performance.

I’ve included below the project title and abstract.

Chemometric Support for Temperature-Programmed Sensing System

The Temperature-Programmed Sensing (TPS) system developed at NIST presents many opportunities and unique challenges. The data output from the system can be quite complex and there are many opportunities to optimize the system for specific sensing scenarios. We propose a program aimed at characterizing the system so that potential problems (such as system drift) can be solved early so that the full potential of the system can be realized. The plan includes studies on the stability and theoretical functionality of the sensors. This will result in procedures for instrument standardization and data base-lining. After this is accomplished, advanced preprocessing methods will be considered, along with the use of multi-way (“second order”) data modeling methods for use in calibration and classification. Finally, procedures for optimizing the system for specific applications will be developed.