Chemical imaging is widely used in many areas of science and engineering, including chemistry, materials science, forensic science, medicine, art conservation, and archaeometry. A chemical image is a picture in which the colors indicate the position and concentration of different atoms and molecules. These images therefore provide the chemical composition of the surface of a sample, which is crucial information for understanding its history, behavior, and properties. Chemical images can be very complex. Biological tissue samples, forensic evidence, and other materials may be composed of hundreds or even thousands of different chemical compounds, in amounts varying over lengths as small as a few nanometers. Tools that can correctly identify and resolve these components and changes are highly desirable.
In this project, new analysis tools and state-of-the-art computing resources will be used to greatly improve the resolution and quality of these chemical images. This improvement is possible because the great bulk of information obtained in chemical imaging techniques is normally not used. The "raw" data produced by high-resolution experiments (imaging mass spectrometry, infrared and Raman microscopy, scanning Auger microscopy, and x-ray photoelectron spectroscopy imaging) can exceed thousands of gigabytes per square millimeter of sample imaged, which up to now has been far too much for individual researchers to even store, let alone completely analyze. The team will provide software that can take full advantage of the power of modern supercomputers, such as those available through NSF's Teragrid, to extract statistically optimal chemical images from these enormous data sets. These tools will also be able to combine many small-area images into large-area chemical images of unprecedented resolution, enabling detailed chemical imaging of much larger samples than previously possible.
This research's goal is to dramatically improve the power, applicability, and ease-of-use of a wide range of chemical imaging techniques, significantly advancing many different fields of science, engineering and medicine. This can only be accomplished through "computational thinking", because the data sets themselves are simply too large and too complex to be comprehensively analyzed by even expert human operators. The software, along with documentation and tutorials, will be freely distributed, and installed for general use at national supercomputing centers.
We have developed a new computational tool for the quantitative analysis of chemical images. In methods such as time-of-flight secondary ion mass spectrometry (TOF SIMS) imaging, an image is created from pixel-by-pixel spectral characterization of a sample surface. From this data our method correctly extracts the number of important chemical components in the sample, as well as the distribution and mass spectrum of each one. We believe we are the first group to systematically account for matrix effects in SIMS image analysis, which is a new paradigm in this field. Matrix effects are the interactions of each molecule (the "analyte") with its surroundings (the "matrix"). These interactions change both the number and type of secondary ions produced during the measurement. If ignored, matrix effects can lead to incorrect interpretation of experimental data. We have also developed a new experimental technique, ionic liquid matrix-enhanced SIMS (IL SIMS). SIMS is often limited to analysis of lower-mass species due to fragmentation of the analyte. IL SIMS can enhance analyte signal intensities by up to two orders of magnitude, and increases limits of detection by up to 3 orders of magnitude. This makes the analysis of high-mass molecules such as proteins and polymers possible. IL SIMS is also suitable for high lateral resolution chemical imaging. We have investigated the mechanisms involved in IL SIMS and identified several "universal" matrices, which can be employed to enhance signals from peptides, proteins, lipids, and polymers. One postdoctoral scholar (Dr. Tammy Milillo), three graduate students (including two women), and two undergraduates (both women) participated in this research. During the project Dr. Milillo, who is permanently disabled and wheelchair-bound, was promoted to Research Assistant Professor in the Department of Chemistry, University at Buffalo. This work has also lead to a number of new collaborations, including one with the Dallas Museum of Art.
