Research
Abstract
Acrylic paint is a modern artistic material made of colored pigment and polymeric binder. Acrylic binder requires fundamental study at the molecular level to understand its physical properties for purposes of art conservation and general polymer chemistry. The research presented in this thesis uses single-sided nuclear magnetic resonance (NMR) as a non-invasive and non-destructive way to measure relaxation and self-diffusion, which provide insight to molecular mobility and physical properties of proton-containing samples. Specifically, this study relies on T2 relaxation to gain insight to regions within acrylic paint with different molecular mobilities. In both dry and wet paint, relaxometry data revealed two relaxation behaviors, each representing a region with unique binder mobility. Furthermore, the effect of pigment concentration on relaxation behavior of wet paint suggested molecules in acrylic binder undergo chemical exchange between these regions with differing mobilities. The characterization of local molecular environments in acrylic paint provides a foundation for future studies of acrylic polymers and contributes fundamental knowledge about the chemistry of acrylic paints to support their long-term preservation.
Kiple, L., Ballenger, J., Keating, K., Balachandra, A., Meldrum, T. Automated optimization of spatial resolution for single-sided NMR. Magn Reson Chem. 2023. Accepted Author Manuscript. https://doi.org/10.1002/mrc.5352
Abstract
Single-sided NMR instruments utilize inhomogeneous magnetic fields with strong gradients to non-destructively probe physical properties of materials. The sensitive region of this type of magnet is often a thin slice above the magnet’s surface; measuring planar samples with high spatial resolution requires coplanarity between the sensitive region of the magnet and the sample region of interest. We developed an algorithmic approach to position flat samples coplanar with the magnet’s sensitive region. The efficient and objective positioning process utilizes an adjustable stage that offers control over three degrees of freedom, and the optimal position for each sample is found with a quadtree algorithm. We show this algorithm is effective for positioning samples with various relaxation behaviors. We report resolution values that describe position optimization, acquisition constraints, and final spatial resolution for each sample. Measurements after optimized positioning had appropriate spatial resolution to distinguish physical regions of layered samples with different physical properties, namely relaxation behavior. Our algorithmic positioning process can be implemented for planar samples in research and industrial settings to enhance spatial resolution of single-sided NMR measurements.
My Reflections on the Research Process
Experimentation
The concept of research was still somewhat ambiguous to me when I started graduate school. I had done experiments in academic labs but those are written out step-by-step and assigned because they are have known results. The science experiments I did as a kid helped instill curiosity but they weren't research. Experimental design in a research lab is something I didn't begin to comprehend until I stepped foot into one. When I arrived in the middle of a project, I jumped in by first learning how to use the instrumentation to collect data. As I followed the prescribed experiments my advisor requested, I tried to understand why we were doing those experiments, how we would use the collected data, and what results we expected. I soon was incorporated into the much broader endeavor of setting up experiments with systematic variability to create comparisons. I began to see the process at work: collecting data, viewing data, compiling data, comparing data... It was a trial and error process that we kept pushing forward to get new information.
Most importantly, I learned that the process hinged on asking questions. It was about asking the right questions to nail down what you want to find out. Then it was about designing the right experiment to make the discovery that would answer your question. This learning curve during my first months in a research lab, which was also my first semester of graduate school, opened my eyes to the possibility, struggle, critical thinking, and exploration that is research and the scientific method.
Data Processing
The biggest new skill I have gained in graduate school is MATLAB. While I am no where near proficient I have become increasingly familiar with the program through data analysis. I have been assigned analysis tasks that I did not know how to do, but struggling, pushing through, and overcoming those challenges helped me learn and made me feel accomplished. I am still learning the proper scripts and protocols for importing collected data into MATLAB and producing something meaningful. Troubleshooting my errors in MATLAB has helped me grow as a problem solver and scientist.
Communication of Results
My first semester courses, especially the introduction to graduate studies, helped me become a better science communicator. (They also showed me how much I enjoy communicating science to a range of audiences!) Through making numerous PowerPoints and rehearsing presentations, I practiced focusing on the big ideas to convey importance and applicability. As I started writing papers, I was given tips on how to make ideas flow and ensure the reader would understand my train of thought. The piece of advice that stuck with me most was to connect sentences to each other through repeated ideas. This technique taught me to avoid assuming knowledge and skipping steps. I believe it has made my science writing easier to comprehend and, thus, more impactful to more people.
Future Interests
In the future, I'd like to expand my research interests into the application of single-sided NMR to various materials important in cultural heritage such as porous materials and layered objects.
I am also interested in applying other techniques to analytical studies of art and cultural heritage. I would like to explore instrumentation including HPLC, Py-GC, FTIR, MS, and Raman for their various strengths and specializations.