Exploring the Future of Polymer Analysis

Bryan Katzenmeyer has over 15 years of expertise in mass spectrometry and separation techniques, specializing in characterizing materials, polymers, monomers, and small molecules. Bryan has held both technical and business roles across multiple organizations. Currently, he works at JEOL, overseeing mass spectrometry business development. Before that, Bryan worked at Waters Corporation, Valspar/Sherwin-Williams, and Lubrizol Corporation. Bryan holds a PhD in polymer/analytical chemistry from The University of Akron, an MSc in chemistry from the University of Wisconsin-Madison, and a BSc in chemistry from Penn State University.

Q: Can you tell us about your journey and what inspired you to specialize in polymer analysis? 

A: I started my academic career like most others, but my path was complicated. Initially, I went to graduate school to research mechanistic studies in various Ziegler-Natta catalysts used to synthesize polyolefins. However, I became burned out after being in school for nearly six straight years. So, I left my program with an MSc in Chemistry after deciding that chemical synthesis was not for me. Afterward, I worked for several years in the chemical industry until the 2008 economic collapse. At that point, I decided to pursue a doctorate, believing it would benefit me once the economy recovered. Given my previous experience, I specialized in characterizing polymers, particularly mass spectrometry (MS), because I felt it could offer good employment prospects. Eventually, I found an excellent doctoral advisor who guided me toward using MS for polymer analysis and helped me develop my passion for this field. Although I no longer work in a laboratory setting, I am grateful to continue working with researchers and lab managers across various industries worldwide. I will always have a special place in my heart for those in polymer analysis.

Q: What advantages does mass spectrometry bring to polymer analysis?

A: When comparing MS to classical techniques, MS offers selective information about the polymer instead of an average. Most classical techniques are “relative” methods that provide general information about the average mixture as a whole and often rely on calibration standards, which usually don’t possess properties similar to the polymer being studied. MS is an “absolute” method that isn’t reliant on calibration standards, which is a significant advantage when standards don’t exist. Another advantage is MS provides data at the molecular level, down to individual oligomer chains. 

Q: How can researchers address the challenges of analyzing high molar mass polymers with MS? 

A: No analytical technique is perfect, and MS is no exception. Researchers commonly request the analysis of high molecular weight (MW) polymers by MS. Polymers are naturally polydisperse and behave differently inside a mass spectrometer than monodisperse analytes. When ionizing and detecting polymers by MS, multiple factors exist, including the ion type generated in the ionization source, a polymer’s chemistry or functional groups, the ionization source, ionization efficiency between polymer chains across polydispersity, and instrument geometry.

Fortunately, there are tools to overcome these challenges. For example, linear time-of-flight (TOF) mass spectrometers allow increased mass range over other instrument geometries. Another option involves pyrolysis, often combined with GC separation. Charge detection mass spectrometry (CDMS), which can analyze macromolecules with masses in the megadaltons, is also an emerging technique. 

Q: How do you recommend researchers choose an MS method? 

A: Choosing the right instrument for polymer MS analysis depends on the application's goal. Generally, there are four major areas of polymer analysis: MW and end group information, sequencing and architecture information, mixtures, and surfaces. Additive characterization is also critical, and methods such as DART-MS, GC-MS, LC-MS, MALDI-MS, and Py-GC-MS are commonly used. Overall, the recommended MS technique(s) vary depending on the size of the polymer, the number of components present in the mixture, and the method.

Q: What do you see as the biggest challenge currently facing polymer analysis? 

A: There are a couple of challenges facing polymer MS analysis, in my opinion. These challenges will take time to overcome and may never reach a solution. One challenge is the knowledge gap stemming from MS’s traditional use in the life and pharmaceutical sciences. Polymers require an understanding of specialized sample preparation, how the polymer’s chemistry can affect the result, and the instrument considerations mentioned earlier, which are not typically taught in academia or industrial settings.

Additionally, there are fewer opportunities for students and researchers to learn polymer MS analysis, leading to fewer specialists. MS vendors primarily focus on the life and pharmaceutical sciences more than the applied markets where polymers typically reside. These other areas are often crucial for driving their MS business forward, making it difficult for polymers to get prioritized within these organizations.

The polymer MS field is a tight-knit group of researchers, including students, professors, and business professionals. A group of these individuals meet regularly at conferences or outside their organizations to discuss polymer MS. This group has an opportunity to promote the use of MS for polymer analysis and to educate others on how MS can be leveraged for polymer analysis—for example, this publication. If you want to be a part of this group, please feel free to contact me. 

Q: What data analysis tools or methods do you find most effective and how are they advancing? 

A: Data analysis is crucial to any characterization method, especially for polymer analysis. Various MS vendors and others have developed software tools for data processing and visualization. Kendrick mass defect analysis is one of the most effective tools for organizing and sorting complex polymeric data sets. Additional polymer-specific software tools have been developed to aid in tasks like identifying monomer repeat units, structure assignments, polymer end group determination, and MW distribution data.

Artificial intelligence (AI) is the next frontier in data analysis. It is now used in polymer analysis, with vendors like JEOL developing software tools that use machine learning to aid data processing. This technology provides automatic structure analysis in MS, a previously unavailable capability that dramatically decreases data processing time for large, complex data sets. This technology also offers a new structure analysis tool for unknowns designed explicitly for high-resolution mass spectrometers. I envision the increased use of AI across the scientific community and that its development will be shaped by many to ensure it responsibly advances society.

Q: What is your advice for adopting new technologies?

A: Selecting a technology among the available MS options can be overwhelming. With a wide range of acronyms and vendor-specific terminology, it's essential to consider many factors before making a decision. I recommend outlining specific questions related to the analysis, like the type of compounds to be screened (known, unknowns, or both), the nature of the analysis (qualitative, quantitative, comparative, or combination), regulatory requirements, and potential future expansion of scope. 

These questions often direct one toward the correct solution. In the case of MS, there are two predominant pathways. Quadrupole-based MS systems excel at screening for known compounds and determining their concentrations. In contrast, high-resolution MS is better suited for unknown or untargeted analyses and offers precise measurements of component masses.

In the end, many prefer to conduct their own research and seek advice from colleagues or peers. I would also suggest reaching out to vendors who have scientists and experts available to discuss your laboratory's requirements, as they can help uncover options that might otherwise be overlooked. 

Readers can contact Bryan Katzenmeyer at bkatzenmeyer@jeol.com. 

Learn more about polymeric materials analysis at https://bit.ly/3vGnwKY