Using Raman Spectroscopy for the Characterization of Zeolite Crystals

Zeolites represent a cornerstone of today’s industry as the most-used catalyst by weight in the world. Zeolites are nanoporous, crystalline, alumino-silicate materials with regular arrays of molecule-sized nanopores, giving rise to shape- and size-selective adsorption, diffusion, and reaction of adsorbed guest molecules. Synthetic zeolites can be fabricated as pure silica (SiO₂) polymorphs, alumino-silicates, alumino-phosphates, or include other elements such as boron, germanium, tin, titanium, zinc, or zirconium. Because of this fabrication, a host of new, energy-relevant applications, such as biomass conversion to biofuels, carbon capture, hydrogen purification, and energy-efficient separations, can be realized with targeted zeolite architectures. Zeolite structure is often thought of in terms of a collection of rings that make up zeolite channels and cavities. Achieving the synthesis of tailor-made zeolites, with targeted rings and cavities, is greatly hampered by a poor understanding of how zeolite crystals actually form in solution. Solving this problem by identifying critical structures that lead to zeolite crystals has presented a difficult analytical challenge. Recently, we spoke to Scott M. Auerbach a professor of chemistry and chemical engineering at the University of Massachusetts Amherst, about his work in the use of Raman spectroscopy for zeolite structure characterization, focusing on a collection of nine all-silica zeolites.

From both theory and experience, it is understood that the precursors for zeolites are not sufficiently well characterized using infrared (IR), nuclear magnetic resonance (NMR) or X-ray diffraction (XRD). In researching the analysis of zeolites, you have recently recognized Raman spectroscopy as an analytical tool capable of understanding the formation of zeolites, especially their individual ring structures (1). Is it possible to assign Raman spectral features to individual ring structures, or should assigned Raman spectral features be associated with other zeolite building units?

Since the pioneering work in the 1980s by Prabir Dutta and others, there is a history of assigning Raman spectra of zeolites to individual rings, especially for Raman bands in the 200–700 cm^–1 range. This is perhaps not surprising, given that Raman spectroscopy can be sensitive to collective, nonpolar vibrations, such as those of silica-based rings. This zeolite Raman assignment approach arises from observed correlations between known rings in zeolites and the locations of Raman bands. For example, zeolites with so-called “four-membered rings” (four alternating pairs of ...-Si-O-... atoms in a ring) are known to exhibit a strong Raman band around 450–550 cm^-1, while zeolites with six membered rings show Raman bands around 300–350cm^-1, and those with eight membered rings show Raman bands at even lower wavenumbers, around 200–250 cm^-1.

The apparent anticorrelation between ring size and Raman wavenumber also makes sense, because frequency is expected to decrease with increasing wavelength, and ring size should be related to vibrational wavelength. So, in the end, there is logic to assigning Raman bands to rings. However, the problem is that these rings exist in environments of other rings, raising the question, “How do nearby rings influence Raman excitations?” We thought to ask this question because of zeolites like Linde Type A (LTA) and Chabazite framework (CHA), which have 4-rings, 6-rings, and 8-rings (in different arrangements), but exhibit different Raman spectra. We wondered why. Answering that question was the original reason for our study.

How is your Raman work on zeolite structural characterization different from what others have done previously to study these structures?

The innovative aspect of our approach is that we engaged in an integrated synthesis, spectroscopy, and simulation study to address two big problems that exist in the previous literature. The first problem is the difficulty in comparing properties for different zeolites, which can exhibit distinct framework structures, and contain different compositions, such as different silicon:aluminum ratios in alumino-silicate frameworks. As such, if Raman spectra for different zeolites were distinct, it’s not obvious why, because we typically have to change two variables at a time, and, in science, we’re not supposed to do that. The second problem is the difficulty in assigning Raman spectra, especially by first principles calculations, because of the large sizes of typical zeolite unit cells. We tackled both problems by identifying nine distinct zeolites all sharing the same properties: 1) they could be synthesized as all-silica materials, which removes ambiguities associated with locations of aluminum atoms in the zeolite framework, and locations of charge-compensating cations in zeolite pores; 2) they have unit cells small enough to be treated accurately with periodic density functional theory, including the calculation of Raman intensities; and 3) they all share a common structural feature—the zeolite four-membered ring to determine if Raman bands associated with four-membered ring vibrations are sensitive to different local environments or not. Studying this disparate collection of zeolites was made possible by our zeolite synthesis capabilities, allowing us to consider relatively novel zeolite materials that are not commercially available. In the end, our approach allowed us to make systematic comparisons among Raman spectra of different zeolite frameworks in ways that others have not.

You and your coworkers have approached the question of zeolite structure using such methods as integrated synthesis, Raman spectroscopy, and periodic density functional theory (DFT) modeling. How do these methods complement one another in understanding zeolite structures?

Insights in materials chemistry often follow the central dogma that structure explains properties. In the case of our study, structure means a given zeolite’s framework structure with its collections of interlocking rings and cages, which after synthesis is tested and confirmed by comparing mea- sured XRD patterns to known standards. Then, property means the corresponding Raman spectra, which were collected with our colleagues at Worcester Polytechnic Institute (WPI). As mentioned above, using zeolite structure (rings) to explain properties (Raman band locations) has, in the past, required assumptions, and lacked accurate, first principles theory. We have bridged that gap in our study by applying periodic density functional theory on zeolite unit cells in a three-step process: 1) optimizing atomic positions and lattice parameters; 2) computing normal mode frequencies and vibrational coordinates; and 3) computing dielectric susceptibilities that underlie the Raman intensities. As such, the theory provides both accurate Raman spectra and illustrative pictures of key vibrations—the normal modes, thus answering whether bands can indeed by assigned to individual rings.

What are the larger questions associated with the accuracy of the assignments of Raman spectra related to zeolite structure? What are the aspects of these spectral assignments that are not well understood?

Zeolite structures consist of “secondary building units,” such as four-membered rings, and “composite building units,” such as small and large cages. The big question is whether Raman bands can be assigned to rings, cages, or both, depending on the given zeolite. This is a burning question, because, after all these years in zeolite science, we still don’t really understand how zeolites form small crystals that grow into larger ones. Discovering this could help us synthesize new zeolites for advanced applications in, for example, creating carbon-neutral biofuels and capturing carbon dioxide. That’s why we’re funded by the Department of Energy.

Two diametrically opposed theories for how zeolites form are 1) random monomer addition, like a sand castle forming from random addition of grains of sand, and 2) hierarchical growth from populations of pre-formed rings and cages in solution, like a sand castle forming from pre-formed buckets of sand. The former seems rather unlikely, but we don’t really have evidence for the latter yet. Being able to assign Raman spectra to rings and cages could answer this grand challenge question in materials chemistry, which is important for the reasons stated above.

Using normal-mode analysis, you discovered that Raman bands can be assigned to tricyclic bridges (which are structures comprised of three zeolite rings that share common Si−O−Si bridges). You also found that the vibrational frequency of a given Raman band can be correlated to the smallest ring of its tricyclic bridge, and not to the ring that is actually vibrating. Lastly, you have discovered a precise anticorrelation between Raman frequency and Si−O−Si angle. How do these discoveries reveal new ways to investigate structures like those found in zeolites?

The new structural concept of the tricyclic bridge offers a new organizing principle for understanding Raman bands from different, though related, materials. Tricyclic bridges are both building blocks for understanding zeolite structure, and objects that collectively vibrate during Raman scattering. Grouping normal modes by the smallest ring of the corresponding tricyclic bridge allows us to categorize and structurally correlate vibrations with similar but slightly different band locations, and also explain the reasons for outliers that don’t obey a given correlation. The angular correlation you mention allows us to identify specific molecular coordinates (the Si-O-Si angle) from Raman band locations. In the end, it’s like finding the key to unlock the treasure of zeolite structure, elucidated by Raman spectroscopy.

How do you properly sample materials like zeolites to understand their general structural properties?

The key to accurate calculations is optimizing both geometrical and electronic structures. The key to a good geometry is a good first guess, and that is furnished by previous experimental XRD studies on zeolite crystals. When we optimize atomic locations and unit cell parameters, we start from experimental data, and make small tweaks consistent with quantum density functional theory (DFT). The Nobel Prize in Chemistry was given to Walter Kohn in 1998 for DFT, which has revolutionized computational materials chemistry. DFT is tractable for zeolites because of the use of “plane waves,” which are great mathematical functions for modeling valence electrons in crystals such as zeolites. The only problem with zeolites is their typically large unit cells, meaning many atoms, many valence electrons, and thus, many functions in the basis set. That’s why we had to focus on materials with smaller unit cells to make calculations tractable.

What were the key challenges you encountered during this research work? Each component of the project—synthesis, spectroscopy, and modeling—had its unique challenges. Synthesizing all-silica materials often requires an esoteric organic molecule to act as a sacrificial “structure directing agent” (SDA). Before our team could actually synthesize a given zeolite, we had to do the multistep organic synthesis of a certain SDA, and that could take weeks. The spectroscopy was challenging to get good Raman signals, we needed to “hunt” for zeolite crystallites with an orientation that happens to give good back-scattering (our WPI collaborator, Dr. Geoff Tompsett, has Jedi skills when it comes to backscattering), and good signal-to-noise (S/N) requires plenty of time to collect and average over spectra. Finally, the quantum calculations of Raman intensities were, by far, the most computationally intensive aspect of our work. Our computations were performed on fast computers at the Massachusetts Green High Performance Computing Center in Holyoke, Massachusetts. In the end, the graduate students on this project, Tongkun Wang and Song Luo, showed heroic resilience and resourceful- ness, leading to our breakthrough findings.

What do you consider to be the most useful contribution of your work?

Actually I see two: one about science, and the other about scientists. The science contribution is about the discovery of a new “Platonic form.” In other words, a new fundamental shape, the tricylic bridge, that helps us understand zeolite structure. The trycyclic bridge is an interesting shape, involving three rings that share one edge. I can’t help but wonder where else in nature we might find the tricyclic bridge. The contribution related to scientists is about how our integrated science approach made a real difference. By crossing disciplinary boundaries in our team, we taught one another about our respective fields, creating fertile ground for new ideas. I am the Executive Director of an integrated science program for science, technology, engineering, and math (STEM) undergraduates at UMass Amherst called the UMass iCons Program (icons.cns.umass.edu), that gave me a head start in launching and running a research program aimed at creating breakthroughs through “radical synergies.”

What are your next steps in this work?

We’d like to connect the dots between Raman spectra of perfect zeolites (our recent JACS paper [1]), to Raman spectra during zeolite formation, to shed light on how zeolites form. To do this, we have to consider what’s in the soup that makes zeolites, the signature vibrations of each of these ingredients, and how interactions among these ingredients can be probed by Raman spectroscopy. Step by step, we will cross the chasm between zeolite structure to zeolite formation with the nexus among zeolite synthesis–Raman spectroscopy–quantum modeling.

Reference

1. T. Wang, S. Luo, G.A. Tompsett, M.T. Timko, W. Fan, and S.M. Auerbach, J. Am. Chem. Soc. 141(51), 20318–20324 (2019).

Dr. Scott Auerbach, UMass Amherst

Dr. Scott Auerbach is a professor of physical chemistry at the University of Massachusetts at Amherst (UMass Amherst). Professor Auerbach’s research focuses on modeling nano-structured materials such as zeolites, which are of importance to renewable energy technologies including biofuels and fuel cells, leading to 2 books and 120 articles.