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 (SiO

) 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

 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

, while zeolites with six membered rings show Raman bands around 300–350cm

, and those with eight membered rings show Raman bands at even lower wavenumbers, around 200–250 cm

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.”

We’d like to connect the dots between Raman spectra of perfect zeolites (our recent 

 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.

1. T. Wang, S. Luo, G.A. Tompsett, M.T. Timko, W. Fan, and S.M. 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.

Articles

Spectroscopy Spotlight

Zeolites are the most-used catalyst in industry. Synthesizing tailor-made zeolites is hampered by a poor understanding of how zeolite crystals actually form in solution. Scott M. Auerbach of the University of Massachusetts at Amherst is addressing this challenge with Raman spectroscopy.

Using Raman Spectroscopy for the Characterization of Zeolite Crystals

As part of our ongoing review of infrared spectroscopic theory, we will discuss why different functional groups have different peak heights and widths. We find that Beer’s law determines peak height, and that the absorptivity in Beer’s law depends upon a mysterious quantity called the dipole moment, and is in large part responsible for why infrared peaks vary in size, from large to non-existent. Infrared peak widths can vary from a few wavenumbers to thousands of wavenumbers in width. We explore the role of intermolecular interactions in this phenomenon. We will wrap up by discussing the importance of integrating peak position, height, and width information to properly interpret spectra.

When I first introduced infrared spectral interpretation theory many columns ago (1), it was interspersed with discussions of the interpretation of specific functional groups. This approach made sense at the time, but, by not presenting all the theory of a piece, some important concepts that you need to know to interpret spectra successfully might have gotten short shrift. Hence the need for this Grand Review.

There are three important pieces of information in an infrared spectrum; peak positions, peak heights, and peak widths. All these pieces of information can be used to distinguish different functional groups from each other, and I have emphasized repeatedly in my columns the importance of integrating all these pieces of information to interpret spectra. It then makes sense for you to learn the reasons why different functional groups have different peak position, heights, and widths.

In my last column, I discussed why different functional groups have different peak positions (2). Here, we will discuss why different functional groups have different peak heights and widths. We will find that Beer’s law determines peak height, and that the absorptivity in Beer’s law depends upon a mysterious quantity called the 

, and is in large part responsible for why infrared peaks vary in size from large to nonexistent. Infrared peak widths can vary from a few wavenumbers to thousands of wavenumbers in width. We will explore the role of intermolecular interactions in this phenomenon.

When plotted in absorbance, the peak heights in an infrared spectrum are proportional to concentration, allowing infrared spectra to be used to determine the concentration of chemical species in samples (3). The relationship between absorbance and concentration is summarized in Beer’s law (3):

 equals absorbance, ɛ equals absorptivity, 

The pathlength is simply the thickness of sample seen by the infrared beam. The absorptivity is the proportionality constant between absorbance and concentration. The absorptivity also has physical meaning; it is a constant for a given pure molecule at a given wavenumber. For example, the absorptivity of pure acetone at 1716 cm

 (if not stated, assume all peak positions are in cm

 going forward) under standard conditions of temperature and pressure is a constant. This means that, under these conditions and for a fixed pathlength, a sample of pure acetone will always absorb the same amount of light at 1716.

Since different molecules have different absorptivities, this quantity must somehow be related to chemical structure. Consider the electronic structure of the CO

, the electronegativity difference between the carbon and the oxygen atoms causes the electrons to be shared unevenly (4), leading to a partial positive charge on the carbon, and partial negative charges on the oxygens. In Figure 1, a partial positive charge is represented by a δ+, and a partial negative charge by a δ-.

A dipole moment can be defined as two charges separated by a distance. Since chemical bonds, such as the ones in CO

, can consist of charges separated by a distance, chemical bonds have what are called 

. The bond dipole is a vector quantity; it has a magnitude and direction. The arrows in Figure 1 show the dipole moment vectors for CO

. Each arrow’s length represents the magnitude of the dipole moment for that bond, while the arrow head denotes its direction.

The magnitude of a bond dipole is simply the size of the charges multiplied by their distance apart, and can be calculated as such:

The bond dipole is a measure of charge asymmetry, and bonds with large charges held far apart having bigger dipole moments than bonds with small charges held close together. Thus, polar bonds, such as O-H and C=O, have large charge asymmetry, and therefore large dipole moments. Covalent bonds such as C-C bonds have small change asymmetry, and therefore small dipole moments.

The net dipole moment for a molecule is the sum of the bond dipoles for that molecule. Note in Figure 1 that, for CO

 at equilibrium, the two bond dipoles are equal in magnitude and opposite in direction. When added together, these two dipole moments cancel each other, giving a net dipole of zero.

So what does all this dipole moment stuff have to do with molecular vibrations and peak heights? Consider the symmetric and asymmetric stretching peaks of CO

During the symmetric stretch, the length of the bonds changes, but, since they stretch in phase with each other, the two bonds are always the same length, and always point in opposite directions. We say the symmetry of the molecule is maintained during the symmetric stretch (hence its name), and further meaning that the net dipole of the molecule is always zero at all points during this stretch. We can then say that, for the symmetric stretch of CO

, with respect to the bond length, dx, is zero. In other words:

An inspection of the infrared spectrum of CO

 shows the intensity of its symmetric stretching peak is zero, which means there is no peak corresponding to the symmetric stretch in CO

’s spectrum. Notice that there is a connection between dμ/dx being zero and the peak intensity being zero.

 undergoes an asymmetric stretch, as seen in Figure 2, the bond’s lengths are different at the same point in time. This means at points during this vibration, the two bond dipoles have different values, and that the net dipole is non-zero during this vibration. In this case, we say the symmetry of the molecule is broken, hence the name 

. Since the two bond dipoles values vary during the asymmetric stretch, this means the net dipole during this vibration has non-zero values. In other words:

. In fact, dμ/dx is quite large for this vibration, since we are stretching large dipole moments asymmetrically. The asymmetric stretching peak in the spectrum of CO

, and is quite intense, as seen in Figure 3. In fact, it is the intensity of this absorbance that makes CO

You may have seen 2350 peak before in sample spectra measured with a Fourier-transform infrared (FT-IR) spectrometer. This peak occurs when the CO

 concentration inside the spectrometer increases between the measurement of the background and sample spectra (please consult my book on FT-IR to learn how to avoid this problem [5]).

Note that dμ/dx for the asymmetric stretch of CO

 is large, and the vibration’s corresponding peak is large. What, then, is the connection between dμ/dx and intensity, and how does this new discovery of ours fit in with Beer’s law?

I will skip the derivation here, but quantum mechanics tells us that (6):

where ɛ equals absorptivity, dμ equals change in dipole moment during a vibration, and dx equals change in bond length during a vibration.

Therefore, the absorptivity depends upon the change in dipole moment with respect to bond length during a vibration squared. In other words, the absorptivity depends upon how the electronic structure of a molecule or functional group changes during a vibration. It is this equation that links peak height to molecular structure, and is why different functional groups have different peak heights.

A vibration such as the symmetric stretch of CO

 for which dμ/dx = 0 and the resultant peak intensity is zero is called an 

. On the other hand, vibrations such as the asymmetric stretch of CO

 for which dμ/dx ≠ 0 and the resultant peak’s intensity is non-zero are called infrared active peaks. One of the reasons that the number of normal modes a molecule has is greater than its number of infrared peaks is that not all vibrations are infrared active.

Polar functional groups such as C-O or C=O have large values of 

, hence have vibrations with large values of dμ/dx, and generally have intense peaks. On the other hand, for non-polar functional groups, such as C-C bonds that have small values of μ and their vibrations have small values of dμ/dx, their peaks are generally small. This means that infrared spectroscopy is better at detecting some functional groups than others, and that polar functional groups are generally the easiest to see. In other words, infrared spectroscopy is not a democracy, and, from the point of view of an infrared spectrometer, not all functional groups are created equal. This actually limits us as to what functional groups infrared spectroscopy can see. Fortunately, other molecular spectroscopy techniques, such as Raman scattering, are excellent at seeing the functional groups infrared (IR) cannot see, and thus can take up the slack.

In addition to peak positions and heights, peak widths can also be used to distinguish functional groups. Infrared peak widths vary greatly across the spectrum of known molecules. Figure 4 shows the infrared spectrum of liquid water.

Infrared peaks are generally narrow at their tops, and wider at their bottoms. How, then, do we measure peak widths in a standard fashion? One way spectroscopists do this is to measure what is called the full width half maximum (FWHM) of a peak (6). To measure a FWHM, take the height of a peak, go to where it is half this height, and then measure its width there. For example, if a peak is 1.0 absorbance unit (AU) high, we would measure this peak’s FWHM at 0.5 AU. The FWHM os the O-H stretching peak of water at 3300 cm

, as illustrated in Figure 4. This is an unusually broad peak for an infrared spectrum.

Figure 5 shows an example of a spectrum with narrow peaks; it is of the molecule benzonitrile.

In Figure 5, the FWHM of the C≡N stretching peak at 2228 is less than 10 cm

. The barely discernible red dot shows the FWHM. This peak is 50 times narrower than the FWHM of the O-H stretching peak of water. Why?

Consider Figure 6, which shows the interactions between neighboring water molecules, known as intermolecular interactions. The O-H bond in water is polar, because of the electronegativity differences between oxygen and hydrogen. This means, in an O-H bond, the hydrogen has a partial positive charge on it, and that the oxygen has a partial negative charge on it. Because of this, individual water molecules coordinate with each other with the partially positive hydrogen of one molecule interacting with the partially negative oxygen on a neighboring molecule, as seen in Figure 6. These two molecules form a weak, but very real, chemical bond called a hydrogen bond. These are represented by the dashed lines in Figure 6. Hydrogen bonds are an example of a strong intermolecular interaction, and note that water has broad peaks. In fact, any functional group that engages in hydrogen bonding, including alcohols, carboxylic acids, and amines, will have broadened peaks as a result of hydrogen bonding.

As noted, benzonitrile has narrow peaks. Why? The bonds in this molecule are non-polar, so there is no hydrogen bonding, and the interactions between neighboring molecules is weak, consisting of what are called van der Waals bonds (6). This shows us that molecules with weak intermolecular interactions have narrow peaks. Ultimately, then, the peak widths in infrared spectra are determined by the strength of intermolecular interactions; this is why peak width differences can be used to distinguish functional groups from each other. An example of this is seen in Figure 7.

The spectrum on the left in Figure 7 shows the N-H stretching peaks of a primary amine and on the right is the O-H stretching peak of an alcohol. Note these peaks all appear around 3300. The FWHM of the N-H stretching peaks is about 70 cm

, whereas the FWHM of the O-H stretch is about 500 cm

. Even though these two functional groups have peaks in similar places, the difference in their peak widths can be used to distinguish them from the other.

The difference in peak width seen here exists because the polarity of an N-H bond is lower than that of an O-H bond; as a result, N-H hydrogen bonds are weaker than those of O-H bonds, meaning that amines have weaker intermolecular interactions than alcohols.

The pieces of information available to us in an infrared spectrum include peak positions, heights, and widths. All these pieces of information can be used to distinguish functional groups. We covered peak positions in the last column. Here, we saw that Beer’s law determines peak size, and that the absorptivity depends upon the change in dipole moment with respect to bond length during a vibration. We also saw that peak widths are determined by the strengths of intermolecular interactions. We now know why integrating peak position, height, and width information to interpret spectra is important.

Quantitative Spectroscopy: Theory and Practice

 (Academic Press, Boston, Massachusetts, 2002).

 (Cornell University Press, Ithaca, New York, 1938).

Fundamentals of Fourier Transform Infrared Spectroscopy

 (CRC Press, Boca Raton, Florida, 3rd Ed., 2011).

 (John Wiley and Sons, New York, New York, 3rd Ed., 1996).

, PhD, is the founder and CEO of Big Sur Scientific, a maker of portable mid-infrared cannabis analyzers. He has over 30 years experience as an industrial infrared spectroscopist, has published numerous peer-reviewed papers, and has written three books on spectroscopy. As a trainer, he has helped thousands of people around the world improve their infrared analyses. In addition to writing for 

, Dr. Smith writes a regular column for its sister publication 

 and sits on its editorial board. He earned his PhD in physical chemistry from Dartmouth College. He can be reached at: 

IR Spectral Interpretation Workshop

Integrating peak position, height, and width information is important to properly interpret infrared spectra. But do you understand why peak heights and widths vary? We explain.

The Grand Review II: Why Do Different Functional Groups Have Different Peak Heights and Widths?

Fundamental research often requires narrowband light to accurately map out fluorescence spectra, reaction energetics, or other key questions in chemical, biological, and physical systems. Tunable narrowband sources are often key to this endeavor. Pulsed or continuous wave, the optical parametric oscillator (OPO) has come a long way, dropping in price while improving in performance. We explore the landscape and some common uses of this essential tool for spectroscopists.

When it comes to spectroscopy, the combination of narrowband light sources with broadband detectors (or the converse, narrowband detection with broadband light sources) are the equivalent of the surgeon’s scalpel. They allow us to see deeply into the spectroscopic fine structure, providing experimental data to feed models, probing atomic and molecular structure, and allowing the science of spectroscopy to move forward.

For nearly a century before the advent of the laser, spectroscopists only had narrowband detectors. Kirchhoff and Bun- sen’s work with their “spectroscope” led to the discovery of rubidium and cesium, and the mapping out of many of the fundamental emission lines in atomic spectroscopy, as the pair feverishly fed element after element into their “Bunsen Burner” and recorded spectra. Of course, Kirchhoff would go on to use the spectroscope to study radiative equilibrium (leading to the eponymous “Kirchhoff’s Laws”) and make contributions across the landscape of physics, including thermodynamics and fluid mechanics.

While Kirchhoff and Bunsen could even then get reasonable resolution and sensitivity on their detectors (Kirchhoff dispersed light from the sun over nearly three meters, for example, and at least for steady-state experiments in flames or studying solar emission, photographic plates could be exposed for long periods), it was hard to obtain sufficient intensity of monochromatic light to do the inverse experiment. In other parlance, while it was easy to obtain fluorescence or absorption spectra, it was difficult to obtain excitation spectra.

This situation changed with the advent of the laser. Early solid state and gas lasers emitted one or more narrowband lines, which had some limited spectroscopic usefulness. It was the development of the organic dye laser in 1966 that provided revolutionary access to narrowband light in the visible and near-infrared. This resulted in an explosion of scientific and spectroscopic work, as nicely summarized by Frank Duarte in 2003 (1). However, as Duarte notes, “...over the years dye lasers have acquired a reputation in some quarters as being ‘user unfriendly.’” Duarte does a good job of defending the many contributions and the unique attributes of dye lasers, including the available high pulse energy, femtosecond, and narrow line width, among several. However, the reputational aspect is hardly addressed, except though technological advances, and your author’s opinion and experience is that although dye lasers offer a certain amount of “fun” and are great tools for certain problems, they are also more difficult to maintain and operate than might be optimal.

Enter the optical parametric oscillator (OPO).

Optical Parametric Oscillator (OPO) Operation

OPOs were actually invented in advance of the dye laser, in 1965, by Giordmaine and Miller of Bell Laboratories (2). Their work utilized lithium niobate, which continues to be an important crystal in many OPOs on the market today. They were able to convert incoming 529 nm light from a CaWO

 laser to tunable light over the range of 970–1150 nm by vary- ing the temperature of the oscillator crystal. They observed conversion of only some of the pump pulses, and stated that the conversion appeared highly dependent on the mode structure of the pump beam. These observations would prove prescient; while research continued, development of the OPO into a practical device would depend strongly on the availability of very high quality crystals and appropriate laser sources and coatings that were not available until the late 1980s and 1990s.

I was fortunate to witness some of the explosion in OPO research as a post-doc and staff member at Sandia National Labs in the late 1990s. Colleagues Tom Kulp, Scott Bisson, and the late Peter Powers at the Combustion Research Facility were among those at the forefront of the developments, and I had a ringside seat. The race was on to develop wavelength coverage, and set new power and pulse-length milestones. Many of the applications of OPOs have been focused around spectroscopy, and OPOs have opened up new vistas for optical sensing.

Harmonic processes for frequency conversion in crystals, including second harmonic generation, sum and difference frequency generation, and nonlinear processes (such as OPO operation) have two fundamental constraints. The first, naturally, is energy conservation—the sum of the energy of photons produced must equal that of the input photons. The second requirement is a phase matching requirement; briefly put, the crystal structure must support the generation of photons in phase, or the photons destructively interfere, and there is no gain built up in the crystal. Changing the angle or the temperature of the crystal changes the effective periodicity of the crystal, which can change the wavelength(s) generated.

The difficulty with the phase requirement is that there are few natural crystals that accommodate particular laser wavelengths, and, of course, those crystals would then only accommodate a single wavelength or its harmonics. The optimum condition (from a developer’s point of view) would be a crystal that could accommodate multiple wavelengths, or indeed any input wavelength.

This is where periodically-poled lithium niobate (PPLN) re-enters the picture. For an incident wave 

 will become out-of-phase and destructively interfere after a certain number of periods. The method of quasi phase matching, which was developed independently by Armstrong and associates (3) in 1962, and by Franken and Ward (4) in 1963 (actually before OPOs were invented), can correct for this destructive interference. If the phase of the generated wave is inverted periodically, after a set “interaction length” L when 

 begin to get out of phase, the phase relationship is effectively reset: if the two waves were φ out of phase at the point of the inversion, they will be -φ out of phase after inversion. If φ is relatively small, this allows gain to build up during each interaction length and minimizes destructive interference.

Physically, quasi-phase matching is accomplished by periodic poling in the crystal. This is done in PPLN by periodically (in space) imposing a very strong electric field on the crystal, that permanently switches the electric dipole (dependent on the position of the Li and Nb ions in the crystal). This periodic poling allows quasi-phase matching for particular sets of wavelength, and, in fact, different poling spacings can be introduced at different points in the crystal. This allows a single crystal to be used for different wavelength regions. Figure 1 provides a conceptual illustration of the periodic poling on the generated wave, in the case that the generated wave has a slightly longer period than the incident wave. Figure 2 provides an illustration of the gain build-up in the generated wave with and without poling. Without poling, destructive interference eliminates gain buildup.

Typical PPLN is doped with magnesium oxide to increase its optical damage threshold. Effective anti-reflective (AR) coatings have also been key, because PPLN has a high refractive index (>2), and, without AR coatings, losses in OPOs would be extreme. Several other crystals are also used in OPOs, for example BBO (barium borate), KTP (potassium titanyl phosphate), and ZGP (zinc germanium phosphide). Different crystals are used in various wavelength regions, some more suited for infrared (IR) generation, and some for the visible and ultraviolet (UV).

With the principle of energy conservation and quasi-phase matching, it is possible to understand not only straight harmonic generation, but nonlinear sum frequency and difference frequency generation. In sum frequency generation, two input photons are converted by the crystal to a single photon with the combined energy of the two input photons. Second harmonic generation is a special case of sum frequency generation, in which the two incoming photons are the same wavelength.

Difference frequency generation may be considered to have several forms: 1) two input frequencies can go into a crystal to produce multiple output photons at the difference frequency; 2) a single input frequency can produce two lower frequency beams (where the higher frequency is called the signal and the lower is called the 

); 3) two input frequencies can be input into a crystal, and one of them can be amplified, with the amplified output at one of the input frequencies, and a lower frequency output, in an optical parametric amplifier (OPA).

With all of these options, it is not surprising that OPOs and their cousins take many forms. OPOs were quite temperamental early on, had limited tuning ranges with gaps in tuning, and were relatively low in power. OPOs available on the market today have considerably improved in range and ease of use, while also coming down in price as crystals and coatings have become less expensive. The ability to grow very spatially precise poled crystals in multiple materials, combined with precision movements, has contributed to this improvement, to large extent, as well as the increasing availability of stable pump lasers at numerous wavelengths. To achieve reliable tuning, modern OPOs are all computer-controlled.

An example of the range of product offerings can be seen in the plethora of OPO and OPA products from Spectra-Physics. The Spirit-OPA is an ultrafast amplifier that is designed for 350 fs pumping at up to 30 W, and outputs 630–1020 nm on the signal and 1040–2600 nm on the idler beams. Either can be modified with harmonics to extend the range further. Their TOPAS Prime OPO is meant for pumping from 770–830 nm with a sub-150 fs beam and has even a wider range. The Spectra-Physics Inspire has a smaller range, but uses only a single set of optics and crystals. They also offer the continuous wave MixTrain laser sum and difference frequency mixers among their many laser offerings.

There are also companies that specialize in OPOs, foremost among them Opotek, headquartered in Carlsbad, California. They focus on the nanosecond market, with OPOs ranging from 210 nm to 3100 nm, typically 10–20 Hz repetition rate, and relatively high energy. Their Radiant and Opolette models are among their leading offerings; with 4 to 7 cm

 line width over the entire tuning range of each device. InnoLas, from Austria, has similar nanosecond-class offerings of OPOs that operate from about 400 to 2100 nm, tailored either to diode-pumped or flashlamp-pumped Nd:YAG, and reach 150 mJ in pulse energy.

Other companies have niche offerings, like APE, which offers an OPO solution able to reach below 200 nm, if deep-UV coverage is required. APE also offers picosecond and femtosecond OPOs to be pumped with either NIR lasers around 1 μm, or Ti:Sapphire. The latter offerings include GHz repetition rates. Coherent markets and sells the Chameleon MPX and Compact OPO systems, produced by APE, that are Ti:Sapphire-pumped and cover wavelengths as broad as 340 to 4000 nm. Toptica offers the TOPO CW OPO laser for NIR and MIR applications; the system covers 1450–4000 nm.

Finally, Ekspla has a notable range of picosecond- and nanosecond-class OPOs built in their factory in Vilnius, Lithuania, with possibly the broadest range of repetition rates and energies among the 15 models of OPO that they sell worldwide. Ekspla systems include models with an integrated pump laser and OPO, and also purpose-built systems like the PhotoSonus, with 150 mJ of pulse energy for photoacoustic imaging. Ekspla also has systems with very narrow, transform-limited line widths.

As one would expect, OPOs are used in a variety of ways to facilitate discoveries in science. They have largely replaced dye lasers as tunable sources, due to their convenience and relatively recent reliability gains. For example, in biophotonics they are a popular tool for fluorescence microscopy. Tissue typically scatters strongly under visible excitation, with the result that visible light penetration is limited to about 100 μm, unless the tissue is specially treated by a “clearing” technique to minimize the scattering. One way to circumvent this problem is to use near-infrared (NIR) light, which penetrates much more deeply (often millimeters). Intense, ultrafast pulses of NIR light can generate multiphoton absorbance and excitation in fluorescent tags and nanoparticles, as we discussed in this column in 2020 (5).

Likewise, OPOs are an indispensable tool in physical chemistry, where the tunable source can be used to probe particular rotational, vibrational, and electronic states of molecules and atoms. This is useful, for example, for probing energy transfer in molecular beam experiments, or to precisely understand energy levels and particular transitions.

In my own home field of combustion science, planar laser-induced fluorescence is used to determine the distribution of radical species (such as OH and CH), as well as pollutants (such as NO), as they form in prototypical laboratory flames and in actual devices. OPOs are indispensable in such studies; their high repetition rates allow flame dynamics to be captured with fidelity, and statistical or spatial distributions can be easily developed.

OPOs have evolved from the temperamental creatures that they were in the relatively recent past, to reliable and versatile tools today. The variety of pump lasers, repetition rates, and wavelength range available on the market today makes the OPO definitely the reigning king of tunable laser sources.

(3) J.A. Armstrong, N. Bloembergen, J. Ducuing, and P.S. Pershan, 

, is the General Manager of the Applied Systems business and Vice President of Engineering at Ocean Insight, and an affiliate associate professor at the University of Washington, and has started and advised numerous companies in spectroscopy and in applications of machine learning. He has approximately 40 peer-reviewed publications and 6 patents. His work in practical optical spectroscopy, such as LIBS, Raman, and TDL spectroscopy, dovetails with the coverage in this column, which reviews methods (new and old) in laser-based spectroscopy and optical sensing. Direct correspondence to: 

Lasers & Optics Interface

Tunable narrowband light sources are essential for measuring fluorescence, reaction energetics, and other challenging measurements in chemical, biological, and physical systems. In particular, the optical parametric oscillator (OPO) is an indispensable tool.

Narrowband Visible Light: The OPO Reigns

Click the title above to open the Spectroscopy January regular issue, Volume 36, Issue 1, in an interactive PDF format.

Spectroscopy-01-01-21

Using Raman Spectroscopy for the Characterization of Zeolite Crystals

The Grand Review II: Why Do Different Functional Groups Have Different Peak Heights and Widths?

Narrowband Visible Light: The OPO Reigns

Vol 36 No 1 Spectroscopy January 2021 Regular Issue PDF