Fran Adar is the Worldwide Raman Applications manager for Horiba Jobin Yvon.

Fran Adar

Any product used for medical purposes has to follow the adage “do no harm.” In the current work, we assess if and how Raman microscopy, in combination with X-ray fluorescence (XRF), can aid in the characterization of material leached from several implantable devices. The results show both the presence of metal oxides and organic compounds. In one case, two crystalline forms of an identical oxide existed simultaneously, and we suggest that such information may be useful in characterizing the oxidation of the metal. One of the samples was tinged pink, and its spectrum was consistent with a resonance Raman (RR) spectrum of a pigment. Because information on the source of these samples was not known, it is not possible to perform a complete characterization, but we can suggest ways that these results can be used in the future when a more complete study can be done.

	
	Any product that comes in contact with the human body or with a product that is subsequently ingested, injected, or inserted into a human body has the potential to carry harmful products. If a polymer is part of an implant, it can release low molecular weight oligomers or additives that are present to engineer the polymer’s properties. If a drug product is contained in a polymer bag or vessel, especially if the drug is liquid, similar problems are possible. An implant is often composed of metallic parts that have the potential to oxidize, even if chipping is not likely. Metallic oxidation products from an implant can either chip away or be dissolved in fluids and carried from the site of the implant, or material from a metallic container can be ingested, injected, or inserted into a human body.

	Pharmaceutical companies are constantly testing their products for extractables and leachables, both before FDA release and during the life of the product. 

 are products that can be released by a product under normal ambient conditions. 

 are products that are released under conditions designed to indicate worse case scenarios. For example, an implant can be immersed in a solution meant to mimic biological fluids, and then subjected to elevated temperatures to determine what can be forced out of that part; this would be called a 

. Exposure to non-physiological solvents would produce extractables.

	We will show here the results of a study of material leached from two implants, and indicate how this information can be of use to the biomedical community.

	Two saline solutions of material leached from two different implants were examined. To aid in the interpretation of the Raman spectroscopy results, X-ray fluorescence (XRF) was also measured. Figure 1a shows the vials of solutions as received. Figure 1b shows some of these materials deposited on a stainless steel microscope slide.

	Elemental analysis performed on an XG7200 (Horiba Scientific) showed that the elements in the green deposit were dominated by titanium, with some silicon, calcium, nickel, and sodium. The pink sample contains a lot of iron, aluminum, and sulfur, with some copper, chromium, manganese, and silicon, and small amounts of zinc and nickel.

	As readers of this column are aware, Raman spectroscopy provides information on both organic and inorganic materials. In fact, the original concept of the Raman microscope (1) was to provide molecular identification of organic molecules, especially manufacturing contaminants that could not be analyzed by the standard microprobes in use at the time. Today, there are extensive Raman spectral libraries that can assist in the identification of unknowns, and even if a library does not provide a match, there are tools to aid in the assignment of functional groups (2).

	The Raman spectrum of organic materials is separated into several characteristic regions. The region below 1500 cm

 because the motions of all the atoms are coupled together, so it is not possible to assign a particular band in the spectrum to the motion of one or a few atoms. Between 1500 and 1800 cm

, there are bands that can be assigned to the >C=C< and >C=O stretches (~1650 and ~1730 cm

, respectively). Between 2000 and 2400 cm

 is the –SH stretch. Then there are CH stretches (2700–3100 cm

), and the OH stretches (anywhere between 3100 and 3600 cm

	Most of the bands of the Raman spectrum of inorganic materials fall below 1400 cm

 but if there are hydroxyl groups, they will appear above 3000 cm

, and the position and width of the bands will also depend on H-bonding.

	In both cases, there are shifts and splittings of bands in the crystal phases, and it is important to point out that there is a significant number of inorganic materials in which there are not identifiable vibrating functional groups, so when these materials crystallize in different forms, the spectra are totally different. Examples are TiO

, and carbon (diamond vs. graphite). One of the examples that is shown below in one of the deposits illustrates this in the spectra of two phases of Al(OH)

	Because the sample deposits did not have any good morphology on which to focus, I recorded maps in order to acquire a good sampling of representative spectra. Spectra were isolated from the maps using either classical least squares (CLS) or multiple curve resolution (MCR).

	The top of Figure 2 shows both bright field and dark field micrographs of the region that was studied by Raman microscopy. While the sample in the vial had a weak pinkish color, that color is not seen in the micrographs. In bright field, one is essentially seeing the reflectivity of the sample, so it is not unusual so see bright areas where the sample is reflective and areas of different tones of gray from less reflective material; sometimes one can see color here if it is intense. In dark field, we are seeing light scattered from the periphery through the sample, and then up the bright field objective. The pink box in the micrograph is the region from which we collected spectra.

	Figure 2 also shows the extracted spectra on the right and the rendered map on the left, using the spectral colors on the right; the spectra and map are color-coded, so that it is clear which spectrum corresponds to which region on the map. The OH bands in the green and blue spectra are good matches to the spectra of bayerite (blue) and gibbsite (green), two crystallographic forms of Al(OH)

.(3), and the fingerprint bands between 300 and 600 cm

 are good matches as well. In the blue spectrum, there is a strong CH band below 3000 cm

 indicating saturated organic material, and a small band at 3073 cm

 indicating the presence of an aromatic. There are (unlabeled) bands in the blue spectrum near 1450 and 1600 cm

, also consistent with these assignments of saturated organic and aromatic species. The spectrum in pink at the bottom of the figure is consistent with an aromatic, but because the CH region is weak and the strongest bands are between 1000 and 1700 cm

, it is believed that this represents the resonance-enhanced spectrum of the pink pigment. Examination of the green spectrum indicates the presence of similar bands.

 bands from Figure 2 compared to the spectra in the cited publication, illustrating a remarkable match in spectral features.

	Figure 3 shows our spectra that we assign to the two phases of Al(OH)

, bayerite in green, and gibbsite in blue.

	The map of this sample was treated in a manner similar to that of the pink sample. Three spectra were isolated and used to produce the Raman rendition shown in the upper right side of the figure. In this sample, there is also evidence for organic material with aromatic functional groups. (The band close to 2330 cm

 is atmospheric nitrogen which is often seen in our spectra.). This sample also shows surprising behavior in the hydroxide region; there is a sharp band at 3574 cm

, with a much weaker but fairly sharp band at slightly higher frequency. This sample was known to have a high content of titanium, and the low frequency region is consistent with an oxide of this metal, although not an exact match to any of the common phases. I made a considerable effort to assign the OH band, but was unsuccessful. However, in searching, I did find a very old article on the OH band in a series of metal hydroxides that occur in this region of the spectrum (± 50 cm

	There are several issues about this report that may be unsettling to analysts who need detailed answers to solve problems. At this point, I am unable to make firm assignments to neither the organic species nor the inorganic species. But because I really have no information on the implants from which these samples came, it seems gratifying that I can make any assignments at all. If I knew the composition of the implants (that is, the type of organic or polymeric material present), I would be in a better situation to identify these materials. What, for me, is surprising is that I am seeing hydroxides of the metals. The real question for the analyst is whether the identity of the hydroxides in these extracted samples is meaningful, or is just an artifact of the preparation of the slurries that we deposited on a slide. The use of a saline solution is, for sure, meant to mimic physiological conditions. The question is whether these species are coming off the implants in the states that we are observing, or are they flaking off in a different form and then changing in solution or during precipitation. Again, we need more information on the original implants, and then need to consult with a physical chemist who would understand the possible chemical and phase changes that are possible under what conditions.

	I chose to do this pilot study on extractables and leachables because I am getting multiple meeting announcements per month on this topic, and the topic just spoke to me about the potential of Raman
	microscopy to be helpful. What is surprising is that vibrational spectroscopy doesn’t seem to be exploited in this field for its potential. Maybe some of my readers can tell me I am wrong, or tell me why it is not being used.

 3, 33–43 (1975). This is the first publication describing the concept of the Raman microscope, with its potential for analytical work implicit in the publication.

https://www.bio-rad.com/en-us/product/knowitall-software-analytical-edition?ID=N19OQWKSY

		H.D. Ruan, R.L. Frost, and J.T. Kloprogge, 

The Raman and Infra-red Spectra of Some Solid Hydroxides Part II. Correlation with Crystal Structure

, Memoir No. 119 from the Raman Research Institute, Bangalore, India.

Fran Adar is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey. Direct correspondence to: 

Articles

Raman microscopy, combined with X-ray fluorescence, can provide detailed information about extractables and leachables from medically implantable devices.

Use of Raman Microscopy to Characterize Extractables and Leachables

Raman spectra of five reference samples of ethylene vinyl acetate (EVA) with vinyl acetate (VA) copolymer composition between 14 and 40% were measured. After normalizing the spectra to the >CH

, the spectra can be compared and band assignments made, enabling a description of morphological changes resulting from the introduction of the vinyl acetate group. The spectra were then analyzed by two-dimensional correlation spectroscopy (2D-COS). The 2D-COS enabled an easy way to use the spectral data to derive information on conformational changes that bypasses band-fitting, which can be non-unique.   

	
	Polymers are experiencing ever-expanding uses in many applications. The successful use of polymers depends on their chemical and physical properties which, in turn, depend on the molecular weight, chemical composition, and morphology. When a polymer is used to dissolve a small molecule (for instance, for drug delivery or for pigmentation), it needs to be noncrystalline. When it is used for a structural application, it needs to be tough, to be capable of resisting fracture when stressed. For those who are new to the characterization of polymers, it is useful to explain that 

 refers to the special arrangement of amorphous versus crystalline composition, orientation of the molecular chains, and the state of amorphous chains in direct vicinity of the crystalline phase, all of which affect the physical properties.

	In my journey into acquiring expertise in two-dimensional correlation spectroscopy (2D-COS), I have been looking for a system where I can connect what I know about the spectra with the changes that 2D-COS is showing. Some of the work that I have published in this column on the spectra of polyethylene terephthalate was designed with this in mind. However, this system is quite complicated, so I have been looking for a  system where we can more easily use the combination of Raman spectroscopy and 2D-COS to reveal useful information about chemical or conformational changes. After I collected spectra of five reference samples of poly(ethylene vinyl acetate) (EVA), I dropped them into 2D-COS and asked my mentor, Isao Noda, if this would be a convenient approach to try. His enthusiastic response was my go-ahead signal. For me, this is exciting, because I am able to rigorously follow the physical and chemical changes in a material. The focus is entirely different from my usual focus on microanalysis. Raman spectroscopy has not been used as much for chemical characterization as infrared (IR) and nuclear magnetic resonance (NMR) spectroscopy, for a variety of reasons. Its application requires detailed knowledge of spectral interpretation, which is not being taught as it was 50 years ago. For me, this has provided an incentive to learn more about spectral interpretation, especially of organic compounds.

	Ethylene vinyl acetate is a copolymer of ethylene and vinyl acetate with the latter usually present at concentrations less than 50%. Whereas polyethylene (PE) can be quite well-ordered (chains parallel with long-range crystalline packing order), the introduction of vinyl acetate interrupts that order. The spectrum of polyethylene has been extensively interpreted (1), so the changes introduced by the presence of vinyl acetate are amenable to interpretation. The first figure shows the Raman spectrum of poly(vinyl acetate) (PVA, top), EVA, 40% vinyl acetate (middle), and PE (bottom). Before examining the spectra, one might expect that the spectrum of EVA will be a superposition of PVA and polyethylene, but, in fact, that is an extreme oversimplification that becomes clearer by examining the spectra of solid and molten high density polyethylene (HDPE) in Figure 2. The top of the figure shows the spectrum of a pellet of polyethylene (2) at room temperature where it is solid versus the bottom spectrum of the same material, recorded at 200 â°C where it has melted. In the case of EVA, as the concentration of the VA is increased, the crystalline order of the polyethylene content is interrupted. Disorder in polyethylene has well-known effects on the spectrum, which can be seen in Figure 2. In molten polyethylene, there are three broad bands in the fingerprint region-the C-C single bond at about 1070 cm

. If polyethylene has a limited number of side branches, when solidified, the chains will stretch out with translational alignment; there are two methylene units in the repeat segment of the chain, and two chains in the unit cell. Using this information, it becomes possible to assign molecular motion to all the peaks in the spectrum of the solid (1,3). With this information in mind, when we examine the spectra of the various compositions of EVA, we note that bands of well-ordered, crystalline polyethylene are decreasing as the VA composition is increasing, and VA bands are increasing with VA concentrations. What the 2D-COS will tell us is what is happening simultaneously (in the synchronous plots), and what is not happening simultaneously (in the asynchronous plots). What is even more important is 2D-COS will tell us the order in which the different bands are changing. Before thinking this through thoroughly, it did not occur to me that the conformational changes would not be simultaneous. This has important implications for the study of temperature dependence and extrusion in polymers.

	In this section, I enumerate the bands that I am using to follow  the influence of VA on the polyethylene order. Figure 2 shows the Raman spectrum of a pellet measured just above room temperature (30 â°C), and then at 200 â°C, which is well above the range of melting temperatures (120–180 â°C). In the liquid phase, there are three broad and prominent bands in the fingerprint region-the C-C stretch near 1070 cm

. Polyethylene is an unusual polymer in that it can exist in a highly crystalline state. In that form, the C-C stretch splits into two sharp bands at 1060 and 1125 cm

, the backbone stretch is sharp and appears near 1295 cm

 deformation region exhibits sharp bands near 1420, 1440, and 1460 cm

. In the CH stretching region, crystalline polyethylene is dominated by a strong sharp band near 2880 cm

, with a second, somewhat broader band at about 2840 cm

, with about 65% of the peak intensity of the first band. As disorder increases, the band at 2880 cm

 band, and higher frequency shoulders grow in intensity and sharpen. These are the features that we will be following in the 2D-COS behavior.

	The spectra of the EVA samples in the fingerprint region are overlaid in Figure 3a, after scaling all the spectra to the intensity at the deformation near 1440 cm

. Note that in this region there will be contributions from the methylene groups in the polyethylene segments, as well as the methyl group in vinyl acetate, and this may introduce some non-linearity to the spectra, but we will use the spectra scaled in this manner for this work, because we are looking more for effects of loss of crystallinity rather than small chemical changes. To make the examination of the spectra easier, I have color-coded the samples from violet to blue to green to red to brown as the concentration of VA changes from 14% to 18% to 25% to 33% and then 40%. The peak labels are brown (when the VA is at high concentration) or violet (when the VA is at low concentration). Thus, the violet labeled peaks correspond to peaks from the crystalline polyethylene units, and the brown labeled peaks correspond to peaks from the VA, and this can be confirmed by comparing these spectra from the various figures to those in Figure 1. Figure 3b shows the same spectra but in the CH stretching region. (The sharp band near 3060 cm

 is an artifact from the room lights and should be ignored.) The purple and brown color-coding is identical to that of Figure 2a, but the apparent peak at 2903 cm

 is highest in the 30% sample, not 14 or 40%, so it is labeled in the color of that sample. What will become apparent is that this intensity at 2903 cm

 is not due to a real peak, but the overlap of bands in the two phases that happen to maximize in this sample.

2D-COS-Following Conformational and Morphological Changes

	My goal is to understand how the conformation of the crystalline phase of polyethylene is changing as the VA concentration increases, and, in particular, what is the order of the changes. Before learning   about 2D-COS, my assumption was that as a polymer changed, all parts of the material changed together. If this were the case, the asynchronous plots would be blank. Now we are able to determine the order of changes in the polymer as reflected by the order of changes in the spectral bands. In particular, we will follow bands of the crystalline versus amorphous forms of polyethylene, keeping in mind that the intensity of the marker bands for the crystalline phase go down, and the amorphous bands go up, as the VA is added. In addition, it is important to keep in mind that the intensity of the spots in the 2D-COS plots indicate the magnitude of the changes, not of the peaks themselves. In fact, in the diagonal points of the synchronous plots, all peaks have the same sign. That means that while the peaks indicating a disordered polyethylene lattice are increasing, those of the crystalline lattice are decreasing, but they are changing together. It is only by examining the off-diagonal points that we can determine which peaks are increasing, and which are decreasing. But to determine the sequence of the changes, we have to examine the asynchronous plots.

	If I plot the entire 2D-COS spectra, it will not be possible to see the details of interest. I will be plotting first blocks along the diagonal, then blocks off the diagonal, to examine the behavior of cross-peaks. For example, I know that the sharp CH peaks near 2840 and 2880 cm

 are attributable to the crystalline phase. If I want to understand how the changes in the CH region line up with changes in the CH

 deformation or the backbone stretch or the C-C stretches, then I will produce the 2D-COS plots with the CH region on one axis and a fingerprint region on the other axis.

	Figures 4 through 9 show the 2D-COS results in the following regions:

	The rules for determining whether the change at ν

, or visa versa (4) are shown in Table I. Table II shows the sequence in which the bands are changing that were derived from the 2D-COS plots. In order to produce this table, one has to inspect each of the partial 2D-COS plots and determine the sequence of the changes for the bands of interest in that plot. Then, when all the plots have been evaluated, it is possible to consolidate the results and produce the list shown in Table II. However, it is a bit tedious to get everything in a self-consistent order; it is more or less like solving a puzzle.

	So what does this list mean? The first bands listed are the two VA bands near 1350 and 1370 cm

. Although polyethylene does have a band in this region (the so-called 

 groups), most of the change here is from the VA. In fact, careful examination of the overlaid spectra in this region (Figure 3a) does indicate that at low VA concentration, the band at 1368 cm

 is sharper, and more intense, than the band at 1346 cm

, but at high VA concentration the two have similar widths and intensities exactly as in the VA and 44% EVA as shown in Figure 1. The next band to exhibit changes is the backbone twist in amorphous polyethylene, which, of course, grows in intensity with VA; note that this band is totally absent in PVA, so its presence indicates only the amorphous content of polyethylene, and no contribution from VA.

	Following this are changes around the >CH

 deformation that was used for normalization at 1437 cm

. What is curious is that the two small spots that seem to indicate symmetric increases in intensity at 1427 and 1440 cm

 in the synchronous plot do not behave in parallel. The 1440 cm

 band of crystalline polyethylene decreases next, and then the 1425 cm

 spot increases. To convince myself that this conclusion really reflects the data, I have plotted this region of the spectra in Figure 10. The figure shows clearly the decrease in the crystal bands at 1415 and 1460 cm

, but it also shows broadening around the centroid at 1437 cm

. The spots appear in the 2D-COS plots because of the -CH

 group on the acetyl that replaces one of the protons on the chain. In fact, the normalization to the centroid of the >CH

 probably enhances the ability to see the increase in width. The spectral plot does not indicate directly the order of the changes, but at least it does support the conclusions from the 2D-COS.

	Following these changes, there is the growth of the amorphous contributions in the CH region at 2932 cm

. Then there is the loss in intensity of the crystalline CH bands near 2880 and 2840 cm

. After that there is growth in the amorphous C-C stretch at 1080 cm

, followed by decrease in the crystalline C-C stretches at 1060 and 1125 cm

. Finally, there is loss in intensity of the backbone twist of the crystalline polyethylene, and then the loss in intensity of the 1460 cm

	When I examine these changes, the trend that I see is that first we observe growth in the VA bands that have no overlap with polyethylene (1346 and 1368 cm

), and then the changes seem to happen with the growth of the amorphous phase before the loss of crystallinity. In most cases, changes of analogous bands (amorphous and crystalline) occur sequentially; see the bands in the CH region and then the C-C region. The last bands to change are the backbone twist of the crystalline phase, and then the last component of the >CH

 deformation region puzzling. While both the 1415 and 1460 cm

 are clearly associated with crystallinity, they did not behave the same. But the following will explain what is going on. In the amorphous phase there is a single chain δ(>CH

symmetry. In the crystal there are two chains per unit cell, and polarization studies have shown that this mode splits into bands at 1415 and 1440 cm

band in the crystalline phase, the overtone of the IR active band is invoked. The single chain >CH

in the amorphous phase, but splits into a doublet at 720 and 730 cm

 in the crystalline phase. Thus the overtone of the 720 cm

 component of the split-off Raman band, and the overtone of the second at 730 cm

 will be the Raman band observed at 1460 cm

. These assignments have been summarized in (1).

	It is tempting to try to rationalize the order in which the band changes occur. That the first change was the introduction of VA bands is easy to understand. But why would the appearance of the backbone twist of the amorphous phase appear so much earlier than the disappearance of the twist in the crystalline phase? Why does the disappearance of the 1415 cm

 crystalline band occur between the two spots that appear in the 2D-COS at 1440 and 1425 cm

; perhaps because the magnitude of the changes on the two sides of the 1437 cm

 band are not equal? One can argue that the changes in the CH stretches precede those of the CC stretches and crystal backbone because it is easier for the protons to adjust to changes than the CC units, which are constrained and coupled together. Likewise, it can be argued that it is easier for there to be adjustments in the single CC bond stretches than the backbone twist, which probably requires some minimum coherent sequence length along the chain.

	After this work was completed experimentally and I was preparing to write, I picked up the text by Noda and Ozaki and it fell open to pages in which analysis of this same chemical system was described (5)! The work cited in this section of the text goes back to 1999, in which Fourier transfer (FT) Raman spectra were analyzed only in the fingerprint region. The results were discussed in terms of the three-phase model (the orthorhombic crystal phase, a melt-like amorphous phase, and an isotropic disordered phase). Some of their conclusions are different than ours, but they did not describe how the spectra were normalized for comparison. I believe that the analysis and conclusions can be different, because I have normalized spectra at 1437 cm

	Of what use is all this? The question is important, because in deriving Table II, much time was involved to check and recheck the sequences and I have to ask myself if it was worth doing. Because polymers are used for many applications with quite differing physical and chemical requirements, and the physical and chemical properties depend not only on their chemical composition, but on their history, this type of analysis can provide information that is difficult to acquire by other means. In particular, polymers are often extruded from the melt and the speed with which they are extruded as well as the temperature and other factors will determine the physical properties, including (but not limited to) orientation, crystallinity, and ductility. Because the 2D-COS results provide information on the atomic/molecular scale, the polymer chemist can access this information to engineer their materials. For instance, the ductility of a polymer depends (inversely) on the crystallinity. If the EVA is being made for an adhesive application, it will need to be ductile, but not so much that it will flow. Using the information provided in Table II, along with MVA (multivariate analysis predictions), one should be able to determine the morphological characteristics effecting the ductility, and then adjust the manufacturing conditions for optimization. For example, tuning the extrusion temperature, or  amount of shear, could potentially change the order in which these changes occur, which could affect the bulk physical properties.

The Vibrational Spectroscopy of Polymers (

Cambridge University Press, Cambridge, United Kingdom, 1989).

		Scientific Polymer Products, Polyethylene High Density Pellets CAS#25213-0209 Cat#041, Ontario, NY.

Two-Dimensional Correlation Spectroscopy 

(John Wiley and Sons, Ltd. Chichester, United Kingdom, 2004).

		Section 10.4 Composition-based 2D Raman Study of EVA Compounds in Reference 4.

is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey. Direct correspondence to: 

Raman 2D-COS spectral data provide information on conformational changes of polymers. Here, Raman spectra of ethylene vinyl acetate and vinyl acetate copolymer are measured and interpreted, enabling a description of morphological changes related to the vinyl acetate group.

Raman Analysis of Ethylene Vinyl Acetate Copolymers–Using 2D-COS for Identifying Structural Changes

The Raman spectrum of water measured on a fixed grating near-infrared (NIR) spectrograph with a dual wavelength laser enables recording the signal in both the fingerprint and the OH stretching region. This article will show spectra recorded between 5 °C and 80 °C, and treated with both band-fitting and the 2D-COS algorithm. The results will show the complementarity of the two data treatments in providing different insights to the spectral behavior.  

	The Raman spectrum of deionized water was recorded at temperatures between 4 °C and 80 °C in a near-infrared (NIR) Raman spectrograph (MacroRam), which is designed around a fixed grating spectrograph and a 785 nm laser. When the Raman spectrum is dominated with broad overlapping bands, it often becomes difficult to use the spectrum for structural analysis. In this case, we will examine the behavior of the spectrum of water as a function of temperature between 4 °C and 80 °C. In principle, there should be an HOH deformation, and symmetric and antisymmetric combinations of the motions of the two OH functional groups. In the liquid phase, while the deformation near 1640 cm

 appears to be a single band, the spectrum in the OH stretching region (3000 to 3700 cm

) is clearly composed of more than two bands. The assumption is that there are complicated effects of H-bonding in the OH-stretching region that can account for this behavior. Our goal here was to determine what structural insight could be gained by comparing band-fitting with 2D-COS analysis. Because the OH stretching vibration is beyond the range of the detector with this excitation wavelength, we used the dual wavelength laser from IPS (1). The OH region of the spectrum (3000 to 3800 cm

) was excited at 680 nm, which places the spectrum at the same wavelength range as the fingerprint part of the spectrum excited at 785 nm. Thus, by using the 680 nm laser line, one gets good sensitivity, even in the OH region of the spectrum. Figure 1 shows the full spectrum of water recorded with this laser at 4 °C and 70 °C. The laser was delivered with a fiber optic, and the Raman light was collected with a fiber bundle. Because there was no effort to make polarization measurements, the spectra reflect the isotropic component.


	Figure 1: The full Raman spectrum of di-ionized water recorded on an NIR Raman spectrograph with a dual wavelength (785/680 nm) laser.

	The Raman spectrum of water has been studied by several groups as a means to understand the structure of water as a function of temperature, in the context of the H-bonding (2,3). In our measurements, the entire spectrum below 1300 cm

 is dominated by the bands of the silica cuvette. In the midrange of the spectrum, one can see the O

 dissolved in the water at 1556 and 2331 cm

. While there are water bands of interest in the region dominated by the silica (3), we are going to focus on the OH stretching bands above 2900 cm

Temperature Dependence of the Water Spectrum

	In the past, I have done temperature-dependent studies of proteins dissolved in water buffer, and observed the important changes with temperature, which can be seen in Figure 2. At first glance, one assumes that the OH envelope can be fit by two bands, but closer inspection indicates that there is more going on. Attempts to fit the spectra to two bands never yielded good fits, as shown in Figure 2; the figure also shows five-band fits, which produced absolutely flat residuals in the spectra. Note that others have also fit the OH spectra to five bands (2).


	Figure 2: Spectral fits of the OH spectra of water at 80 °C and 5 °C. On the left are shown two-band fits with significant residuals displayed in purple. On the right are five-band fits with vanishing residuals.

	Figure 3 shows the spectra taken as a function of decreasing temperature. On the right side is the integrated intensity between the cursors shown in the spectral multifile. However, after band-fitting the entire hyperspectral cube shown in Figure 3, we can plot the peak position, the integrated intensity, and the peak width as a function of time (which means temperature because the sample was cooling), and that behavior is shown in Figure 4.


	Figure 3: Spectra of water in the OH stretching region (on the left) taken as a function of time while cooling from 80 °C to 4 °C. The vertical red and green lines indicate the brackets used to calculate the integrated intensities. On the right is shown the integrated intensities as a function of time and temperature.

Temperature-Dependence of the Fitted Bands

	Figure 4 shows the results of the band-fitting of the entire file. A total of five peaks were used for the fitting. In addition to the strong peaks near 3200 and 3400 cm

, peaks were added on the low side, on the high side, and one between the two strong bands. The peak positions were fixed during the fitting procedure. The integrated intensities of the strongest peaks near 3200 and 3400 cm

 are changing in opposite directions, which is consistent with what is seen in Figure 3. The widths of the middle band (~3340 cm

, considerably sharper than the other bands, although their behavior with temperature are somewhat different. The width of the lowest frequency band (blue) has almost the same width as the strong bands, but it increased as the sample was cooled.


	Figure 4: The peak position, integrated intensity in each peak, and the peak width as a function of time and temperature.

	Another method of analysis that is possible is to subject the hyperspectral cube with temperature dependence to multiplicative curve resolution (MCR), something that was done in the cited publication (2). Figure 5 shows the results of this calculation, but doesn't seem to offer any insight. The two factors identified are very close to the cold spectrum (red) and the warm spectrum (green), and the addition of a third factor did not produce an additional independent spectrum. Because the spectra were acquired while cooling, the cold spectrum increases as the warm spectrum decreases.


	Figure 5: Factors and Scores from the MCR calculation.

	Because spectral fitting is an "art", and in particular that the fits are often not unique, when these spectra are fit more than once, there is some variation in the results (in fact, that is why the fitting shown in Figure 4 was done by fixing the peak positions). For this reason I felt that implementing 2D-COS might be a more rigorous technique, so I was optimistic that applying 2D-COS to this set of measurements might provide a more "accurate" reflection of the behavior of the various overlapping features in the spectra.

	What will the 2D-COS show us in comparison? Figure 6 shows the results of the 2D-COS application to this set of measurements. The synchronous and asynchronous plots are shown with four sets of intensity scales. The reason for this is that I only observe two peaks in the 2D-COS plots; one peak is center at 3114 cm

, neither of which overlays with the peaks in the band-fitted spectra. I was surprised that I did not see a correspondence with fitted spectra, so I used multiple plots to determine what was "hidden" in the results. In fact, changing the scale for plotting did not reveal any new structure. But I was able to accurately determine the centers of the 2D-COS structures, and determine if they corresponded to peaks in the original spectra.


	Figure 6: 2D-COS of the OH stretching part of the water spectrum, displayed with varying intensity scales. Synchronous plots on top, and asynchronous on the bottom.

	Figure 7 shows the 2D-COS results plotted along with the 5 °C and 80 °C fitted spectra, in order to determine more detail in the results. What we find is that the center of the 2D-COS higher frequency structure is at 3460 cm

 which is shifted from the higher frequency strong band in the spectra at 3406 cm

. The lower frequency structure is peaked near 3114 cm

, which is shifted from the lower frequency strong band at 3189 cm


	Figure 7: 2D-COS results enlarged and superimposed on the 4 °C and 80 °C band-fitted spectra.

	How do we make sense of the 2D-COS results compared to the band-fitted results? The important fact to keep in mind is that the 2D-COS is sensitive to anything in the spectrum that is changing, while the band-fitting reports bands whether they are changing or not. In fact, because there are asynchronous peaks, there must be more than two components (this follows from the fact that when there are only two components, one is simultaneously replaced by the other, and there is no asynchronous component). Notice as well that the asynchronous peak spans the space for both positive and negative synchronous peaks. If the peaks in the 2D-COS plots do not correspond to the peaks in the band-fitted spectra, that means that changes in all the peaks are contributing to the changes encoded in the 2D-COS.

	What is interesting is that the intensities of the three small spectral features are fairly constant over this temperature range, yet their widths are all varying, but with different behaviors, which can be seen in Figure 4. Actually, the band-fitted spectra show us that only the two strong bands are changing in intensity to any significant amount; one is increasing in intensity, and one is decreasing, and they are broad enough that they overlap. This accounts for the inflection point in the 2D-COS plots at 3260 cm

. All of this, of course, is a result of hydrogen bonding and the formation and evolution of various clusters in the water, which is discussed with much more detail in the references (2,3).

	Interestingly, temperature-dependent NIR (near IR) measurements have also been measured and subjected to 2D-COS analysis (4). It is gratifying to see that similar conclusions have been drawn here. There is a broad asymmetric combination band of the symmetric and anti-symmetric OH stretches that appears as two components in the 2D-COS. PCA was applied and it was shown that 99% of the spectral variations occurred at 7089 and 6718 cm

 combination in two different species of water whose relative concentrations were changing with temperature. While the full story of the structure of water (2,3) is much more complicated than what I have described here, it is always encouraging to see that different techniques produce similar information.

	Something that did not occur to me while I was doing this work is whether changing the peak positions of the band-fitted spectra would provide a different comparison to the 2D-COS. After submitting the manuscript, I did vary the fitting parameters, and confirmed that these fits are not unique. A future study where the fitting would be varied systematically might provide better insight, but I leave this to a future column.

	I was motivated to do these measurements for several reasons. I wanted to see the results of using this analytical approach to analyze water, whose OH band would not be detectable with a 785 nm laser. But I have also been fascinated by the potential of 2D-COS to reveal differences that are difficult to see spectrally because of band overlap. Unfortunately (for me) the story is much more complicated than I expected, a fact that was revealed to me by Professor Isao Noda. Again I am grateful to him for helping through this painful learning process, and I hope that my candor in describing what I am doing will be helpful to my readers.

	(1) Innovative Photonic Systems, Monmouth Junction, NJ 08852.

	(3) T. Morawietz, O. Marsalek, S.R. Pattenaude, L.M. Streacker, D. Ben-Amotz. and T.E. Markland, 

Two-Dimensional Correlation Spectroscopy: Applications in Vibrational and Optical Spectroscopy 

(John Wiley & Sons, Chichester, England, 2004).

 is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey. Direct correspondence to: 

The spectral behavior of water is studied using Raman with an NIR spectrograph and dual wavelength lasers for measurements of both the fingerprint and the OH stretching regions. Raman spectra are recorded between 5 ⁰C and 80 ⁰C, and treated with both band-fitting and the 2D-COS algorithm revealing improved insights into the spectral behavior of water.

Measurement of the Temperature Dependence of Water Using a Near-Infrared Raman Spectrograph

Raman spectroscopy, a laser-excited technique for measuring molecular vibrations, will be used to analyze ethanol–water and ethanol–methanol mixtures. It is well known that some solvent mixtures have a smaller volume than the sum of the volumes of the components, presumably a result of the strong hydrogen bonding. H-bonding is known to be one of the strongest noncovalent interactions, so we will examine mixtures of two solvents with H-bonded species. Initially, one would band-fit bands of vibrations to determine which functional groups are contributing to the interactions. But, because the Raman bands of the OH stretches where we expected to see effects are so broad, band-fitting can be problematic. This article will show how two-dimensional correlation spectroscopy (2D-COS) plots can reveal subtle changes that would be difficult to see in individual spectra.

	Anyone who follows my columns or hears me talk at conferences knows that most of my work is done on a Raman microscope. When I accepted this job many years ago, Raman microscopy was an entirely new technology that offered totally new information for the scientist trying to understand the structure of new materials with engineered microstructures, or to solve manufacturing problems, or to understand processes like corrosion. My formal training was in physics, so I actually had to educate myself on the structure and spectroscopy of the materials that I was studying. But, given my background in physics, I understood immediately that the technology was capable of doing things that the chemists who invented the technology never considered. I understood that we could easily differentiate crystalline phases of organic and inorganic materials, and we could determine orientation. Because of my limited background in chemistry, I tended to stay away from purely chemical studies that involved a level of understanding of chemical structure, for which my background was weak. In this column, I am going outside my comfort zone to examine chemical solutions where it is known that there are chemical interactions. I am doing this as a result of several hardware and software innovations.

	Because of the ubiquitous occurrence of fluorescence in many materials, it has become clear that using the 785 nm laser for excitation avoids such fluorescence in many, if not most, problematic samples, especially organic ones. In fact, inorganic materials may exhibit real fluorescence when excited at such a long wavelength because of the presence of transition metal and rare earth impurities, but I will not deal with that here. I have been aware of the phenomenon described in the preceding paragraph for a long time, so I thought it might be interesting to do a study of these mixtures on an instrument designed only for macro work (Macro RAM, Horiba Scientific, Edison, NJ). However, there is a major problem in such a study. When exciting at 785 nm, the detector, which is based on optical absorption of silicon, sees diminishing sensitivity as one scans farther out into the spectrum, because, as we approach the indirect bandgap, the capacity for absorption decreases. It is possible to correct for decreasing sensitivity, but only as long as the silicon is absorbing some light. Its bandgap, beyond which it will not absorb at all, is about 1.05 µm (3200 cm

), which places it right in the middle of the OH region of the spectrum. At one point, I thought I would be clever and attempted to reconstruct the latter part of the OH spectrum, which goes out to 3600 cm

, by doing a correction, but found that I couldn't make it work. What has recently become interesting is the introduction of a dual wavelength laser from IPS which easily switches between 785 nm and 680 nm. Exciting at 680 nm places the CH, NH, and OH part of the Raman spectrum at the same wavelength region as the fingerprint spectrum excited at 785 nm. So, I was able to record the full spectrum on an instrument with no moving parts, and with no sacrifice in spectral resolution.

	The next part of my motivation was to use 2D-COS to analyze the spectra of mixtures with varying relative concentrations. What I was hoping to find was that I could visualize subtle changes in the OH region due to changes in H-bonding. I was disappointed to see that, in fact, there is not much evidence for changes in the OH envelopes. I did, however, observe other subtle changes that I did not expect to see, and you will see what I am talking about as you read further. For those of you who need an introduction to 2D-COS, I recommend the text by Noda and Ozaki (1).

	I have collected spectra from two sample sets, the first being an ethanol–water mixture, and the second an ethanol–methanol mixture. The ethanol–water mixture is a known azeotrope, which means that distillation cannot separate the two completely. The strong hydrogen bonding creates a 95.5% mixture that will distill without separation. In both sets of samples, I expected there to be strong H-bonding. The goal of this exercise is to determine how the H-bonding affects the spectra. Table I shows the composition of the mixtures that were used for this study. Since the dilutions were the same, the table labels two compounds as A and B, where A and B mean either ethanol and water or ethanol and methanol.

	In my naïve understanding of chemistry from the point of view of a physicist, I would have thought that methanol would behave the same way as ethanol in a water–alcohol mixture. But, in fact, methanol does not form an azeotrope with water. It apparently does not form an azeotrope with ethanol, either. My question was whether the Raman spectra would provide any insight into these different behaviors.

	Figure 1 shows that Raman spectra of water and ethanol taken on the macro Raman instrument with the 785 and 680 nm laser combination.


	Figure 1: Raman spectrum of water (grey) and ethanol (black) in the fingerprint region (left, below 2000 cm

) excited by 785 nm, and in the CH, NH, and OH region (right, above 2000 cm

	The black spectrum is that of ethanol, and the grey that of water. All the sharp bands are attributed to vibrations of the ethanol molecule. The broad low frequency band in both samples is related to the speed of sound in the fluid, and, even though this spectral feature is truncated by the edge filter, it is seen to be different in the two liquids. Water also has an H-O-H deformation near 1640 cm

, which is very weak because of the decreasing sensitivity of the charge-coupled device (CCD) detector, and which will not appear in any alcohol. In the high frequency part of the spectrum (now excited by 680 nm), there is an OH stretching envelope for both water and ethanol. In the water spectrum, however, the intensity extends farther in frequency shift, and if one assigns "peaks," it is clear that they are different.

	Figure 2 shows the 2D-COS results for the 11 samples in the fingerprint region. The major changes will be the increasing intensity of the ethanol bands as the ethanol concentration is increased, and that is demonstrated clearly in the synchronous plots. In the lower left corner of the asynchronous plot, you can see evidence of the decreasing low frequency intensity of the ethanol band as the water band increases. In addition, there are more subtle changes occurring in the fingerprint region, which will become more evident when the plot is expanded.


	Figure 2: 2D-COS spectra, synchronous on the left and asynchronous on the right.

	Figure 3 shows the synchronous and asynchronous plots between 850 and 1650 cm

: in the upper right hand corner of the figure is the region around the intense band near 900 cm

 of the asynchronous plot expanded for clarification. What you can see is clear evidence for a downshift of the ethanol band as the concentration of water is increased. While it is tempting to argue that these plots indicate a systematic, continuous frequency shift, analogous to what we see in solid materials, it is more likely that there are two (or more) closely spaced bands whose relative intensities are changing with composition (2).


	Figure 3: 2D-COS spectra as in Figure 2 but expanded between 850 and 1650 cm

. The pattern in the lower left corner is expanded to show evidence of a change in intensity and an apparent peak shift.

	Further expansion of this region shows shifts in almost all of the ethanol vibrational features, and, in the 1050 to 1100 cm

 doublet, there even appears to be a third band that is not obvious from a visual inspection of the spectra. In addition, because some of the bands apparently shift down as the ethanol concentration decreases, and some shift up, you can eliminate the possibility of an artifact from an unstable wavenumber calibration. For sure, inspection of the literature can provide information on the band assignments as well as bond order and angles in ethanol. With this information, you can then use the shifts to determine how the interactions with the water are affecting the molecular configuration or bonding of the ethanol, or both.


	Figure 4 : 2D-COS asynchronous plot between 950 and 1350 cm

	Figure 5 shows the high frequency part of the spectrum that covers the CH, NH, and OH region. As expected in the synchronous plot, the bands between 2700 and 3000 cm

 are increasing with the ethanol concentration, and the off-diagonal stripes indicate that the OH signal is decreasing in the opposite direction. At first, this last observation is a bit puzzling, because the water and the ethanol both have OH functional groups. The reason for this observation is that the OH signal in the water is higher than that from the ethanol. Expanding the asynchronous plot in the CH region in Figure 6 indicates the possible presence of four null points along the diagonal. What is curious is that these null points do not always superimpose on the spectral peaks. But note also that we really only see three spectral peaks, so, again, we consider the possibility that 2D-COS indicates the presence of unresolved peaks. The fact that the nodes do not coincide with the peaks is simply a result of the 2D-COS signal indicating changes, not absolute signals.


	Figure 5: The 2D-COS synchronous and asynchronous plots of ethanol and water mixtures in the CH, NH, and OH region.

	Figure 7 shows the 2D-COS results of the OH region of the spectrum. Because the OH intensity in the water is higher than in the ethanol, the higher frequency lobe that is assigned to water is increasing; the asynchronous plot shows a redistribution of intensity as the concentration of water is changed.


	Figure 6: Asynchronous plots of CH region of the ethanol and water mixture.

	Figure 8 shows the spectra of ethanol and methanol acquired on the macro Raman instrument with the two laser wavelengths. Looking at these individual spectra enables us to start to predict where to look for effects.


	Figure 7: 2D-COS of the OH region of the ethanol and water mixture. 

	Figure 9 shows the 2D-COS results for the region between 850 and 1150 cm

. Again, the asynchronous plot is where the most information can be seen. The ethanol band, which is fairly isolated in the spectrum, shows similar behavior in the asynchronous plot to what we saw in the ethanol-water system: an apparent peak shift with changing concentration. The more complicated pattern in the upper right hand quadrant is due to the fact that there are overlapping bands of the two species. The asynchronous plot in the low frequency region, shown in Figure 10, also shows a pattern that indicates peak shift with concentration. Figure 11 shows the asynchronous plot in the CH, and OH region. There is no intensity in the OH region, but peak shifting behaviors in the CH stretching bands.

Two-dimensional correlation spectroscopy (2D-COS) can reveal subtle chemical interactions that are difficult to discern when analyzing individual spectra. A demonstration
of the subtleties of this technique can be seen in the analysis of ethanol–water and ethanol–methanol mixtures.

Author | Fran Adar

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