Investigating Problems in Industry and Beyond

Molecular Spectroscopy in Practice

Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy

 Raman Spectroscopy in Analyzing Fats and Oils in Foods

Raman Spectroscopy, an Analytical Technique of Choice

Lithium-Ion Battery Manufacturing and Quality Control

Study of Nondestructive Testing of Nanguo Pear Quality Using Vis-NIR Spectroscopy

While laser metal deposition is a rapidly evolving method for 3D printing and the fabrication of high-quality parts by depositing materials on substrates, the quality of production depends on instrumental design and operational parameters that require constant quality control during the process. Igor Gornushkin and colleagues at BAM Federal Institute for Materials Research and Testing in Berlin, Germany studied the feasibility of using optical spectroscopy as a control method for laser metal deposition, and he recently spoke to us about this work. Gornushkin is the 2022 recipient of the Lester W. Strock Award from the New England Chapter of the Society for Applied Spectroscopy (SAS). This interview is part of an ongoing series of interviews with the winners of awards that are presented at the annual SciX conference, which will be held this year from October 2 through October 7, in Covington, Kentucky.

Your paper (1) examines possibility of detecting surface defects, such as cracks, cavities, and thuds, with optical emission spectroscopy (OES), both theoretically via simulations and experimentally via measuring the optical signal from a sample with artificial defects. How effective is OES in finding defects and do you have good data to support your conclusion? Have you found a way to estimate the percentage of defects that will be found using OES?

OES was proposed as one of many diagnostic tools for detection of defects during additive manufacturing (AM). We did this work in a framework of a large interdisciplinary project initiated in our Institute to help improving the AM process. The other diagnostic techniques employed were thermography, optical tomography, eddy current testing, laminography, X-ray backscattering, particle emission spectroscopy, and photoacoustic methods. Among this group, the OES seemed to be the simplest and easiest to try, so we set a proof-of-principle experiment and ran it under rather artificial conditions. We have not yet come to a stage when we could detect real defects; we only established the validity of the method in detection of artificial defect. The natural defects appear randomly and have unpredictable nature; to find the functionality between the optical signal and printing irregularity, special reference samples would be required that had defects of known types situated at known spatial position. We did not have such samples, therefore, we prepared them by carving artificial defects on a metallic plate. We then verified if OES is able of detecting them. The artificial defects were a strongly exaggerated version of real ones; having samples with only artificial defects, it was difficult to estimate the efficiency of OES for detection of real defects.

Answering your question, yes, with OES, we were able to reliably identify all artificial defects during the printing, although the detection sensitivity was decreasing with each next overlaid printing, because it smoothed underlying irregularities. More work would be needed to answer your question about the percentage of identified real defects.

What inspired you to consider optical emission spectroscopy as a means for online defect measurements?

It is a simplicity of OES plus our previous experience in classification of materials by various chemometric techniques, like using correlation or principal component analyses. The problem of detection of printing defects is alike to a calibration or classification one: in requiring a training set. The calibration requires a set of spectra from a defectless surface; having that, one should find a subset of spectra that are “spoiled” by defects. If a training set contains spectra that are characteristic to known defect types, such as cracks, cavities, or thuds, even more advanced classification is possible that identifies the specific character of a defect.

What benefits did you anticipate by using OES over other analytical techniques for your intended analysis?

Here we speak about diagnostic rather than analytical techniques, because we did not (and could not) use OES for elemental analysis. In comparison to the above-mentioned methods, OES is probably the best suited for the continuous online monitoring of the AM process; the optical head can easily be attached to any printing machine.



Briefly discuss your overall findings and their implications. Were you surprised by the results?

We developed a thermal model of the printing process and combined it with a geometric model of collection of light from a molten metal pool that forms during the printing. This allowed us to calculate emission signals produced by different types of printing defects (grooves, holes, thuds, ripples, and so forth) and compare them to those measured experimentally. By “emission signals,” we understand one of three metrics: integral intensity, temperature, or correlation coefficient, which we plot versus the printing distance during laser scanning. As we initially expected, the signals showed strong variation in the vicinity of defects and that variation turned out to be very specific to a defect type. For example, a surface thud produced the emission signal that looked like a spectral line, and a hole produced the signal that looked like a self-reversed spectral line. Other signals were also very characteristic to defect types. From that, we concluded that OES was capable of not only detection of defects but also identification of their types. That was quite a surprising observation.

What are the biggest challenges that you have encountered in developing this method? What options or alternative developments are available to overcome these challenges or to improve your approach?

The first challenge was a necessity of working with continuous rather than discrete spectra. Under the best printing conditions, the laser created a pool of molten metal on the printed surface with a bright hallo above it. These two produced a broadband emission spectrum, and even though plasma spectra randomly appeared, they were rare and stochastic and not useful for diagnostics. The next problem was strong instability of the emission signal; it was caused by fluctuations in the powder supply rate, appearance of fumes and particulates above the printed spot, and sporadic ignition of plasma. It was a challenge to distinguish the signal change due to the surface defect from that caused by other reasons. Another challenge was a limited spectral range of our spectrometers for the detection of thermal radiation. Our available range was 200–1650 nm, and this was not enough to measure quality blackbody spectra. The extension into further infrared (IR), for instance, to 3 μm, would be beneficial. Yet another challenge was a limited spatial resolution of our instrument. We collected light from a spot that was bigger than even a biggest artificial defect. To improve that, the Czerny Turner spectrometer with an 1D detector can be replaced by the imaging spectrometer with a 2D CCD detector; this could be realized by using a circular-to-linear fiber bundle attached to a slit of the imaging spectrometer.

In the computational domain, the challenge was a need for using a very fine spatial discretization to accommodate small defects; that resulted in many hours of computational time. So, optimization of the software is needed to make computations more economical. Beside the optimization, new features must be added in the model to make it more realistic—these are the flow of a liquid phase and supply of a metallic powder into a molten pool of metal on the printed surface.

What sort of feedback did you receive from your paper and the study results?

Even though the paper is recent (available since September 2021), it has been noticed and received three citations in both theoretical (2) and experimental (3,4) studies. Besides, the colleagues from our Institute, who work with 3D metal printing, rely on our results to continue using OES for monitoring the process.

Are there any additional analytical applications or processes where your findings might be beneficial?

Sure! OES can be used for monitoring and analysis of any process accompanied by optical or thermal emission. Regarding the material processing by lasers, OES is beneficial in laser welding, where plasma is created, and plasma spectrum can provide information about a composition of the welded metal and solder and the heat affected zone. In applications that combine a 3D metal printing and laser induced breakdown spectroscopy (LIBS) (several works are available on this topic), OES is the natural choice. In both the welding and 3D printing, the thermal-emission model, which we developed, can be used to roughly predict process parameters and desirable outcome without doing tedious optimization experiments.

What are your next steps regarding this research?

In theory, we will extend the model to accommodate the liquid motion and powder supply (these features are currently absent). In further experiments, we will do spatially resolved measurements with both the thermal camera and imaging spectrometer. In addition, we plan to use a combination of OES with absorption and light scattering measurements; the correlation of signals from these techniques will improve the detectability of printing defects. We also plan using LIBS for online monitoring the 3D printing; this will allow us to control compositional uniformity of a printed workpiece. Finally, we will implement the data fusion and machine learning methods to make the detection of printing defect a more robust procedure.

I.B. Gornushkin, G. Pignatelli, and A. Straße, 

Igor Gornushkin is a senior scientist at the BAM Federal Institute for Materials Research and Testing in Berlin, Germany. His major expertise is in fundamental and applied spectroscopy including LIBS, emission, absorption, and fluorescence with a focus on modeling and simulation of laser induced plasma and development of spectroscopic methodsfor environmental, industrial, and laboratory applications.

He received his PhD from the University of Florida in 1997 where he worked until 2008, when accepted a position at BAM. Over the last two decades, he and his colleagues developed a comprehensive theoretical model of laser induced plasma, numerous methods for plasma diagnostics and LIBS-analyses. Recent works include application of spatial heterodyne spectrometry for LIBS and Raman qualitative and quantitative analyses, laser induced plasma for chemical vapor deposition and texturing materials, and optical spectroscopy for controlling metal 3D printing. The instrumental works are always being accompanied by theoretical modeling and numeric simulations of relevant processes.

As of now, Igor’s research activity has resulted in ca. 150 publications and several book chapters, which collected over 3500 citations, several patents, and copyrights. He is a member of the editorial board of Spectrochimica Acta part B and recipient of the 2001 Elsevier SAB best paper award. Over the years, Igor has been a mentor to many PhD students from USA, German, and other Universities.

Articles

Igor Gornushkin and colleagues at BAM Federal Institute for Materials Research and Testing in Berlin, Germany studied the feasibility of using optical spectroscopy as a control method for laser metal deposition, and he recently spoke to us about this work. Gornushkin is the 2022 recipient of the Lester W. Strock Award from the New England Chapter of the Society for Applied Spectroscopy (SAS). 

Optical Detection of Defects during Laser Metal Deposition: Simulations and Experiment

Professor Martin Zanni, the Meloche-Bascom Professor of Chemistry at the University of Wisconsin-Madison, and his colleagues explored how to resolve diagonal peaks using a polarization scheme that can be implemented in pump-probe beam geometry—not only in two-dimensional infrared (2D-IR) spectroscopy but also in TA spectroscopy. Zanni is the 2022 recipient of the Ellis R. Lippincott Award. This interview is part of an ongoing series of interviews with the winners of awards that are presented at the annual SciX conference, which will be held this year from October 2 through October 7, in the Greater Cincinnati/Northern Kentucky area.

Transient absorption (TA) spectroscopy is a spectroscopic technique routinely used for biological and chemical applications, including kinetics and nanostructure analysis. Because TA spectroscopy generally deploys only two laser pulses, there are limitations on polarization control. Often in TA spectroscopy studies, cross peaks overlap with diagonal peak features, resulting in spectroscopists’ being unable to determine the molecular structure of what is being studied. Professor Martin Zanni, the Meloche-Bascom Professor of Chemistry at the University of Wisconsin-Madison, and his colleagues explored how to resolve diagonal peaks using a polarization scheme that can be implemented in pump-probe beam geometry—not only in two-dimensional infrared (2D-IR) spectroscopy but also in TA spectroscopy. Zanni is the 2022 recipient of the Ellis R. Lippincott Award. This interview is part of an ongoing series of interviews with the winners of awards that are presented at the annual SciX conference, which will be held this year from October 2 through October 7, in the Greater Cincinnati/Northern Kentucky area.

You and your colleagues conducted a 2019 study that helped determine the role of amyloids in cataracts (1). Why is studying amyloids an important problem to solve, and how can 2D-IR spectroscopy contribute to understanding the relationship between amyloid β-sheet secondary structure and cataract lenses?

Understanding the nature of the disease is an important step in developing treatments. Before our 2D-IR imaging studies, it was not known that cataracts contained amyloid fibrils. Amyloid fibrils are a type of structure in which many copies of proteins stack on top of one another to create long fibrils of very stable beta sheets. Amyloid is involved in many diseases, including Alzheimer’s and Type 2 diabetes. Knowing that cataracts also contain amyloid fibrils and that the fibrils are the deposits that scatter light, this information suggests that those are the structures that one should target with therapies. It is especially intriguing that fibrils form so early in one’s life, much before the symptoms of cataracts. It suggests that a therapy that targets amyloids, if applied early in life, might help prevent cataracts later in life.

2D-IR spectroscopy has some unique characteristics that make it well suited for addressing amyloid deposits in vitro and in tissues. 2D-IR signals scale quadratically with the size of secondary structures and so amyloid fibrils, which are very large, create large and sharp signals that are easily identified. There are other identifying markers, including anharmonicities and cross peaks, that are also useful. 2D-IR spectroscopy can be applied to tissues to create hyperspectral images. This cataract report is one of the first examples of 2D-IR applied to tissues, but we expect many more in the future.

Would you explain for our general readers what is meant by cross-peaks and diagonal peaks when using 2D spectroscopy?

Most spectroscopies produce a peak for each subset of molecules that they are measuring. For example, a spectrum of pentacene has an absorption peak at approximately 600 nm, whereas rubrene is at approximately 550 nm, because they have different conjugated ring systems. In 2D spectroscopy, those absorptions will each create a diagonal peak. For example, if the molecules are electronically coupled to one another because they are touching, then cross peaks will also appear in the spectrum. If the molecules are floating in solution and not interacting, then the cross peaks would be missing. By utilizing various theories or calculations for the coupling, they have enabled the cross peaks to be related to the relative distances and orientations of the molecules or simply the connectivity between molecules or functional groups within a molecule. It can often be difficult to distinguish these two situations, touching or not, with standard absorption spectroscopies. 

 published an article on 2D spectroscopy back in 2013 (2). Figure 1 from that article shows an example of a 2D-IR spectrum of a solution made from two compounds.

Figure 1: Experimental FT-IR and 2D-IR spectra for a mixture of W(CO)

 and a rhodium dicarbonyl (RDC). For each peak in the FT-IR spectrum, the 2D-IR spectrum exhibits a pair of diagonal peaks. The cross peaks in the 2D-IR spectrum reveal that the two higher frequency peaks are coupled to one another, which is because peaks 2 and 3 are from a rhodium dicarbonyl (RDC) whereas peak 1 is from W(CO)

 and RDC do not have cross peaks between them because the mixture is too dilute. (Data collected by Tianqi Zhang.)

Fast forwarding to your most recent study on pulse polarization (3), how precisely does this polarization scheme enhance signal and reduce spectral congestion for 2D spectroscopy and how is this an improvement over previous applications of 2D spectroscopy?

2D-IR spectra were first invented 20 years ago at a time when laser and spectroscopy technology was much less developed than it is now. The technology is 1000 times better now, literally, and both technological and conceptual advances continue to be made. We believe the polarization scheme reported in this 

 article is an important technological step because it allows the diagonal peaks to be removed from the spectra, leaving the cross peaks, which are what give connectivity (couplings) that are related to structure. Figure 2 below shows data using our technique on a model compound with two carbonyl vibrations. The spectra on the left are a normal 2D-IR spectrum, with pairs of diagonal and cross peaks. The spectra on the right are using our polarization trick, which makes the cross peaks the most intense feature. Also shown are transient absorption spectra (often called pump-probe spectra) using the same polarization. In a normal transient absorption spectra, the features that are equivalent to the cross peaks in the 2D spectra are obscured by the much stronger diagonal features. With the new polarization, the only features in the transient absorption spectra are from the cross peaks. Thus, 2D spectroscopy is not necessarily needed. The cross peaks of 2D spectra can be measure using more standard pump-probe spectroscopy when this polarization experiment is used. It is an idea that could have been implemented 20 years ago but was somehow overlooked until my student Kieran Farrell discovered it.

Figure 2: Diagonal peak suppression using a new polarization trick. (left) Standard 2D-IR spectrum and transient absorption spectrum of a model compound in which both laser pulses (the pump and probe) have the same polarization, as show in the top. Both diagonal and cross peaks are observed. In more interesting compounds, with smaller separations or more modes, the cross peaks are not as easily resolved from the diagonal peaks. (right) The new polarization scheme in which the pump and probe pulses are oriented 60° to one another and a polarizer is added after the sample at -60°. The only features now seen in the 2D-IR and transient absorption spectra are the cross peaks.

As mentioned in the literature (2), multidimensional spectroscopy has a significant advantage in that it can observe cross-peaks, but they often overlap with diagonal peak features. What did you do in your study to better resolve cross-peaks that is different from what other researchers have done?

One tricky part about 2D spectroscopy is that the cross peaks are not always well-resolved from the diagonal peaks. They are smaller than the diagonal peaks and so can be obscured if they are close to the diagonal. Several techniques have been developed to better resolve the cross peaks. One of the most successful techniques is using polarizations of the laser pulses. Many people think that absorption spectroscopies involve a single-laser pulse and pump-probe spectroscopies involve a two-laser pulse (a pump and a probe), but molecules need to interact twice with the electric field of each laser pulse. Thus, there are two electric field polarizations in absorption spectroscopy and four in pump-probe spectroscopy. Each field polarization can be set independently with the right kind of spectrometer. 2D spectroscopies are like pump-probe spectroscopy and have four electric field interactions. It turns out that if the absorption of two molecules is not parallel to one another (absorptions have directionality, vectors, such as along the length of conjugated rings in pentacene), then the cross-peak intensities and phases differ from the diagonal peaks. As a result, a spectrometer that has all four electric field polarizations set at different locations will remove the diagonal peaks from the spectra, leaving just the cross peaks that give the structure information that everyone is after.

Your study demonstrated that polarization could expand the capabilities of pump-probe spectroscopy measurements, including TA and 2D spectroscopy. What are your next steps in this work, and which applications would benefit from testing this new polarization scheme?

The elimination of the diagonal peaks from the spectra significantly decongests the spectra, resolving cross peaks that would otherwise be unnoticeable. We are currently using the technique to study amyloid aggregation of a hormone, hIAPP, involved in Type 2 diabetes. We are uncovering features in hIAPP that have been hidden to us for the past 10 years. But its application is also much broader. There are thousands of pump-probe spectrometers on the planet, all of which can be easily modified to do this polarization trick and thereby measure the same cross peaks that was previously relegated to 2D spectroscopy. Hyperspectral microscopy is also an exciting application. By adding a polarizer after the sample in a transient absorption microscope, one only collects the spectra of cross peaks. Thus, images that report on the connectivity of molecules can be measured. In our own research, I do not expect that we will ever again publish a manuscript that does not utilize this polarization trick!

A.M. Alperstein, J.S. Ostrander, T.O. Zhang, and M.T. Zanni,

 is the Meloche-Bascom Professor of Chemistry at the University of Wisconsin-Madison. He received his PhD from the University of California-Berkeley, working with Dan Neumark, and was an NIH Postdoctoral Fellow at the University of Pennsylvania with Robin Hochstrasser. He is one of the early pioneers of 2D-IR spectroscopy and has made many technological innovations that has broadened the capabilities and scope for a wide range of multidimensional spectroscopies and microscopies. He utilizes these new techniques to study topics in biophysics, chemical physics, photovoltaics, and surfaces. He has received many national and international accolades for his research. Notably, he is the only person to have received the ACS Nobel Laureate Signature Award as both a student and a mentor and the first person to receive both the Craver and the Coblentz Awards. He founded PhaseTech Spectroscopy Inc., which is the first company to commercialize 2D-IR and 2D Electronic spectroscopies. Direct correspondence to: 

Resolving Diagonal Peaks in Two-Dimensional and Transient Absorption Spectroscopy Using a New Polarization Scheme

The global cost of corrosion imposes a significant burden on society, with safety and environmental consequences in addition to its direct impact. One challenge to its management is that the process of corrosion is complex, and common experimental techniques provide only limited information. The neutral salt spray test, a standardized test used for the evaluation of the corrosion resistance of steels and coatings, creates an aggressive environment not suitable for many analytical methods. Here, we report the first use of in situ Raman spectroscopy to detect the formation, growth, and evolution of corrosion products on metal surfaces, monitoring a mild steel plate in alternating salt fog and dry atmospheric conditions over a period of eight days to identify and track key corrosion products. Assisted by multivariate curve resolution alternating least squares (MCR-ALS) analysis and online weighing of the sample, we found the chemical conversion of iron hydroxides to oxides takes much longer than the physical drying of the corroded surface. Together, these results pave the way for real-time online observation of corrosion inside a salt fog chamber to obtain chemically relevant information with a single, compact apparatus.

Corrosion is the process of degradation of metals because of atmospheric phenomena or environmental conditions, incurring costs of approximately 3.4% of the global gross domestic product (1). To combat corrosion, many coatings and alloys have been developed to extend the life span of steel products. Research institutions and industries, including but not limited to the steel, automotive, oil and gas, and aerospace industries, have adopted different techniques to understand, predict, and ultimately control corrosion-related processes (2). The key to improving these techniques is a better understanding of the corrosion mechanisms involved.

Standardized tests, such as the neutral salt spray (NSS) test, are used to accelerate the corrosion process for research purposes (3). Samples are placed in a chamber with a fog of neutral 5% NaCl solution at 35 °C. Once the samples are corroded in the chamber, they are removed and can be analyzed with a variety of methods (4–6). Raman spectroscopy is a common analysis technique that can provide detailed chemical information with high specificity (7–9).

In contrast to such offline analyses, here we present for the first time a setup that collects in situ Raman spectra inside a working salt fog chamber, demonstrating the feasibility of online investigation of the corrosion process in mild steel samples (also known as low carbon steel).

In this study, we used a custom tabletop corrosion chamber constructed from a glass basin 45 x 25 x 30 cm in size (see Figure 1). A solvent-cleaned sample of 10 x 10 cm mild steel (DC04, voestalpine Stahl GmbH) was corroded for 8 days as follows: 48 h of salt fog, 48 h of drying, an additional 72 h of salt fog, and a further 24 h of drying. To generate salt fog, the basin was filled with several centimeters of a neutral 5.0 wt% sodium chloride solution (NaCl, Pharmasal, Salinen Austria AG). The salt fog was produced by a floating ultrasonic fog generator, and homogeneously dispersed with a stirrer. To monitor sample weight changes, the steel plate under study was suspended from a balance which was mounted on top of the chamber. The sample was placed at a 20° angle from vertical to facilitate run-off of the condensed salt water. During the salt fog experiments, the temperature of the sample and chamber was kept at 35±2 °C. These settings are similar to standardized tests such as EN ISO 9227 NSS (3) or ASTM B117 (10). The drying was performed in laboratory atmosphere conditions at 23±2 °C with 40–60% relative humidity.

FIGURE 1: Schematic representation of custom tabletop salt fog chamber with Raman probe.

The in situ Raman measurements were performed with a hermetically enclosed Raman probe pointed perpendicular to the sample surface and observed the same spot throughout the experiment. The working distance was kept to within ±0.5 mm of the optimal working distance of 11 mm to maintain good signal-to-noise (S/N) ratio, maintaining a 90° sample-laser angle for optimal signal intensity. The presence of salt fog in the chamber reduced the Raman signal intensity by approximately 50%.

The Raman system used for monitoring was comprised of a WP-785-R-SR-LMMFC-25 spectrometer (Wasatch Photonics) with an integrated diode laser (450 mW, 785 nm), connected to an RP-785 Raman probe (Wasatch Photonics) using a 600 μm core SMA-coupled detection fiber and a 550 μm core FC-coupled laser delivery fiber. The laser spot size on the sample was 900 μm, large enough to prevent local heating of the corrosion products, which could induce background emission in the Raman spectrum. Spectra were recorded at 6-min intervals with an integration time of 1 s, and 25 averages. The laser was turned off between acquisitions, and a new averaged dark spectrum was acquired prior to every measurement. The laser power was set to the full 450 mW during the salt fog periods, but reduced to 300 mW during dry conditions to avoid local sample heating.

Figure 2 shows examples of Raman spectra obtained for the corrosion process of the sample during the four phases of the experiment (salt fog I and II, drying phases I and II). Before the start of corrosion (0 h), the signal represents the broadband Raman signature of the encapsulation windows and focusing lens.

FIGURE 2: Representative in situ Raman spectra recorded during the different phases of the corrosion experiment with tentative assignments to corrosion products (top figure), and a schematic representation of the deduced chemical transformations (bottom).

The emergence of the low-intensity bands at 297 and 378 cm

 (marked with solid diamonds) during the first salt fog phase marks the onset of corrosion with appearance of α-FeO(OH), also known as goethite (11–12). During the first drying phase, bands with more significant Raman emission at 289, 402, 489, and 604 cm

 (marked with solid triangles) were observed. Using library spectra (13) and literature spectra (14,15) with very similar band positions, we assign these bands to hematite (α-Fe

). Therefore, we interpret these spectral changes as the drying of the iron oxyhydroxides to iron oxides through the removal of chemically bound water.

Additional features were observed at 647 cm

 (marked with the open triangle and the star, respectively). Based on library spectra, we assign these bands to magnetite (Fe

) (17), respectively. We interpreted the emergence of the siderite band as an indication of a chemical reaction between the sample surface and CO

 from the laboratory atmosphere during the drying phase.

During the second salt fog phase, we observed bands at 306, 346, 376, and 525 cm

 (marked with the open diamonds). We assign these bands to lepidocrocite (γ-FeO[OH]) (18). An additional band at 649 cm

 was observed, and can again be assigned to magnetite (marked by an open triangle, as before). The persistence of magnetite throughout the salt fog phase can be explained by the stable nature of this iron oxide.

During the second drying phase, many of the aforementioned bands attributable to iron oxyhydrides and oxides reappeared (as marked), namely hematite, lepidocrocite, and magnetite, although it is hard to distinguish the α and γ oxyhydroxides bands around 376 cm

. Hence, the nature of the exact crystal structure present was uncertain.

In addition to qualitative assessment of the chemical changes, in situ Raman observations also allowed the extraction of dynamic information about the corrosion process. Even though corrosion occurs slowly, changes in the spectrum could be observed within a few hours, as shown in Figure 3. The bands at 298 and 376 cm

 (assigned to α-FeO(OH) above) emerged once the sample was exposed to the salt fog.

FIGURE 3: Evolution of the in situ Raman spectra during the first hours of a neutral salt fog exposure indicating the formation of corrosion products on mild steel.

It was also possible to monitor and analyze a shift in the position of the dominant Raman band during drying phase I and II, as shown in Figure 4, revealing a shift in intensity from the band at 374 cm

. We interpret this spectral change as the transformation from FeO(OH) to α-Fe

FIGURE 4: Recorded Raman spectra of a corroding mild steel sample during drying phase I (left) and II (right) in a salt fog experiment designed to monitor corrosion.

The position of the main peak within this spectral window was determined through quadratic interpolation across the band maximum. Additionally, as shown in Figure 5, we compared the resulting shift of the maximum Raman peak position (in blue) with the recorded weight changes (in green). The sudden weight changes were caused by surface water evaporation in the drying phase and sample wetting in the salt fog phase, respectively, and take approximately 1–2 h to occur.

FIGURE 5: Weight changes of the steel sample compared to the shift to the main peak around 375–400 cm

 measured by the position of the most intensive peak in this region, indicating the transformation from FeO(OH) (375 cm

Comparing the sudden weight changes during drying phase I to the change in Raman band position, we find a much more gradual change in the spectrum, taking about 30 h. Based on earlier band assignments, this spectral change can be attributed to the chemical transformation from α-FeO(OH) to α-Fe

. For the second drying phase, Figure 4 illustrates a decreasing band at 376 cm

, leading to the appearance of an instantaneous transition of the maximum in Figure 5. We assign this change to the transformation from γ-FeO(OH) to α-Fe

, which may explain the difference between drying phases I and II.

Using in situ Raman spectroscopy, it was possible to identify the chemical transformations and also determine their timelines. Access to this information would be of significant benefit to both fundamental corrosion research and in the development of new coatings and alloys.

The complete set of in situ Raman spectra recorded during the week-long experiment contains a full representation of the chemical changes during this corrosion process. Multivariate statistical analysis tools can be used to transform the full set of spectra into a sum of spectra from individual chemical species, each with a contribution varying as a function of time. Therefore, this analysis provides a potential chemical interpretation of the process based on the recorded set of in situ Raman spectra.

One such analysis is multivariate curve resolution (MCR), often using alternating least squares (ALS) during the fitting process. The analysis is dependent on the chosen starting values; here, we use the orthogonal projection approach (OPA) to estimate the time changes of each species’ contribution. Five components were fitted, with the four main contributions discussed below. This number was determined through a combination of principal component analysis (PCA), chemical intuition, and trial and error. It should be noted that MCR-ALS has difficulties separating spectral contributions that are significantly correlated in time.

The MCR-ALS analysis resulted in spectra for four components, shown in Figure 6, which were assigned to chemical species based on the spectra referenced above. Three components were assigned as “FeO(OH),” “Fe

.” However, the fourth component was not clearly identifiable and is hence simply labeled as “other components.”

FIGURE 6: Component spectra from MCR-ALS multivariate analysis of the entire experiment. The component spectra are tentatively assigned to actual chemical species based on best agreement with published band positions.

Based on these component spectra, the MCR-ALS analysis generated a visualization of the change in each component’s contribution to the experimental spectrum as a function of time, as shown in Figure 7. These plots represent the dynamics of the process, observed with in situ Raman spectroscopy, but interpreted from a chemical perspective. However, as MCR-ALS is a fitting technique, the results can only show one potential representation of the experimental spectral data set.

FIGURE 7: Dynamics and time profile of the contributions of the four components to the experimental spectra set, as determined with MCR-ALS analysis, here tentatively assigned to four chemical species as indicated.

The MCR-ALS results show even more clearly the slow transition from oxyhydroxides to oxides during the drying phases of the experiment, both in the disappearance of the “FeO(OH)” contribution, as well as in the appearance of the “Fe

“ contribution. The sudden jump in the various contributions during the first drying phase most likely stems from a small shift in the observed location, or some macroscopic change in the monitored spot on the sample surface.

This study shows how in situ Raman spectroscopy can facilitate the investigation of surface chemistry processes, such as the corrosion of mild steel, even in a harsh salt fog environment. Detailed chemical insights into the corrosion process that would otherwise be inaccessible can be obtained.

By performing a cyclical corrosion experiment consisting of alternating salt fog and drying periods for 1–3 d each, we find that initially α-FeO(OH) is formed after about 2 h of salt fog exposure. During the first drying phase, a slow change to α-Fe

 was accompanied by the formation of iron carbonate and magnetite; while the former rapidly converts to a mixture of γ-FeO(OH) and α-FeO(OH) during the second salt fog phase, some magnetite remains on the surface. The second drying period leads to a continuous conversion from iron oxyhydroxides to iron oxides. The results show that physi- cal drying of the sample occurred within 2 h (in contrast to the chemical conversion, which took about 24 h), with MCR-ALS analysis of the spectra revealing the detailed dynamics of these transformations.

The results confirm that a compact Raman spectroscopy setup can be used to monitor and understand complex corrosion processes taking place in a custom salt fog chamber, allowing new insights to be generated for academic and industrial corrosion investigations. The coupling of in situ Raman spectroscopy and a salt fog chamber represents a valuable tool for future investigations of corrosion mechanisms, and for evaluating the resistance of newly developed coatings and alloys.

The Comet Center CEST is funded within the framework of COMET—Competence Centers for Excellent Technologies by BMVIT, BMDW as well as the Province of Lower Austria and Upper Austria. The COMET program is run by FFG. This work originates from research in iMESA (FFG 865864, CEST-K1, 2019–2022) project. The authors gratefully thank voestalpine Stahl GmbH and Recendt GmbH for support, and Wasatch Photonics for providing the Raman spectroscopy system.

(1) G. Koch, J. Varney, N. Thompson, O. Moghissi, M. Gould, and J. Payer, “International Measures of Prevention, Application, and Economics of Corrosion Technologies Study” (NACE International, Houston, TX, 2016), pp. 1–6.

(2) S.K. Dhawan, H. Bhandari, G. Ruhi, B.M. Bisht, and P. Sambyal, 

Corrosion Preventive Materials and Corrosion Testing

(3) International Organization for Standardization, “Corrosion Tests in Artificial Atmospheres — Salt Spray Tests,” ISO 9227, 2017.

(4) E.D. Kiosidou, A. Karantonis, G.N. Sakalis, and D.I. Pantelis, 

, 127–150 (2018). DOI: 10.1016/j.corsci.2018.03.037.

(5) M. Ito, A. Ooi, E.Tada, and A. Nishikata, 

(10), 101508 (2020). DOI: 10.1149/1945-7111/ab9c85.

(6) J. Duchoslav, M. Arndt, T. Keppert, G. Luckeneder, and D. Stifter, 

(22), 7133–7144 (2013). DOI: 10.1007/s00216-013-7099-3.

(7) K. Hedenstedt, J. Bäckström, and E. Ahlberg, 

(9), H621–H627 (2017). DOI: 10.1149/2.0731709jes.

(8) T. Doi, T. Adachi, T. Kudo, and N. Usuki, 

, 108931 (2020). DOI: 10.1016/j.corsci.2020.108931.

, 207–218 (2017). DOI: 10.1016/j.jnucmat.2017.05.043.

(10) ASTM International, ASTM Standard B117-19 “Standard Practice for Operating Salt Spray (Fog) Apparatus” (West Con- shohocken, PA, 2019).

(11) Raman spectrum RHX124, KnowItAll Informatics System 2021 (John Wiley & Sons, Inc., Hoboken, NJ, 2001–2021)

(3), 941–948 (2009). DOI: 10.1111/j.1365-246X.2009.04122.x.

(13) Raman spectrum RMX267, KnowItAll Informatics System 2021 (John Wiley Sons, Inc., Hoboken, NJ, 2001–2021)

(4), C147–C154 (2012). DOI: 10.1149/2.013204jes.

, 940–950 (2017). DOI: 10.1016/j.eng-failanal.2017.03.022.

(16) X. Nie, X. Li, C. Du, Y. Huang, and H. Du, 

(17) Raman spectrum RHX183, KnowItAll Informatics System 2021 (John Wiley & Sons, Inc., Hoboken, NJ, 2001–2021)

(18) Raman spectrum RHX145, KnowItAll Informatics System 2021 (John Wiley & Sons, Inc., Hoboken, NJ, 2001–2021).

 are with the CEST Competence Center for Electrochemical Surface Technology GmbH in Linz, Austria. Dieter Bingemann and Cicely Rathmell are with Wasatch Photonics. 

 are with voestalpine Stahl GmbH in Linz, Austria. 

 are with the Vienna University of Technology in Vienna, Austria. 

 are with the Research Center for Non Destructive Testing GmbH in Linz, Austria. Direct correspondence to: 

In this study, in situ Raman spectroscopy was used to detect the formation, growth, and evolution of corrosion inside a salt fog chamber. These results pave the way for monitoring the real-time observation of corrosion on metal surfaces.

In situ Raman Spectroscopy Monitors the Corrosion of Mild Steel in a Salt Fog Chamber

In the last 30 to 40 years, various new types of carbon materials have been engineered for multiple industrial uses. It is now well-known that the Raman spectrum is sensitive to the structure, even though the spectrum is rather uncomplicated. Because Raman spectroscopy now has a reputation for providing good information, potential users of Raman equipment can request information on the quality of their sample. However, they are often not able to define clearly what they mean by “quality.” If they are growing diamond films, they may or may not want interstitial sp

 carbon to glue polycrystalline diamond together. If they are growing hard diamond-like carbon (DLC) films, they may want to correlate the spectral characteristics with physical characteristics of the film. In this column, I explain how the Raman characteristics can aid in characterization of carbon materials.

 is the Principal Raman Applications Scientist for Horiba Scientific in Edison, New Jersey.

There are essentially two ways that elemental carbon bonds to itself—sp

. The atoms crystallize in a tetrahedral lattice, and the zone center phonon falls at 1332 cm

. In perfect graphite, all the carbons are arranged in infinitely extended aromatic planes that stack in the A-B-A-B arrangement. The zone center phonon falls near 1580 cm

, and has been called the G band. However, graphite rarely appears without a second band in the mid-1300 cm

 region. In 1970, Koenig reported that he could correlate the relative intensity of this band to the crystallite size of the graphite sample being measured, as determined by X-ray diffraction (XRD) (1); the intensity of this band that came to be known as the D (“disorder”) band was related inversely to long range order. It was believed that the D band simply represented the disorder in graphite. However, in 1978, Vidano and Fischbach and, in 1990, Wang, Alsmeyer, and McCreery found that its frequency depended on the laser excitation wavelength. By exciting graphite with varying laser wavelengths they observed that the D band occurred near 1390 cm

 when excited in the UV, and 1270 when excited in the NIR (2,3). They also documented the presence of a band in the mid-2700 cm

 region that fell close to precisely twice the frequency of the D band. In the discussion by Wang and associates, they surmised that the dependence of the D band frequency on the excitation wavelength followed from each wavelength exciting different subpopulations of the materials. However, in 2004, Reich and Thomsen produced a theory based on double resonance that explained the “excitation-energy dependence (of the D band), the overtone spectrum, and the difference between Stokes and anti-Stokes scattering” (4). Although the theory is an elegant solid-state physics exposition of the energy band structure and its effects creating double resonance Raman conditions, it is probable that many readers of this article would not be in a position to become competent in this area. The point is to be cognizant of the best understanding of the origins of the Raman spectra of carbon-based materials so that the spectra can be used to derive empirically physical and chemical information on manufactured materials.

Before the double resonant theory was developed, the D nomenclature was further used to denote “diamond-like,” which was probably a misnomer. The term came from the development of hard carbon films for the computer hard disc industry where the conclusion was that the hardness of the films was related to the D or “diamond-like” band, whose frequency was not far from that of diamond. In fact, the spectra of these films were fit to D and G bands and the characteristics of the fits (peak positions, peak widths, peak amplitudes, % Gaussian in the fitting function, and integrated intensity in the fits) were used to correlate to the physical characteristics of the films. Because of the wavelength dependence of the D band intensity, for empirical correlations to be effective, measurements must be made in a consistent manner—same excitation wavelength, same spectral dispersion, and so forth. Irmer and Dorner-Reisel explained the spectral behavior related to the size of graphitic nanoclusters, hydrogenation, and hydrogen-free tetrahedral amorphous carbon (ta-C) with sp

-bonded content, and how these properties are correlated with hardness and wear properties (5).

In this column, I show examples of measurements of all these materials, so that someone interested in using the spectra for empirical correlations has a roadmap of where to start.

, as seen in Figure 1. A clean diamond has a flat baseline, with some evidence for the second order band near 2600 cm

. Figure 1 shows the Raman spectrum with a picture of the small chip from which the spectrum was acquired. Figure 1 also shows the spectrum plotted in wavelength units, rather than Raman units, and shows the photoluminescence (PL) that sometimes appears in diamond because of impurities. These PL spectral features have been used to characterize diamond, both natural and man-made (made under pressure or by chemical vapor deposition [CVD]) (6).

FIGURE 1: (a) Photoluminescence and (b) Raman spectra of a small diamond chip (insert image).

Figure 2 shows the results of a mapping measurement of a polycrystalline diamond film. The two colored maps were made using the Raman frequency of the two peaks fitted by the software. This material is clearly not high quality, single crystal diamond, but if the small crystals are grown to be used for polishing, the material would work well. If, however, one wanted a diamond window, this sample would clearly not be useful. In addition, it is useful to point out that the peak shifts in this data set are understood to be because of stress.

FIGURE 2: (a) Photomicrograph of a polycrystalline diamond film. (b) Representative Raman spectra illustrating the multiplicity of bands all over this film. (c, d) The mapping renditions are color-coded for the peak frequency for the two bands, (c) the lower band, and (d) the higher band.

Figure 3 shows the spectrum of crystalline graphite measured with a 600 and 1800 g/mm grating on the same instrument. Note that the relative intensities do not exactly overlay because of differences in line widths relative to the instrument’s dispersion with the two gratings. Because of this phenomenon, one must make measurements under consistent conditions if meaningful correlations from the spectra want to be derived. Note that the D band appears in this spectrum because truly single crystal natural graphite is rare.

FIGURE 3: Raman spectrum of graphite recorded with (a) the 1800 vs. (b) the 600 groove per mm gratings.

Figure 4a shows a Raman map of graphene. Graphene is technically a single layer of graphite, but the spectra of several-layer graphene have been used to determine the number of layers. In the figure, the sets of red, green, and blue cursors were used to calculate the integrated intensities of the D, 2D, and G bands. Clearly, the D band is quite low in intensity in this sample, which is expected for a near perfect lattice. A mark of true single layer graphene is a high I(2D)/I(G) ratio, which can be seen in the light teal map on the lower right. However, the intensity maps do not show the entire picture. Figure 4b shows a few spectra extracted from the map, indicating how the spectra change in this data set from point to point. A good reference for the Raman spectra of graphene is an article in 

 (7) which discusses many interesting phenomena that can be monitored with Raman spectra.

FIGURE 4a: Map of graphene on silicon. Upper right is micrograph, with box showing area of map. Upper left shows all the spectra in the mapped area, overlaid. Lower right shows the mapped rendition of the three graphene bands; the colors in the map correspond to the colors of the cursors in the upper left part of the figure. The teal colored map represents the ratio of the 2D to G bands. The higher the ratio, the fewer the layers of graphene.

FIGURE 4b: Left: three spectra extracted from the map in Figure 4a. Note the differences in the 2D to G intensities, as well as the line shapes. The arrows indicate where on the map each spectra was generated.

There is also the possibility of monitoring single-walled and multi-walled carbon nanotubes (SWCNTs and MWCNTs). In addition to the D and G bands (and their derivatives discussed above), the SWCNTs have bands in the low frequency region(<300 cm

), which are diagnostic of the chirality and size of the tube. However, the intensity of the spectra are extremely dependent on the type of tube, which changes the resonance conditions. That means, if you are looking for a particular type of tube, you may or may not see it with the laser wavelength that you are using. There are many good references on the solid states physics of these materials, but in another 

 article, there is a figure that clearly shows the relationship between the excitation wavelength and the type of tube measured (Figure 12 in reference [8]).

We have also measured the spectra of various carbon fibers that were shared with us by Herman Noether, retired from Hoechst Celanese. Thornel was manufactured from pitch, and its structure was known to exhibit radial graphite planes. Other fibers manufactured from polyacrylonitrile (PAN) exhibit turbostratic graphite planes that wrap around the fiber axis. Figure 5 shows the spectra of these three samples. Both Thornel and PAN GY70 were fairly crystalline as determined by their Raman spectra, both of which have sharp G and D bands. The PAN fiber called Celion 3000 exhibits very broad D and G bands and very weak features in the 2D region. I have band-fit all three spectra. Thornel and GY70 both exhibit a weak but sharp band between 1610 and
1620 cm

, and this band has been seen in intercalation studies. To fit the Celion spectrum, two extra bands are required to flatten the residual. If you will be using these spectra to characterize or compare samples, again the samples have to be oriented in the same way, and all instrumental conditions have to be the same (laser wavelength, spectral dispersion, and polarization). Then, you can export the fitted parameters to an Excel file to determine quantitatively how the samples compare. In fact, you can correlate physical chemical properties with these parameters, even if you do not understand what they mean at the nanoscopic level. Tables Ia–Ic show the fitted parameters from these spectra.

FIGURE 5: Raman spectra of carbon fibers of 3 different structures, (a) heat treated PAN (polyacrylonitrile) gy70 graphite fiber, (b) Thornel graphite fiber pulled from pitch, and (c) PAN Celion 3000 fiber. The cartoon figures on the left represent the structure of these fibers.

An example of how these spectra can be useful is something I came across on the internet while assembling this article. An article that goes back to 1979 discusses the uptake of certain metal chlorides that are used to treat carbon fibers to raise their electrical resistance. This study was sponsored by NASA because of there being several industrial accidents that were a consequence of electrical sparking in manufacturing facilities (9). What was found was that the chemical uptake was much higher in the pitch fibers than in the PAN fibers. Looking at the cartoon figures that I made for Figure 5, it is easy to understand why there is more penetration in the pitch fibers than in the PAN fibers. Polarized Raman spectra would enable differentiating PAN from pitch fibers because of the orientation of the graphite planes in the fiber.

There is also a material called glassy carbon, a spectrum of which I show in Figure 6. Note that it also has D and G bands, wide enough to overlap a bit, but sharper than the bands of DLC, which you will see in a moment. In addition, the D band is more intense than the G band which is rather unusual. According to Professor William White at Penn State, the adjective “glassy” for this material is actually a misnomer. It is highly crystalline, but the crystals are quite small. Professor White told me this more than 30 years ago (10), and the current theories of the structures of sp

 carbons might affect this description, but if you do a Google search on the Raman spectrum of glassy carbon, most spectra are similar to the one in Figure 6.

FIGURE 6: Raman spectrum of glass-like carbon exhibiting medium sharp bands, with the D band more intense than the G band.

The Raman spectra of these films have been used extensively for physical characterization. Figure 7 shows the spectra of two such films, with the band-fitting, and Table II shows the band-fit parameters extracted and imported into Excel for further manipulation that will enable correlation with physical properties. There is an excellent review by Whitley that describes how thistechnology is used in the computer disk industry (11). The figure shows two spectra, both of which were successfully fit to a D and G band. I say “successfully fit” because the residual shows only noise, but no indication of the necessity for another band. Comparison of the D bands in the two spectra indicate significant differences in band width. More interesting, however, is the I(D)/I(G) ratio when using the highest count rate in the region of the two bands vs. the amplitude (height) of the fitted peaks vs. the integrated intensities of the fitted peaks. Table III shows the results of these simple calculations for the two samples. Reading across the table from left to right for each sample, it is clear that the calculated ratios are quite different. People tell me that they want to know the D/G ratio as a quality index. But which ratio do they really want? The problem is that the non-spectroscopist is not aware of this complexity. It appears that calculating the D/G ratio should be a simple calculation, but, clearly, the answer is different depending on how the calculation is done. Which one will produce the best correlation with the desired properties? Even if you do not understand what is the origin of these spectral characteristics, you can see that what you choose to calculate will determine what number you will get. Maybe the thing to do is to correlate separately all of these ratios to the physical characteristics of the sample to see which one actually predicts the data the best.

FIGURE 7: Raman spectra of two DLC films that exhibit clear spectral differences that are further clarified in the band-fitting.

We showed representative spectra of many classes of carbon allotropes, with an indication of the origins of the spectra. We tried to indicate what about the spectra can be important for some obvious applications, providing literature references in order for the reader to start planning their work.

(3) Y. Wang, D.C. Alsmeyer, and R.L. McCreery, 

(6) www.gia.edu/gems-gemology/spring-2016-photoluminescence-spectroscopy-diamond-applications-gemology

(7) L.M. Malard, M.A. Pimenta, G. Dresselhaus, and M.S. Dresselhaus, 

(8) M.S. Dresselhaus, G. Dresselhaus, R. Saito, and A. Jorio, 

(9) T.E. Thompson, NASA-CR-166201 Carbon Fiber Modification NASA Contract NAS 2-10091 Nov 1979 SRI Project 7976

(10) Private Communication, Professor William White, Penn State University. 

(11) A. Whitley, “The Use of Raman Spectroscopy to Monitor the Quality of Carbon Overcoats in the Disk Drive Industry,“ in 

Handbook of Raman Spectroscopy from the Research Laboratory to the Process Line

, I.R. Lewis and H.G.M. Edwards, Eds. (Marcel Dekker, Inc. New York, NY, 2001), pp. 975–998.

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

Here, we explain how Raman spectroscopy is valuable for characterizing industrially important carbon materials.

Use of Raman Spectroscopy to Qualify Carbon Materials

A transdermal patch is a method of drug delivery through the skin into the bloodstream that is composed of layers that store and release the active pharmaceutical ingredient (API) over time. The ability to analyze these layers without damaging the patch allows for quality control and stability measurements. Raman spectroscopy has excellent chemical identification abilities, thanks to the fingerprint like spectrum it produces, that deliver rapid discrimination between components typically found in transdermal patches, such as polymers and APIs. Coupling this with a confocal microscope gives high spatial resolution on all three axes, with the pinhole providing otherwise unachievable axial resolution, permitting depth profile analysis. Using this technique, the patch can be analyzed from top to bottom without any risk of damage. This article highlights the use of confocal Raman microscopy to acquire a 3D Raman map of a transdermal patch for layer evaluation. The map revealed six layers which were composed of four polymers and the API layer at the center of the patch.

A transdermal patch is a method of drug delivery through the skin into the bloodstream (Figure 1). The patch contains a drug that absorbs into the skin; the patch releases a consistent and controlled amount of medication into the body over long periods time. This method of delivery is preferred in cases where the drug can have negative side effects on the gastrointestinal tract, for prolonged drug release, or as an option for people with a fear of needles looking to avoid medication delivered via injection.

FIGURE 1: A typical transdermal patch and its layer composition.

One of the most common uses for transdermal patches is to help combat nicotine addiction. Nicotine patches were first developed in the 1980s as a method to help people quit smoking after several studies proved the absorption of nicotine into the body reduced cravings. Nicotine replacement therapy (NRT) releases small amounts of nicotine throughout the day, without the additional harmful components of cigarettes, such as tar. A recent review of data from the public and from clinical trials revealed that using NRT can increase the chance of successfully quitting smoking by up to 60% (1).

Analyzing multilayer polymers, such as transdermal patches, can provide manufacturers and researchers with important information on the quality of their product. Probing into the layers allows validation of the amount and distribution of the active pharmaceutical ingredient (API) within a patch, as well as identifying any contaminants. Achieving this without sample damage or alteration is critical for providing meaningful data on real-life products. Raman microscopy is an extremely sensitive technique for polymer identification, and coupling to a confocal microscope allows high spatial resolution in the 

-axis for investigating different layers.

Raman spectroscopy can be used for identification of polymers due to its fingerprint-like level of sensitivity. Each peak in a Raman spectrum can be thought of as the fingerprint of a specific molecular vibration from the sample, meaning polymer spectra can easily be distinguished. For example, the technique is sensitive enough to identify co-polymers and determine differences between high- and low-density polyethylene. Furthermore, its non-destructive nature allows analysis of samples to be analyzed without any damage or need for sample preparation (2).

Databases of known polymers can be developed, meaning unknown samples can be compared to these spectra for identification, thus adding to the speed and accuracy of polymer identification by Raman spectroscopy. For example, the database applies a best fit comparison of the measured spectra with those in its library, and carries out a series of optimized corrections to provide the best possible match against its database.

The skill set of Raman spectroscopy can be further enhanced by incorporating a confocal microscope where the Raman laser will be directed though a microscope containing a pinhole, enabling the analysis volume to be defined with simultaneously high lateral (

) resolution, allowing the analysis of discrete sample features and layers down to less than 1 μm in size (3).

When laser light is focused into a sample by a Raman microscope, a three-dimensional excitation volume is formed within the sample. This volume is known as the 

, and a representation of its shape is shown in Figure 2a. As the light converges towards the focal point, the diameter of the PSF decreases, reaching a diffraction-limited minimum diameter at the focal plane followed by an increase in diameter as the light diverges. The exact shape of the PSF is heavily dependent on the optical properties of the sample, such as opacity at the excitation wavelength, refractive index, and how it scatters the incident excitation. Raman scattering will occur throughout the entire excitation volume, and simply measuring the Raman spectrum from this excitation would, therefore, result in spectral contributions from the entire volume with limited axial (

FIGURE 2: Role of the confocal pinhole in a Raman microscope: (a) representation of the excitation 3D point spread function, (b) excitation cone induces Raman scattering throughout the sample volume, (c) Raman scatter originating from above the focal plane is rejected by the confocal pinhole, (d) Raman scatter originating from below the focal plane is rejected by the confocal pinhole, (e) Raman scatter originating from the focal plane is passed through the pinhole and enters the spectrograph for detection.

The role of the confocal pinhole is to spatially filter the analysis volume so that the Raman scatter is detected from the focal plane only; its operation is illustrated in Figures 2b-e. Raman scatter that originates either above Figure 2c or below Figure 2d the focal plane is not brought to a focus in the confocal plane; rather, it is blocked by the confocal pinhole and not detected. In contrast, Raman scatter that originates from the focal plane is brought to a focus in the confocal plane and passes through the pinhole and is detected. This is the meaning of the word confocal; to share the same focal point, and Raman scatter from inside the excitation focus will also be in focus at the pinhole (4).

The addition of the pinhole allows for 3D analysis, which is particularly useful for multilayer samples, such as composite plastics. The polyethylene terephthalate (PET) and polyvinyl chloride (PVC) polymers have unique Raman spectra, and the Raman depth profile can identify the discrete layers within the PET-PVC-PET polymer stack. It can clearly be seen in Figure 3 that, as the diameter of the pinhole is decreased from 100 μm to 25 μm, it becomes increasingly effective at blocking the out-of-focus scatter (increased confocality), and the effective axial resolution improves. Using narrow pinhole diameters, axial resolutions of <1 μm can be achieved. The tradeoff with narrowing the pinhole diameter is that the throughput of the microscope is significantly lowered, and measurements will therefore take longer, due to decreased Raman scatter intensity at the detector. Due to this tradeoff, most Raman microscopes use a variable diameter confocal pinhole enabling the microscope mode to be adjusted from high confocality/low throughput to non-confocal/high throughput, depending on the application.

FIGURE 3: Raman depth profile of a multilayer polymer sample comprised of a PET-PVC-PET stack measured with a 532 nm laser and a 100x 0.9 NA objective and (a) 100 μm pinhole and (b) 25 μm pinhole.

As an alternative drug delivery route, transdermal patches control the amount of medication a patient receives. With confocal Raman microscopy it is possible to create a Raman 3D map and thus visualize and analyze the layers of a transdermal patch without damaging it, allowing for quality control and stability measurements.

Featured Content

Molecular Spectroscopy in Practice

Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy

Raman Spectroscopy in Analyzing Fats and Oils in Foods

Lithium-Ion Battery Manufacturing and Quality Control

Study of Nondestructive Testing of Nanguo Pear Quality Using Vis-NIR Spectroscopy