Measuring Heavy Metals and Other Elements in Alternative Proteins

Simulation of an Algorithm for Space Target Materials Identification 

Data Transforms in Chemometric Calibrations

Analysis of Serum from Acute Leukemia Patients Using Surface-Enhanced Raman Spectroscopy (SERS)

In this paper, we show surface-enhanced Raman spectroscopy (SERS) of serum from acute leukemia and analyze the SERS through the multivariate statistical method of principal component analysis (PCA). First, the changes in the structure of proteins, lipids, carbohydrates, and other substances in the serum of patients with acute leukemia are analyzed from the molecular perspective. The results showed that the ordered structure of the protein main chain and the side chain conformation was weakened, the main chain structure was almost broken, and the protein became loose and disordered. In addition, the content of esters, amino acids, glycoproteins, and carbohydrate-related substances decreases, indicating a change in the environment. Finally, the PCA method was used to analyze the SERS signal of the serum of healthy people and patients with acute leukemia to accurately distinguish the two serum samples. The above conclusions can provide a favorable experimental basis for diagnosing leukemia and the study of the biochemical mechanism involved.

Hongwen Han is with the College of Science, Yantai Nashan University, China.

Jingjing Gong is with the College of Business at Yantai Nanshan University in Yantai, China.

Yannan Tian is with the College of Science at Yantai Nanshan University in Yantai, China.

Leukemia is a malignant tumor of the hematopoietic system. It is characterized by the neoplastic proliferation of a certain type of leukemia cells in the bone marrow or other hematopoietic tissues, which can infiltrate various organs and tissues in the body and impair the function of various organs, producing corresponding symptoms and signs. Currently, leukemia is diagnosed mainly through blood and bone marrow, but the bone marrow examination brings great pain to the patient (1,2). In some patients with acute leukemia, there are no typical blood and bone marrow changes in the early stage, but there are abnormal blood images. During this period, comprehensive clinical and laboratory examinations cannot confirm the diagnosis of leukemia.

Surface-enhanced Raman spectroscopy (SERS) is superior for identifying the molecular composition of complex materials because the enhancement of Raman intensities as much as 10

 times by molecules adsorbed onto the nanostructured metal surface can possibly detect very small amounts of the substances (3–5). Therefore, it has become one of the best techniques in assay single molecule and bimolecular spectroscopy. The study of Raman spectroscopy on serum has received extensive attention in the field of pathobiology (6–8). However, there are few articles using SERS to study the serum of leukemia patients. Here, SERS is used to probe the serum from acute leukemia.

The experimental serum were selected from 20 healthy volunteers as the control group, and 20 patients who were diagnosed with acute leukemia by the People’s Hospital as the other group. The serum was mixed with the silver colloidal nanoparticles according to a 2:1 proportion and placed in a quartz glass capillary for Raman measurement. The Raman spectra were collected with a confocal Raman microspectrometer (Renishaw) in the range of 400–1800 cm

, with a near-infrared (NIR) 780 nm laser whose power was maintained at 25 mW and a spectral resolution of less than 2 cm

. Spectrometer scans, data collection, and processing were controlled by a personal computer.

Preparation of Silver Colloid Nanoparticles

Here, the silver colloidal nanoparticles were synthesized by a deoxidizing method using trisodium citrate and silver nitrate. The configuration of the silver glue is characterized by a scanning probe microscope (9–11). The atomic force microscopy (AFM) morphology of the silver glue is shown in Figure 1. It can be seen that the particles were distributed in a relatively aggregated uniform size and exist in the form of nearly spherical nanoparticles. The average size of the particles was in the range of 30–50 nm.

FIGURE 1: AFM morphology of silver colloidal nanoparticles.

Research on “Hot Spots” of Silver Colloid Nanoparticles 

During their study of the SERS mechanism, Nie and others found the “hot spot” phenomenon in the SERS experiment—that is, in SERS, there were some aggregation points of silver nanoparticles. The Raman scattering intensity of the sample adsorbed on it was abnormally enhanced, which is called the “hot spot” (12,13). The combination of the silver colloidal and serum under the microscope are presented in Figure 2. It can be seen that the aggregation of multiple silver nanoparticles is shown in the marked part of the graph, which meets the conditions for forming a “hot spot.”

FIGURE 2: The combination of silver gel and serum image under a microscope (with close-up insert).

The mean SERS of serum from the acute leukemia group and control group are presented in Figure 3. The major contribution to these bands is from proteins, lipids, and carbohydrates. The ratio of relative intensity can be used to analyze the characteristics of biomacromolecules after the group and chemical bond changed and be defined as (14,15):

 are representative for the relative intensity of the peak from the acute leukemia group and control group, respectively. By selecting the intensity of 725±1 cm

as the main criterion, the relative intensity of the peak, 

In equation 2, In represents the absolute intensity of the serum Raman peak of patients with acute leukemia. 

 represents the absolute intensity of the Raman peak of healthy human serum.

The bands at 1445, 1333, 1099, 1016, and 725 cm

, which are characterized by lipids, carbohydrates, tryptophan, asparagine, and protein side chain CS bonds, look similar to one another (16–18), but some peaks show differences in intensity and in the Raman shift (see Figure 3 and Table I for more details).

FIGURE 3: (a) SERS of normal human serum and (b) SERS of the acute leukemia patient serum.

The spectral line of the protein backbone conformation changes significantly: 528 cm

 in the Raman peak of healthy human serum belongs to the disulfide bond in the protein structure. The characteristic spectrum of stretching vibration disappears in the serum of patients with acute leukemia. The spectral line contributed by amide VI in the serum of acute leukemia patients shifted from the normal 591 cm

, and the relative intensity was reduced by 37%. The Raman characteristic peaks 953 cm

 contributed by the protein backbone C-C bond and amide III (19,20), respectively, increased by 51% and 54% in the patient’s serum, indicating that the protein backbone structure is almost broken and ordered.

The change in the protein side chain was also obvious: the spectrum line 635 cm

 in the serum of healthy people belonged to the twisted conformation of the C-S bond of the protein side chain, and it shifted to 631 cm

 in the serum of patients with acute leukemia, with the relative intensity being reduced by 48%. The line of the indole ring of tryptophan shifted from 1617 cm

 in the serum of patients with acute leukemia, and the relative intensity was reduced by 27%. The characteristic peaks of 810 cm

 contributed by aspartic acid, amino acid C-N (21,22), and tryptophan in healthy human serum disappeared in acute leukemia patients. It can be seen that the amino acid content in the metabolites of acute leukemia patients was reduced. The spatial structure of the protein side chain was destroyed, and the protein became loose and disordered.

 in carbohydrates disappeared in the serum of patients with acute leukemia, and the relative intensity of the characteristic peak at 1133 cm

 was reduced by 53%, indicating that the serum of patients with acute leukemia was related to glycoproteins and carbohydrates.

To improve the sensitivity of Raman spectroscopy, we established a PCA result. 

 is a statistical method of dimensionality reduction. It uses an orthogonal transformation to transform the original random vector whose components are related to a new random vector whose components are not related. The idea of PCA is to use fewer comprehensive variables. To replace the original variables simultaneously, it is required that these comprehensive variables reflect the information of the original variables as much as possible, and they are not related to each other (23). Then, the new matrix is transformed into a diagonal matrix, which is geometrically expressed as transforming the original coordinate system into a new orthogonal coordinate system. The new orthogonal coordinate system points to the 

-orthogonal directions where the sample points are most dispersed before reducing the multidimensional variable system. This dimensional processing allows the system to be converted into a low-dimensional variable system with a higher precision; by constructing an appropriate value function, the low-dimensional system is further transformed into a one-dimensional (1D) system. After using PCA, we can select the first two principal components, or a couple of them, and draw the distribution of 

 samples on a two-dimensional (2D) plane based on the scores of the principal components, which can be visually seen from the graph. Doing so helps to find out the position of each sample in the principal component and then classify the sample, finding outliers far away from most sample points from the graph (24).

The calculation steps of PCA are as follows (25,26):

(1) Collection of the original index data p-dimensional random vector 

. Construct a sample array and perform the following standardized transformations on the sample array elements:

(2) Find the correlation coefficient matrix for the standardized matrix 

(3) Solve the characteristic equation of the sample correlation matrix 

 and get a characteristic root to determine the principal component. Determine the value of 

So that the utilization rate of information reaches over 85%, the following system of equations are solved

(4) Convert standardized indicator variables into main components

(5) Perform a weighted summation on the m principal components to obtain the final evaluation value, and the weight is the variance contribution rate of each principal component.

To improve the sensitivity of Raman spectrum recognition, we performed background subtraction, smoothing, derivation, and area normalization of the original spectrum data as shown in Figure 4. We selected the region of 400~1800 cm

 in the Raman peak concentration to do multivariate statistical analysis, and we used Matlab 7.0 software to do the PCA analysis.

FIGURE 4: Mean SERS spectra of serum from (a) healthy and (b) acute leukemia are processed by baseline correction, second-order derivative, and area normalization.

PCA was performed on 19 vibration modes shared by 20 acute leukemia patient serum and 20 healthy human serum. The components whose cumulative variance contribution rate was greater than 85% were selected as the main components to achieve the extraction of spectral feature information. The first three principal components can be used to represent the main information on the original spectrum. The characteristic values and cumulative reliability of the principal components are shown in Table II.

Figure 5 is a three-dimensional (3D) projection diagram of the main components 1, 2, and 3. As can be seen from Figure 5, “O” represents the serum of patients with acute leukemia, and “☆” represents the serum of healthy people. From the graph, we can intuitively see the serum of patients with acute leukemia and healthy people. The vibration peaks fall in different intervals, so that the samples can be classified and processed. The distribution of the main components of the serum of patients with acute leukemia is relatively scattered compared with healthy people. It may be because of the changes in certain components in the serum of patients with acute leukemia that the internal material distribution is uneven. It can be seen that the PCA method can accurately distinguish the serum of acute leukemia patients from healthy human serum.

FIGURE 5: Three-dimensional (3D) mapping of PCA from the acute leukemia group, and healthy volunteer group. Shown are the discrimination results of PCA from healthy volunteer group (o) and acute leukemia group (☆). They can be separated completely.

Through the comparative analysis of the SERS of the two serum, the difference in Raman signals between the serum of patients with leukemia and the serum of normal people was obtained. The results show that the protein, lipid, and carbohydrate content in the serum of leukemia patients decreased. The ordered structure of proteins in the serum of patients with acute leukemia changes, and the content of amino acids, glycoproteins, and carbohydrate-related substances in the serum decreases. After PCA analysis, the serum of patients with acute leukemia can be accurately distinguished from the serum of healthy people. These conclusions provide a favorable experimental basis for the diagnosis of leukemia and the study of the biochemical mechanism involved.

We thank the Provincial Natural Science Foundation of Shandong for financial support (Grant No. ZR2019PB010).

(1) D. Rohleder, W. Kiefer, and W. Petrich, 

(4) W.C. Chen, Y. Kang, H. Zhang, et al, 

(5) C.R. Lee, S.J. Bae, M.S. Gong, K. Kim, and S.W. Joo, 

(8) A. Callegari, D. Tonti, and M. Chergui, 

(9) H.W. Han, X.L. Yan, R.X. Dong, et al, 

(11) F.M. Zehentbauer, J. Rüger, and J. Kiefner, 

(12) Y.D. Lu, Y.S. Lin, Z. Zheng, et al, 

(15) DA Silva Wagner Rafael, S. Landulfo, and F.A. Barrinha, 

(16) S.Y. Feng, J.J.Pan, Y.Y. Wu, et al, 

(18) K. Matthias, S. Christian, R. Bernhard, et al, 

(19) X.G. Shao, J.H. Pan, Y.Q. Wang, et al, 

(20) B. Yan, B.Li, Z.N. Wen, X.Y. Luo, L.L. Xue, and L.J. Li, 

(21) S. Breuninger, H. Fischer, U. Schmidt, et al, 

(22) A. Pérez, Y.A. Prada, R. Cabanzo, et al, 

(23) R.X. Zhou, Y.H. Xiong, N. Wang, and X.Z. Wang, 

(24) A. Park, S. Baek, and A. Diagnosis, 

 is with the College of Science, Yantai Nanshan University, China. 

 is with the College of Business, Yantai Nanshan University,Shandong, Yantai, China. 

 is with the College of Science, Yantai Nanshan University, China. Direct correspondence to 

Articles

This study shows that surface-enhanced Raman spectroscopy (SERS)
of serum can provide an experimental basis for diagnosing leukemia in patients.

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.

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.

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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.

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