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Infrared Spectroscopy of Polymers, XI

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Aaron Acevedo is an assistant editor for LCGC and Spectroscopy at MJH Life Sciences.

Pittcon annually recognizes those who are most accomplished in various areas of specialization within the field of analytical chemistry. One such award is the Pittsburgh Spectroscopy Award; established in 1957, this award is given to people who demonstrate outstanding achievements in the field of spectroscopy. At Pittcon 2023, the award went to Robert Tycko of the National Institutes of Health.

For over 30 years, Tycko has evolved the field of magnetic resonance spectroscopy, advancing the fundamentals of solid-state nuclear magnetic resonance (NMR) and its use in physical and biological sciences. Most recently, his laboratory has been working on how to perform high-resolution MRI at extreme temperature lows, developing novel time-resolved NMR methods, and applying NMR techniques to the analysis of structural studies of protein assemblies related to neurogenerative disease.

The selection committee noted that Tycko’s work “combines new conceptual insights and experimental approaches with detailed investigations of specific systems that are of central importance to large communities of scientists outside of magnetic resonance.”

Hosted by Pittcon’s John Baltrus of the U.S. Department of Energy’s National Energy Technology Laboratory, the symposium for this award featured several speakers, including Tatyana Polenova of the University of Delaware, Stephan Grzesiek of the University of Basel, Songi Han of the University of California at Santa Barbara, and Tycko. Tycko’s talk, titled “Time-Resolved Biomolecular NMR and Micron-Resolution MRI at Very Low Temperatures,” focused on his laboratory’s most recent research endeavors at the time, which heavily relied on “large enhancements of NMR signals that occur at temperatures below 30 K through dynamic nuclear polarization (DNP).”

The award symposium took place on Monday, March 20, with Pittcon itself being held from March 18–22.

Articles

Robert Tycko received the 2023 Pittsburgh Spectroscopy Award for his contributions in the field of spectroscopy. He was recognized at Pittcon 2023, which took place in Philadelphia, Pennsylvania.

Robert Tycko to Receive Pittsburgh Spectroscopy Award at Pittcon 2023

There are many biological functions of glycans, spanning the spectrum from relatively subtle to absolutely crucial, pertaining to the development, growth, maintenance, or survival of the organism that synthesizes them, including (but not limited to) structural development, energy storage, and system regulation. Zac Schultz (ZS), a professor at the Ohio State University, and his graduate student Hannah Schorr (HS), are working on the use of surface-enhanced Raman spectroscopy (SERS) for the differentiation of glycans. Schultz and Schorr spoke to 

Why is identification and quantification of glycans on a protein important?

HS: Glycoproteins are becoming widely used in the pharmaceutical industry as both drug delivery targets and as therapeutic drugs themselves. In 2013, 11 of the top-20-grossing pharmaceuticals were glycoproteins. In 2021, the second-highest grossing pharmaceutical was a glycoprotein, second only to the COVID vaccine. Glycosylation, where enzymes attach sugars to specific protein residues, is the most common post-translational modification of proteins, and it can affect protein folding and function. Being able to rapidly identify and quantify which glycans are present can speed up the quality control system and ensure that the therapeutic drugs are being made properly. Understanding the glycome of a protein can also aid in figuring out where binding or other interactions with the protein are occurring, possibly opening new avenues for diagnostics and drug delivery systems.

ZS: Glycation, where sugars are reacting with free amines of proteins, is another area that appears to be important for understanding diseases such as diabetes. Being able to identify and quantify the sugar attached to proteins is difficult. In general, sugars play key roles in numerous biological functions, but the techniques to characterize them are rather limited, presenting an important research challenge.

Why was surface-enhanced Raman spectroscopy (SERS) chosen as the technique to detect and differentiate glycans and monosaccharides?

HS: Because glycans, and monosaccharides in particular, are often structural isomers, it can cause a real challenge in differentiation and detection. Many have the exact same mass, with the only difference being whether or not a hydroxyl is above or below the carbon ring. With SERS, we are able to probe the vibrations of the bonds in the molecule, and by using chemometrics, we are able to tell structural isomers apart, even when their spectra are quite similar.

ZS: A glib answer is because our lab does SERS. But in all seriousness, previous work in our lab suggested SERS could characterize sugars, and monosaccharides in particular. We saw previously that phosphorylated sugar isomers could be differentiated. Distinguishing metabolites was important, and applying SERS to characterize protein modifications was a logical next step.

What specifically makes SERS an analytical method of choice for glycan analysis in terms of sensitivity and selectivity to these analytes?

HS: SERS can be a useful tool for glycan analysis because of its insensitivity to water. Most of the time sugars are going to come from a biological sample, and the matrix is going to be an aqueous solution. The water insensitivity is also useful for liquid chromatography (LC) analysis due to the aqueous portion of the mobile phase. We don’t have to worry about any competing signals or figuring out how to ionize a sample. Since the goal is to analyze the whole glycome, we actually aren’t looking for a super selective technique. It’s good for us that we can detect all of the sugars and not just certain ones. With different surface monolayers, we can get detection down to near-physiological concentrations as well, which will be useful as we move forward with the project.

ZS: SERS provides information that can identify the glycan, which is an important part of the challenge. And, as Hannah noted, we have been able to adapt the methods we are developing into a technique that can address this previously unmet research need.

What is the optimum method of sampling for this analytical technique?

ZS: Ideally, you would like a sampling method that requires the least amount of sample manipulation. With SERS, you have to combine the sample with the SERS-active nanoparticles. Our sheath-flow SERS method enables liquid solutions to be directly analyzed without drying or mixing, both of which can result in misleading artifacts. This direct analysis of the solutions is really beneficial. Since the Schultz lab reported sheath-flow SERS almost 10 years ago, we have continued optimizing the sampling method, but it has been useful for the glycan analysis reported here and in other applications from our lab.

What was the most surprising result in your work? Were there any memorable limitations or challenges that you encountered?

HS: The most surprising result is definitely that we can use chemometrics to differentiate between the monosaccharides. Looking at the raw data, it all looks incredibly similar. It is challenging to pick out distinctions by eye. Principal component analysis (PCA) was able to take the small differences in relative peak heights between the sugar spectra and classify them into distinct clusters. It was really exciting to see that there was clustering based on structural similarities between the monosaccharides as well. The most challenging thing that we encountered doing this work is that sugars and SERS do not get along well. It’s very difficult to get a SERS spectrum of a bare monosaccharide, as the sugars seem to be repelled from the metallic substrates. Finding a way to encourage interaction between the analyte and the substrate was the biggest hurdle in this project, and luckily phenyl boronic acid (PBA) was the key to our success there.

ZS: I was surprised that the reaction with phenyl boronic acid improved the polarizability of the sugar as much as it did. This suggests other reactions may be appropriate for detecting other difficult analytes.

What feedback you have received from other researchers about this work? 

HS: I had some positive feedback and interest while presenting at SciX 2022, especially regarding the in-flow portion of the research, but we’re still waiting for feedback on the published work. In the meantime, my mom thinks it’s pretty cool.

ZS: The work has been well received. Sugars are a challenging problem and people get excited if it looks like progress is being made. I have been given great suggestions for specific biological problems that would benefit from this analysis.

What discoveries do you hope to realize using this method of identification, and what do you think its impact will be in the future?

HS: My biggest hope is that we can actually implement this identification method when it comes to cleaving sugars off of a protein. The glycans on a protein are typically much more complex than monosaccharides, so we have quite a way to go before we can get this to a place where it would be applicable in a more commercial setting. If we can get there, this could be a big tool in pharmaceutical quality control, and it could also be used as a tool for general protein analysis.

What are the next steps in this research?

HS: There are a few different directions we’re heading next. The first is that we are working on figuring out a good method for separating mixtures of the PBA–sugar conjugates using liquid chromatography. The next is moving on to more complex sugars such as disaccharides and oligosaccharides. And finally looking at some glycosylated peptides to see if we can still pull out the signal from the present sugar. We have a lot of work ahead of us, but I’m excited to get into it!

ZS: Hannah has a good plan, and I am excited to see how far she can push this forward.

, is a professor at The Ohio State University. Schultz earned his B.S. degree from the Ohio State University in 2000 and his PhD from the University of Illinois at Urbana-Champaign in 2005. As a graduate student, he was recognized with an ACS Division of Analytical Chemistry Graduate Fellowship (2004). Upon completing his PhD, he was awarded a National Research Council Postdoctoral Fellowship to conduct research at the National Institute of Standards and Technology (NIST). Following his postdoctoral training at NIST, Schultz continued as a research fellow at NIH using vibrational spectroscopy and microscopy to study biomembrane systems. While at the NIH, Schultz was awarded an NIH Pathway to Independence Award. Schultz began his independent career as an assistant professor of chemistry and biochemistry at the University of Notre Dame in 2009 and was promoted with tenure to associate professor in 2015. In January of 2018, Schultz moved his research program to Ohio State, where he was promoted to professor in 2022. Schultz was recognized as a Cottrell Scholar in 2013, elected a Fellow of the American Association for the Advancement of Science (AAAS) in 2019, and was awarded the Craver Award for applied vibrational spectroscopy from the Coblentz Society in 2021. Schultz’s research focuses on developing innovative approaches utilizing the unique interactions between light and nanostructured materials for spectroscopic imaging and ultrasensitive label-free spectroscopic detection. Direct correspondence to: 

 is a graduate student in the Schultz Lab at The Ohio State University. She earned her B.S. at Saint Francis University in Loretto, Pennsylvania, in 2018. Hannah received a FACCS student poster award at SciX 2021 for her research, which focuses on trace detection of analytes in flow using surface-enhanced Raman spectroscopy (SERS).

Zac Schultz, a professor at the Ohio State University, and his graduate student Hannah Schorr, are working on the use of surface-enhanced Raman spectroscopy (SERS) for the differentiation of glycans. Schultz and Schorr spoke to Spectroscopy about this work. 

Differentiating Glycans by Surface-Enhanced Raman Spectroscopy

Carbamazepine (CBZ), a pharmaceutical used in the treatment of epilepsy, is also used for the treatment of some diseases or symptoms such as alcoholism, opiate withdrawal, and depression. Unfortunately, this drug has often been detected in rivers and surface waters, thus raising concern regarding its impact on the environment. A recent paper discusses how flax shives (the material left after fiber is extracted from flax stems) have been used to remove CBZ from simulated wastewater. Catherine Niu, a professor at the College of Engineering of the University of Saskatchewan in Canada and co-author of this paper, spoke to 

 about how she and her associates used X-ray photoelectron spectra and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy analysis in their research.

Your paper (1) discusses how adsorbents based on hydrochars and steam activated hydrochars developed from flax shives were used to adsorb carbamazepine (CBZ) from simulated contaminated water. What made you decide that using flax shives would be an effective way to accomplish this?

Flax shives containing cellulosic components have been abundantly generated as a byproduct after flax fiber is extracted, which have not been effectively utilized. Previously research showed that cellulosic components or their derivatives have potential for removing pharmaceutical or related chemicals from water. The decision of using flax shives as a feedstock to make hydrochars and steam activated hydrochars adsorbents to remove pharmaceuticals in this work was made in order to develop novel uses of the material for water treatment. In addition, the results from this work may be transferable to treat other agricultural byproducts containing cellulosic components.

Please describe the mechanisms of CBZ adsorption.

The results obtained from this work show π-π electron donor-acceptor interaction may play an important role on the adsorption mechanism of CBZ on the above-mentioned adsorbents. Hydrophobic interaction also contributed while the roles of hydrogen bonding and electrostatic attraction between CBZ and adsorbents with opposite charges is insignificant. Pore filling may also contribute to the ability of flax shives to act as adsorbents.

You used X-ray photoelectron spectra and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy analysis in your research. What do these techniques reveal about the adsorption process you were studying?

These spectral analyses provided evidence indicating that the adsorption mechanism of π-π electron donor-acceptor interaction may play an important role in the adsorption of CBZ on the above-mentioned adsorbents. This interaction provides a mechanism whereby CBZ adsorbs to the cellulose of the flax shives.

What advantages do these spectroscopy techniques offer that other techniques available may have lacked?

Experiments of adsorption of pharmaceuticals from water by adsorbents are able to demonstrate whether the adsorbents can adsorb the pharmaceuticals or not. However, other methods have not been able to provide adequate information on what reasons or the mechanism that causes the pharmaceuticals to be adsorbed on the surface of the adsorbents. The aforementioned spectroscopy techniques may provide information to assist of understanding the adsorption mechanisms, which is what we are interested in learning.

How was the Canadian Light Source (CLS) essential for this research?

The Canadian Light Source (CLS) was required in order to utilize the near-edge X-ray absorption fine structure (NEXAFS) spectroscopy analysis. Without this source there would not be sufficient X-ray energy to complete this analysis. This work actually provided the detailed evidence that a π-π electron donor-acceptor interaction may play a critical role in the adsorption mechanism of CBZ on the abovementioned adsorbents. This mechanism may also be present for other similar pharmaceutical molecular structures. 

How does your research differ from what was previously done by yourself or others?

This work used flax shives as a feedstock to make hydrochars and steam activated hydrochars for CBZ pharmaceutical contaminant adsorption. To the best knowledge of the authors of this work, such an adsorption system has not been reported prior to this published research work.

Were there any particular limitations or challenges you encountered in your work?

We encountered challenges to effectively desorb adsorbed carbamazepine from the loaded adsorbents. This limits the regeneration of the adsorbents for reuse. If we are not able to regenerate the adsorbents they will have a one-use only limited applicability. In addition, we were interested in how to reduce the uses of acid and thermal energy in the process of developing the adsorbents. For us to learn how to minimize the use of energy and chemicals in this process is an ongoing challenge.

Can you please summarize the feedback that you have received regarding this work?

This work has made contributions to demonstrate flax shives were successfully made into adsorbents which adsorbed carbamazepine with capacity and speed comparable with some other commercial adsorbents reported. The mechanisms which may play important roles in the adsorption were explored. However, there are the abovementioned challenges which still need to be addressed to optimize this process for real-world application.

Can this work translate to detecting other pharmaceuticals in water, or even detecting them in other classical elements (earth, air)?

This work may be transferable to detect other pharmaceuticals similar to carbamazepine in water or in earth after earth samples are dissolved in water. However, this question needs to be verified by future research.

The next steps are to address the challenges and limits described in the previous answers and to continue to find ways to enhance adsorption capacity. Choices of additional feedstocks and pharmaceuticals will also be investigated. If successful, collaboration may be established with industrial partners to develop a pilot or an industrial scale water treatment process. We are hopeful that this research will be fruitful.

(1) Aghababaei, A.; Babu Borugadda, V.; Dalai, A.; Hui Niu, C. An Investigation on Adsorption of Carbamazepine with Adsorbents Developed from Flax Shives: Kinetics, Mechanisms, and Desorption. 

, 138–155, DOI: 10.1016/j.cherd.2022.11.008

 is a professor in the Department of Chemical and Biological Engineering at the University of Saskatchewan. She obtained her Ph.D. in chemical engineering from McGill University in Canada. In addition, she received her bachelor and master's degrees in chemical engineering from Sichuan University in P.R. China. Dr. Niu has passion on biosorption and adsorption research. Her research area focuses on biosorption which utilizes inexpensive non-living biomaterials called biosorbents to sequester chemicals of interest. Biosorbents are prepared from the naturally abundant or waste biomass of algae, moss, fungi, bacteria or waste parts of plants and animals, which act just as adsorbents of biological origin. Dr. Niu's research includes developing and characterizing novel biosorbents, optimizing biosorption processes, and studying the adsorption kinetics, equilibrium, mechanism, and desorption. The diversified and promising applications of biosorption technology lead to the development of her research projects in the areas of water treatment, gas purification, biochemical adsorption, and more.

Catherine Niu, a professor at the College of Engineering of the University of Saskatchewan in Canada and co-author of this paper, spoke to Spectroscopy about how she and her associates used X-ray photoelectron spectra and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy analysis in their research.

Investigating Adsorption of Carbamazepine with Adsorbents Developed from Flax Shives with X-ray Spectroscopy

Toptica Photonics, a German laser manufacturer, reached an agreement to buy the French fiber laser technology company Azurlight Systems SAS. The deal became official on December 22nd, 2022.

Azurlight Systems makes continuous wave (CW) infrared (IR) and visible fiber lasers and amplifiers designed for low noise—for application in quantum technologies, high power argon gas laser replacement, laser Doppler velocimetry, high brightness laser pumping, laser holography, metrology, and interferometry.

Under the agreement, Azurlight will continue to build its laser business under the Toptica name and form the French hub for Toptica. Toptica believes that this acquisition will allow for better consumer access to an experienced team of experts covering laser technology and technical support.

Toptica Photonics Acquires Azurlight Systems

Current benchtop instruments with edge filters providing Raman spectra down to 50 cm

 can be used to study the crystallization of PHBHx (polyhydroxybutyrate-hexanoate) without the complexity of larger instruments. By collecting the averaged signal from spherulites, the effects of orientation can be averaged out, enabling the use of multivariate techniques to compare samples. These polymers are being commercialized because they are created by fermentation and are biodegradable, making them an ideal replacement for petroleum-derived polymers that do not biodegrade. In addition, it is understood how to control the polymer’s physical properties by controlling the molecular weight and the percent of sidechains which, in turn, controls the crystallinity. To compare spectra of different samples, it is necessary to remove the effects of polymer chain orientation, which averaging the spectrum of a spherulite will enable.

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

Although a polymer physicist is well aware of the formation of spherulites in semicrystalline polymers, it is an amazing experience to see them for the first time in an optical microscope with crossed-polar illumination. Figure 1 shows a spherulite in a film of PHBHx polyhydroxybutyrate-13% hexanoate that had been annealed for 36 h at 70 

C. Our understanding of the structure that accounts for what is observed is that the crystallites are nucleated at the center of these features and grow radially along the axis of maximum crystal growth. The sizes of the spherulites will depend on a variety of factors (including the number of nucleation sites, the structure of the molecules, and the cooling rate or annealing temperature), and can range between micrometers to centimeters. One might assume that the radial direction corresponds to the chain axes, but, in fact, it is often a different axis. In PHBHx, it is the 

-axis that is the axis of optimum growth (1), and 

 is also the axis of the unusual interaction between one of the methyl protons on one chain in the unit cell and the carbonyl in the second chain in the unit cell (2).

FIGURE 1: Image of a spherulite of PHBHx (13% hexanoate) recorded with 100x magnification between crossed polars. The directions are labeled so that the polarization properties can be discussed.

The spherulites are actually a three-dimensional (3D) spherical structure comprised of lamellae that align along the radial axes. Lamellae are polymer chains that fold back and forth on each other in a direction normal to the growth direction. Lamellae define the crystalline regions, with the disordered chains between the lamellae being amorphous. For those readers who are unfamiliar with these concepts, I recommend looking at the Wikipedia article on polymer spherulites. In the case of PHBHx, the lamellae actually twist around the growth axis (3).

These concepts should affect the Raman polarization behavior. In the instrument being used, which is the XploRA Plus, the laser (532 nm) polarization is east-west (EW) at the microscope stage. Therefore, in an EW line scan through the center, the laser will be polarized along the 

-axis. In a north-south (NS) line scan, the laser polarization will be normal to 

-axis would not be unique in this system because the lamellae are rotating around the growth axis (3). If we do a line scan at 45

 to NS or EW, we expect to see a spectrum that is an average.

Figure 2 shows profiles through the center nucleation site (as accurately as could be determined) for the three bands that have been associated with crystallinity. The phonon at 80 cm

 represents beating of the two chains in the unit cell. The carbonyl band is actually several overlapping bands where the frequency downshifts in the crystalline state. The CH band at 3000 cm

 becomes a doublet in the crystalline form, and it has been interpreted to be a splitting of the band at 3000 cm

 when the chains in the unit cell interact (4). Except for the carbonyl profiles in the 0% Hx sample, the diagonal profiles (not shown) are approximately equal to the average of the vertical and horizontal profiles. We believe that the reason for this discrepancy is the large increase in intensity of the crystalline component in the vertical profile of the 0% sample, as shown in Figure 3.

FIGURE 2: Line profiles of the spectral lines that are associated with crystalline PHBHx for 0 and 13% Hx. The darkest lines are for vertical profiles, the lightest lines are for diagonal profiles and the middle intensity lines are for horizontal profiles. For all subfigures, the abscissa label is 

FIGURE 3: Spectra averaged over a thin slice of the spherulite maps for high crystallinity (0%) and low crystallinity (13%) Nodax. The spectra were scaled to the maximum intensity at approximately 2930 cm

Figure 3 shows the spectra (from top to bottom) of narrow horizontal and vertical slices of the low crystallinity samples, and then horizontal and vertical slices of the high crystallinity samples. To validate the conjecture in the previous paragraph, I show the carbonyl region expanded in Figure 4. Note that the spectra of the low crystallinity material at the top shows only approximately a 30% increase in intensity in the vertical slice, but in the spectra of the high crystallinity material at the bottom, there is a very large increase in the crystalline component for the vertical profile. In addition, I have placed arrows to indicate some subtle features buried in the broad amorphous bands.

FIGURE 4: The carbonyl region from the previous slide is expanded to indicate better the differences in behavior.

I am currently recording line profiles with a higher groove density grating and more closely spaced data points. Under these conditions, we are clearly able to correlate the bands in the micrograph to differences in the Raman spectra. Figure 6 shows one such scan, revealing the variation in the intensity of the carbonyl band in a horizontal cut through the center of a spherulite of the pure PHB sample. What is curious is that the 0% sample does not show the banding in the micrograph recorded between crossed polars. By systematically measuring radial slices of spherulites, we should be able to understand what the role of polymer orientation has to do with the bands seen in the micrographs.

FIGURE 5: Expansion of the spectra of Figure 3 in the CH region. In particular the relative intensities of the anomalously high CH bands (2997 and 3009 cm

FIGURE 6: Intensity of the carbonyl band as a function of distance along a horizontal cut through a spherulite center in a pure PHB sample (0% Hx).

This project started because I wanted to compare spectra of different PHBHx samples with different concentrations of Hx. Because I need to eliminate polarization and orientation effects on relative intensities to compare samples, it occurred to me that mapping a spherulite and calculating the average spectrum would eliminate these effects. This idea stunningly opened up new possibilities. Not only can I see intensity ripples as a function of radial distance, but by comparing spectra between horizontal and vertical cuts I can start analyzing the polarization and orientation behavior. Clearly, there is a lot of information coming from these measurements that we will be able to exploit in a more complete analysis of the spectra of this material.

(1) Liu, C.; Noda, I.; Martin, D. C.; Chase, D. B.; Ni, C.; Rabolt, J. F. Growth of Anisotropic Single Crystals of a Random Copolymer, Poly[(R)-3-hydroxybutyrate-co-(R)-3-hydroxyhexanoate] Driven by Cooperative –CH···O H-Bonding. 

, 111–118. DOI: 10.1016/j.polymer.2018.08.046.

(2) Sato, H.; Dybal, J.; Murakami, R.; Noda, I.; Ozaki, Y. Infrared and Raman Spectroscopy and Quantum Chemistry Calcula- tion Studies of C–H···O Hydrogen Bondings and Thermal Behavior of Biodegradable Polyhydroxyalkanoate. 

, 744–747, 35-46, DOI: 10.1016/j.molstruc.2004.10.069

(4) Adar, F.; Street, R.; Noda, I. Isothermal Crystallization of Polyhydroxyalkanoate (PHA) Utilizing Raman Spectroscopy to Follow Chain Packing As Well As Molecular Motion, 

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

Studying the crystallinity of biodegradable polymers can be done without the complexity of larger instruments. Here’s how.

Measuring the Crystallinity of PHBHx with Varying Amounts of Sidechains on a Benchtop Instrument

Artificial intelligence (AI), and its subfield machine learning (ML), are major buzzwords in today’s technology world. In a two-part series, let’s begin to take a look under the hood and behind the scenes to see what AI is and its applicability to analytical chemistry and spectroscopy for future discussion and elaboration. What are the benefits and limitations, as well as the praises and detractions, of AI? How does AI relate to chemometrics?

Howard Mark serves on the Editorial Advisory Board of 

 and runs a consulting service, Mark Electronics that provides assistance, training, and consultation in near-IR spectroscopy as well as custom hardware and software design and development.

As with any modeling technique, it might serve us well to remember Robert A. Heinlein’s reference: “There ain’t no such thing as a free lunch (TANSTAAFL)” (1), and supercalibratestatisticexpedientalgorithmadnauseous, a play-on word inspired by the wisdom of Mary Poppins (2), reminding us about the hype of new algorithms. The term was coined to mock the then-current practice of trying all-possible combinations of available algorithms and test statistics to automate the process of developing calibration models for near-infrared (NIR) analysis. It took a long time, but eventually, the NIR community recognized the futility of that methodology. Remember, if you are going to make mistakes, automation allows you to make them more quickly and repetitively. One must be cautioned when using powerful and automated regression-based methods to remember that modeling algorithms are only able to build an accurate predictive model based on the information they are presented. As Mark Twain once said, “There are three kinds of lies: lies, damn lies, and statistics” (3–5). And as Rasmus Bro, a leader in the field of chemometrics, has said:

“As most people are aware, there is currently a hype on machine learning and artificial intelligence (AI), and that is all fine and good. Part of the hype is that these methods are sometimes oversold, and so then people are relieved that now they don’t have to think about things, saying, ‘I don’t have to think about experimental design, sampling error, analytical quality, etc.’ And when that happens, those projects typically fail, because these new and excellent methods don’t actually replace the responsibility for you to actually know what you are doing” (6).

With all these precautionary tales in mind, we now move ahead with our initial overview discussion of AI and chemometrics.

Basically, AI and machine learning (ML), as they are used today, consist of applying signal processing and multiple layer neural networks to model a function or set of functions that has been loosely associated with the intelligent activity of living organisms. The same challenges for building successful models may be present in all modeling activities, including chemometrics; these may include sparse or limited data, noisy data, unknown limits to the potential data and predicted values, a lack of basic understanding of the nature of the data, and so forth. In chemometrics, it is generally accepted that a chemist or chemical engineer is deriving (or at least interpreting) a quantitative, qualitative, or classification model for a chemical system, and therefore has domain knowledge of the nature of the data and the expected model prediction results. 

, or fundamental knowledge of the system one is working with, is essential to avoid major errors, using computer modeling. That is why, if one is using AI to build a self-driving automobile, it would be very handy to have expert drivers involved during the modeling process. Without domain knowledge, one will undoubtedly see major errors in relying on models generated without such knowledge used as input information. These caveats also apply to modeling weather (where one asteroid or erupting volcano changes everything), the future economy (with the potential for wars and famines), and other unbounded or somewhat unknown domains.

For an “official” AI response, we asked the question, “How are machine learning and chemometrics related?” to the program Chat.OpenAI.com (7), an AI program in development. The AI answer is transcribed below. Note that as the AI program “learns,” its responses will change over time.

“Machine learning and chemometrics are both fields that involve the use of statistical and computational methods to analyze and interpret data. However, they are applied in different contexts and for different purposes. Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from and make predictions or decisions based on data. These algorithms and models are trained on a set of labeled data, which consists of input data and corresponding output data, and are then used to make predictions or decisions about new, unseen data. Machine learning methods are widely used in a variety of applications, including image and speech recognition, natural language processing, and predictive modeling.

Chemometrics, on the other hand, is the application of statistical and mathematical methods to the analysis of chemical data. It is used to extract meaningful information and knowledge from chemical data sets, such as those obtained from spectroscopic or chromatographic measurements. Chemometrics methods are used to analyze and interpret chemical data in order to gain insights into the underlying chemical processes and to make predictions 

about the properties and behavior of chemical substances.

Both machine learning and chemometrics involve the use of statistical and computational techniques to analyze and interpret data. However, while machine learning is generally applied to a wide range of data types and applications, chemometrics is specifically focused on the analysis of chemical data.”

As with many technical matters, it is useful to begin by discussing the various concepts involved with the subject of interest, and compose a list of (and a set of definitions for) those terms. If one is to develop AI technology and ML algorithms, it is helpful to know what 

 are. These concepts seem self-evident or intuitive to sentient beings; however, clear definitions of terms are helpful for classification and understanding. There have been many attempts to precisely define 

 from Merriam-Webster includes the following:

1) “...(T)he ability to learn or understand or to deal with new or trying situations: Reason; also: the skilled use of reason; and 2): the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as tests)” (8).

The definition of intelligence from Oxford Learner’s Dictionaries is

“...(T)he ability to learn, understand, and think in a logical way about things; the ability to do this well....” (9).

A definition of AI from the Oxford English Dictionary is as follows:

“(T)he capacity of computers or other machines to exhibit or simulate intelligent behavior; the field of study con- cerned with this. Abbreviated AI” (10).

The often-referred to Wikipedia source has a more detailed definition:

“AI is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans....”

Wikipedia notes that older definitions of AI referred to machines that mimic human cognitive skills, such as learning and problem solving. A more modern definition if AI uses phrases such as “machines that act rationally,” which somewhat muddies the waters in terms of a precise definition (11). We might summarize the various ideas about a definition of AI, for our purposes, as:

“(A)n automated technology using computers and algorithms that is capable of simulating learning, thought, creativity, rationality, and analysis; and that is able to perform routine or complex tasks that have been historically accomplished by humans.”

ML may then be defined as a subfield of AI where computers (machines) are programmed in such a way that they are able to self-iterate and optimize problem solving for modeling data in an automated way, using computational algorithms specifically designed for such purposes. Let us be frank here: the perceived intelligence or learning is a computer simulation using automation and optimization routines of computer algorithms—no more and no less. Once programmed with appropriate algorithms, the computer (machine) is capable of simulating problem solving and learning functions that are normally attributable to humans.

The foundation of learning AI is to understand the variety of ML (deep learning) algorithms, and then learn how to apply these ML algorithms for various applications. Next, one may design a user interface to interact with the AI programming. The main subfields involved include using the various models for defining an AI problem, learning how to design AI programs, deciding how humans will interact with the AI programs, and learning how to modify and teach an AI program. One may add here that it is also important that experts evaluate or check the output of an AI program to test its legitimacy.

It may surprise those analytical chemists completely unfamiliar with AI that many of the chemometrics (multivariate) algorithms they are familiar with are also classified as AI algorithms—these are algorithms that are routinely taught and used by AI programmers. We refer our readers to the following articles for a head start on this discussion (12–14). This previous series of articles provides definitions and a set of references for signal preprocessing, component analysis, quantitative (calibration) methods, qualitative (classification) methods, and programming platforms.

Table I shows a comparison of the chemometrics algorithms and the AI and ML algorithms as derived by the authors. Table II is a comparison table generated by an AI program (Chat.OpenAI.com) that compares AI and ML algorithms with chemometrics algorithms using its own AI terminology.

Figure 1 shows a basic flow chart of commonality between AI approaches and chemometric modeling. Note that any modeling system is only capable of accurately modeling the data it has been shown or data that follow a logical framework, such as game theory. Within this limited framework, modeling techniques are able to interpolate within its known boundaries and generally produce an accurate result, or at least, a result with a bounded estimate of error. If the modeling system has not seen the data, or if there is not a rational or known framework for potential results, then the results are merely an automated or computerized guess.

FIGURE 1: Flow chart of commonality between model optimization in AI approaches and chemometric modeling. A finished model must include all possible data variables to be a closed system (interpolation) or be forced to predict outside the modeling data space with potential for greater error (failure to predict accurately) if in an open system (extrapolation). The red rectangle illustrates the “never-ending” process of collecting more data when modeling applications try to predict “open systems.” The iterative process of modeling, testing, and adding additional data is illustrated. Note that the algorithms and modeling steps may also be varied with more modeling iterations.

The concept of an open or closed model is helpful at this point, because we may imagine what could happen when we model a constrained data set and when the nature of the entire potential data set is not well understood. As Figure 2 illustrates, the limitation of models using powerful fitting algorithms is the “completeness” of the data used and the assumptions made about the nature of the data. Modeling “uncharted” data in science can be, and often is, problematic and unpredictable. Model prediction outside of a closed system is based on hypothesized assumptions about the nature of the data. If the nature of the data and the assumptions about the data are unknown or not clearly understood, then the ability of any modeling system to predict a result accurately is also unknown. Therefore, caution when using extrapolation in a modeling system is wise.

FIGURE 2: Illustration of closed and open model data showing commonality between AI approaches and chemometric modeling. Data shown only in three dimensions (D1, D2, D3). A finished model must include all possible data variables to be closed (interpolation) or forced to predict outside the modeling data space with potential for greater error if open (extrapolation). Note that a model based on fitting to the black dots would not necessarily accurately predict either the green dots or red dots. The limitation of models using powerful fitting algorithms is the “completeness” of the data used and the assumptions made about the nature of the data. Modeling “uncharted” data in science can, and often is, problematic and unpredictable. Model prediction outside of a closed system is based on hypothesized assumptions about the nature of the data.

One can imagine that game playing is a good example of what a modeling system or AI could easily master, because the universe of possible game moves is well-known and constrained to a finite set of options. With the high speed of computation possible in today’s computers, a modeling system is able to compute all possible combinations of moves and describe the best option (or options) available based on probability, separate decision algorithms, or a competitor’s responses to its moves. It would seem that a well-written AI or computer system would be the master of game playing and associated tasks. A next level of complexity may arise in self-driving automobiles, but this activity is still fairly well-defined under normal circumstances. That being said, there are occasionally extreme circumstances, such as weather, unusual sky or light reflections, sudden objects appearing on the roadway, multiple car collisions, and so forth, that would be extremely difficult to model and negotiate.

AI computers would also seem to be able to master inventory, logistics, traffic control, basic war games, and prediction of basic human behaviors—all with relatively constrained circumstances. Even a form of pseudo-creativity would seem to be relatively straightforward with access to the Internet and computerized data processing. For new inventions, a computer system is capable of reviewing the prior patent art and literature, evaluating the known physical laws and basic science regarding the subject, and then “suggesting” potentially novel ideas not yet tried. However, issues of compassion, love, sacrifice, selflessness, and generosity would be extremely difficult to model; these are also factors that weigh in heavily for positive human decisions.

So let us get back to the task at hand, which is to discuss AI for applications in analytical spectroscopy. Potential uses would be for instrument alignment (instrument calibration) and diagnostics, instrument measurement conditions and parameter optimization, experimental design, signal preprocessing, quantitative and qualitative calibration, library searching, and automated imaging and image enhancement functions.

There are several steps in developing both AI and chemometric models that are basic and somewhat unchangeable. These would include the following steps, which can be accomplished either manually or in an automated fashion:

1) Define the problem that needs to be solved.

2) Define the type and amount of data required to train the model.

8) Repeat the process to optimize the model .

There are three main skill sets that are essential in learning how to work with AI. They are as follows:

1) Learn how to operate basic software tools (such as Python), language, and syntax, and the types of algorithms needed.

2) Learn the basics of machine learning algorithms: regression, k-nearest neighbors (KNN), support vector machine (SVM), artificial neural networks (ANNs), reinforcement learning methods, computer vision tools, and so forth.

3) Learn how to specifically solve the problems you are interested in by researching other examples and those researchers already problem solving in your area of interest.

In Part I of this two-part set of introductory articles on AI, we have attempted to give a very concise and top-level overview of AI, a technology topic that is now taking the world by storm. AI discussions are now ubiquitous across media, politics, business, academia, and especially in science and engineering. There is so much more to be said, but we have here brought an introduction to a topic we may continue to explore through additional articles and social media, such as in the “Analytically Speaking” podcast series, Episode 9, featuring Rasmus Bro as the guest speaker (6). In addition, in Part II of this written series, we plan on exploring actual applications of AI and ML to different vibrational spectroscopy methods, such as Raman, Fourier transform infrared (FT-IR), NIR, and UV–visible (UV-vis) spectroscopic techniques.

 (John Wiley & Sons, New York, NY, 1991), pp. ix.

 (University of California Press, Berkeley, CA, 2010), p. 228. (Attributed to Twain dictating this to his secretary in Florence, April, 1904, in “Notes on “Innocents Abroad”). 

http://gutenberg.net.au/ebooks02/0200551h.html

https://www.york.ac.uk/depts/maths/histstat/lies.htm

(5) Velleman, P.F. Truth, Damn Truth, and Statistics. 

https://doi.org/10.1080/10691898.2008.11889565

(6) Spectroscopy, Analytically Speaking Podcast, Episode 9, Feb. 1, 2023. 

https://www.spectroscopyonline.com/analytically-speaking-podcast 

(7) OpenAI, Build Next-Gen Apps With OpenAI’s Powerful Models. 

(8) Merriam-Webster Dictionary, Intelligence. 

https://www.merriam-webster.com/dictionary/ intelligence#:~:text=1%20%3A%20 the%20ability%20to%20learn,noun 

(9) Oxford Learners Dictionaries, Intelligence. 

https://www.oxfordlearnersdictionaries.com/us/definition/english/intelligence

(10) Oxford English Dictionary, Artificial intelligence, n. 

https://www.oed.com/viewdictionaryentry/Entry/271625

(11) Wikipedia, Artificial Intelligence. 

https://en.wikipedia.org/wiki/Artificial_intelligence

(12) Workman, Jr, J.; Mark, H. A Survey of Chemometric Methods Used in Spectroscopy, 

(13) Workman, Jr, J.; Mark, H. Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy: Part I, 

(14) Workman, Jr, J.; Mark, H. Survey of Key Descriptive References for Chemometric Methods Used for Spectroscopy: Part II, 

serves on the Editorial Advisory Board of Spectroscopy and is the Senior Technical Editor for 

. He is also a Certified Core Adjunct Professor at U.S. National University in La Jolla, California. He was formerly the Executive Vice President of Research and Engineering for Unity Scientific and Process Sensors Corporation.

 serves on the Editorial Advisory Board of Spectroscopy, and runs a consulting service, Mark Electronics, in Suffern, New York. Direct correspondence to: 

There were some errors in the October 2022 Chemometrics in Spectroscopy column that evaded detection until after the column was published (1). Therefore, we hereby publish these corrections:

 was given as 10000; this number contains one more zero than the correct amount. The correct expression for 8

In the first text column on page 20, under “Errors in Representation,” there were several instances of inserting the incorrect subscript to define the number system the specified number belongs to. For example, in the passage“. . . 2

 = 0.25 is too small...” it is clear that the digit 2 cannot belong to a binary number, since a binary number can only contain the digits 0 and 1, as we stated several times. The correct statement is “. . . 2

The same error occurred several times over the subsequent six lines. Instead of belaboring each occurrence of the error, here we simply present the corrected passage, starting with “for example”:

, which is again too large. We note that 0.0101

, which is still too large, while 0.01001

, is again too small. Finally, we are closing in on 0.3

In Table III, the last two entries under the column heading “Truncated Decimal Number” simply repeat the second-to-last entry instead of reflecting the continuing truncation of pi. The correct table entries are 3.141000000000000

(1) Mark, H.; Workman, Jr, J. Decimal Versus Binary Representation of Numbers in Computers. 

https://doi.org/10.56530/spectroscopy.mm1179p4

Are you intrigued by artificial intelligence, but unsure what it really means for analytical chemistry? Read on.

Artificial Intelligence in Analytical Spectroscopy, Part I: Basic Concepts and Discussion

The solution-cathode glow discharge (SCGD) is a low-power, atmospheric- pressure, ambient-atmosphere microplasma that is proving to be a proficient excitation source for atomic emission spectroscopy (AES). The analytical figures of merit of SCGD-AES experiments often compete with established, conventional approaches despite the fact that it is a simple, small, low-cost instrument. The operating principles of the SCGD are reviewed here, including experimental operating parameters, plasma conditions, analytical performance, matrix interferences, and application examples.

The solution-cathode glow discharge (SCGD) is a low-power, atmospheric-pressure, ambient-atmosphere microplasma that is proving to be a proficient excitation source for atomic emission spectroscopy (AES). The analytical figures of merit of SCGD-AES experiments often compete with established, conventional approaches despite the fact that it is a simple, small, low-cost instrument. The operating principles of the SCGD are reviewed here, including experimental operating parameters, plasma conditions, analytical performance, matrix interferences, and application examples.

A new class of miniaturized plasma sources that exploit the liquid–plasma interface of an atmospheric pressure glow discharge are gaining popularity as excitation sources for atomic emission spectroscopy (AES). Reports of glow discharge electrolysis date back to at least 1887, but it was not until 1993 that the first modern instrument for AES was developed by Cserfalvi and coworkers (1). The solution-cathode glow discharge (SCGD) experiment is much different from conventional plasma or flame-based AES strategies. In SCGD-AES, the plasma itself is responsible for sampling material directly from a liquid sample solution, as well as desolvating, vaporizing, atomizing, and exciting the constituent atoms for quantitation by emission spectroscopy. Since the introduction of SCGD, several decades of instrumental refinement and an improved fundamental understanding of the plasma has allowed it to achieve analytical performance comparable to traditional AES and atomic absorption approaches (2,3).

The potential advantages of using a miniaturized plasma, like the SCGD, in place of the argon inductively coupled plasma (ICP) or pre-mix flames were immediately recognized. The SCGD operates in the open ambient atmosphere and does not require compressed purified argon or flammable gases. The miniaturized plasma also requires only modest power (100 W) and uses simple electronic circuitry compared to the popular 1 kW radio-frequency ICP. Because the SCGD plasma directly samples the analytical solution, there is also no need for the nebulizers and spray chambers that are used with other methods. Moreover, because the liquid surface is constantly regenerated by a flowing stream, memory effects are nearly elimi- nated, and the plasma is naturally suited to coupling with chromatographic separations. The discharge predominantly produces neutral atomic emission lines, leading to a relatively uncluttered emission spectrum. Although the SCGD is certainly not a panacea to all limitations, these attributes make the SCGD attractive as a simple, low-cost AES excitation source with significant advantages best suited for applications like continuous, portable monitoring studies.

A schematic of a typical SCGD experiment is shown in Figure 1. The SCGD is an ambient atmosphere, atmospheric-pressure glow discharge formed between a metal pin-type anode electrode (tungsten, titanium, or molybdenum) and the liquid surface of a sample solution, which acts as the discharge cathode. The SCGD is sustained by a potential of 700–1000 V, requiring a power of approximately 100 W. The electrical circuit is completed by a graphite rod or a platinum wire placed within the liquid waste reservoir, and the circuit often includes a ballast resistor to prevent arc formation. A peristatic, or syringe, pump is used to introduce samples into the discharge capillary at a typical rate of 1–3 mL/min. As the liquid emerges from the capillary tip (0.4 mm i.d.), the liquid surface forms a cathode that is constantly replenished by the flowing stream. Excess liquid that is not sampled into the discharge overflows from the capillary tip into the waste reservoir.

FIGURE 1: Schematic of a typical SCGD experiment.

Samples are typically prepared as acidic solutions (1% HNO

). Because the solution forms a part of the electrical circuit, maintaining constant solution conductivity is an important factor in ensuring a reproducible response. A variety of supporting electrolytes, detergents, and chemical additives have been reported as means of controlling solution conditions. Overall, most studies have shown that a low solution pH is the most important factor for optimum AES response in the SCGD, which makes acid digestion a particularly convenient sample pretreatment method. Atomic emission that originates in the SCGD is captured and analyzed in the typical manner, although many studies make use of portable fiber-optic-based spectrometers.

A large number of discharge geometries, solution conditions, and powering schemes have been reported, and several are critically compared in recent reviews (2–5). One popular modification of the SCGD is to simply reverse the applied voltage polarities, forming a solution-anode glow discharge (SAGD). The SAGD retains many of the benefits of the SCGD (low power, simple spectra, and no support gas required), but it has been found to provide enhanced sensitivity and limits of detection to select elements, including silver, bismuth, cadmium, mercury, indium, thallium, and zinc (6). The SAGD is sustained over the same current and voltage range as the SCGD and can use modestly acidified sample; thus, it can be sustained in the same instrumental arrangement as the SCGD.

A representative SCGD atomic emission spectrum is shown in Figure 2. The background emission from the SCGD plasma differs from argon-based plasmas such as the ICP. Significant broadband molecular background features exist in the range of 250–350 nm because of emission from NO, OH, and N

Π) emission bandhead at 306.4 nm is a particularly significant feature because the SCGD is essentially a water vapor-based discharge. Nitrogen vibronic emission N

 penetrating the interior of the discharge from the surrounding atmosphere. Background atomic emission is also common (oxygen at 777 nm, oxygen at 846 nm, hydrogen at 486 nm, and hydrogen at 656 nm). Figure 2 also includes atomic emission lines of several analyte elements, including gallium, indium, thallium, sodium, lithium, and cesium in a 0.1 M HNO

 matrix. Neutral atomic emission lines are prevalent in the emission spectrum, and emission from ionic lines is weak or nonexistent. Atomic lines used for quantitative analysis are often commonly used in ICP or flame emission spectroscopy, or they are selected based on the spectral regions where the spectral background is low.

FIGURE 2: Example of an SCGD atomic emission spectrum.

Figure 3 summarizes current SCGD-AES limits of detection (LODs) for the elements across the periodic table, where each element is color coded according to approximate LOD. The lowest LODs are found with group I alkali metals, (0.16 μg/L for lithium, 0.04 μg/L for sodium, 0.09 μg/L for potassium, and 1.1 μg/L for cesium), which are values that compare well to those offered by modern ICP-AES instruments. Group II alkali earth metals show slightly higher LODs (3 μg/L for magnesium, 11 μg/L for calcium, and 210 μg/L for strontium). Transition metal LODs vary significantly by element, but often fall between 1–10 μg/L (0.011 μg/L for indium, 0.18 μg/L for silver, 1.1 μg/L for lead, 13 μg/L for cadmium, and 12 μg/L for copper) (2–4). To our knowledge, no data on the analysis of lanthanides and actinides have yet been published, although we have analyzed scandium, yttrium, and lanthanum in our laboratory using the metal oxide vibronic emission and measured LODs of 560 μg/L for yttrium, 2000 μg/L for scandium, and 3700 μg/L for lanthanum. Metalloids (such as tin, antimony, and arsenic) are not analyzed under typical SCGD conditions, but they are often introduced to the plasma as hydrides yielding low LODs (for example, 0.3 μg/L for arsenic, 0.36 μg/L for antimony, 0.2 μg/L for selenium, and 92 μg/L for mercury). Of special note are the poor LODs or complete lack of emission of elements with high oxide bond strength (for example, above 550 kJ/mol), such as boron and tin. This comes as a consequence of the water-vapor nature of the SCGD and SAGD plasma, which encourages formation of oxide and hydroxides.

The SCGD ambient-atmosphere microplasma has emerged as an alternate excitation source for atomic emission spectroscopy that is able to perform admirably compared to established, conventional approaches—with lower cost.

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