Chemometric Methods Used In Spectroscopy

A Newcomer’s Guide to Using Surface Enhanced Raman Scattering

Demystifying SERS

The Analysis of Microplastics by Infrared Microscopy

In your paper (1), you state that single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) is an emerging methodology that holds promise to enable multielemental analysis of low-volume samples in the future. Can you explain why you believe this?

In my view, SC-ICP-MS has become a hot topic in the field of elemental analysis, and rightly so: Scientist working in the field of bioanalytics applied this approach to look into the smallest units of life. As a matter of fact, most biochemical processes take place in cells. Elemental bioanalysis in cells makes it possible to obtain deep insights into cellular processes and the biological status of specific cells. In comparison, the analysis of bioliquids mainly provides insights on a systemic level of the corresponding organism. Over the last decades, many scientists applied ICP-MS to study the elemental composition in all kinds of biofluids and tissues. Due to the latest technical developments it is now possible to achieve ever lower detection limits. Thus, it is now possible to examine individual cells and their elemental composition—at least partially. This may open a completely new field of application for ICP-MS and there are many scientists who are about to prove this.

Why is ICP-MS generally considered being the method of choice for multielemental analysis as compared to other ICP techniques (AES or OES) or more traditional atomic absorption (F-AAS or GF-AAS)?

ICP-MS is unarguably the gold standard for elemental and especially multielemental analysis. This is mainly due to the fact that this technique is extremely versatile compared to ICP-OES/AES and atomic absorption spectroscopy. Most AAS devices apply element-specific radiation sources (hollow cathode lamps) that enable the detection of a single element at the same time. Although there are some commercially available AAS instruments that allow the detection of 10-20 elements simultaneously, sensitivity limitations hamper the analysis of most trace and ultratrace elements. ICP-OES allows multielemental analysis, is relatively easy to setup, and well-suited for high-throughput analysis—however, spectral interferences can be challenging. ICP-MS allows high-throughput, multielemental analysis with the lowest-possible detection limits. It offers a linear detection range of up to 10 orders of magnitude, enables isotopic analysis (isotope dilution analysis for quantification, control of spectral interferences), and it is possible to analyze any type of sample material (gases, liquids, solids).Furthermore, high-resolution separation techniques such as HPLC, GC, CE etc. can be hyphenated to the ICP-MS. Overall, this makes ICP-MS the most powerful and versatile technique for elemental analysis.

What motivated you to focus on this specific application?

Several times, we have encountered situations, where limited sample volume was available. For example, limited volume of biofluids (blood serum or plasma) might be available in human sample cohorts for interdisciplinary clinical studies. In such cases, the principal investigator often selects the panel of analytical tests according to the required sample volume. Sample volume restrictions can also arise from the donor itself, even if there is no competing analysis. This is the case for animal models, especially for studies with mice where only a few µL of blood serum or plasma are available per animal. Since most of the published multielemental profiling methodologies require relatively large sample volumes, our aim was to provide a tool that overcomes this challenge. For this reason, in 2016 we have developed an ICP-MS based method, which allows the analysis of 29 elements in a 150 µL blood serum sample. However, we soon realized that there is a need for multielement analysis in even smaller sample volumes. This is for example the case when only small amounts of serum and sample are available (for example, in precious clinical cohorts or preclinical studies with rodents). We accepted the challenge and found that it is possible to detect 20 to 30 elements in 3–10 µL of biological fluids. These positive results have motivated us to optimize and validate the method. Finally, we have successfully applied the method in a preclinical model. The method turned out to be very easy to use, it does not require expensive or complicated changes in the experimental setup, and, above all, it has shown very good analytical performance. We then decided to publish the results of our study as well as the experimental setup in order to encourage other readers to perform multi-element analyses in the smallest sample volumes in a straightforward way.

What are some cancer-specific multielemental profiles that you have identified? What type of data analysis techniques were used to determine these profiles? Are there a few predominant elements that seem to be most often present in the mice cancer cells?

In the mouse cancer modes under evaluation, the presence of the tumor influences the serum concentration levels of various elements. Some elements, such as sulfur, calcium, manganese, zinc, strontium and molybdenum, were not affected significantly by the presence of the tumor. On the other hand, elements such as phosphorous and copper were increased in the serum samples of tumor bearing mice, independently of the tumor type. Iron, on the other hand, was depleted. These results seem to indicate that the presence of tumor systemically influences the concentration levels of some serum elements, independently from the tumor type. On the contrary, elements such as vanadium, chromium, bromine, rubidium, cesium and cobalt show trends for tumor-specific concentration changes. Furthermore, our results show that different immune T cells (CD4+ and CD8+) have specific mineral profiles reflecting their specific immune function. Moreover, the presence of a tumor seems to have an impact in the mineral contents of T cells. Indeed, we observed elemental modulations within CD8 cells that could be associated with presence of cancer, such as higher levels of magnesium and manganese and lower levels of iron, cobalt and rubidium. These findings might indicate an increased metabolic need of immune cells for Mn and Mg in the presence of a tumor.

What are the biggest challenges that you encounter in this analysis method? Are there any limitations to using this technique? What options or alternatives are available to overcome these challenges?

While the analysis of small amounts of biofluids is quite straightforward (centrifugation to remove particles and a simple dilution step), the analysis of cells is more challenging. In fact, one hurdle we faced during method development was to remove the cell sorting buffer (containing a high concentration of various elements of interest) from the cells. As large volumes of this buffer are required for a single cell-sorting experiment (roughly 10 liters in our case), it was not possible to prepare a buffer solution based on reagents which are suitable for trace elemental analysis. For a washing step, we applied filters or considered osmosis for buffer removal, but none of these sample preparation strategies proved to be suitable. Finally, applying a mild centrifugation step to form a cell pellet, followed by removing the supernatant with a pipette, provided satisfying results. However, the removal of a few microliters from an Eppendorf tube, and at the same time maintaining the integrity of a tiny cell pellet, remains the Achilles heel of the method and requires some finesse and experience.

With such small samples, is contamination an issue, and how have you overcome this potential problem?

Contamination is always a challenge in ultratrace elemental analysis. Acid-washing, working in a clean environment and under a fume hood are the minimum requirements to obtain high quality results. However, contamination is not more challenging in this method, as compared to most other ICP-MS based methods dealing with trace and ultratrace analysis. This is mainly due to the fact that a relatively moderate sample dilution of (1:10) and few sample preparation steps are applied. Contamination could arise, for example, from plastic materials. We mitigate this risk by applying an acid-washing step up front. Another, often underestimated source of contaminants are chemicals and reagents applied during sample preparation. They often contain elements (for example, iron) that enter the sample in this way and thus mix with the analytes. Due to the inherent properties of elemental mass spectrometry, it is then no longer possible to distinguish easily between endogenous and exogenous elements. This can lead to an overestimation of the element concentration. In our study we only apply buffer solutions and nitric acid during sample preparation. Thus, the overall contamination risk is relatively low.

Can you briefly summarize the results that you have achieved so far?

We have developed a quantitative analytical strategy that enables the analysis of 20 biologically relevant elements in low-volume samples (10 µL) of serum. By applying an optimized workflow, the method is also applicable for the quantification of 12 elements in a low number of sorted cells (250,000 cells). In our paper, we give detailed descriptions of the experimental setup. This enables scientist to apply the approach to study biological-relevant elements (major trace and ultratrace elements) in very small sample volumes. Applied to tumor cells, we could demonstrate that the presence of certain cancer types influences differentially the elemental composition of serum concentrations. Furthermore, we could show that different immune T cells (CD4+ and CD8+) have specific mineral profiles reflecting their specific immune function. Last but not least, the presence of a tumor effects the mineral composition of T cells.

Is there anything that you have done in your study that is different from what other researchers have done as they have examined the same problem?

Most of the ICP-MS-based approaches aiming to detect major trace and ultratrace elements in cells are challenged by the media or solvent in which the cells are transported into the plasma ionization source. If the cells are present in Milli-Q water, the “elemental background” is very low. This facilitates the detection of very small quantities of cellular elements. However, cells are prone to lyse in Milli-Q-water. This makes the cellular membranes permeable for ions and small molecules, which are eventually leaking out of the cell and changing the elemental composition of the cell. Fixation strategies, aiming to stabilize cellular membranes, can prevent lysis. Unfortunately, fixation methods tend to increase permeability of the cell membranes and consequently alter the endogenous elemental composition of the cell. Moreover, aggregation of cells is often seen after fixation and needs to be monitored. In fact, the presence of cells in the monodisperse state has to be confirmed by complementary analysis. Many of the reported SC-ICP-MS methods are overcoming this issue by using yeast cells. The use of yeast cells has two advantages: first, the cells are relatively big, meaning higher absolute quantity of elements per cell. Second, these cells are extremely robust compared to immune cells. Re-suspended in Milli-Q water, yeast cells can stay alive for a couple of hours without undergoing lysis. This timespan allows conducting SC-ICP-MS experiments with intact cells while maintaining an extremely low elemental background of the solvent. Suspensions of cells in buffer solutions, on the other hand, allow keeping the cells alive and preserve the integrity of the cell. However, such buffer solutions contain “contaminants”; for example, major or trace elements which are present in the raw materials. This makes it difficult to distinguish between “cellular” elements and elements from the buffer solution. We were able to overcome this limitation by using a sample preparation strategy that includes a washing step with a suitable buffer solution (containing a relatively low elemental background).

What are your next steps in this work? Do you know how well the discoveries of multielemental profiles in mice cancer cells may extrapolate to human cancer cells?

I am now working in a company which is rather focused on elemental analysis is food and pharmaceutical products. However, my former colleagues are continuing our research work and other scientist are working on different approaches to study trace- and ultratrace elements in cells. I am confident that rather sooner than later we will see new findings in this field, both in pre-clinical and clinical studies.

Tobias Konz is currently leading the Elemental Analysis group at UFAG Laboratorien (in Sursee, Switzerland), specializing in quantitative analysis of elements in food and pharmaceutical products (GMP and ISO environment). He studied biomedical chemistry at the Johannes-Gutenberg University (2000-2007), and completed his diploma thesis and PhD in Analytical Chemistry in the research group of Alfredo Sanz-Medel, under the supervision of Maria Montes-Bayón (University of Oviedo, Spain). In 2014, he started a professional career as ICP-MS specialist at the Nestlé Institute of Health Sciences (Lausanne, Switzerland) in the Nestlé Research organization. The main focus of his work has been to develop quantitative methods for multielemental analysis in low volume samples (biofluids) in a GLP and ISO regulated environment.

(1) T. Konz, C. Monnard, M. Rincon Restrepo, J. Laval, F. Sizzano, M. Girotra, G. Dammone, A. Palini, G. Coukos, S. Rezzi, J.-P. Godin, and N. Vannini, 

https://dx.doi.org/10.1021/acs.analchem.9b05643

Articles

Tobias Konz of Nestlé Research, Lausanne, Switzerland and various associates have developed and validated what they describe as a reliable, robust, and easy-to-implement quantitative method for multielemental analysis of low-volume samples. The ICP-MS-based method comprises the analysis of 20 elements (Mg, P, S, K, Ca, V, Cr, Mn, Fe, Co, Cu, Zn, Se, Br, Rb, Sr, Mo, I, Cs, and Ba) in 10 μL of serum and 12 elements (Mg, S, Mn, Fe, Co, Cu, Zn Se, Br, Rb, Mo, and Cs) in less than 250,000 cells, and involved the analysis of elemental profiles of serum and sorted immune T cells derived from naıv̈e and tumor-bearing mice. The results indicate a tumor systemic effect on the elemental profiles of both serum and T cells. Konz and his colleagues believe their approach highlights promising applications of multielemental analysis in precious samples such as rare cell populations or limited volumes of biofluids that could provide a deeper understanding of the essential role of elements as cofactors in biological and pathological processes. Konz spoke to us about this work.

Quantitative Methods for Multielemental Analysis in Low Volume Biofluids 

 Magazine is pleased to announce the addition of seven new members to its editorial advisory board: Karl S. Booksh, John Coates, Paul J. Gemperline, Barry K. Lavine, Igor K. Lednev, and Yukihiro “Yuki” Ozaki.

Karl S. Booksh is a professor in the Department of Chemistry and Biochemistry at the University of Delaware. He holds a BS in Chemistry from the University of Alaska and a PhD in Analytical Chemistry from the University of Washington. He is an American Chemical Society Fellow (2015) and a Society for Applied Spectroscopy (SAS) Fellow (2014), and is the president elect of SAS for 2021. His research group develops small chemical sensors capable of reliable measurements in the field or in the process chemical sensors for environmental, biomedical, and industrial process monitoring. His group also conducts chemometric analysis of collected data and multivariate sensor calibration. In terms of analytical techniques, Booksh is focusing largely on Raman, Laser-induced breakdown spectroscopy (LIBS), and fluorescence sensors with a bit of Raman imaging for fun. He is the author of over 100 peer-reviewed articles.

John Coates was educated and started his career in the United Kingdom. He first worked as an analytical chemist for Castrol Oil Company. He graduated from the Royal Society of Chemistry (RSC) and obtained his PhD in analytical chemistry at Brunel University. After moving to the United States as a senior staff scientist at Perkin Elmer Corporation, Coates accepted positions at Spectra-Tech (Stamford, Connecticut) and then at Nicolet Instruments (Madison, Wisconsin) before returning to a position at PerkinElmer’s Real-Time Systems Division (Wilton, Connecticut) a joint-venture with Dow Chemical Company (Midland, Michigan).

In 1996, Coates opted to leave the corporate world and formed his own company, Coates Consulting LLC, a consulting company focused on applied and industrial instrumentation, optical spectroscopy, and analytical instruments and sensors for dedicated applications, with a primary focus on instrument miniaturization and spectral sensors. Coates has been involved in the development of more than 100 different instrument and sensor products for dedicated applications and received the Williams-Wright Award for Industrial Spectroscopy, presented by the Coblentz Society, in 2013. Coates has 50 years of industrial experience.

Paul J. Gemperline has been the dean of the graduate school at East Carolina University (ECU) since 2009 and a faculty member in the department of chemistry since 1982. He earned his BS and PhD in chemistry from Cleveland State University. Gemperline’s area of research is chemometrics with more than 60 publications and $1.8 million in external grant funds from government and industrial sources. Gemperline's research focuses on the development of new computer algorithms and software tools for analysis of multivariate spectroscopic data. His work has led to advances in methods for monitoring, understanding, and controlling batch chemical and pharmaceutical processes. He has developed methods of cluster analysis and classification for rapid non-destructive testing of raw materials and finished products. His work with collaborators led to the development of a licensed patent for designing optical filters for chemical calibration.

Gemperline also served as the editor-in-chief of the 

Barry K. Lavine is a professor of chemistry at Oklahoma State University (OSU). Lavine’s research interests encompass many aspects of chemical analysis including optical sensors, vibrational spectroscopy, infrared and Raman imaging, and chemometrics. Lavine has published more than 115 research papers, 21 book chapters, and 16 review articles, along with being the editor for three ACS monographs. Lavine is on the editorial board of several journals including the 

. He has served as chair of the Northern New York (1997–2004) and Oklahoma (2006–2008) sections of the ACS. Lavine has also been the program chair for several meetings including SciX (1992), the Northeast Regional ACS Meeting (1999), and the Pentasectional Meeting of the local Oklahoma Sections of the ACS (2005). Lavine is the recipient of the 2015 Kowalski prize from the 

and the 2017 Chemometrics Award sponsored by the Eastern Analytical Symposium. He is also a SAS fellow.

Igor K. Lednev is a professor at the University of Albany, State University of New York. He received his PhD from the Moscow Institute of Physics and Technology and worked in several leading laboratories around the world in the United Kingdom, Canada, Germany, and Japan. Lednev accepted a faculty position at the University at Albany in 2002 and was promoted to full professor in 2013. His research focuses on the development and application of novel laser spectroscopy techniques for medical diagnostics and forensic purposes. Lednev is a cofounder of two startup companies commercializing his patented technology. He served as an advisory member on the White House Subcommittee for Forensic Science and is a fellow of SAS and the Royal Society of Chemistry. Lednev also launched a new annual National Institute of Justice (NIJ) Forensic Science Symposium at Pittcon in 2018.

He has co-authored about 240 publications in peer-reviewed journals and seven patents.

Yukihiro (Yuki) Ozaki received his PhD in 1978 from Osaka University. He joined Kwansei Gakuin University in 1989. From 1993 to March 2018, he was a professor in School of Science and Technology. Currently, Ozaki is a professor emeritus of the university. He has been a guest professor or scientist at Kobe University, Fukui University, and Riken and Toyota Physical and Chemical Research Institute. Ozaki is involved in studies of a wide range of molecular spectroscopy techniques, ranging from far-ultraviolet to far-infrared to Terahertz and Raman spectroscopy. His research focuses on both electronic and vibrational spectroscopy.

Ozaki has been a member of SAS for more than 30 years and fellow since 2013. He received several awards including the Bomem-Michelson Award (2014), Chemical Society of Japan Award (2017), The Medal with Purple Ribbon (2018), Pittsburg Spectroscopy Award (2019), and Charles Mann Award (2020).

Barry Wise received BS degrees in chemistry and chemical engineering from the University of Washington in 1982. After three years with Battelle Pacific Northwest National Laboratory, he returned to the University of Washington, where he received MS (1987) and PhD (1991) degrees in chemical engineering. Wise has authored more than 50 scientific publications, patents, and book chapters. He has also created a popular chemometrics software package, PLS_Toolbox, used by scientists and engineers worldwide. Wise is president and cofounder of Eigenvector Research Inc., which provides chemometrics software, training, and consulting. Thousands have attended Wise’s chemometrics short courses at conferences and at Eigenvector University.

Wise was named the winner of the Eastern Analytical Symposium Chemometrics Award for 2001 in recognition of his significant contributions to the field. He also received the Herman Wold Medal in Gold in 2019 for his deep commitment to the proliferation of chemometrics.

Spectroscopy Magazine is pleased to announce the addition of seven new members to its editorial advisory board.

Seven New Members Join Spectroscopy's Editorial Advisory Board

Of course, this year’s meeting is different because of our response to the presence of COVID-19. 

 There will be LIVE on-line sessions each day during the week of 

 Additionally, the EAS presents pre-recorded ON-DEMAND sessions available at any time through December 31 to those who register for the Symposium. And, be sure not to miss the on-line virtual exhibition for the latest innovations from the vendors you have come to rely on.

This year, the EAS Awards for achievements in spectroscopy recognize the outstanding contributions of Barbara S. Larsen (mass spectrometry), Arthur Palmer (magnetic resonance spectroscopy), and John Reffner (vibrational spectroscopy), and you can tune in to these award sessions, LIVE. Also live are the sponsor awards such as the SAS Gold Medal honoring Howard Mark and Jerome Workman, as well as the Ernst Abbe Award to Brian J. Ford. But there also are many pre-recorded on-demand sessions and posters on spectroscopic analysis of materials, forensic analysis, biological and pharmaceutical analysis, microscopy, Raman imaging, LIBS, and many more.

Two highlights of the EAS in years past have been the keynote lecture and the plenary lecture, and this year’s lectures should be exceptionally entertaining for attendees. The keynote lecture by Eric Green of the National Institutes of Health is titled “The Human Genome Project was just the Beginning: Research Opportunities at the Forefront of Genomics.” The plenary lecture, “The Fascinating Impact of Nanoscale Structure on Chromatography and Mass Spectral Ionization,” will be given by Susan Olesik, this year’s winner of the EAS Award for Outstanding Achievements in the Fields of Analytical Chemistry. Her lecture combines two themes that should interest a wide audience. In addition, this year there is a special lecture presented by Adam Lanzarotta of the USFDA on the “FDA’s Role in the 2019 Vaping Crisis.” These three lectures focus on important issues in science and how it affects the world around us.

The Eastern Analytical Symposium is known for its large suite of short courses tailored to the needs of analysts. This year we are proud to continue the tradition of short courses on a wide range of traditional and novel topics in analysis. Whether it’s gas or liquid chromatography, hyphenated methods, spectroscopic methods including imaging, NMR spectroscopy, FT-IR spectroscopy, or topics like communication or process analysis, there is a course at EAS for you!

These will be presented in a LIVE video format that allows interaction with the instructor, over several weeks, allowing interested persons to attend multiple short courses.

, for the latest news and registration info for this year’s virtual EAS. Be sure to take advantage of the discounted pricing we offer to those who register early!

Once again, the Eastern Analytical Symposium takes the spotlight November 16–19, providing analysts with the latest developments in areas ranging from biopharmaceutical analysis to materials characterization, from quality assessment to green analysis, and from environmental contaminant determination to antibody analysis, to name a few of the many areas covered.

The 59th Eastern Analytical Symposium

The Society for Applied Spectroscopy (SAS) Atomic Section is proud to honor four deserving students with the 2020 SAS Atomic Section Student Awards. These awards provide the winning students with the opportunity to present their work at the conference and normally include travel funding to attend the conference. This year, the winners will present during an on-demand access session as part of the SciX 2020 virtual conference. The awards also include a two-year membership to the society. The four winners are listed below.

who is a student at Rensselaer Polytechnic Institute under Prof. Jacob Shelley, will present his work on “solution-cathode glow discharge ionization sources for mass spectrometry.”

of the University of Natural Resources and Life Sciences Vienna (BOKU), who is studying under Prof. Thomas Prohaska, will present her work on “LA and MC-ICP-MS for isotope analysis of bone for biomedical applications.”

, a student at the University of Münster, under adviser Prof. Uwe Karst, will present his work on “LC–ICP-MS for Gd speciation in surface and drinking waters.”

 of the University at Buffalo, The State University of New York, whose adviser is Prof. Steven Ray, will present her work on “Exploration of microelectrochemical devices as temporal gating devices for LIBS and LAMIS.”

More information about the awards can be found on the SciX conference 

The 2020 SAS Atomic Section Student Awards

Jerome Workman Jr. serves on the Editorial Advisory Board of Spectroscopy and is currently with Unity Scientific LLC. He is also an adjunct professor at Liberty University and U.S. National University. He can be reached at jworkman04@gsb.columbia.edu.

Landry received her PhD from the University of Illinois at Urbana-Champaign in 2012. She then completed a postdoctoral fellowship at MIT before taking her current position at the University of California, Berkeley. She is also currently a faculty scientist with Lawrence Berkeley National Laboratory, an investigator at the Chan-Zuckerberg Biohub in San Francisco, and an investigator with the Innovative Genomics Institute in Berkeley. Landry’s research focuses on the intersection of single-molecule biophysics and nanomaterial-polymer science to develop new tools to probe and characterize biological systems. Her research has generated nanoparticle-polymer conjugates for imaging and spectroscopic detection of neuromodulation in the brain, and for the delivery of genetic materials into plants for crop biotechnology applications.

In recognition of her work, Landry is the 2020 winner of the Emerging Leader in Molecular Spectroscopy Award, which is presented by 

 magazine. The award was scheduled to be presented to Landry at the SciX 2020 conference in October, where she was to give a plenary lecture and be honored in an award symposium, but the in-person conference has been cancelled because of the COVID-19 pandemic. This interview with Landry describes her early and most recent research.

Please tell us about some of your earliest research interests. How did you become interested in science? What has kept you motivated? What are the most exciting aspects of your work each day?

I am a physicist by training, having developed several instruments capable of measuring piconewton-scale forces and imaging at the nanometer-scale (1) for my doctoral work. My goal as a postdoc was to leverage my expertise in single-molecule biophysics to design molecular recognition tools, during which time I developed a generic platform to create synthetic near-infrared nanosensors (2,3), enabling the detection of any molecule with purely synthetic molecular recognition elements. When I started my lab at UC Berkeley in 2016, I grew increasingly interested in developing tools to study biological systems: As a scientist, I find the complexity of thought and intricacy of the brain fascinating. As a friend of several individuals suffering from mental health illness, I find it astounding that the status quo for diagnosing and treating psychiatric disorders remains qualitative. Understanding how our brain cells communicate (and miscommunicate) needs to be addressed by a collective and interdisciplinary approach to overcome the many limitations currently rendering this information inaccessible. Motivated by this, one of my lab’s research directions is in developing optical probes to image brain chemistry—the neurotransmitters in the brain that support healthy brain function but can also lead to psychiatric or neurodegenerative disease when their signaling is disrupted.

What have been the most difficult aspects of your research to date? How have you worked to overcome these challenges?

Research is inherently challenging because we initiate projects with big goals, and don’t usually have a clear path to achieve those goals. For every successful dopamine imaging experiment my lab performs today, there are dozens of failed experiments that have led up to it. The students and postdocs in my lab are the reason for the success of our lab’s research: Their tenacity, intelligence, and clever approaches to their research projects are what enable our science to progress.

In your postdoctoral work, you and your colleagues created single-walled carbon nanotubes (SWNTs) that were inserted into the lipid envelope of extracted plant chloroplasts to promote higher photosynthetic activity and maximum electron transport rates (4). SWNTs have allowed near-infrared (NIR) fluorescence monitoring of nitric oxide both ex vivo and in vivo,demonstrating that a plant can be altered to function as a photonic chemical sensor. How did you conceive this idea and what has been learned from this work?

In this work, we hypothesized that the broad absorption spectrum of nanomaterials such as single walled carbon nanotubes, which extends well into the near infrared and past the absorption spectrum of chlorophyll pigments, could capture a broader range of photons from broad-spectrum sunlight. This work demonstrated that these nanotubes 1) could internalize into chloroplasts and 2) could increase their photosynthetic activity as assessed by in vitro assays. This paper opens the opportunity for the incorporation of nanomaterials into plants for energy-harvesting purposes and for detection of chemicals in the environment to which plants may be exposed.

Also as a postdoctoral researcher, you used corona phase molecular recognition (CoPhMoRe) to identify adsorbed polymer phases on fluorescent single-walled carbon nanotubes (SWCNTs) for the selective detection of neurotransmitters, such as dopamine (5). This research suggested that polymer–SWCNT constructs may be useful as fluorescent neurotransmitter sensors, potentially within living tissue. Is this work continuing to show promise? And what has been unique about this approach?

This is an exciting body of work, showing that molecular recognition can be conferred synthetically. Normally, to detect biomolecules, natural molecular recognition elements such as antibodies are used to detect the target molecule over all other molecules that can be found in a biological system—for example, detecting dopamine over other neurotransmitters. With this approach, we show that synthetic polymers can be adsorbed to the surface of carbon nanotubes, and while most polymers will take on random conformations, every once in a while a polymer will take on a conformation that can selectively bind the target molecule, such as dopamine. In this manner, we can screen for polymer–nanotube conjugates that can in turn serve as synthetic probes to image biomolecules.

The work you have done on carbon nanomaterials has been highly cited and has been shown to be useful in biomedicine for imaging, sensing, targeting, delivery, therapeutics, catalysis, and energy harvesting (6). Are these materials and techniques restricted to research and discovery use or do you think this technology will lead to a clinically viable tool for measurements on patients? Are there any plans for creating a diagnostic method for physicians using carbon nanomaterials?

For the foreseeable future, the tools developed with nanomaterials in my lab are intended for research purposes. Until the nascent field of nanotechnology achieves consensus on the toxicity and environmental impacts of nanomaterials, their restricted use in a research setting is best. However, one advantage of abiotic (non-biologically based) tools in neuroscience is that they can be deployed in non-model organisms. This feature of our neurotransmitter imaging technologies could be incorporated into form factors enabling their use in humans, for instance, using our dopamine probes to measure changes in brain dopamine in organisms other than those traditionally used for neuroscience research, such as mice.

Of all your research papers so far, what are the most meaningful papers you would like to highlight for our readers that describe your current research interests?

Our most meaningful work at this point involves both genetic engineering and spectroscopy as separate, but related, pursuits.

In our genetic engineering related work, my lab demonstrates the use of various nanomaterials and their associated surface chemistries to controllably graft and quickly deliver functional biomolecular cargoes such as whole DNA plasmids to various agriculturally relevant plants (7,8). We accomplish nanoparticle-mediated heterologous and transient expression of a protein in plants, and show that there is no transgenic DNA integration into the host plant genome. We demonstrate gene expression in both model and non-model plant species of agricultural relevance: 

 (cotton, non-model dicot plant). Wheat and cotton are cash crops; cotton, in particular, is challenging to genetically transform with current technologies. We further demonstrate with single-molecule imaging that the process of grafting DNA on nanoparticles prevents DNA degradation by nucleases, providing a complete picture of how this nanotechnology both promotes delivery of functional biomolecules to plants, and also protects cargo from enzymatic degradation en route to its intracellular function.

In our spectroscopy-related work, my lab has developed a tool that newly enables imaging of the chemical communication between neurons in the brain at the spatial (micrometer) and temporal (millisecond) scales the brain uses to communicate (9). In particular, chemicals known as neuromodulators tune neuronal networks that control a wide variety of brain function, including motor control, learning, and attention. Aberrations in the chemistry of neuromodulators lies at the core of many psychiatric and neurological disorders such as schizophrenia, Parkinson’s disease, addiction, depression, and anxiety. Yet, the inability to measure the dynamics of neuromodulation at high spatial and temporal resolution has hindered fundamental neuroscience research and the validation of neuropsychiatric drugs. Thus, a technology that enables neuromodulator visualizationcould be influential for our understanding of basic neurobiology and for the treatment of our psychiatric and neurodegenerative disorders.

We have developed a near-infrared nanoscale catecholamine probe (nIRCat) that can be used to image the neuromodulator dopamine in the brain, and is validated against “gold standard” techniques in neuroscience, including electrophysiology and optogenetics. This work elucidates the core of what neuroscientists can use to implement this technology to image the dynamics of dopamine neuromodulation at the spatial and temporal scales that have eluded existing methods of inquiry. To our knowledge, this is the only optical probe for dopamine that enables dopamine imaging in the presence of dopamine receptor agonists and antagonists, and thus the only probe that can be implemented to study how dopamine dynamics gets modulated by full-panel antidepressants and antipsychotics that are used to treat psychiatric and neurodegenerative disease. Owing to its synthetic nature, our probe is remarkably flexible in implementation, requiring no reliance on genetic engineering techniques that protein-based optical probes typically demand. This latter point is important because nIRCats are based on a purely synthetic molecular recognition element for dopamine. This last feature has enabled us, for example, to optically record previously undetected heterogeneity in D2 dopamine autoreceptor modulation of presynaptic dopamine release upon exposure to various drugs with micrometer spatial resolution. This result is important because most psychiatric drugs target dopamine receptors, thus it is imperative to know where receptors are expressed within neurons—pre- vs. post-synaptically—and whether they respond to drugs similarly or heterogeneously. The non-genetically encoded nature of the probe has also enabled us to newly measure dopamine modulation in bats, a largely genetically intractable species. This method should therefore uniquely support similar explorations of the effects of other dopaminergic drugs at the level of individual synapses, experiments that are currently unachievable.

What is the most difficult or challenging aspect of your current research? How do you manage a research group, while at the same time mentoring and directing students?

Research is inherently hard. By far the best part of my job is mentoring and directing students and postdocs—working with talented lab members to come up with ideas and research directions together. Of my tasks as a professor, meetings with students and postdocs to discuss research are the highlights of my day. And of the “outputs’” of my work, more so than papers, awards, or grants, what I am most proud of are the scientists that emerge from the lab, and the careers they subsequently build for themselves.

How were you able to direct your research toward discovery of important biomedical and agricultural challenges? What made you choose the path of your research versus industrial, chemical, or environmental problems?

The goal of our lab is to develop tools that can be as broadly useful as possible. The tools we develop for neuroscience and for plant biomolecule delivery are meant to fill voids in the toolkits available to study the brain, and to genetically modify plants, respectively. Therefore, the directions that my lab takes seek to avoid redundancy in the tools we develop with pre-existing tools, and developing technologies that uniquely enable studies that would not be possible without our developments.

What areas would you like to see this research expand into when looking toward the future? Do you plan to stay your current work with carbon nanomaterials or are there another analytical techniques or areas of research that look promising?

In the future, I am excited to take the tools my lab has developed and to use them to learn more about biological systems. This will require parallel developments in nanotechnology, and also in near-infrared spectroscopy and microscopy that are necessary to complete our studies. While carbon nanotubes in particular, and their unique spectroscopic properties, have been very useful as the basis of our research thus far, we are also exploring other nanomaterials such as graphene quantum dots and gold nanoparticles.

What would you tell young people interested in science about how to prepare for a career in research?

It is never too late to get involved in science and in research. Before starting as a professor at UC Berkeley, I had never been involved in research with living systems. This transition from physics to the research areas my lab works on today was not an easy change, and required a lot of time spent with introductory level textbooks to get up to speed on this new field of research. Just to say, if I can learn new areas of science and research this late in my career, aspiring young scientists should feel empowered to get involved by volunteering in research labs, taking science classes, and participating in extracurricular activities in STEM.

(1) M.P. Landry, X. Zou, L. Wang, W.M. Huang, K. Schulten, and Y.R. Chemla, “DNA target sequence identification mechanism for dimer-active protein complexes,” 

(2) J. Zhang, M.P. Landry, P.W. Barone, J.H. Kim, S. Lin, Z.W. Ulissi, D. Lin, B. Mu, A.A. Boghossian, A.J. Hilmer, and A. Rwei, “Molecular recognition using corona phase complexes made of synthetic polymers adsorbed on carbon nanotubes, 

(3) M.P. Landry, H. Ando, A.Y. Chen, J. Cao, V.I. Kottadiel, L. Chio, D. Yang, J. Dong, T.K. Lu, and M.S. Strano, “Single-molecule detection of protein efflux from microorganisms using fluorescent single-walled carbon nanotube sensor arrays,” 

(4) J.P. Giraldo, M.P. Landry, S.M. Faltermeier, T.P. McNicholas, N.M. Iverson, A.A Boghossian, N.F. Reuel, A.J. Hilmer, F. Sen, J.A. Brew, and M.S. Strano, “Plant nanobionics approach to augment photosynthesis and biochemical sensing,”

(5) S. Kruss, M.P. Landry, E. Vander Ende, B.M. Lima, N.F. Reuel, J. Zhang, J. Nelson, B. Mu, A. Hilmer, and M. Strano, “Neurotransmitter detection using corona phase molecular recognition on fluorescent single-walled carbon nanotube sensors,” 

(6) S.F. Oliveira, G. Bisker, N.A. Bakh, S.L. Gibbs, M.P. Landry, M.S. Strano, “Protein functionalized carbon nanomaterials for biomedical applications,” 

(7) G.S. Demirer, H. Zhang, J. Matos, N. Goh, F.J. Cunningham, Y. Sung, R. Chang, A.J. Aditham, L. Chio, M.-J. Cho, B. Staskawicz, M.P. Landry, “Nanoparticle-Guided Biomolecule Delivery for Transgene Expression and Gene Silencing in Mature Plants,” 

(8) H.Zhang, G.S. Demirer, H. Zhang, T. Ye, N.S. Goh, A.J Aditham, F.J. Cunningham, C. Fan, M.P. Landry,

Low-dimensional DNA Nanostructures Coordinate Gene Silencing in Mature Plants,” 

(15), 7543-8 (2019). DOI: 10.1073/pnas.1818290116

(9) A.G. Beyene, K. Delevich, J.T.D.B. ODonnell, D.J. Piekarski, W.C. Lin, A.W. Thomas, S.J. Yang, P. Kosillo, D. Yang, L. Wilbrecht, M.P. Landry, “Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor,” 

By combining single-molecule biophysics and nanomaterial-polymer science, Markita Landry of the University of California, Berkeley has developed new tools for understanding biological systems. Using a combination of nanoparticles, imaging, and spectroscopy, her work has led to discovery of the aspects of neuromodulation in the brain, and for the delivery of genetic materials into plants for crop biotechnology. 

The 2020 Emerging Leader in Molecular Spectroscopy: Advancing Spectroscopy, Biotechnology, and Nanotechnology

Micro- and nanoplastics enter the environment as manufactured products in cosmetics and other industrial products, and as breakdown products of plastics of larger size (macroplastics). Recent estimates suggest that the mass of microplastics in aquatic habitats will surpass that of fish by 2050; therefore, plastic pollution is a major environmental issue faced at global scale. Marine scientists have been working in this field for over a decade, and more recently the study of microplastics has captured the attention of other scientists in the environmental field. Despite efforts focused on identifying, quantifying, and assessing the size of microplastics in the environment, standardized methods are lacking. As a result, studies cannot be compared or reproduced. Infrared (IR) microscopy has become an essential technique in the toolbox for the analysis of microplastics in environmental samples. In this work, we conducted a methodical evaluation of significant operational parameters of IR microscopy for accurately determining the size of microplastics, with the overarching goal of outlining performance parameters that will help in the standardization of microplastics analysis.

Plastic pollution is a major environmental issue faced at global scale. Micro- and nanoplastics enter the environment as manufactured products in cosmetics and other industrial products, and as breakdown products of plastics of larger size (macroplastics). According to a recent perspective paper (1), the most pessimistic estimates suggest that the mass of microplastics in aquatic habitats will surpass that of fish by 2050. Interest in the scientific community and public has exponentially increased in the past few years, resulting in a significant increase in the number of publications focused on this topic (2). Marine scientists have been working on this field for a decade, and more recently the study of microplastics has captured the attention of other scientists in the environmental field. Despite efforts focused on identifying, quantifying, and assessing the size of microplastics in the environment, standardized methods and agreement of reporting parameters are lacking (3). As a result, studies cannot be compared or reproduced. Common requests from scientists engaged in the field include the need for reference materials, proficiency tests and interlaboratory studies, and detailed descriptions of instrumental and spectral treatment parameters (2). Multiple international groups (2,3), scientific organizations (such as the Society of Environmental Toxicology and Chemistry), and standardization agencies (such as the American Society for Testing and Materials [ASTM] and the International Organization for Standardization [ISO]) are currently engaging in efforts to fulfill these requests.

 (4) provides an excellent summary of various techniques suitable of the identification of microplastics and their scope. Infrared (IR) microscopy has become an essential technique in the toolbox for the analysis of microplastics in environmental samples. IR microscopy is often used for two purposes: the identification and sizing of microplastics present in the sample. While lack of appropriate standard spectra in the libraries could cause challenges in proper identification, especially for weathered plastics, this problem can be overcome with custom-created libraries and analyzing specific regions of the spectra (unique peaks, valleys, and intensity ratios, to name a few). Therefore, the real challenge is to understand the capability of IR microscopy for microplastics analysis, mainly in terms of smallest measurable microplastic particle size.

The objective of the work presented here was to collect data to support current standardization efforts for the analysis of microplastics in environmental samples by IR microscopy. For this purpose, we conducted a methodical evaluation of significant operational parameters of IR microscopy for accurately determining the size of microplastics, including optical mode, number of scans, and background contribution from filters.

Microplastic standards were purchased from Sigma-Aldrich. They were polymer based micro particles with various diameters: 100 µm polystyrene (P/N 59336), 50 µm polymethacrylate (P/N 74161), 50 µm polystyrene (P/N 74491) and 10 µm polystyrene (P/N 72986).

Three types of filters commonly used to hold the microplastics samples for Fourier transform infrared (FT-IR) spectroscopy studies were evaluated: a stainless-steel mesh filter (Utah Biodiesel Supply, 5 μm pore size), a cellulose ester filter (Whatman, 0.6 μm pore size) and a glass fiber filter (Whatman, 934-AH grade, 1.5 μm pore size).

The measurements were carried out with a Shimadzu AIM-9000 FT-IR microscope in combination with an IRTracer-100 FT-IR spectrophotometer. Instrumental control and data acquisition were done automatically by the AIMSolution software. Data were collected in reflection mode and attenuated total reflectance (ATR) mode. When measuring in the reflection mode, background was acquired using a silver mirror. Experiments conducted in ATR mode were conducted with the Shimadzu ATR attachment with a Ge objective on the FT-IR microscope. The only post-processing steps performed were baseline correction of the data whose signal to noise ratio were calculated, and Kubelka-Munk transformation of the spectrum of the cellulose ester filter. The instrumental parameters are listed in Table I.

All samples were diluted first to 1 mL of Deionized water, except for the 100 μm polystyrene based microplastics sample. For reflection mode measurements, the samples were dispensed onto the filters and dried under ambient conditions. For ATR mode studies, 1–2 drops of the diluted samples or the 100 μm polystyrene-based stock sample were dispensed on a low-E microscope slide, and dried under ambient conditions.

To get a better understanding of types and distribution of the particles on the filters, infrared mapping measurements were carried out using the reflection mode on the FT-IR microscope. The information obtained from mapping is often qualitative; however, some quantitative information can also be obtained with further data analysis. In-depth measurements using more sensitive setup and parameters were carried out on areas of interest determined by mapping.

Samples containing microplastics of decreasing particle size (described in the section 

) were analyzed in reflection mode with the conditions listed in Table I. These experiments were repeated using three filter types (stainless-steel mesh filter, cellulose ester filter, and glass fiber filter) to assess the impact of the background spectra on the detection threshold of the particle sizes.

The reflection mode measurement of the samples was obtained as-is in the filter, and although this measurement mode allows for faster data acquisition with the ability to analyze multiple particles or an entire area automatically, it is susceptible to background noise from the filter element, surrounding particles, and matrix. Therefore, once the particles were identified using the reflection method, a more targeted measurement with better signal-to-noise ratio (S/N) using the ATR attachment on single particles was carried out. Samples containing microplastics of decreasing particle size were analyzed in ATR mode with the conditions listed in Table I. Because ATR is a contact-based measurement for surface measurement (no more than 2–5 µm into the surface), only particles pressed against the ATR crystal are measured, ensuring
higher accuracy.

Evaluation of S/N and Threshold of Detection Based on Sample Size

An experiment was conducted for IR spectral acquisition (reflection mode) on the 100 μm polystyrene-based sample with varying aperture sizes. The aperture sizes started from 5x5 μm, then increased to 10x10 μm. The aperture sizes were gradually increased from 10x10 μm to 100x100 μm, with an interval of 10 μm (Figure 1). This change in aperture size can be directly correlated to the difference in sample size. In addition, and more importantly, using the same particle but with different aperture sizes also ensured that any other experimental variations (inter-sample variation and instrument conditions) were eliminated. In a second experiment, the impact of the number of scans in the S/N of the measurement at each aperture size was evaluated.

Spectral Search Using the FT-IR Polymer Library

Spectral searches for all spectra were performed using polymer libraries provided by the microscope software. The Shimadzu AIM-9000 FT-IR microscope adopts a score system from 0 to 1000 to report search results. In this study, the differential derivative algorithm was used, which calculates the primary differential of each data and compares the differential factor at each point with fitting of whole curves.

Reflection Mode Mapping Measurement Results

Microplastics samples dispensed on filters were scanned automatically using the mapping function of the AIMSolution Software. Aperture sizes were adjusted according to the dimension of the microplastics particles. The ratio of the signature peak intensity over the baseline intensity could be mapped to reflect the specific microplastics abundance at the locale. Figure 2 shows the mapping area settings and the resulting chemical mapping of microplastics samples of four different sizes on a stainless-steel mesh filter.

In Figure 2, it can be observed that mapping measurement in the reflection mode can provide relatively quick qualitative analysis of the microplastics samples in a potentially large area. When measuring particles with a size of 10x10 μm, it became challenging to capture focused images of the samples because the unevenness and the structure of the filters made it almost impossible for the microplastics particles to lie within the focal plane. As a result, the FT-IR spectra acquired for the 10 μm samples tended to reflect signals contributed by a subset of particles rather than that of an exact individual particle.

Similar reflection mapping measurements were obtained for microplastics samples held on a cellulose ester filter and a glass fiber filter. These filters were selected because they are commonly used in environmental laboratories for sample filtration. When those filters were used, signal interferences from the filters were evident (Figure 3). The cellulose ester filter showed IR peaks across the whole measured region (Figure 3e), whereas the glass fiber had IR absorption mainly in the low wavenumber region (Figure 3j). The stainless-steel mesh filter is highly reflective in the IR and can be considered to share similar features to that of the metal mirror used for background measurement; therefore, the spectra were not included in Figure 3. Figure 3 also demonstrates qualitatively how the background spectra negatively affects the detection threshold of the particle size. Overall, using the stainless-steel filter had the advantage of suppressing the signal contributions of the filters.

When measuring in the reflection mode, IR signals from the microplastics samples may be unable to be determined because of unwanted interference, like neighboring contaminants or substance attaching to the samples. It is desirable to measure in the ATR mode because it greatly assists in getting signal only from the targeted area. With a tip diameter of only 12.5 μm, the Shimadzu ATR attachment for AIM-9000 can acquire IR spectra from particles with high specificity. Figure 4 illustrates the microplastic samples and the FT-IR spectra acquired using ATR mode. However, samples were analyzed in triplicates; only one spectrum per particle size is included in the figure. The pictures on the left side are visible images captured for each of the samples analyzed. The FT-IR spectra are shown on the right side, and they illustrate the characteristic absorption bands of the microplastics. Spectral search was performed using polymer libraries provided by the microscope software; search results showed ≥75% match for the four samples analyzed, independently from the size of the particle. Qualitatively, the results demonstrate that FT-IR spectra of microplastics as small as 10 µm can be detected in ATR mode.

Evaluation of S/N and Detection Threshold

Measurements made in reflection mode are known to be more affected by the presence of interferences and background than ATR measurements; therefore, it can be assumed that the threshold of detection for determining the size of microplastics will be higher in reflection mode than ATR mode. To quantitatively determine the threshold of detection under the most challenging conditions, a series of experiments was conducted in reflection mode. FT-IR spectra were acquired on a 100 μm polystyrene particle, with variable aperture sizes gradually increasing from 5x5 μm to 100x100 μm (Figure 1); four replicate measurements at each aperture were performed. This change in aperture size can be directly correlated to the difference in sample size (See the Experimental Approach section for more details). Peak-to-peak S/N values at each aperture setting were evaluated; results are shown in Figure 4. Threshold of detection was determined based on measurements that presented a S/N > 3 (5). According to the results included in Figure 5, it can be concluded that when using the typical operational conditions of FT-IR microscopy (included in Table I), the smallest particle size that can reliably measured is 10x10 μm. From Figure 5, it was clear that the greatest increase in S/N took place when the sample (aperture) size was increased from 5x5 μm to 10x10 μm. After the sample (aperture) size was increased to 40x40 μm and higher, the rate of increase of S/N remained constant.

To further confirm the threshold of detection in terms of particle size, another study was carried out with sample sizes of 5x5 μm and 10x10 μm. In this study, the number of scans averaged to obtain a spectrum were gradually increased. The number of scans was set at 32, 64, 128, and 256 scans for the two sample sizes mentioned above. Measurements were replicated five times. Peak-to-peak S/N was evaluated at each measurement. As can be concluded from Figure 6, S/N in the 5x5 μm sample size did not show any substantial improvement with the increased number of scans. The averaged S/N values stayed close to 0.5, suggesting that only noise was observed. When the sample size was changed to 10x10 μm, S/N showed a noticeable increase as the number of scans increased. S/N should increase as more scans are averaged when a real signal is detected, this result further verifies that the signal from the 10x10 μm was real and that from the 5x5 μm sample was primarily noise; thereby, this confirms the threshold of 10x10 μm.

This work demonstrates a series of method and experimental conditions in FT-IR microscopy in both reflectance and ATR mode for identification and accurate determination of the size of microplastics. The results presented in this paper are a subset of data being prepared for standardizing the analysis of microplastics in environmental samples. We have demonstrated that, by using typical operating conditions of FT-IR microscopy, it is possible to identify and distinguish between microplastic particles as small as 10x10 μm in dimension. This threshold value held true for both the reflection measurement method (which is faster, but can experience signal interference from neighboring particles and matrix) and the ATR method, which is specific to a particle to get “cleaner” measurements but is slower because of the setup involved. In the reflection measurement itself, this threshold value was determined and verified by two separate analysis methods, further verifying the capability of the technique.

J.M. Andrade, B. Ferreiro, P. López-Mahía, and S. Muniategui-Lorenzo, 

W. Cowger, A. Booth, B. Hamilton, S. Primpke, et al., 

is a Product Specialist in the Molecular Spectroscopy group at Shimadzu Scientific Instruments in Columbia, Maryland. 

is the Product Manager of the Molecular Spectroscopy group at Shimadzu Scientific instruments in Columbia, Maryland. 

 is the Environmental Market Manager at Shimadzu Scientific Instruments, in Columbia, Maryland. Direct correspondence to: 

Here we conduct an evaluation of significant operational parameters of IR microscopy for accurately determining the size of microplastics, with the overarching goal of outlining performance parameters that will help in the standardization of microplastics analysis.

Insights Toward Standardization of the Analysis of Microplastics by Infrared Microscopy

Fourier transform infrared spectroscopy (FT-IR) is an established technique for the qualitative determination of organic structure. Absorption infrared spectroscopy can be employed quantitatively using Beer’s law. However, reliable quantitative analysis requires that the pathlength is known within 1%. Calculations based on nominal spacer thickness are not sufficient for accurate work, due to variances in cell assembly and thermal expansion. The “interference pattern” method provides an accurate determination of cell pathlength, but requires plotting of empty cell spectra and counting of the fringes. This work provides a MATLAB application with a graphical user interface implementing an interactive plot where the user selects the spectral region for the calculation. Then, the program automatically computes the cell pathlength, providing a rapid determination of pathlength for practitioners.

Absorption infrared (IR) spectroscopy is an established instrumental method for qualitative determination of molecular functionality via characteristic bond resonance frequencies and absorbance intensities. Modern IR spectrophotometers utilizing Fourier transforms allow for fast and efficient data collection. This technological advance has enhanced the utility of IR spectroscopy, making it an expedient method for qualitative as well as quantitative measurements.

Vibrational excitation occurs when chemical bonds absorb photons whose frequency matches that of the bond. For this excitation to be IR-active, the magnitude of the dipole moment vector must change with oscillation. In absorption spectroscopy, the fraction of incident light absorbed by the sample at a specific wavelength is proportional to the concentration of absorbing molecules in the optical path, as described by the Beer-Lambert law

where ϵ is the molar attenuation coefficient (known as the molar extinction coefficient in older literature), 

 is the concentration of the chromophore, and 

 is the pathlength. Provided that the molar attenuation coefficient and pathlength are known, the formula can be rearranged, allowing for concentration to be calculated from an absorbance peak height or area. Harris (1) provides in-depth discussion of the interplay of concentration, pathlength, and intensity.

Cell pathlength is typically set using lead or Teflon spacers sandwiched between two salt plate windows. According to Meloan (2), quantitative reliability of IR measurements necessitates that the pathlength must be known within 1%. Because cells are often disassembled for cleaning and polishing of windows, the pathlength can vary upon reassembly relative to calibrations and the nominal spacer dimensions provided by the manufacturers are not adequate for quantification. Since cell pathlength may change due to thermal expansion (3), in-situ measurements provide the most reliable pathlength (4). The pathlength should be calculated during calibration, followed by determination of the attenuation coefficient based on the known concentration and absorbance measurement:

If the cell is reassembled, the pathlength

 should be measured before the sample is loaded, and then the concentration is determined using:

Pathlength Measurement Using the Interference Pattern

When the cell is empty, the refractive indices are significantly different for the salt plate windows and the inert gas (such as nitrogen) in the sample cell. Thus, a measurable amount of the irradiating light is internally twice reflected at the gas-salt interfaces within the cell before exiting on the detector side. Constructive and destructive interference occurs between the irradiating light and internally reflected light at all wavelengths. Maximum intensities are observed when the wavelength of light exiting the cell is an integer multiple of twice the cell pathlength. Likewise, minima in intensity occur when the wavelengths of exiting light are half integer multiples of twice the cell pathlength. Wavelengths lying between half and full integer multiples give rise to intermediate absorbance intensities. The resulting spectrum is comprised of successive oscillations known as an 

, which allows for the pathlength to be calculated from the span of several adjacent minima using Equation 4:

 is the number of cycles (fringes), and ν̃

 are the high and low wavenumbers selected for the calculation (4).

The application can be run on any operating system where MATLAB is installed. No additional MATLAB toolboxes are required. The code is developed for absorption spectroscopy. Before scanning the empty cell, a background scan should be collected to remove environmental effects. The cell should be clean, and the windows free of surface imperfections or clouding. Next, the empty cell should be scanned, ideally with a dry inert gas in the sample space, and the instrument output should be saved in a .csv format, with the first column containing wavenumbers and the second column containing absorbance or percent transmittance. The app will disregard any lines in the file containing text.An example .csv file is provided at the link that appears in the Appendix section of this article.

A screenshot of the application graphical user interface is shown in Figure 1. The empty cell spectrum is loaded by clicking the 

 button, which opens a standard file browser allowing the practitioner to select the empty cell .csv file. After the file has loaded, the file name will be displayed as the title of the plot; in Figure 1, the file is Path.csv. The entire empty cell spectrum is displayed as a blue line (obscured by the smoothed red line in Figure 1), enabling the user to visually inspect the spectra before selecting the region to be used for the pathlength calculation. The user can vary the region of the spectrum used for the calculation by adjusting the maximum and minimum values in the Wavenumber Window box. Data resolution is calculated and displayed.

A smoothing filter is provided since noise in the spectra can interfere with determination of the intensity minima used for counting cycles. The smoothing filter applies a quadratic spline fitted to a specified wavenumber span. Setting the smoothing span to the width of the top third of the cycle typically works well. The plot is interactive, thus clicking on the curve displays coordinates, permitting rapid determination of a reliable span to use
for smoothing.

Once the desired region has been established, and the span specified, depressing the 

 button performs the calculation, and generates a value for the cell pathlength in centimeters. The smoothed spectrum is displayed as a red line. The number of fringes used in the calculation are determined automatically, eliminating tedious counting where user errors may occur. Circles appearing on the plot of Figure 1 denote the fringe minima detected by the program and the 

) relative to the first minimum. The first and last circle observed on the plot coincide with the actual wavenumber values used to perform the calculation. The calculation should be performed two to three times, using different wavenumber ranges to confirm the pathlength. Values should vary by less than 0.2% (4). The app is configured so that smoothing can be changed without resetting the selected wavenumber range. Noise in the spectra can cause incorrect identification of the cycle minima if the smoothing span is too small. The user can identify this behavior when viewing the circles on the calculated plot, reset the smoothing filter, and recalculate the pathlength. The complete cycles within the active plot window are used in the calculation, not the nominal wave numbers used in the GUI to select the window.

The pathlength application provides a convenient method to accurately and quickly calculate the pathlength of IR cells. The app is easy to use, and the visualization of the fringe pattern provides an opportunity for the user to interactively change the selected wavenumber window and smoothing to use for the pathlength calculation. The app eliminates tedious counting where user error may occur. Code is provided in the Appendix, and the pathlength app is distributed via the MATLAB Central File Exchange repository at 

https://www.mathworks.com/matlabcentral/fileexchange/77353-pathlength

. Documentation is provided with the app download for users who may need to edit the data .csv file as well as an example data file and step-by-step instructions.

A text file containing a description of the Excel data file, and the actual MATLAB code, is published online at 

https://www.egr.msu.edu/thermoprops/pathlength

. An example Excel data file (InstrumentFile.csv) is also provided at the link.

This work was supported by the National Science Foundation under Grant No. 1603705 and USDA National Institute of Food and Agriculture, Hatch/Multi-State project MICL04192. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

 (W. H. Freeman: New York, New York, 7th Edition, 2006).

K.E. Amunson, B.A. Anderson, and J. Kubelka, 

Chemical Infrared Spectroscopy, Vol. 1: Techniques

 (John Wiley & Sons, Inc, New York, New York, 1963).

William G. Killian, Jr., Jackson A. Storer, and Carl T. Lira 

are with the Department of Chemical Engineering and Materials Science at Michigan State University, in East Lansing, Michigan. 

is with the Department of Physical Sciences at Ferris State University, in Big Rapids, Michigan. Direct correspondence to 

Reliable quantitative FT-IR measurements require that the pathlength be known to within 1%. Pathlength estimations based on nominal spacer thickness are not reliable and require that the actual pathlength be measured for accurate data. We demonstrate how.

A MATLAB Application for Calculation of Cell Pathlength in Absorption Transmission Infrared Spectroscopy

It is fairly easy to generate a surface enhanced Raman scattering (SERS) signal. However, there are severalissues to be aware of when trying to use these signals in analytical applications. The SERS signal arises from the combination of the number of molecules (

), the polarizability or cross section of the molecule (σ), and the electric field (

) experienced by the molecules. Understanding how these different variables interact to generate the enhanced SERS response is the key to applying SERS accurately. Here, we try to share experiences and some helpful references to assist people interested in applications using SERS.

It has been more than40 years since the realization that Raman signals are enhanced on nanostructured metal surfaces (1). Figure 1 illustrates the advantages of surface enhanced Raman scattering (SERS), which provides a larger signal from fewer molecules. Over those past 40 years, significant progress has been made toward understanding the origin of the signal enhancement, enabling SERS to become a promising technique for chemical-specific analytical analysis. It is now understood how the electric fields originating from localized surface plasmon resonances on nanoparticles can concentrate the incident laser and generate significant enhancements in Raman scattering from molecules at the surface (2). In particular, nanoparticle dimers and larger aggregates with nanometer-scale gaps and crevices produce some of the largest electromagnetic field enhancements (3). In fact, it is fairly easy to generate a SERS signal; however, as many SERS practitioners will testify, there are a few things to be aware of when trying to use these signals in analytical applications (Table I). The SERS signal arises from the combination of the number of molecules (

) experienced by the molecules. Understanding how these different variables combine to generate the SERS response is the key to applying SERS. Here, I share my experiences and try to provide some helpful references to help people interested in applications using SERS. The following sections highlight some key concepts that people should be aware of when using SERS.

Much of the pioneering work in SERS has been performed using a molecule such as rhodamine or other molecules that have electronic resonance in the visible region (2). These experiments are often called 

surface enhanced resonance Raman scattering

 (SERRS). Just as resonance Raman increases the cross-section (σ) of spontaneous Raman scattering, it also enables the lowest limits of detection in SERS experiments. Most people studying single molecules use this additional enhancement (4). It turns out that many of these molecules are fluorescent dyes, and even with excitation away from the peak absorption, some level of pre-resonance is accessed boosting the SERS response.

The other class of molecules that is commonly detected in SERS is aromatic thiols and pyridines. Although these molecules do not have an intrinsic resonance, they are reported to form charge transfer complexes on the plasmonic surfaces that also increase the relevant cross-section of the analyte and boost the enhancement. These charge-transfer mechanisms are commonly associated with “chemical enhancement” (which has become a default category for enhancement that is not from the enhanced electric field).

Some molecules are just difficult. Glucose is one example. Although glucose has been detected by SERS, this often involves some form of surface functionalization or capture, such as reacting with boronic acid on the surface (5,6). It is often worth measuring the spontaneous Raman limit of detection for a molecule; with SERS one should be able to detect the molecule at a lower concentration. This is one way to distinguish SERS from weak spontaneous Raman scattering. However, if molar concentrations are needed to see the spontaneous Raman signal, the SERS signal may be difficult to measure.

SERS Enhances the Signal from the Molecules at the Surface

The SERS effect is a very short-range enhancement. Studies done on the distance dependence of SERS show the signal enhancement goes away within a few nanometers (7,8). Other studies indicate the molecule is physisorbed to the surface (9). There was even discussion of a “first layer effect,” where the molecules adsorbed to the surface showed a larger enhancement (10).The number (

) of molecules at the surface can often be modeled with a Langmuir isotherm, which correlates the surface coverage to the affinity of the molecule to the surface. At concentrations where your molecule is unlikely to have significant coverage, it is unlikely that one will detect SERS. Both electromagnetic and chemical enhancement mechanisms increase the signal of molecules at the surface. For nanoparticles dropped onto a substrate and dried, the molecules that stick to the nanoparticles are easier to detect.

The idea of a SERS tag is to put a molecule on the surface that gives a strong signal (see above) and then incorporate some form of chemical recognition agent (such as DNA, an antibody, or an aptamer) that binds the target molecule (11,12). Although these approaches have been very successful, it is worth remembering that your limit of detection and sensitivity will now be controlled by the chemical recognition agent on your tag.

Intensities Depend on the Nanostructures, Too

It has been shown that the large majority of the SERS signal originates from “hotspots,” (13) generally regarded as the high electric-field (

) regions associated with gaps and crevices. This means that one molecule in an electric field enhanced region of 106 will be as bright as either 106 molecules in solution, or 105 molecules in a region with an electric field enhancement of 10. This tells us that small changes in the number of molecules in “hotspots” can create large intensity variations. This is particularly problematic when using colloidal nanoparticles, where it can be very challenging to aggregate nanoparticles in a reproducible manner.

A lot of effort has gone into making patterned nanostructures as SERS substrates (14). Many of these substrate approaches still have some variation in the electric field enhancements, and it is common to observe intensity variation on the scale of 10% in these systems. One approach to averaging out this heterogeneity is to measure multiple spots. One study suggested that more than 100 spots are needed to properly capture this variance (15).

This is not to say that SERS cannot be quantitative (16). The use of internal standards, assumed to have the same intensity distribution as an analyte, can correct for this variance. This correction can be a co-adsorbed molecule (17), or, perhaps preferably, a stable isotope variant of the target molecule (18).

The metric used to compare the enhancement observed from a SERS active material is the enhancement factor (

) (19). In theory, this number represents the increase in the Raman signal expected from a molecule. In practice, this is really difficult to determine. It requires that you measure a SERS signal (

SERS), and you have the spontaneous Raman signal (

Raman) from a known number of molecules (

Raman) under similar experimental conditions. With these four numbers, the EF can be calculated as follows:

In perhaps the simplest case, a monolayer of thiol on a patterned nanostructured surface, approximations are still required. Because of the influence of hotspots, the number of molecules detected by SERS (

SERS) is difficult to know. Further, the free molecule may have a different cross-section than the adsorbed molecule, making 

Raman also uncertain. Given these uncertainties, the 

 is at best a relative number for the conditions under which it was determined. When reporting 

s, it is important to provide as much information as possible about how the 

SERS offers many advantages. However, there are several issues to be aware of when trying to use SERS signals in analytical applications.

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