Spectroscopic literature often conveys the impression that peak shifts are generally related to structural changes on the molecular or unit cell level. Here, we discuss several situations in which peaks shift for reasons unrelated to chemical structural changes. To avoid misinterpretations, a practitioner should be aware of these reasons.

When you are a student and learn about spectroscopic theory, you are told that every peak shift in an infrared (IR) or ultraviolet-visible (UV-vis) spectrum is related to changes in chemical structure or chemical composition. However, this important lesson sometimes does not hold up in the first few months that you start working on your bachelors, masters, or PhD thesis. The latter happened to one of us when working on two isostructural compounds for a thesis. In one of these isostructural compounds, a heavy element was replaced by a much lighter one. Based on the accepted theory, the expectation was that only some peaks should be blueshifted, because they are related to vibrations of the heavier and lighter element. However, when the spectra were recorded, it was a shock to see that nearly all peaks were blueshifted, and X-ray diffraction (XRD) confirmed that the two samples where chemically isostructural. This raises an important question: What was the reason behind this phenomenon?

Peak Shifts Are Consequences of Increased Oscillator Strength and Molar Concentration

Spectroscopic theory, as we know it today, is a simplified version of a merger of wave optics and dispersion theory, where the wave properties of light were removed to make it easily digestible (1). When Hendrik Antoon Lorentz and Max Planck worked to understand dispersion based on the damped harmonic oscillator model, it was clear to them, for example, that peaks shift with oscillator strength (2,3). The damped harmonic oscillator model, often called the 

, generates a peak in the absorption index spectrum that blue-shifts with increasing oscillator strength and molar concentration, whereas the oscillator position (the band position) remains fixed (4). The Lorentz oscillator model is not common knowledge because Lorentz also derived the Lorentz profile, the peak of which does not shift because of some approximations (2,5). In academic curriculum, only the Lorentz profile is consistently taught. The approximations are usually valid for weak oscillators, but easily noticeable, for example, for the C=O vibration, where it is of the order of 5 cm

. This might seem hardly worth mentioning, but the oscillators in inorganic materials are much stronger and as a result, so are the corresponding shifts.

Why should we care about shifts that we might not even be able to see in spectra? Because nowadays, techniques are in use like 2D-correlation spectroscopy (2D-COS), principal component analysis (PCA), or multiple curve resolution (MCR) (6,7). Even if you do not see shifts in conventional spectra, these techniques are so sensitive that they will show the resulting nonlinearities (8). Accordingly, in experimental spectra, because you will always get an asynchronous spectrum in 2D-COS, you will always detect more principal components by PCA than you have chemical components, even in ideal systems. Meanwhile, MCR will help you to find components that are often nothing but a mere linear combination of the spectra of the real components, which indicates nothing but nonlinearities caused by peak shifts and deviations from Beer’s law.

Peak Shifts as a Consequence of the Polarization of Matter by Light 

Sometimes these shifts are cancelled by an effect known by Lorentz and Planck (2,9). When light hits matter, the latter becomes polarized. As a consequence, the local electric field is not the one externally applied (it is the same effect you know from capacitors). For example, these local fields lead to increasing redshifts if you put your solute in different solvents with increasing refractive index or dispersion strength (10,11). Figure 1 displays a comparison between the Lorentz profile, a Lorentz oscillator, and this effect for various concentrations. The same is observed when changing from potassium bromide (KBr) to cesium iodide (CsI) and finally to silver bromide (AgBr) as a matrix material for the pellet technique (12). This transformation occurs because of an interaction that is induced by light and vanishes if switched off, which is a macroscopic manifestation of the uncertainty principle: By observation, you change what you want to see.

Peak Shifts as a Consequence of the Influence of the Refractive Index on Spectra

Comparably strong redshifts that are not related to matrix effects, but are also because of the influence of the refractive index, this time not of the embedding, but of the incidence medium and the sample, occur for attenuated total reflection (ATR) (4,13). In the case of ATR, the absorbance is modulated by the refractive index dispersion of the sample. Because the refractive index has a maximum redshifted relative to that of the maximum of the absorption index, the absorbance maximum is also redshifted, easily by 10 cm

 and more, even for organic substances if you use the very common combination of a zinc selenide (ZnSe) ATR crystal with an angle of incidence of 45° (4). Note that the shift scales again with oscillator strength, which means that for mixtures the shift increases with concentration because it is often combined with the one resulting from local field effects. To avoid corresponding problems, you can either use a high index incidence medium like germanium at a high angle of incidence, which only gives problems with inorganic materials. As long as the critical angle of the sample is always lower than the incidence angle of your accessory, you can also correct the problem with an advanced ATR correction procedure, or, with an even more advanced procedure, which lets you determine the optical constants of your sample, but this requires use of a polarizer (4). Figure 2 compares the influence of the sample geometry, technique, and substrate on absorbance spectra.

Peak Shifts as a Consequence of the Wave Nature of Light and Interference Effects

Quite often, samples consist of layers on substrates. It seems to be clever to use refractive-index matched substrates to avoid interference effects within the layers (which change the internal fields from the ones applied), but because absorption is accompanied by refractive index changes, this approach can easily fail, particularly for thinner layers (4 μm or less in case of calcium fluoride) (14). If you use a highly reflecting substrate, like a low-e glass or a metal-coated substrate, the interference effects can be so strong that not only the peaks can shift by 25 cm

 or more, but new peaks not directly related to vibrations may emerge (15,16). New peaks emerge because the product of electric field intensity and absorptivity because of a neighboring peak is particularly high. Such interference effects are not restricted to layers—for example, they also exist in spheres (17). Correspondingly, Mie scattering-related effects in heterogeneous samples do not only generate disturbing baselines, but also cause interference-related shifts in spectra. Keep in mind that just getting rid of ripples or fringes in the baseline does not correct the shifts in your spectra!

Peak Shifts as a Consequence of Anisotropy and the Continuity Relations of the Electric Field at Interfaces

A last effect that we will touch upon that causes shifts in spectra is anisotropy. This may be surprising, but the signal in your spectrum does not only depend of the orientation of the transition moment relative to the polarization direction, as you usually hear. The orientation of the transition moment relative to the interface is also important because this orientation can cause shifts (1,18,19). Why? Because if the transition moment is perpendicular to the surface, then the transmittance or reflectance does not depend on the dielectric function, but on its inverse (this is a consequence of the continuity relations of the electric field between two different media). The maximum in the spectrum is, therefore, at the minimum in the dielectric function, which is always blueshifted. For organic materials this shift is usually 5 cm

 or smaller, but for inorganic materials a shift of 50 cm

 is nothing out of the ordinary (1). Actually, this was also the case for the example we mentioned in the beginning—have a look at Figure 3. Funnily enough, these two samples were randomly oriented polycrystalline samples, but it was nevertheless an effect related to the anisotropy of the individual crystallites and how orientation averaging takes place. The latter is different if the crystallites are small or large compared to the resolution limit, which causes the blueshifts. The simple (but not always cheap) solution is to use the corresponding single crystals instead.

From the potpourri of examples that we presented, you get a good impression of the many different effects that can be at work, sometimes in combination, and that you may have to correct before you can interpret remaining peak shifts in the classical way. In principle, such a correction is adequate with the determination of the optical constants of a sample, which can require significant effort at times. Before we close, a word of caution: At the moment, many exciting innovations are becoming available, such as new modalities with which you can record spectra via surprising ways like photothermal IR upon quantum cascade laser irradiation, but we are still investigating the resulting consequences of the new measurement schemes or radiation sources. However, we can say that all effects described in this paper will stay, but additional ones will be introduced.

. DOI: 10.13140/ RG.2.2.14293.55520: 2021.

Sitzungsberichte der K ̈oniglich Preussischen Akademie der Wissenschaften

(4) T.G. Mayerhöfer, S. Pahlow, and J. Popp, 

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

Quantitative Infrared Spectroscopy for Understanding of a Condensed Matter

(8) T.G. Mayerhöfer, O. Ilchenko, A. Kutsyk, and J. Popp, 

Beyond Beer’s law: Ideal binary liquid mixtures

Sitzungsberichte der K ̈oniglich Preussischen Akademie der Wissenschaften zu Berlin

(13) L.A. Averett, P.R. Griffiths, and K. Nishikida, 

(14) T.G. Mayerhöfer, S. Pahlow, U. Hübner, and J. Popp, 

(16) T.G. Mayerhöfer, S. Pahlow, U. Hübner, and J. Popp, 

(17) T.G. Mayerhöfer, S. Höfer, and J. Popp, 

 are with the Leibniz Institute of Photonic Technology (IPHT), in Jena, Germany, and the Institute of Physical Chemistry and Abbe Center of Photonics at Friedrich Schiller University, in Jena, Germany. Direct correspondence to: 

Articles

Peak shifts in infrared spectra may occur for many reasons other than structural changes on the molecular or unit cell level. Here, we discuss several examples.

Five Reasons Why Not Every Peak Shift in Infrared (IR) Spectra Indicates a Chemical Structure Change

As forensic analysis continues to advance, such as in the understanding of source identification and analysis of trace quantities of bodily fluids, spectroscopic techniques and machine learning are playing a significant role. Igor K. Lednev, a chemistry professor at the University at Albany, SUNY, in Albany, New York, has been working in this field with his team. The analytical methods currently under investigation include Raman spectroscopy, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy, and advanced chemometric classification and analysis methods. We recently interviewed him about his work.

In one recent study, you reported the development of a multivariate discriminant model using ATR FT-IR spectra of dry urine to identify the sex of the donor (1). Which specific analytes are being measured for this discriminant analysis? What specific chemometric approach was required for optimizing the discrimination? 

We utilized one of the multivariate statistical methods called partial least-squares discriminant analysis (PLS-DA).To improve further the discrimination, we also applied genetic algorithm (GA) analysis to the derivative urine spectra. The GA is a computational method that identifies spectral regions that contribute the most to the differentiation power of the model. In addition, the spectral regions selected by GA may be instrumental for understanding the chemical origin of sex differences in urine samples. Although a specific biochemical assignment based on the regions selected by GA was challenging, there was good agreement with the characteristic spectral peaks of creatinine. Creatinine is a waste product from creatine, which is consumed in muscles. Detection and quantification of creatinine in biological fluids, particularly urine, have been previously investigated. It has been shown that the concentration of creatinine in urine depends on the muscle mass and donor sex, as the urine of male subjects typically has a higher concentration of creatinine than that of females. Let me also mention that this was a collaborative project between our laboratory and Prof. Takeaki Ozawa’s laboratory at the University of Tokyo. A very talented PhD student, Ayari Takamura, spent half a year in our laboratory conducting research.

Research using ATR FT-IR spectroscopy was used to discriminate sex and race based on the analysis of bloodstain samples (15). In this work, ATR FT-IR spectra were acquired from dry bloodstains and PLS-DA was used to classify these sample spectra. What do you think is the basis of the fundamental chemical differences in these samples that was detected using ATR FT-IR?

To address this question, we used GAs and determined ATR FT-IR spectral regions that contributed the most to the differentiation of blood samples based on the donor sex and race. Specifically for the sex differentiation, we observed a region corresponding to lipid contribution that is consistent with the literature, reporting different levels of high-density lipoprotein (HDL) cholesterol and double bond index in fatty acids within the blood serum of males and females. In addition, a spectral region with a contribution from carbohydrates was selected by GA that is consistent with the literature data, indicating higher levels of glucose in blood of female donors. As for the race differentiation, the results of our analysis of ATR FT-IR spectral data are consistent with the literature indicating that the levels of lipoproteins, apolipoproteins, hemoglobin, and total protein concentration in blood varies with the donor race.

What would you consider to be the most meaningful contributions of your work, including teaching and patent work?

An academic career is attractive to me because it gives plenty of opportunity to make various meaningful contributions including those to a scientific field, profession, and even society. Training the next generation of experts is probably the most important duty of the university professor. I am very happy that all students graduated from our lab are developing successful careers in pharma, major crime laboratories, and academia. I guess publishing about 250 research papers and reaching the 

-index of 58 might indicate a noticeable contribution to the scientific field, but I leave it for my peers to evaluate. After many years of fundamental research, I have shifted the focus of our program toward applied science during the last decade or so, specifically targeting the development of new methods for medical diagnostics and forensic purposes. Most recently, I cofounded two startup companies aiming at the commercialization of our patented technologies. Should this activity be successful, it will probably make the most meaningful societal contribution.

Would you share with our readers to describe your work ethic, philosophy, and how you plan your daily or weekly work schedule?

This is a serious question, which deserves a separate conversation. Briefly, my work ethic is probably quite standard: be honest, be respectful, and work hard. Although this might sound trivial, it is not. One of the key elements of my teaching philosophy is to make sure that students learn how to learn. As for the research training, my main approach is to give students as much independence and responsibility as they can handle. In my opinion, and based on many years of experience, this is the best way to prepare students for their future careers. I am investing time and significant effort to generate a priority “wish” list for my activities. My daily and weekly work schedule includes my duties (teaching, service, and professional) and then, if there is time left, top activities from the priority list.

What words of wisdom do you have for any young people interested in a scientific research career?

Go for it! However, do not expect that it will be easy. Good luck!

(1) A. Takamura, L. Halamkova, T. Ozawa, and I.K. Lednev, 

(2) C. Taylor, E. Mistek, L. Halámková, and I.K. Lednev, 

(3) C.K. Muro, K.C. Doty, L.d.S. Fernandes, and I.K. Lednev, 

(4) R. Rosenblatt, L. Halámková, K.C. Doty, E.A.C. de Oliveira, and I.K. Lednev, 

(6) K.C. Doty, G. McLaughlin, and I.K. Lednev, 

(7) A. Wojtowicz, A. Weber, R. Wietecha-Posluszny, and I.K. Lednev, 

Spectrochim. Acta A Mol. Biomol. Spectrosc.

(8) K.C. Doty, C.K. Muro, and I.K. Lednev, 

(9) B. Vyas, L. Halamkova, and I.K. Lednev, 

. 100247 (2020). https://doi.org/10.1016/j.forc.2020.100247

(11) G. McLaughlin, M.A. Fikiet, H.-o. Hamaguchi, and I.K. Lednev, 

(12) G. McLaughlin, V. Sikirzhytski, and I.K. Lednev, 

(13) A. Sikirzhytskaya, V. Sikirzhytski, G. McLaughlin, and I.K. Lednev, 

(14) E. Al-Hetlani L. Halámková, M.O. Amin, and I.K. Lednev, 

e201960123 (2019). https://doi.org/10.1002/jbio.201960123

(15) E. Mistek, L. Halámková, and I.K. Lednev, 

 is Professor of Chemistry at the University at Albany, State University of New York. His research is focused on the development of novel laser spectroscopy for medical diagnostics and forensic purposes. He has authored over 240 publications in peer-reviewed journals reaching 

-index of 58. He is a cofounder of two startup companies targeting the commercialization of his technology protected by seven patents. He is on the editorial boards of 

 journal. He served as an advisory member on the White House Subcommittee for Forensic Science. He is a cofounder of the NIJ Forensic Science Symposium at Pittcon (the world-largest Analytical Chemistry congress); the symposium became a regular annual event including 34 invited talks and a poster session. He is a Fellow of the Society for Applied Spectroscopy and the Royal Society of Chemistry (UK). Media covered his work over 90 times including TV and radio interviews, publications in the Wall Street Journal, Chemical & Engineering News, Forensic Magazine, etc. Discovery Channel Canada featured his work on forensic Raman spectroscopy. Congressman Tonko featured his research at the U.S. House of Representatives Hearing on the advancements in forensic science in September 2019. Dr. Lednev received several prestigious awards including Gold Medal Award from NY/NJ Section of the Society for Applied Spectroscopy, Guest Prof. Fellowship from the Friedrich-Schiller-University, Research Innovation Award from Research Corporation, Chancellor’s Award for Excellence in Scholarship and Creative Activities, and CAS Dean’s Award for Outstanding Achievements in Teaching. For his development work, Lednev and coworkers have just received a Phase I Small Business Technology Transfer (STTR) grant from the U.S. National Science Foundation.

Spectroscopy Spotlight

Igor K. Lednev at the University at Albany SUNY in New York explains advances in forensic analysis using a variety of chemometrics techniques to classify ATR FT-IR and Raman spectra of bodily fluids.

Forensics Applications of Raman Spectroscopy, ATR FT-IR, and Chemometrics

Mun Seok Jeong of the Department of Energy Science at Sungkyunkwan University in Korea is exploring the nanoscale of material science using a suite of new spectroscopy tools. A prominent analytical tool for this nanoanalysis is infrared nanoscopy. We recently spoke to him about his work.

You have used infrared nanoscopy to measure and study the degradation of organic–inorganic hybrid perovskite (OHP) films in the field of optoelectronics (1). Why is this important, and what is unique about the application of infrared nanoscopy to this problem?

One of the significant issues of the perovskite material is its long-term stability due to environmental degradation. Generally, perovskites decompose easily when exposed to environmental factors such as oxygen, moisture, heat and light. Therefore, there are still difficulties in commercialization using the perovskite materials. Many studies have been investigated, but it is still insufficient to clarify this issue. Herein, we conducted infrared (IR) nanoscopy measurement to investigate the degradation of OHP film in nanoscale. The IR nanoscopy is a high spatial resolution chemical analysis apparatus to measure changes in physical chemistry properties on a nanoscale that were not previously identified. As a result, we obtained the clear mechanism of degradation in perovskite film. This research is a unique approach in the field of nano chemistry research because it can be intuitively investigated in the nanoscale region (~10 nm) using the AFM-tip.

How would you describe the differences between classic infrared microspectroscopy and infrared nanoscopy?

Unlike classic IR microspectroscopy which can investigate the materials with several tens of micrometer spatial resolution, IR nanoscopy combined with AFM can identify substances in nanoscale region. Also, this technique can observe the structural and chemical properties of a sample simultaneously with the tens of nanometer scales. For realizing nanoscale spatial resolution, the amplitude of ring-down motions of the atomic force microscope (AFM) tip on the sample due to thermal expansion of sample at certain wavenumber were recorded along the incident IR laser. Finally we can achieve the mapping image of chemical vibrational modes and topography with ~10 nm spatial resolution.

We purchased a commercial instrument system and adapted it for our work.

You have also applied infrared nanoscopy to identify defects and to improve the quality and performance of low-dimensional organic lead halide perovskites (OHPs), which comprise white light-emitting diodes (LEDs) (2). What has infrared nanoscopy revealed for this application that other analytical techniques have not shown?

The white emission in 2D OHPs have been explained by self-trapped exciton at polaron states (STEs). Large organic cation for 2D OHP structure induces the size restriction of polaron and distortion of PbX6 octahedrons resulting in unique polaron properties. In this point of view, the direct observation of organic cation at nanoscale is very important in 2D OHP studies. Other nanoscale mapping techniques such as photoluminescence (PL) or Raman scattering hindered to observe organic cation directly because of indirect effect of organic cation in band structure or high photoluminescence intensity. However, using IR nanoscopy, the effects of organic cations and moisture related to white emission in 2D OHP can be observed simultaneously. Herein, we revealed that the correlation of white emission and organic cation defect site by using the IR nanoscopy. In addition, white emission is not only originated from STEs, but it also came from defect sites by water molecules.

What is most important about the quality of white-light emission in LEDs?

The LEDs are the systems with several materials such as active layer, electrode, and charge injection layers. The performance of LEDs with outstanding materials can be poor without understanding of interaction with each material. Thus, the most important for better performance of white-light emission in LEDs is the understanding of interaction with materials. For that, we study single or heterostructure of material step by step.

(1) H.M. Yu, H.M. Oh, D.Y. Park, and M.S. Jeong, 

(2) D.Y. Park, S.J. An, C. Lee, D.A. Nguyen, K.N. Lee, and M.S. Jeong, 

(3) D.H. Kim, C. Lee, B.G. Jeong, S.H. Kim, and M.S. Jeong, 

(4) C. Lee, B.G. Jeong, S.J. Yun, Y.H. Lee, S.M. Lee, S.M. and M.S. Jeong, 

(5) K.D. Park, M.B. Raschke, J.M. Atkin, Y.H. Lee, and M.S. Jeong, 

(7), 1603601 (2017). doi.org/10.1002/adma.201603601.

(6) C. Lee, S.T. Kim, B.G. Jeong, S.J. Yun, Y.J. Song, Y.H. Lee, D.J. Park, and M.S. Jeong, 

 is an associate professor at Department of Energy Science, Sungkyunkwan University (SKKU), Korea. He received his PhD from Chonbuk National University in 2000. He then worked as a postdoctoral research associate at University of Illinois at Urbana Champaign, USA (2000-2001), senior research scientist at Electronics and Telecommunications Research Institute (ETRI), Korea (2001-2003), and principal research scientist at Advanced Photonics Research Institute, GIST, Korea (2003-2013). He joined SKKU in 2013. His current research is seeking new physics in the emerging materials including 2-D nanoplates and halide perovskite by novel approaches in synthesis, optical characterization, and optoelectronic devices.

Infrared nanoscopy is a valuable new spectroscopy tool for exploring the nanoscale of materials science. Mun Seok Jeong of Sungkyunkwan University in Korea explains.

Advancing Spectroscopy into the Nanoworld of Materials Science: Vibrational Nanoscopy and Infrared Nanoscopy

In this article, inline monitoring of active pharmaceutical ingredient (API) crystallinity by Fourier transform near-infrared (FT-NIR) during a hot melt extrusion process is described. The area of the peak at 6100 cm

 was used to track the transitions between different solid dispersions during a hot melt extrusion in real time. The NIR observations are further corroborated by an offline terahertz (THz) Raman imaging analysis. Overall, both high process temperature and high screw speed are conducive to the formation of amorphous acetaminophen (APAP) dispersions, but temperature appears to more directly impact the formation of the most desired amorphous dispersion. FT-NIR is effective in providing real-time information on the solid physical state transformation, but through bulk measurements. Raman microscopy complements FT-NIR by offering direct visualization of the spatial distributions of different solid physical states of the API.

Hot melt extrusion (HME) is gaining increased interest in the pharmaceutical industry because of its high manufacturing efficiency and economic benefits. HME has been utilized to develop versatile dosage forms, especially for poorly soluble active pharmaceutical ingredients (API) (1–3). In an HME process, APIs are compounded with thermoplastic polymers and extruded to form solid dispersion. The polymer matrix acts as a solid solvent for the drug molecules. Amorphous API dispersions in a polymer matrix at the molecular level is highly desirable because the drug is in a dissolved molecular state with no lattice energy to overcome prior to dissolution. Furthermore, amorphous API dispersions can provide a physical barrier to recrystallization or aggregation of drug particles and reduce the risk of physical change post-manufacture.

To ensure the consistency and the product quality, it is imperative to have a thorough understanding of the critical process parameters, such as screw design, rotational speed, and zone temperature, in real time as well as their impact on the quality of the final products. To that end, Fourier transform near-infrared (FT-NIR) spectroscopy in conjunction with multivariate analysis (MVA) techniques have long been used as an in-process analytical tool to provide molecular level chemical information through direct measurements of the material being processed (4–6). Recently, Repka and others reported the successful implementation of FT-NIR for inline API concentration and process stability monitoring during HME processes (7,8).

Another integral part of the overall HME process design and optimization is characterizing the solid physical state of extrudates (amorphous, crystalline, or partially amorphous), which provides critical information on product stability during shelf life, crystallization propensity, drug dissolution, and drug bioavailability, as well as process efficiency (2). Previously, we reported a method employing terahertz (THz) Raman imaging to characterize the solid state of the acetaminophen (APAP) within a solid dispersion prepared by HME (9). The low-frequency Raman shift region of ≤ 6 THz or ≤ 200 cm

, is sensitive to the variations in crystal structures, such as lattice form and orientation. Therefore, THz Raman spectroscopy readily identifies different polymorphs of APIs and monitors API crystallization and dissolution processes (10–12).

In this article, we describe the use of inline FT-NIR to characterize the solid physical states transformation of acetaminophen (APAP) in hydroxypropyl methylcellulose (HPMC) matrix prepared by HME. By tracking the changes in the peak area associated with crystalline APAP during HME, the underlying transitions between different solid dispersions under different process conditions are rationalized. The FT-NIR data is further corroborated by an offline THz Raman imaging analysis.

Acetaminophen (APAP) (Spectrum Chemicals), a crystalline biopharmaceutics classification system (BCS) I drug with a melting point of 169°–170 °C, was chosen as the model API. Hydroxypropyl methylcellulose (HPMC) (Ashland) E5 grade, a common hydrophilic matrix material for amorphous solid dispersions, was used as the polymer matrix.

An 11-mm twin-screw extruder, Thermo Scientific Process 11 was used for the HME processes. Figure 1 is a schematic showing the screw profile design as well as the process configuration. APAP and HPMC were tumbling mixed for 30 min before fed into the extruder at the Zone 1 position. The Zones 2 through 8 were mixing, conveying and discharge zones. The Zone 9 is a die that reduces the barrel configuration to a single circular orifice with 5 mm in diameter.

The blending ratio of APAP and HPMC was 30:70 (w/w). Four different HME processes were performed with two different screw speeds at two different temperatures, as shown in Table I. For each HME process, after the extrusion reached the steady state, the extrudates from the die (zone 9) was collected and pressed into thin films using a hydraulic press with heating panels at 40 °C to be analyzed by Raman imaging.

A Thermo Scientific Antaris II MDS FT-NIR spectrometer equipped with a diffuse reflectance probe was used for spectral acquisition. The probe and a metal reflector were screwed opposite into the sensor ports of the die (zone 9) with a gap of 4 mm. NIR spectra were collected every 20 s with the spectral range of 4000–10,000 cm

 spectral resolution, and 16 scans were co-added (8 s scan time). FT-NIR data acquisition was performed using the Thermo Scientific Result v3 software package. Data processing and modeling was performed using the Thermo Scientific TQ Analyst software.

To track the polymorphous change of APAP during the extrusion processes, the spectral peak at 6100 cm

 was chosen as the indicator for crystalline APAP. All spectral data was first processed with 11-data point and third-order Savitzky–Golay smoothing. The peak area was then integrated between 6055.39–6124.81 cm

Raman spectroscopy and Raman imaging were performed using a Thermo Scientific DXR3xi Raman imaging microscope, using a 532 nm laser operated at 8 mW power at the sample. Data acquisition and processing were carried out using the Thermo Scientific OMNICxi software.

Raman images were obtained by the Correlation Profiling option of the OMNICxi software, using the THz region (≤ 200 cm

) of the crystalline APAP Raman spectrum as a reference. The correlation profile shows the correlation between the spectrum at each pixel (sampling point) in the Raman image and the specified reference spectrum. Each color in the rainbow-colored intensity scale represents a degree of correlation with the reference spectrum. The higher the intensity value, the greater similarity to the reference spectrum. A value of 1.0 indicates that the sample spectrum and reference spectrum are identical and is displayed as red. For the purpose of direct comparison, a correlation range of 0.75–1 were applied to all the Raman images.

As shown in Figure 2, all FT-NIR spectra have strong features in the first overtone C–H stretching region around 6000 cm

. The amorphous and crystalline APAP, however, are noticeably different (Figure 2). The crystalline APAP spectrum has a sharp peak around 6100 cm

, whereas the amorphous APAP has a broad feature around 6600 cm

Spectral peak wavelengths are predominately determined by molecular structures, interactions among molecules, as well as their environment, and peak intensities are often proportional to component concentrations. Therefore, the area of crystalline APAP peak at 6100 cm

 is an indicator for the concentrations of crystalline APAP in the samples. When the extrusion process was operated at 130 °C and 50 rpm (PRC 1), the spectral peak at 6100 cm

 is apparent. As the extrusion condition moved to a higher screw speed (PRC 2), a higher extrusion temperature (PRC 3), or a combination of both (PRC 4), the crystalline peak at 6100 cm

 decreased while the amorphous peak at 6600 cm

 increased concurrently, indicating a solid physical state transformation from crystalline to amorphous. A high temperature promotes amorphization as well as diffusion of the API molecules through the polymer chains for adequate mixing. On the other hand, increasing the screw speed increases the dissolution of the drug into a molten carrier and dispersion by imparting the energy generated by the friction between screws and extruded material.

Figure 3a shows the calculated peak areas of crystalline APAP under different HME process conditions. Under PRC 1 (130 °C, 50 rpm), the extrudates exhibit the highest crystallinity. When the screw speed was increased to 200 rpm while maintaining the same temperature at 130 °C (PRC 2), the crystallinity decreased. When the process conditions were changed back to PRC 1, the crystallinity also reverted to its original value, suggesting the reversible nature of the transition from PRC 1 to PRC 2 and back to PRC 1. Next, the temperature was increased to 170 °C while maintaining the screw speed at 50 rpm (PRC 3). The crystallinity decreased again, but to a level below the value observed in PRC 2, as indicated by the gap bracketed by the two green arrows in Figure 3a. Clearly, PRC 3 resulted in an overall more amorphous solid dispersion than PRC 2. However, increasing the screw speed to 200 rpm (PRC 4) did not further decrease the crystallinity. Finally, the process was changed back to PRC 1. Interestingly, the crystallinity remained at the low value and did not change to the value previously observed in PRC 1.

A two-component, API-polymer carrier solid dispersion can form multiple structures depending on their composition and process- ing history (13), as illustrated in Figure 3b. The drug can be molecularly dispersed within the polymer matrix, forming a thermodynamically stable, homogeneous solid solution (SS), the most desirable structure of solid dispersions. Another form is a dispersion of crystalline drug particles in a polymer matrix, referred to as a crystalline solid dispersion (CSD). Alternatively, an intermediate, meta-stable structure where amorphous drug aggregates are dispersed in a polymer matrix (ASD) can be formed. The initial process changes from PRC 1 to PRC 2 and back to PRC 1 likely induced a transition of CSD to ASD and back to CSD, at least in portions of the sample, causing the rise and fall of the overall crystallinity value. Because ASD is a meta-stable state, it can be converted to the equilibrium CSD provided that the thermodynamic driving force is sufficient to overcome the Gibbs free energy barrier. The later transitions of PRC 1 to PRC 3 by raising the temperature to 170 °C resulted in the formation of SS, indicated by the lowest crystallinity value. Once the solid dispersion reached the SS, the polymer matrix provided both a physical barrier to aggregation and stabilization through the drug–polymer interaction. As the result, the API stays in that solid solution. Further transitions from PRC 3 to PRC 4 then back to PRC 1 did not change the crystallinity.

The observations in the FT-NIR experiments were further corroborated by THz Raman imaging analysis. At 130 °C and 50 rpm (PRC 1, Figure 4a), agglomeration of crystalline APAP (CSD, red regions), partially amorphous dispersion (ASD, green regions), and amorphous dispersion (SS, blue regions) coexist. Note that the processing temperature was well below the temperature of APAP (169 °C), and the energy imparted to APAP by screw rotation was not enough to accomplish complete amorphization. At this low processing temperature, even with a higher screw speed of 200 rpm (PRC 2, Figure 4b), the sample still contains noticeable amount of partially amorphous dispersion (ASD). Figures 4a and 4b are in sharp contrast to those of the extrudates processed at a higher temperature of 170 °C (PRC 3 and PRC 4; Figures 4c and 4d). By raising the temperature, near-complete amorphous APAP dispersion (SS) were formed with both screw speeds. The comparison between the four processes clearly illustrate that screw speed is a critical process variable that dictates the energy supply to the extruded material, the degree of mixing, and, to a lesser extent, the residence time. Increasing screw speed increases dissolution of drug into a molten carrier and dispersion. However, the higher temperature is more conducive to the formation of SS.

In this study, inline monitoring of API crystallinity by FT-NIR during an HME process was demonstrated. The crystallinity of APAP, measured by the peak area at 6100 cm

, shows three distinct values, which are speculated to be associated with three different forms of solid dispersion: crystalline solid dispersion, amorphous solid dispersion, and solid solution. Overall, high process temperature and high screw speed are conducive to the formation of amorphous APAP dispersions, but temperature appears to have a more direct impact on the formation of the desired amorphous APAP dispersion. These observations are further corroborated by an offline THz Raman imaging analysis. The extrudates with the highest crystallinity value (least amorphous) show the coexistence of appreciable amount of CSD with ASD and SS, whereas the extrudates with the lowest crystallinity value (most amorphous) contain predominantly SS. FT-NIR is effective in providing real-time information on the solid physical state transformation, but through bulk measurements. Raman microscopy complements FT-NIR by offering direct visualization of the distribution of different solid physical states of the API.

(1) M.A. Repka, S. Bandari, V.R. Kallakunta, A.Q. Vo, H. McFall, M.B. Pimparade, and A.M. Bhagurkar, 

(2) S.D. Schaber, D.I. Gerogiorgis, R. Ramachandran, J.M.B. Evans, P.I. Barton, and B.L. Trout, 

(3) R. Censi, M.R. Gigliobianco, Cr. Casadidio, and P. Di Martino, 

(4) N. Furuyama, S. Hasegawa, T. Hamaura, S. Yada, H. Nakagami, E. Yonemochi, and K. Terada, 

(6) A. Paudel, D. Raijada, and J. Rantanen, 

(7) A.Q. Vo, H. He, J. Zhang, S. Martin, R. Chen, M.A. Repka, 

(8) A.Q. Vo, G. Kutz, H. He, S. Narala, S. Bandari, and M.A. Repka, 

(9) M. Ibrahim, J. Zhang, M.A. Repka, and R. Chen, 

(11) A. Paudel, D. Raijada, and J. Rantanen, 

(12) F. Tres, K. Treacher, J. Booth, L.P. Hughes, S.A.C. Wren, J.W. Aylott, and J. C. Burley, 

 are with Thermo Fisher Scientific. Direct correspondence to: 

Inline FT-NIR and offline terahertz Raman imaging analysis are used to characterize active pharmaceutical ingredient (API) crystallinity and to monitor different solid physical states of the API, to control process parameters of hot melt extrusion.

Solid Physical State Transformation in Hot Melt Extrusion Revealed by Inline Near-Infrared (NIR) Spectroscopy and Offline Terahertz (THz) Raman Imaging

Resonant surface-enhanced infrared spectroscopy (resonant SEIRA) overcomes the limitation of traditional Fourier transform IR (FT-IR) spectroscopy for minute sample sizes of low infrared absorption cross-sections (relatively low absorptivity). This technique provides orders of magnitude enhancement in the detection of infrared vibrations of molecules located in the proximity of resonantly excited (plasmon related) electromagnetic fields. Resonant SEIRA has been demonstrated in applications such as the monitoring of dynamic processes, the detection of proteins, and in hyperspectral infrared chemical imaging. We were recently able to interview Frank Neubrech, PhD, postdoctoral research associate at Heidelberg University, and Professor Harald Giessen at the 4th Physics Institute at Stuttgart University, Germany regarding this research.

You have developed a new SEIRA technique using resonant metal nanoantennas (resonant SEIRA) (1). How did you come to discover this phenomenon? What is unique about the capabilities of your approach as compared to traditional FT-IR and earlier work in SEIRA?

The original discovery was by Frank Neubrech, published in 

 in 2008 (2). Resonant plasmonic nanoantennas and coupling to electronic transitions of emitters such as dye molecules or quantum dots was a very hot topic at the time.

Increasing the nanoantennas’ size to shift their localized surface plasmon polariton resonance from the visible into the near- and mid-IR was a sensible research direction. Given that molecular vibrations are situated in the mid-IR, it was clear that in that case one might also couple resonant energy to molecular vibrations and phonons instead of electronic transitions.

Traditional FT-IR has limited sensitivity when it comes to single-molecule layers. Only in geometries such as IR reflection absorption spectroscopy (IRRAS) is it possible when utilizing grazing incidence and large areas to get enough signal-to detect IR vibrations of single-molecular layers. Traditional SEIRA utilized mostly just the fact that at a surface (or an interface) such as metal-to-air or dielectric-to-air, electric fields of incident light could be enhanced. This held particularly true for rough surfaces where hot-spots of light potentially formed, however with the plasmonic resonance strongly detuned from the molecular vibration.

In contrast, resonant SEIRA took the unknown out of this technique by tailoring the localized surface plasmon polariton resonance of a plasmonic nanoantenna (or as many call it, 

) such that it was in resonance with molecular vibrations or phonons in solids. The field enhancement was thus larger, more quantifiable, and more reproducible.

How does resonant SEIRA overcome the limitation of low IR absorption cross-sections inherent to common FT-IR spectroscopic measurements?

In resonant SEIRA, the incoming light is absorbed and scattered by a plasmonic nanoantenna and excites a localized surface plasmon polariton resonance in it. The electric field distribution around the nanoantenna is enhanced up to 1-2 orders of magnitude, and this light then interacts with the molecular vibrations or phonons of the molecules in solids that are situated right in these 

Would you briefly explain to our readers the principle of resonant coupling between plasmonic and vibrational excitations?

When two harmonic oscillators of the same frequency are brought in close vicinity, they can couple to each other. If the oscillators are of similar oscillator strength and line width, then typically normal mode coupling and splitting of the resonances occurs in case of strong coupling. If the oscillators are dissimilar in oscillator strength and line width, then a Fano-type situation arises, which is indicated by a strongly asymmetric (dispersive) narrow line shape that sits within or on top of the Lorentzian line shape of the broader oscillator.

In SEIRA, the plasmonic oscillator typically takes the role of the broader, stronger oscillator, whereas the molecular vibration or phonon takes the role of the weaker and narrower oscillator. Microscopically, it is the polarizations of the displaced dipoles (in one case the collective motion of the electrons in the surface plasmon of the nanoantenna and in the other case the dipolar molecular polarizability upon its vibrational motion) that couple with each other due to Coulomb interaction.

Would you explain the issues associated with maximizing the resonant SEIRA signal enhancement and how this affects the choice of nanostructures geometry, arrangement, and materials? In other words, how do these choices affect the intensity of localized surface plasmon polariton resonances (LSPRs)?

It is necessary to increase the local electric field at the position of the molecules or solids as much as possible. This can be done by choosing an appropriate geometry, such as a rectangular nanoantenna or a gap antenna or sharp-tipped geometries such as bow-tie antennas. We found that also low-index substrates such as calcium fluoride (CaF

) help as they expel the electric field out of the substrate and thus towards the molecules.

Furthermore, single crystalline metals such as gold for the nanoantennas seem to be beneficial as well. The resonance of the antenna should be as large and as narrow as possible. Lower quality factor plasmons in materials such as titanium nitride (TiN) or aluminum (Al) are not beneficial for the maximum resonant SEIRA signal. High Q resonances in graphene nanoribbons or in silicon nanoresonators (as used by Hatice Altug’s group in [3] and ([4]) are beneficial.

What improvements would resonant SEIRA have for 

Again, due to the large local field enhancements, smallest amounts of chemical traces can be locally detected and hence imaged with appropriate detectors. We have demonstrated this by using patterns of Buckminsterfullerene (C

What are the unique aspects of sample preparation for resonant SEIRA? Are there sample size limitations that limit the applicability of this technique? Are you surprised at the level of signal enhancement for this technique?

The molecules or solids should be brought to the positions of maximum field enhancement of the nanoantenna array. This can be done by tailored chemical functionalization, evaporation techniques, or simple dropcasting.

There are in principle no sample size limitations if one can prepare large nanoantenna arrays by suitable methods, such as electron beam lithography, ion beam lithography, photolithography, interference lithography, direct laser writing, or bottom-up self-assembly methods such as colloidal hole lithography or colloidal etching lithography.

We were glad that the signal enhancement was up to 1 million compared to conventional FTIR measurements, but in hindsight this corresponded to the enhancement values given the local field enhancements and the local field distributions.

What special discoveries have you made using this method and which of these has surprised you the most?

Apart from identifying many chemical species, we were able to detect in vitro protein folding and are working on doing so at the single nanoantenna level.

What do you believe is the potential for this method to achieve single-molecule sensitivity? Would you tell us a little about the potential for real-time monitoring of lipid membranes?

The vibration-specific detection of single molecules is our ultimate goal. If the antenna resonance is optimized and the light source is brilliant enough and spectrally stable and has small enough noise, and if good coupling and photostability are present, vibration-specific single molecule detection should be feasible. Also, real-time monitoring of lipid membranes was already demonstrated (5).

What do you anticipate is your next area of research in this field, and what would be your next steps in advancing this work? And do you anticipate your team doing any work in the THz region in the near future?  

We hope to take this method also into detection of adsorbed gaseous molecules. We have been able to go into the THz region as well, both in the double- and single-digit THz region (6). Whether the method can be taken into the 1 THz or even to the hundreds of GHz region to couple to really low-energy excitations remains to be seen.

(1)   F. Neubrech,  C. Huck, K. Weber, A. Pucci, and H. Giessen, 

(7), 5110–5145 (2017). doi: 10.1021/acs.chemrev.6b00743.

(2)   F. Neubrech, A. Pucci, T.W. Cornelius, S. Karim, A. García-Etxarri, and J. Aizpurua, 

(3)   A. Tittl, A. Leitis, M. Liu, F. Yesilkoy, D.Y. Choi, D.N. Neshev, Y.S. Kivshar, and H. Altug, Imaging-based molecular barcoding with pixelated dielectric metasurfaces. Science, 

(4)   D. Rodrigo et al., Mid-infrared plasmonic biosensing with graphene, Science 

, 165-168 (2015). doi: 10.1126/science.aab2051.

(5)   O. Limaj, D. Etezadi, N.J. Wittenberg, D. Rodrigo, D. Yoo, S.H. Oh, and H. Altug, 

(2), 1502–1508 (2016). doi: 10.1021/acs.nanolett.5b05316.

(6)   K. Weber, M.L. Nesterov, T. Weiss, M. Scherer, M. Hentschel, J. Vogt, and F. Neubrech, ACS Photonics, 

 is a postdoctoral research associate working with Professor Harald Giessen at the 4th Physics Institute at Stuttgart University, Germany, and since 2016 with Professor Na Liu at the Kirchhoff Institute for Physics at the University Heidelberg, Germany. He received his Ph.D. in Physics from Heidelberg University in 2008 under the supervision of Professor Annemarie Pucci. In 2012, he joined Professor Harald Giessen’s group after a postdoctoral position at Heidelberg University. His main research interests include surface-enhanced infrared spectroscopy, infrared spectroscopy, and plasmonics.

Resonant SEIRA overcomes the limitations of traditional Fourier transform infrared (FT-IR) spectroscopy for minute sample sizes of relatively low absorptivity.
Frank Neubrech of Heidelberg University is exploring resonant SEIRA in applications such as monitoring dynamic processes and hyperspectral infrared chemical imaging.

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Solid Physical State Transformation in Hot Melt Extrusion Revealed by Inline Near-Infrared (NIR) Spectroscopy and Offline Terahertz (THz) Raman Imaging

Resonant Surface-Enhanced Infrared Spectroscopy (Resonant SEIRA) Using Metal Nanoantennas