Articles

Special Issue June 2020

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Vol 35 No s2 Raman Technology for Today's Spectroscopists

Manufacturing of medical devices requires the highest level of quality to ensure optimal device performance and patient safety. Raman spectroscopy and imaging techniques are well suited for the characterization of surfaces, interfaces, and coatings to support research, development, and manufacturing of medical devices. Their strengths include chemical information (molecular fingerprint), imaging sub-micrometer chemical features, depth profiling of chemistry nondestructively, identification of drug polymorphs, and the ability to image in various environments. All of these are important for understanding material properties as they are received, processed, and after exposure to different environmental conditions. Raman spectroscopy and imaging can also be used for identifying unknown or foreign materials. This article highlights several real-world applications, including characterization of medical device surface modifications and coatings, differentiation of drug polymorphs, degradation of biomaterials, and forensic identification of unknown materials.

	
	Medical devices are designed to target specific disease states and to treat trauma injuries, ultimately improving the quality of life for patients  by restoring bodily function, mobility, and longevity. The field is very diverse, with several large markets, including cardiovascular (stents, grafts), cardiac rhythm devices (implantable pacemakers and cardioverter-defibrillators, atrial fibrillation ablation catheters), perfusion devices (blood pumps, oxygenators), neuromodulation (implantable drug pumps, spinal cord stimulation), diabetes management (glucose sensors, drug delivery), and surgical tools and consumables (staples, sutures). Common analytical challenges in medical device manufacturing include analysis of organic and inorganic species, coatings and thin films (such as diamond-like carbon and Parylene), identification of drug polymorphs (such as paclitaxel or rapamycin), degradation of polymers and biomaterials under different environmental conditions (such as biological fluids and sterilization), and unknown material identification (such as fibers and particles). Raman spectroscopy is well suited to the characterization of medical device materials and interfaces to gain insight into surface cleanliness, contamination, adhesion, lubricity, and intentional surface modifications.

	Raman spectroscopy measures the intensity of inelastically scattered light (monochromatic light) by a sample as a function   of wavelength (1). An incident laser causes chemical bonds in the sample to gain or lose characteristic amounts of energy corresponding to different vibrational modes. Because Raman activity results from a change of polarizability, the resulting spectra are dominated by peaks that correspond to symmetric stretching and bending modes. In contrast, Fourier transform-infrared (FT-IR) spectroscopy activity results from a change in dipole moment by absorption of IR light, and IR spectra are dominated by peaks that correspond to asymmetric stretching and bending modes (1). Therefore, Raman spectroscopy is a complementary technique to  FT-IR spectroscopy, and a great addition to a material testing laboratory. A comparison of the two techniques is highlighted in Table I. One strength of the Raman technique is that measurements can be performed through glass, in fluids, and in tissue. Furthermore, the confocal Raman microscope allows non-destructive chemical depth profiling with lateral and depth resolution of ~350 nm and ~500 nm, respectively (2). This is important for analyzing buried interfaces through transparent and semi-transparent materials.

	Good sample handling and preparation techniques are necessary for the analysis of medical devices, because often they are large, non-flat, and have been handled or been in contact with other materials (3). For example, fingerprints, disposable laboratory gloves, and polyethylene bags can easily deposit residues (such as fatty acids, siloxanes, and slip agents) onto the material surface of interest that will easily be detectable by Raman (4,5). It is also advised to capture optical images of the samples of interest prior to conducing any analytical testing. For the analysis of material interfaces, it is recommended that the material or device component of interest be embedded in epoxy and mechanically cross-sectioned, polished, or microtomed, to preserve the integrity of the interfaces. If these techniques are not available, then the use of a clean uncoated razor blade is another efficient way to cross-section a material to gain access to bulk or interfacial regions. The examples provided below highlight the use of confocal Raman for the characterization of medical devices.

	The Confocal Raman instrument used for the applications described here consisted of a WITec alpha300 R (Suite 5.1 software) with a 532 nm laser. The Scope A1 was outfitted with two objectives: Zeiss EC EPIPLAN 20x/0.4 and Nikon Plan 100x/0.9 NCG, WD 0.26. A Zygo NewView 6300 White Light Interferometer was used to measure the drug eluting stent (DES) coating thickness. A Bruker Tensor 27 System with a Hyperion 2000 microscope and 20x attenuated total reflection (ATR) objective was used for the PVC particle example. A Leica UC7 Ultramicrotome was used to prepare the cross-section of polyurethane tubing.

	Identification of drug polymorphism is an important criterion when designing and manufacturing drug-containing medical devices. Polymorph structure during the crystallization process is influenced primarily by solvent effects, impurities, and temperature. The crystalline form will control activity and solubility of the drug. In addition, understanding the use condition of the device therapy and desired drug release kinetics is very important during the design stage. Raman spectroscopy is a good technique to determine drug crystallinity, since it is relatively quick, nondestructive, and only a small amount of sample is required in comparison to X-ray diffraction (XRD) and differential scanning calorimetry (DSC) (6,7).

	Paclitaxel, a cytotoxic chemotherapy drug, is commonly used on medical devices for its anti-proliferative properties to treat restenosis (narrowing of arteries) in cardiovascular disease patients. Two products on the market include drug-coated balloons (DCB) and drug-eluting stents (DES) (8,9). Their use conditions differ in that DCB require a fast bolus release within several minutes, whereas DES require a slower release over several months from the substrate to the tissue. The most stable polymorphic states of paclitaxel are dihydrate, anhydrous, and amorphous, and their solubility in water ranges quite dramatically: dihydrate (0.7 µg/mL), anhydrous (6 µg/mL), amorphous (30 µg/mL) (6). A thorough understanding of the polymorph composition is important for designing, manufacturing, and registering drug combination products containing paclitaxel.

	For this example, Raman spectroscopy was used to discriminate between different paclitaxel polymorphs. Figure 1 shows representative Raman spectra of anhydrous, dihydrate, and amorphous paclitaxel. Spectral differences are clearly notable among the three spectra, specifically in the carbonyl stretching region (C=O) between 1680 and 1780 cm

. The dihydrate form shows three peaks centered at ~1700, ~1713, and ~1738 cm

. The anhydrous form shows two peaks centered at ~1705 and ~1713 cm

. The amorphous paclitaxel shows only one broad peak centered at ~1722 cm

. These results demonstrate that Raman spectroscopy can differentiate between the three common polymorphs. In addition, confocal Raman imaging can also be used to evaluate the composition of paclitaxel coatings on DES and DCB.

 insert subfigure). Spectral differences are clearly notable in the carbonyl and amide stretching region from 1760 to 1500 cm

 and can be used to distinguish between the three  different polymorphs.

Example 2: Non-Destructive Analysis of a Drug-Eluting Stent Coating

	Drug-eluting coronary stents are designed to slowly elute an anti-proliferative drug (for example, rapamycin or paclitaxel) into surrounding tissue over an extended period after the initial implantation of the device to reopen arteries (9). The release of drug from the device is controlled by the degradation of a biodegradable polymer matrix into which the drug is formulated. Also, it should be noted that a thin Parylene barrier layer is commonly applied to the base metal substrate (stainless steel, CoCr) to increase coating adhesion (10,11). The thickness, uniformity, and surface texture of drug-eluting stent coatings are important parameters to understand, control, and measure during device manufacturing.

	For this example, the thickness and uniformity of a rapamycin/polylactic acid (PLA) coating applied to a Parylene-primed metal stent were characterized by confocal Raman imaging and white light interferometry. Representative optical images of the DES sample are shown in Figure 2a to highlight the analysis area of interest. The confocal nature of the technique allowed spectra to be acquired near the surface of the coating (red spectrum) and approximately 20 µm beneath the top surface (blue spectrum) as show on Figure 2b. The red spectrum showed bands consistent with rapamycin and PLA, whereas the blue spectrum showed bands consistent with Parylene.

	The spatial composition and thickness of the stent coatings (rapamycin + PLA + Parylene) were analyzed by acquiring an x-z cross section image parallel to the major axis of the stent. Raman spectra were obtained confocally from the top layer of the rapamycin/PLA coating down to approximately 20-µm sub-surface in increments of 0.5 µm. The 1632 cm

 band was selected to represent the distribution of drug and the band at 1607 cm

 was selected to represent the distribution of Parylene. The confocal Raman band map is shown in Figure 2c. The intensity of the band consistent with Parylene was strongest just above the metal surface, whereas the intensity of the band consistent with rapamycin was strongest above the Parylene layer up to the top of the coating.

	The average coating thickness was measured by white light interferometry. The refractive index of the coating is an important parameter required to accurately determine the coating thickness. Here the refractive index of PLA (~1.4) was used to correct the coating thickness, because the refractive index of the coating (ratio of drug/ polymer 1:1) and drug is not known (12). The average coating thickness was measured to be 25.9 ±3.5 μm (

	Understanding the failure mechanism of materials can aid in the design of new and better materials to slow the degradation of current materials. Polyurethane has long been used in medical devices due to its chemical stability in the body over time. Explanted leads made of polyurethane often exhibit a cloudy or cracked appearance on the surface of the lead in portions with mechanical fatigue, where superficial oxidative degradation causes cracking of the surface and as a result appears white or opaque (13,14). In other areas, the surface is unchanged, according to the naked eye. The cracking and cloudiness are thought to be due to degradation of the urethane. By evaluating the extent of chemical changes at the surface, it is hoped to gain better insight into the mechanism of degradation.

	Raman spectra taken at various points along the cloudy surface of an explanted lead show chemical differences from the pristine or clear polyurethane. Figure 3a shows the cloudy regions of polyurethane exhibit a shift in the carbonyl peak from 1620 cm

 that is likely related to the hard segment chain scission and an increase in hydrogen bonding (15).  Additionally, there is a new peak that is observed at 1156 cm

, which could be attributed to the C-C crosslinking of the soft segment that is formed during the degradation process.

	Using confocal Raman to map these chemical changes in a cross-sectioned lead sample provides a visual distribution of where the oxidized and non-oxidized polyurethane are located. The Raman images in Figure 3b show a thin 1–2 μm oxidized layer of polyurethane at the surface of the lead tubing, and down into the cracked regions of the tubing. However, past this oxidized layer the polyurethane does not show further degradation.

	This analysis confirms that the chemical degradation mechanism of the polyurethane is a surface degradation as opposed to a bulk material type of degradation such as a hydrolysis (13,14).  Understanding the method of degradation can then allow further engineering of lead materials to either eliminate or minimize the surface degradation. Modification of polyurethane leads could include surface treatments such as barrier coatings, or additives that would help scavenge the oxygen and prevent surface degradation.

Example 4: Forensic Analysis of Unknown Material

	Confocal Raman spectroscopy is well suited for the analysis of unknown materials that can originate from numerous sources during device manufacturing and packaging (15,16). The technique has excellent spatial resolution (~350 nm) for probing suspect regions of interest on the surface, subsurface, or bulk. In addition, the technique provides information below 650 cm

 for inorganic species and provides complementary information to infrared spectroscopy (1).

	For this example, an unknown green particle (approximately 0.7 cm diameter) was detected in the packaging of a medical device product. The particle was isolated onto the surface of a KBr pellet, and analyzed by vibrational spectroscopy to determine the identity of the material so its origin could be investigated. Raman and ATR-IR spectra were acquired from the particle. The spectra obtained for the green particle are shown in Figure 4. The ATR-IR spectrum (top) yielded signals for methyl and methylene (3000–2800 cm

, probably due to a conjugated ester or ketone), ester (1285 cm

), and aromatic groups (3071, 3034, 1603, and 1577 cm

 could be due to ester groups; however, fingerprint bands can also appear in that range. The Raman spectrum (bottom) yielded signals for aromatic (3069, 1596, and 1574 cm

), methyl and methylene (3000–2800, 1440, and 1384 cm

 are consistent with C-Cl bands; however, C-Cl bands typically appear over a very broad range (600–800 cm

), and their identification can be challenging (1).

	A library search was conducted of the ATR-IR spectrum, and several phthalate species were identified as possible matches. Phthalates are compounds having the general structure of an ortho-substituted aromatic ring with ester side chains. Phthalates are commonly used to plasticize polyvinylchloride (PVC). As signals that are consistent with C-Cl groups were observed in the Raman spectrum, it is likely that the green particle is composed of PVC heavily plasticized with a phthalate compound. This is consistent with the pliable nature of the particle.

Example 5: Non-Destructive Analysis of Parylene Barrier Coatings

	Barrier coatings are commonly used to prevent interaction between a material and the environment in which it will be used. In the medical device field, there are a small subset of patients who are sensitive to titanium, which is a tissue contacting material found in existing, market released devices (17).  A barrier coating, such as Parylene, can prevent further complications for a patient after having a device implanted.

	Being able to characterize coatings in a non-destructive manner is very useful for in-line inspections, and quality control testing where the cost of the product is too great to sacrifice devices on a regular basis. Confocal Raman is well suited for non-destructive analysis due to its limited interaction volume and the capability to image through transparent layers and films.

	For this example, the thickness and uniformity of Parylene barrier layers covering silicone and polyurethane substrates were characterized non-destructively by generating spectral images in the 

 dimensions. A spectrum is collected at each pixel in the image, and then a partial least square fitting routine is performed to generate the component distribution images. The Raman images in Figure 5 show the Parylene coatings measure approximately 5 μm thick, and show no gaps or uncoated regions along the substrate materials.

	In summary, the examples provided here demonstrate that confocal Raman spectroscopy and imaging are well suited for the characterization of medical devices. This technique provided chemical identification of drug polymorphs, characterization of drug and polymer coatings, and identification of unknown materials, all in a non-destructive manner. The confocal nature of the technique provides high spatially resolved chemical information, both laterally and with depth, to further understand the composition and integrity of coatings and material interfaces in both transparent and semi-transparent samples. Confocal Raman spectroscopy is a great technique to compliment other analytical tools in a material testing laboratory.

		N.B. Colthup, L.H. Daly, and S.E. Wiberley, 

Introduction to Infrared and Raman Spectroscopy

 (Academic Press, Cambridge, Massachusetts, 3rd Ed., 1990).

		S. Breuninger, M. Henrich, and T. Dieing, 

		ASTM E334-01, Standard Practice for General Techniques of Infrared Microanalysis (2013).

		B.R. Strohmeier, J.D. Piasecki, and A. Plasencia, 

		R.T. Liggins, W.L. Hunter, and H.M. Burt, 

		Specialty Coating Systems, www.scscoatings.com

 (ChemTech Publishing, Toronto, Ontario, Canada, 2nd Ed., 2016).

 is a Principal Scientist of Microscopy and Surface Analysis at Medtronic, in Minneapolis, Minnesota. Tony Anderson is a Senior Scientist of Microscopy and Surface Analysis at Medtronic, in Minneapolis, Minnesota. Direct correspondence to: 

Raman spectroscopy and imaging techniques are well suited for the characterization of surfaces, interfaces, and coatings to support research, development, and manufacturing of medical devices. Here, we describe applications in surface modifications and coatings, differentiation of drug polymorphs, degradation of biomaterials, and forensic identification of unknown materials.

Applications of Confocal Raman Spectroscopy and Imaging in the Medical Device Industry

Microplastic particles in aqueous ecosystems constitute a worldwide environmental problem. Raman spectroscopy is uniquely positioned to detect and classify even the smallest, and most abundant, particle sizes, down to micrometers in size. However, the current “filter and scan” method used to concentrate and detect dilute microplastics in water is slow, and prone to contamination from handling and interference from the filter substrate. Here, we present a simpler approach to concentrate the particles in situ, without any sample preparation, in the focus of a Raman probe. The technique uses an ultrasonic resonator with a 2 MHz standing acoustic wave to capture 3.4 µm poly(methyl methacrylate) (PMMA) microspheres in the sample solution for direct detection. When tested with a 90 ppm solution, ultrasonic capture for 5 min enabled a clear PMMA Raman signal to be distinguished from the background. The signal enhancement provided by ultrasonic capture was estimated to be greater than 1500x.

	
	The majority of plastics, like the 78 million tons of plastic packaging manufactured every year, is produced for single-use items, approximately a third of which ends up in the ocean (at the rate of one garbage truck every minute). Engulfed by these vast waters, it gradually breaks apart into progressively smaller particles, and eventually enters the food chain as microplastics, which are defined as plastic particles smaller than 5 mm in size. Slowly, and unbeknownst to the world until recently, microplastic particles have spread to every continent, ocean (1–3), the air (4), and most food and beverages (5–8).

	Studies into the nature and distribution of microplastics across the world have found both bottled and ocean water to contain concentrations on the order of 100 particles per liter of microplastics 5 µm or larger in size, and several 1000s of microplastic particles greater than or equal to 1 µm in size. The most common microplastic particles found are polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET) in drinking water, and PE, PP, and polystyrene (PS) in the ocean.

	Currently, the most common approach to detecting microplastics requires filtering large sample volumes of water to concentrate particles for detection, then scanning the filter substrate for any particles (9), and chemically identifying the particles with Fourier-transform infrared spectroscopy (FT-IR) or Raman spectroscopy (10–11). However, this approach is very time-consuming (12), and can be prone to contamination of the filter and to interference from the filter material itself (13). This approach has shown variable sensitivity to the most abundant, but smallest, particles (14). Furthermore, the lack of a unified protocol for filtering and detection leads to inconsistent results, and thus compromises the collection and comparison of quantitative data for global monitoring (15–19).

	Raman microscopy on filter substrates is currently the only tool capable of detecting and identifying the smallest, and most numerous, particles of 20 µm in size or less, as its short wavelength, tight laser focus, and ability to raster systematically across the sample is sensitive to even small particles at low density. However, Raman microscopy in the present form uses large bench top-sized instruments, scanning slowly across the membrane used for the filtration of a water sample. As one review put it: “There is an urgent need for a fast and easily implementable monitoring tool capable of detecting small microplastics (20).”

	Instead of filtration, a different approach to capture and bring these microparticles to the point of measurement is proposed here as a proof of concept investigation (21). It employs a strong ultrasonic standing wave of about 2 MHz frequency in a resonator composed of a piezo transducer on one end and a Raman probe on the other. Small particles are trapped in the nodes of the standing wave field, where they are held by acoustic radiation forces (22). If one of the nodal planes of the ultrasonic trap is colocated with the laser focus of a Raman immersion probe, the particles can immediately be studied in situ. A trap of this design can be employed directly in the sample stream as collected, bypassing all preparation steps. This reduces the analysis time from hours to minutes, prevents any contamination of the sample from handling, and eliminates the Raman background commonly seen when concentrating samples onto a filter in the currently accepted method.

	The Raman system consisted of a compact Wasatch Photonics WP 785 Raman spectrometer with an integrated 785-nm laser set to 350-mW, equipped with a temperature-regulated CCD detector, and a 50 µm slit leading to a resolution of 10 cm

. The spectrometer was chosen for its f/1.3 input aperture and transmissive optical design, as the application requires a high degree of sensitivity in a portable design to enable eventual deployment in the field. The laser and spectrometer were fiber-coupled to a Raman probe with a 1/2” immersion probe tip terminated by a sapphire ball lens with a very short working distance.

	The ultrasonic resonator used for particle capture, soniccatch by usePAT, has been described in detail elsewhere (23). Briefly, a piezo-electric transducer generates the outgoing ultrasonic wave with an approximate frequency of 2 MHz, while the Raman probe ball lens forms the reflector for the resonator at a distance of about 3 mm from the surface of the transducer. Superposition of the incoming and reflected waves generates a standing wave with a small number of nodes, as shown in Figure 1.

	The radiation forces imposed by the ultrasonic wave are predominantly dependent on the particle size, but also depend on their mass density and compressibility relative to the medium. The forces exerted are due to the scattering of the wave at the particles’ surfaces, and lead to agglomerations in the nodal or anti-nodal regions of the standing wave (23).

	The resonator assembly consisting of the ultrasonic transducer and the Raman probe was lowered directly into a stirred 90-ppm suspension of 3.4 µm poly(methyl methacrylate) (PMMA) microspheres in water, in which the microspheres had been dispersed with a drop of detergent. PMMA is a lightweight, transparent plastic most commonly known as acrylic or plexiglass, and is also used as microbeads in personal care products.

	The integration time of the spectrometer was set to 2.5 s, and 500 individual non-averaged spectra were continuously recorded for each experiment. Five repeat runs were performed, four in which ultrasonic capture was activated at the beginning of each run, and one static reference run (no capture).

	Comparing the Raman spectra with and without ultrasonic capture of the PMMA microspheres, as shown in the bottom panel of Figure 2, we identified three main contributions to the signal arising from the sample and setup. The first contribution comes from a series of bands at ~414 cm

, present in all spectra, which do not change with ultrasonic capture. These were attributed to the sapphire of the ball probe lens, an effect also reported in Raman imaging using sapphire fiber (24). The second contribution, present in all spectra with ultrasonic capture in the steady state, agrees well with a bulk PMMA signal shown for comparison in the top panel of Figure 2, with key peaks at ~596 cm

. The third contribution, a broader peak at roughly ~1640 cm

, was present without ultrasonic capture, but disappeared over time once capture was activated. This more elusive signal can be attributed to the front window of the transducer. As particles are trapped, the Raman laser no longer reaches the surface of the transducer, but instead gets scattered by the trapped particles, leading to the marked reduction in signal and to the enhancement of the signal of the particles of interest. A direct comparison of the Raman spectra with and without ultrasonic capture to bulk PMMA in Figure 2 indicates that trapping leads to a large enhancement of the PMMA signal. The difference is distinct, from no discernible signal without ultrasonic capture to a very large and clean signal after ultrasonic trapping is activated.

	To study the ultrasonic capture process in greater detail, we recorded the Raman signal as a time series after activating the ultrasonic transducer and were able to observe the continuous evolution of the PMMA trapping in time as illustrated in Figure 3. Here, we have bound the region associated with the main PMMA Raman lines using vertical lines just beyond 750 cm

. In this region, we subtracted the baseline to obtain a clearer illustration of the growth of the PMMA signal in time (Figure 4). The asymmetric least squares (ALS) method (25) (26) was used for baseline correction.

	With background subtraction applied, the PMMA signal was clearly seen to rise after an initial induction period with no discernible signal change, during which we saw no indication of any PMMA signal in the baseline to indicate ultrasonic capture. Once initiated, the signal grew and reached a maximum quickly.

	For a quantitative analysis, we separated the signal contributions to the spectra set using multivariate curve resolution (MCR). MCR is an iterative method that can be used to identify a potential separation of the spectra set into fixed component spectra and time-varying contributions for each component. This separation is “ill-defined,” and does not lead to a unique solution, that is, the results depend on the initial guess for the spectra. As we already expected three independent contributions (dip probe, PMMA, and transducer), we limited the decomposition to three components and seeded the analysis with the first three loadings from the principal component analysis (PCA) of the same spectra, setting those three component spectra as our initial guesses for the MCR spectra.

	In addition, we enforced additional non-negative constraints on both the component spectra and the component contributions of the separation, an approach known as the ALS (alternative least squares) variant of MCR (27). This matches the expectations of actual spectra and “concentrations”, each of which would be expected to contribute additively to the observed spectra.

	Figure 5 shows the component spectra resulting from the MCR-ALS analysis. We assigned components 1 and 3 to the Raman signal arising from the dip probe and the transducer, and component 2 to the Raman signal from the PMMA microspheres in solution. Comparison to the Raman spectrum of neat PMMA (Figure 2, top panel) shows very good agreement; the PMMA component spectrum shows little interference from the signal caused by the setup (sapphire ball lens or transducer material).

	The MCR-ALS result for the time-varying contribution of component 2, which we assigned to PMMA, can be interpreted as the time evolution of the PMMA signal, as shown in Figure 6 for four independent repeat runs of the same experiment. The figure presents a similar growth curve of the signal for each run, with an induction period of approximately 250 s, and a full, steady state PMMA signal after about 1000 s. The rapid signal evolution after the initial induction period may be due to a feedback effect in which captured PMMA particles slow down other particles near the trapping nodes of the standing wave.

	To determine the Raman signal enhancement for PMMA attributable to ultrasonic capture in the 90 ppm dispersion, we averaged the MCR-ALS result for the contribution of the component assigned to PMMA with and without ultrasonic trapping activated. The average for the signal with trapping is formed across all four runs for all measurements past the 1000 second mark (steady state capture), leading to an average signal of about 23,100 (arbitrary units).

	For the reference signal, we used a separate experiment with the same sample in the same setup, but without trapping activated, and determined the MCR-ALS signal contributions using the same decomposition as used in the four runs with trapping activated. Averaging over the entire run of 100 spectra, we found a reference signal of 15±20 (arbitrary units) with the uncertainty given by the standard deviation across the 100 spectra. Even at “one sigma,” this signal is not significantly different from zero, limiting the possibilities of standard approaches for calculation of a lower limit for the enhancement factor. Though the true magnitude of the enhancement factor for ultrasonic capture cannot be fully quantified, a minimum enhancement of 1500x can safely be reported using this reference signal value.

	Growing concern over the increasing presence of microplastics in our environment and their potential environmental impact has heightened the need for a simple, robust, and portable measurement method. Capture of PMMA microspheres with an ultrasonic standing wave for direct detection using a compact Raman system is one possible alternative to current filtration-based methods. Here, we demonstrated a more than 1500-fold enhancement of the Raman signal using ultrasonic capture and a dip probe from a 90 ppm suspension in water. The captured microplastic sample amount increased in time up to the capacity of the resonator trap, reaching the full enhancement in about 500 s. While still experimental, the method shows a possible route to microplastic measurements in water requiring little to no sample preparation, and which can be performed using compact, portable instrumentation in the field.

N. P. Ivleva, A.C. Wiesheu, and R. Niessner. 2017, 

L. Van Cauwenberghe, A. Vanreusel, J. Mees, and C.R. Janssen, 

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T.M. Karlssona, A.D. Vethaaka, B. Carney Almrothd , F. Ariesee, M. van Velzena, M.Hassellöv, and H.A. Leslie. 

B.E. Oßmann, G. Sarau, H. Holtmannspotter, M. Pischetsrieder, Silke H. Christiansen, and W. Dicke, 

A.C. Wiesheu, P.M. Anger, T. Baumann, R. Niessner, and N.P. Ivleva, 

G. Sarau, L. Kling, B.E. Ossmann, A-K. Unger, F. Vogler, and S.H. Christiansen, 

R.Lenz, K. Enders, C.A. Stedmon, D. M.A. Mackenzie, and T.G. Nielsen, 

L. Vethaak, G.H. Tinnevelt, J.J. Jansen. 

L. Frère, I. Paul-Pont, J. Moreau, P. Soudant, C. Lambert, A. Huvet, and E. Rinnert, 

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Microplastics in Aquatic Systems – Monitoring Methods and Biological Consequences

 YOUMARES 8 -Oceans Across Boundaries: Learning from each other Proceedings of the 2017 conference for YOUng MARine RESearchers.

 C.F. Araujo, M.M. Nolasco, A.M.P. Ribeiro, and P.J.A. Ribeiro-Claro, 

 K. Wieland, S. Tauber, C. Gasser, L. A. Rettenbacher, L. Lux, S. Radel, and B. Lendl, 

 P.H.C. Eilers and H.F.M. Boelens, “Baseline Correction with Asymmetric Least Squares Smoothing,” Leiden University Medical Centre, Leiden, The Netherlands, 2005.

 M. Garrido, F.X. Rius, and M.S. Larrechi, 

are with usePAT GmbH, in Vienna, Austria. 

are with Wasatch Photonics, in Morrisville, North Carolina. Direct correspondence to: 

Of the 78 million tons of plastic packaging manufactured every year, approximately one-third ends up in the ocean, the air, and most foods and beverages. To monitor the proliferation of these plastics, an ultrasonic capture method is demonstrated that produces a 1500-fold enhancement of Raman signals of microplastics in water.

In Situ Enhancement of Microplastic Raman Signals in Water Using Ultrasonic Capture

Raman spectroscopy has been successfully used to study the microscopic nature of the structural and morphological properties of nanopowders. Nanoparticles of titanium dioxide (TiO

 are essential parts of dye-sensitized solar cells and materials for catalysis. Dye-sensitized solar cells are advantageous alternatives to conventional solid-state photovoltaic devices because of performance, environmental compatibility, and cost. Grain size and thickness of TiO

 film show a dominant effect on the efficiency of the photovoltaic devices. We have investigated commercial TiO

 nanopowders with different sizes using Raman spectroscopy. The conservation of momentum for light scattering from the phonons means that the only modes at the center of Brillouin Zone (

 = 0) are Raman active. The localization of phonons in nanoscale materials leads to the relaxation of this selection rule. It gives rise to an increase in the number of Raman active modes away from the center of the Brillouin zone, and, therefore, results in peak shifts and broadening. This study presents and compares the details of the evolution of the E

 from different nanopowders. This research likewise discusses experimental results in the frame of the model of confined phonons. It was found that the Raman spectra of nanopowders are very sensitive to methods and preparation conditions.

	The  past few decades been rapidly emerging as the era of nanotechnology. The technological applications grow when an understanding of the fundamental properties of nanomaterials as a function of grain or particle size allows us to predict it. Grain size and thickness of titanium dioxide (TiO

film have shown a dominant effect on the efficiency of the photovoltaic devices and the application in catalysis (1–4). Nanomaterials synthesized by various methods and the properties of nanomaterials with the same size of grains may be different. This effect is due to defects in structures. Isolated or weakly connected nanoparticles form a nanopowder. Raman scattering, a nondestructive technique that is comparable with other methods used for characterization of nanopowders, requires only a small quantity of a sample and has a short measurement time. Peak position and width of the Raman band are susceptible to the local structure of the materials. The purpose of this study is to develop the understanding relationship between grain size and the structure and properties of commercial TiO

, (which include rutile and brookite), the anatase phase is the most important in applications of catalysis and dye-sensitized solar cells.

	The crystalline structure of anatase is tetragonal; the unit cell composes of two primitive cells, each with two TiO

 ). Group theory predicts six Raman active modes, which are E

, due to its being very sharp and intense.

	The properties of nanopowder do not differ from those of the bulk, unless the dimension of grains is less than 10 to 20 nm. Raman scattering probes only the optical phonons in the center of the Brillouin zone (

 = 0). This selection rule is mainly a consequence of the infinite periodicity of the crystal lattice (

→∞). The wavefunction of phonons with wavevector 

) has a periodicity of lattice. Therefore, â 

= 0; one would expect that the line shape of the Raman band will be Lorentzian. The first-order Raman spectrum can be calculated by integrating the contribution of all phonons over the Brillouin zone and expressed by equation 2:

 is the natural line width of the center zone optical phonon in an infinite crystal.

	In the case of nanocrystalline materials, the periodicity of the crystal is interrupted, and this rule is relaxed. In an isolated grain, the phonon can remain confined within the grain. For a spherical particle of size 

, the phonon wavefunction decays to a minimal value close to the boundary. The function Φ

in equation 1 may be replaced by a new function Ψ(

) is a phonon weight function. There are several weight functions proposed for an isolated grain. These are: 1) Gaussian function, 

) (8); 2) function by analogy with the ground electron state of an electron in a hard-sphere, 

  (9); and 3) function by analogy with a wave in a lossy medium, 

) (9). Parameter α will define how fast the amplitude of the phonon decays toward the boundary. For some nanomaterials, rigid confinement of the phonons (amplitude vanished at the boundary) in nanocrystals is required to explain experimental data.

	When phonons are confined in nanocrystals, wavefunction Ψ

 is no longer an eigenfunction of the phonon at 

 = 0. Instead, it is a superposition of eigenfunctions with the 

) with a possible expansion in a Fourier series (equation 4):

), and that phonon transitions matrix elements |<

 have a non-zero value. The eigenfunctions are weighted through coefficients C (

	where Ô is a photo-phonon operator. In this case, the first-order Raman spectrum is expressed by equation 7:

	The additional contribution to the Raman spectrum from these phonons results in an asymmetric broadening of the line shape. The increased intensity in the high or low wavenumber side of the Raman band arises from the contribution of either positive (ω

) of the phonon branch away from the center of Brillouin zone. A dispersion curve assumes to be isotropic. Depending on the material, it may be approximated by several functions as ω(

 is the frequency of the phonon in the center of the Brillouin zone, while α is the lattice parameter. The zone boundary corresponds to 

 = π and A, β are parameters specific to the material.

 nanopowders from two vendors were used for this study. One is US Research NanoMaterrials Inc. (USRNM), which provides nanopowders with the following profile: purity 99.98% Super Grade, APS ( 30 nm, 15 nm, and 5 nm) and SSA: 50 m

/g, respectively. Color: white; bulk density: 0.42 g/cm

; pH: 5.5-6.5, Loss of weight in drying: 1%; loss of weight on ignition: 3%. The other supplier was Nanostructured & Amorphous Materials (NAM), which provides nanopowder with purity: 99.8%; SSA: 150–300 m

/g; color: white; APS: 5 nm; bulk density: 0.25-0.3 g/cm

. The single crystal of anatase was from Roy Harltburt Minerals, with a size of 2 x 3 x 4 mm.

	Raman spectra of the samples were collected at room temperature in a backscattering configuration using a LabRam Evolution Raman microscope (Horiba Scientific) equipped with an Olympus BX-41 microscope (objective x100, NA 0.9), TE cooled CCD detector (Synapse OE 1024x256), Horiba Scientific), 632.8 nm HeNe laser, and 1800 gr/mm grating. The spectral resolution of the instrument was 0.35 cm

. The laser power was 7 mW, and the acquisition time was between 2 and 20 s depending on the sample. Two to ten spectra were registered for each sample at a different position to verify sample homogeneity and the absence of photoinduced phenomena. The reference spectrum of Si (peak position of 520.6 cm

) was measured after each spectrum to avoid temperature drift.

	The unpolarized Raman spectrum of crystalline anatase is shown in Figure 1. The Raman peaks observed at 142.9, 196.5, 396.5, 514.4, 521.0, and 638.2 cm

 modes of the anatase phase, respectively. The position of peaks is in agreement with previously reported data (5–7).

	Raman spectra of crystalline anatase and TiO

 nanopowders from USRNM with a grain size of 30 nm and 15 nm are shown in Figure 2a. The Raman band at ~142.9 cm

 for nanopowders becomes broader with decreasing the grain size and shifts for that of the TiO

 crystal. The spectra of the 5 nm nanopowder taken in 10 different spots are not reproducible from point to point and show in Figure 2b. From the Raman spectroscopy point of view, 5 nm nanopowder is not considered a homogeneous material on the scale of the spot size of the focused laser, which is 430 nm ((0.61 × λ)⁄(N.A.); λ = 0.6328 μ, N.A. = 0.9).

	Because of that, 5 nm nanopowder from USRNM was excluded from future work. Nanopowders with a grain size of 30 nm and 15 nm show excellent reproducibility of the position and spectral line shape in different spots. Raman spectra of nanopowder with a grain size of 5 nm from NAM is shown in Figure 2a. Spectra taken at different points are reproducible. The grain size for two of these 5 nm nanopowder samples (from NSRNM and NAM) are reported to be the same; however, Raman spectra reveal significant differences in the materials.

	The broadening and shift of the Raman peak at 142.9 cm

 observed in the spectra can be originated to the surface pressure or confinement effects. It is unlikely that there is a shift in the position and line shape of the Raman band due to the surface pressure or stress and strain. These materials are in the form of nanopowder, which is isolated or weakly connected nanoparticles. In nanopowder, nanoparticles are free and not embedded in any medium. Therefore, phonon confinement is the main cause of this phenomenon. The occurrence of the strong line at 142.9 cm

 nanopowders possess a certain degree of long-range order.

	The broadening of the peak compared with the single crystal, and a shoulder from the high wavenumber side suggests the contribution of the phonons away from the center of the Brillouin zone. 

	Among many proposed confinement functions, the Gaussian confinement function is a simple model considering that particles are homogeneous spheres, and the parameter α defines how fast the wavefunction decays when approaching the  boundary. In this case, weight coefficient |

 is the diameter of nanoparticle, while α is a parameter, which is specific to the material. A phonon amplitude decays toward the boundary of the nanoparticle at 2

 = 1 at the actual boundary determined by 

. Rigid confinement, at which the amplitude of the phonons vanished at the boundary, corresponds to a larger α value.

	As a model of the phonon dispersion curve, we use the analytical expression 10

 being a lattice parameter and zone boundary corresponding to the 

 = π (11,12). Based on this model, parameters of the E

 were calculated, assuming that the central frequency is 142.9 cm

; this frequency does not change with the size of the grains.

	The insert in Figure 1 shows the Raman spectrum of crystalline TiO

 fitted using Lorentzian line shape because in single-crystal (ideal conditions) |

 is very narrow with full width at half maximum (fwhm) of 7.6 cm

	Figures 3 and 4 show spectra and fitting parameters for nanopowders with 30 nm and 15 nm grain size respectively. Parameters 

 are calculated using the weight coefficient |

 in equation 9. The size of the nanopowder 

 extracted from the Raman spectra is in good agreement with the size of the grains obtained from XRD (vendor’s specification). Parameter 

 increases with a decrease of grain size, and it shows similar behavior as reported in (13). Parameter α decreases in value as grain size becomes smaller. It corresponds to the condition that the amplitude of phonons requires more rigid confinement for large-size grains, and therefore, the amplitude should vanish toward the boundary much faster when compared with small-size grains.

	Figure 5 shows the Raman spectrum and fit parameters of the TiO

 nanopowder with a grain size of 5 nm. If a Gaussian weight function is in use, the spectrum only fits with ω

 and grain size of 5 nm. However, there is no evidence that phonon frequency in the center of the Brillouin zone changed with the size of nanoparticles. Therefore, Gaussian weight function 

	Few other functions were proposed for confined phonons model (11,12), and one of them is by the analogy of the ground state of an electron in hard-sphere, defined by equation 11:

 in this case, is expressed by equation 12.

	Figure 6 shows the spectrum and fitting parameters for the nanopowder with the grain size of 5 nm. The spectrum fitted with ω

 of 6.8 nm, which is close to reported grain size 5 nm from the XRD data.

	The fact that the line shape of nanopowders with 30 nm and 15 nm grain size is different from the line shape of the 5 nm nanopowder indicates that the properties of the nanomaterials are very different in the sense of phonon scattering. Using different weight functions to fit the experimental data confirms that phonons scatter in the material differently; the decay of phonons close to the boundary is also different.

	In this work, I demonstrate the use of micro-Raman spectroscopy for non-destructive characterization of nanocrystalline TiO

 nanopowders. Grain size can be extracted from the spectrum of TiO

 nanopowders by applying a model of confined phonons. Results showed a good correlation between grain sizes obtained from Raman spectra and XRD. Raman spectra are more sensitive to the structure of nanopowders compared with XRD because Raman spectra of nanopowders may be different even though the XRD data shows the same grain size.

		N.-G. Park, G. Schlichthörl, J. van de Lagemaat, H.M. Cheong, A. Mascarenhas, and A.J. Frank, 

		P. Ettireddy, N. Ettireddy, S. Mamedov, P. Boolchand, and P. Smirniotis, 

		S. Mamedov, T. Moriyama, T. Numata, N. Nabatova-Gabain, S. Yanagida, and L. Yan, 

		O. Frank, M. Zukalova, B. Laskova, J. Kürti, J. Koltaib, and L. Kavan, 

		E. Roca, C. Trallero-Giner, and M. Cardona, 

Raman spectroscopy is proving to be a powerful technique for characterizing the structural and morphological properties of nanopowders. Specifically, Raman spectroscopy can provide details of the grain size and thickness of titanium dioxide (TiO2) nanopowder films. These measured film properties affect the efficiency of photovoltaic devices, such as solar cells, and also the effectiveness of nanopowders in catalysis applications.

Special Issues-06-01-2020

Vol 35 No s2 Raman Technology for Today's Spectroscopists

Applications of Confocal Raman Spectroscopy and Imaging in the Medical Device Industry

In Situ Enhancement of Microplastic Raman Signals in Water Using Ultrasonic Capture

Characterization of TiO2 Nanopowders by Raman Spectroscopy