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Articles 8 Library-Based Screening of Pharmaceutical Materials by

Handheld Raman and Near-Infrared SpectrometersChelliah V. Navin, Latevi S. Lawson, and Jason D. RodriguezA report on screening common anti-infective drugs using spectral libraries built and transferred from laboratory-based instruments to handheld near-infrared and Raman spectrometers

16 The Versatility of Portable Raman in Process DevelopmentThomas Padlo and Katherine BakeevA demonstration of the ability for portable Raman spectroscopy coupled with univariate and multivariate analysis tools in the process development stage to gain insight and process understanding of chemical reactions

24 Analysis for Lead in Laundered Shop Towels Using Handheld X-ray Fluorescence SpectroscopyKyle W. Scott and Wade R. ThompsonThe ability of portable handheld X-ray fluorescence spectroscopy to measure harmful contaminants, such as lead, in towels is tested.

29 Field-Portable VNIR Spectrometry: Applications for Mars Rover Operational Strategies Testing at Terrestrial Analog SitesSarah R. Black, R. Aileen Yingst, and Brian M. HynekHow is visible–near infrared spectroscopy being tested for planetary exploration?

36 Carbon Nanotube Characterization and Quality Control Using Portable Raman: 532-nm Versus 785-nm Laser ExcitationAleksandr V. Mikhonin, Laurence A. Nafie, and Rina K. DukorIs 532-nm or 785-nm laser excitation better for carbon nanotube characterization?

Cover images courtesy of VICTOR HABBICK VISIONS/SCIENCE PHOTO LIBRARY/ Rubberball/Mike Kemp/ Photography by ZhangXun/ Digital Vision.

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A nti-infective medicines such as antibacterial and antiviral drugs play an important role

during a pandemic outbreak (1). In an effort to promote nondestructive screening of anti-infective drugs, the United States Food and Drug Admin-istration (FDA) Division of Pharma-ceutical Analysis (DPA) has developed spectral library–based approaches (2,3) for confirmation of drug prod-uct quality on portable spectrometers. Spectral libraries can be used to screen counterfeit products by comparing them against spectral signatures of

authentic finished drug products (4–6). In this article, we report our at-tempt to screen common anti-infective drugs, such as those that would be used during an emergency situation like a global pandemic, using spectral libraries built and transferred from laboratory-based instruments to hand-held near-infrared (NIR) and Raman spectrometers. The methods devel-oped are easy to use by nonexperts and provide the opportunity to screen materials based on pass–fail determi-nations (7). Typically, these techniques are fast and deliver results in less than

Library-Based Screening of Pharmaceutical Materials by Handheld Raman and Near-Infrared SpectrometersThe availability of quality drugs is crucial in the event of a pandemic. Here, we report our pilot efforts to perform rapid screening of anti-infective drugs for confirmation of drug product quality using near-infrared (NIR) and Raman library methods. The methods reported are nondestructive toward the sample and are designed to facilitate rapid physical testing of drugs at the point of use or in a field setting. We built a representative library through voluntary collaboration with six manu-facturers of antibiotic and antiviral drugs. The drugs supplied by these manufacturers are representative of imported United States Food and Drug Administration (FDA) approved finished products. We successfully transferred the spectral libraries from laboratory-based instruments to field-deployable handheld NIR and Raman instruments and challenged the library methods using independent samples from different batches.

Chelliah V. Navin, Latevi S. Lawson, and Jason D. Rodriguez

September 2016 Advances in Portable and Handheld Spectroscopy 9

60 s, thereby streamlining the process of performing analysis on finished drug products (6). In addition, these nondestructive approaches require no sample preparation and can analyze a sample directly through a transparent container or blister packaging (8–13).

MethodsA list of antibacterial and antiviral drugs used to build the master library is pro-vided in Table I. A total of 150 finished drug product samples (including differ-ent batches and dosage strengths) from six generic drug manufacturers were in-

Table I: Antibacterial and antiviral drugs obtained from Indian pharmaceutical companies

Finished Pharmaceutical Drug Source

Acyclovir 400 mg Manufacturer 5

Ciprofloxacin 250 mg Manufacturer 1



Cephalexin 250 mg Manufacturer 4

Clarithromycin 250 mg Manufacturer 1

Clarithromycin 500 mg Manufacturer 1

Famciclovir 250 mg Manufacturer 3






Levofloxacin 250mg Manufacturer 2

Levofloxacin 750 mg Manufacturer 2




Metronidazole 500 mg Manufacturer 3

Minocycline 100 mg Manufacturer 1



Minocycline HCl 50 mg Manufacturer 3

Minocycline HCl 75 mg Manufacturer 3

Minocycline HCl Extended Release 90 mg Manufacturer 5

Minocycline HCl Extended Release 135 mg Manufacturer 5

Valacyclovir 1000 mg Manufacturer 1




Valacyclovir HCl 500 mg Manufacturer 6




cluded in the study. The samples were re-ceived on a voluntary basis directly from the manufacturers. The laboratory NIR spectrometer used to build the library was an Antaris II Fourier transform NIR spectrometer (Thermo Fisher Scientific). Spectral collection for the library entries was performed using the integrating sphere assembly at 4000–10,000 cm-1 at 8 cm-1 resolution and a gain of 1×. Each spectrum collected consisted of 32 scans. The laboratory Raman spectrometer used to build the library was a Kaiser Raman Workstation 785 nm (Kaiser Optical Systems Inc.). The Raman 3-mm wide-beam probe assembly was used to collect the spectra and the total collec-tion time for each spectrum was 30 s (1 s integration × 30 scans). The laser power at the sample was ~250 mW.

The spectrum used in the NIR and Raman master libraries was the aver-age of multiple tablets or capsules taken from each sample received. The average spectrum for each sample was obtained by randomly sampling six tablets or cap-sules from each sample bottle. Each tablet or capsule was analyzed twice, one spec-

trum for each side in the case of tablets and two random orientations for cap-sules. The same exact tablets and capsules were analyzed by both NIR and Raman libraries. The resulting 12 spectra for each tablet and capsule were averaged into a single entry for each spectral library.

The NIR and Raman libraries were transferred to handheld instruments and the performance of the library was chal-lenged by assembling three different test sets. The test sets consisted oft� the batch used for library develop-

ment (master); t� a different batch received through the

manufacturer submitting the library lot (control); and t� the same product procured in the

United States through a commercial distributor (commercial).

Each test set included 10 drug products, which are listed in Table II. Spectral measurements were acquired on each of the 10 sample test sets using a handheld Phazir NIR spectrometer (Thermo Sci-entific) in the 1595–2400 nm wavelength range. A 785-nm EZ Raman H handheld spectrometer (TSI Inc., formerly Enwave

Table II: Spectral correlation values calculated for the control and commercial sets using a handheld NIR spectrometer

Pharmaceutical Drug

Manufacturer Number

SC Values for NIR NIR Determination

Control Set

Commercial Set

Control Set

Commercial Set

Ciprofloxacin 1 0.988 0.988 Pass Pass

Ciprofloxacin 2 (set 1) 0.984 0.984 Pass Pass


Famciclovir 3 0.981 0.981 Pass Pass

Levofloxacin 4 (set 1) 0.973 0.973 Pass Pass




Metronidazole 3 0.983 0.983 Pass Pass

Valacyclovir 5 0.989 0.989 Pass Pass


Optronics Inc.) was used to analyze the 10 sample test sets in the 350–1800 cm-1

range. Raman spectral collection was achieved using custom software built in-house. The software uses variable ac-quisition time based on a 1-s preliminary acquisition. The software is designed to optimize collection parameters to achieve a 50,000-cps intensity.

Each sample used in the test set was run multiple times (15 times for NIR and 10 times for Raman), and the aver-age spectrum was used to compare to the library spectrum. The NIR and Raman libraries were preprocessed before dis-tribution to the NIR and Raman instru-ment. The processing used for the NIR library before transfer to the handheld spectrometer was interpolation of the master library to the wavelength region used by the handheld spectrometer fol-lowed by first derivative preprocessing (second order, five-point window). The Raman library was corrected following the previously reported procedure (7) and the library and test Raman spectra were pretreated with first derivative prepro-cessing (second order, 31-point window) before comparison. Comparison between library and test samples was achieved by

calculating the spectral correlation (SC) value according to the following equation:

�SC(Library t�Test)2

(Library t Library)(Test t Test) [1]

The SC value is calculated based on the correlation coefficient algorithm and the SC value ranges between 1 and 0, with 1 indicating a perfect correlation and 0 indicating a poor correlation between two different spectral measurements. In this work, a SC value of 0.90 was used as the pass–fail threshold to justify the applicability of using a spectral library generated using the handheld NIR and Raman spectrometers.

Results and DiscussionNIR spectra of the test set for a 500-mg famciclovir tablet are shown in Figure 1. The handheld NIR spectrometer was used to collect the spectral signatures of the control and commercial samples and compared against the library set spectra acquired on the benchtop NIR spectrom-eter. As can be seen from visual inspec-tion of Figure 1 (left), there are slight dif-ferences between the spectra acquired by the benchtop (library set) and handheld NIR spectrometer (control and commer-

1500

Library set Control set Commercial set

Wavelength (nm) Wavelength (nm)


1700 1900 2100 2300

1500 1700 1900 2100 2300

Figure 1: Left: Comparison of NIR spectra for famciclovir. The spectra have been offset for clarity. Right: Comparison of first derivative spectra for famciclovir. All spectra have been normalized to facilitate visual comparison.


cial sets). The differences between the spectra acquired on the benchtop and handheld units are not surprising since the benchtop instrument is a high-per-formance Fourier transform system and the handheld units are dispersive instru-ments. Some of the resolution differences can be seen in the spectra as well because the master library spectrum features slightly narrower features at ~1700 and 2000 nm. These differences are largely preprocessed when visually comparing between the smoothed first derivative spectra, which are shown in Figure 1 (right). The SC values for the famciclovir’s control and commercial samples are 0.981 and 0.971, respectively, and are listed in Table II along with the NIR SC results for all the samples included in this work. An image of the handheld NIR spectrometer user interface indicating a “Pass” for the famciclovir commercial set is given in Figure 2 to demonstrate the view that would be seen by the investigator. The

results for each of the 10 products in the control and commercial sets listed in Table II yield SC values greater than 0.90, which was used as the pass–fail thresh-old. All samples passed the screening and there was not any significant difference in the SC values between the control and commercial sets compared to the master library set. The similarity in the control and commercial SC values indicates that the spectral library signatures chosen to populate the library are representative of the supply chain for each particular drug.

Unlike the spectra acquired using the handheld NIR spectrometer, noticeable differences were observed in compar-ing the Raman spectra acquired on the laboratory spectrometer and handheld unit (Figure 3). Figure 3 shows a visual comparison of the spectral plots for famciclovir’s control and commercial sets with the library set. Four boxes in the Raman spectra of Figure 3 show “hot pixels” present in the handheld spec-trometer spectra. These hot pixels are manifested as sharp, artificial peaks in the spectra and are due to specific pixels in the detector that are close to satura-tion even with the laser off (14). Hot pixels add spectral artifacts that result in low SC values. The lowering of the SC values occurs because the signal present at the hot pixels is not real although it may appear to be a real feature at first glance. The most easily discernible dif-ference between the benchtop and hand-held spectra occurs at ~1417 cm-1 where the library spectrum does not contain a feature but the control and commercial sets do. Figure 4 shows the Raman spec-tral comparison of famciclovir without hot pixels. Elimination was performed using an in-house algorithm that in-volves the application of a median filter with a 15-point window to the spectral

Figure 2: Typical handheld NIR spectrometer result screen indicating a “Pass” for the famciclovir test set.


regions surrounding the hot pixels. The algorithm was applied to the handheld

spectra to eliminate the hot pixels in the data acquired on the handheld Raman

1500 170013001100900700500


300

Raman shift (cm-1)

Figure 3: Raman spectral plot comparison of famciclovir with hot pixels indicated by the black boxes.

300


500 700 900 1100

Raman shift (cm–1)

1300 1500 1700

Figure 4: Raman spectral plot comparison of famciclovir without hot pixels. The black box indicates the locations where the hot pixels were seen before elimination.


for the control and commercial sets. The corrected Raman spectra without hot pixels were used for SC comparison be-tween with the library set spectrum. The SC values shown in Table III were above 0.90 for all samples except two: cipro-floxacin commercial (manufacturer 2, set 1) and a metronidazole control, which were 0.889 and 0.895, respectively. As in the NIR study, the results show that spectral library signatures chosen to populate the library are representative of the supply chain for each particular

drug. A typical Raman pass–fail screen is shown in Figure 5.

ConclusionsThis study shows that library-based SC methods can be used to screen finished products in a nondestructive and rapid fashion. The NIR and Raman results indi-cate that both types of portable techniques can be used to perform reliable screening of finished products. The broader impli-cations of this study indicate that libraries can be built on different NIR and Raman

Table III: Spectral correlation (SC) values calculated for the control and com-mercial sets using handheld Raman spectrometer after hot pixel elimination

Pharmaceutical Drug

Manufacturer Number

SC Values for Raman Raman Determination

Control Set

Commercial Set

Control Set

Commercial Set

Ciprofloxacin 1 0.942 0.934 Pass Pass

Ciprofloxacin 2 (set 1) 0.930 0.889 Pass Fail


Famciclovir 3 0.960 0.959 Pass Pass





Metronidazole 3 0.895 0.988 Fail Pass

Valacyclovir 5 0.918 0.900 Pass Pass

Figure 5: Handheld Raman spectrometer user interface indicating a “Pass” for one of the replicate runs in the famciclovir commercial set.


spectrometers and transferred success-fully to field units. These techniques are straightforward and allow for use by nonexperts. Spectral libraries can be used with portable NIR and Raman spectrom-eters in a field setting, thereby providing an efficient way to increase the number of products that undergo physical testing before reaching consumers in the event of a global pandemic.

AcknowledgmentsThis project was supported in part by the Center for Drug Evaluation and Research (CDER) Critical Path and Regulatory Science & Review Enhancement Programs. The project was supported in part by an appoint-ment (C.V.N) to the Research Partici-pation Program at the CDER admin-istered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of En-ergy (DOE) and U.S. Food and Drug Administration (FDA).

DisclaimerThis article reflects the views of the au-thors and should not be construed to rep-resent the FDA’s views or policies.

References(1) A. Patel and S.E. Gorman, Clin. Pharmacol.

Ther. 86, 241–243 (2009).

(2) P.H. Merrell, L. Buhse, and J.D. Rodriguez,

Tablets and Capsules 11, 22–26 (2013).

(3) J.D. Rodriguez, C.M. Gryniewicz-Ruzicka, J.F.

Kauffman, S. Arzhantsev, A.L. Saettele, K.A.

Berry, B.J. Westenberger, and L.F. Buhse,

Am. Pharm. Review 16, 9–18 (2013).

(4) Y.L. Loethen and J.D. Rodriguez. Am. J. Anal.

Chem. 6, 559–568 (2015).

(5) F.H. Long, Handbook of Stability Testing

in Pharmaceutical Development

(Spectroscopic Solutions, LLC, New Jersey,

2009), chap. 11, pp. 223–239.

(6) M. Kayat, G. Ritchie, S. Pieters, C. Heil, R.

Kershner, R. Cox, G.L. Reid, and M. Mabry,

Am. Pharm. Rev. September/October

(2012).

(7) J.D. Rodriguez, B.J. Westenberger, L.F.

Buhse, and J.F. Kauffman, Analyst 136, 4232–4240 (2011).

(8) K.M. Morisseau and C.T. Rhodes,

Pharmaceutical Research 14(1), 108–111

(1997).

(9) C.M. Hodges and J. Akhavan, Spectrochim.

Acta, Part A 46(2), 303–307 (1990).

(10) G. Reich, Adv. Drug Delivery Rev. 57(8),

1109–1143 (2005).

(11) S.H.F. Scafi and C. Pasquini. Analyst 126, 2218–2224 (2001).

(12) J.S. Day, H.G.M. Edwards, S.A. Dobrowski,

and A.M. Voice, Spectrochim. Acta, Part A

60(3), 563–568 (2004).

(13) M.D. Hargreaves, K. Page, T. Munshi, R.

Tomsett, G. Lynch, and H.G.M. Edwards,

J. Raman Spectrosc. 39(7), 873–880

(2008).

(14) R. McCreery, Chem. Anal. 157, 193 (2000).

Dr. Chelliah V. Navin is a Research Fellow in the U.S. FDA Division of Pharmaceutical Analysis in St. Louis, Missouri. Dr. Latevi Lawson devel-ops and improves on both the ana-lytical and statistical methodologies for the rapid screening team with the FDA’s Department of Pharmaceutical Analysis. Dr. Jason D. Rodriguez is a Research Chemist in the FDA Division of Pharmaceutical Analysis. Direct correspondence to: [email protected] ◾

For more information on this topic, please visit our homepage at: www.spectroscopyonline.com




P rocess analytics has been in use for nearly 70 years, starting in the petrochemical and chemical

industries using infrared (IR) photom-eters and different types of devices such as oxygen and conductivity sensors as process analyzers to control manufac-turing and refining processes (1). These univariate sensors and others continue to be used, and other sensors including spectroscopic systems have also been ad-opted. The value of process analytics is that it can provide the pulse of a process. Process analytical technology (PAT) is the use of off-line, at-line, or in-line analyz-ers to obtain analytical data faster, more often with high precision to increase manufacturing controls of biological or chemical materials, as well as aid in pro-

cess understanding for optimization and improvement (2). PAT has seen increased interest since 2005 with the publication of a guidance on PAT utilization in the pharmaceutical industry from the United States Food and Drug Administration (FDA). Spectroscopic tools including Raman can be used in situ or can be in-terfaced to a sampling loop on a process to monitor the chemical composition with full spectral information, which is the molecular picture of the changes oc-curring in the process. The amount of data that can be collected may exceed the speed of a process and in the early phases of process development this can be used to understand a process and its dynamics, possible side reactions, and kinetic pathways.

The Versatility of Portable Raman in Process DevelopmentRaman spectroscopy is a well-suited spectroscopic technique for process development and control within development laboratories in chemical, pharmaceutical, and other industries. This article demonstrates the utility of portable Raman spectroscopy as a simple and versatile tool for in-situ monitoring of reactions using univariate analysis techniques such as peak trending, as well as multivariate analysis approaches to predict the end point of chemical reactions. The use of portable Raman systems allows analysts to make measurements in the laboratory, but also serves as a proof of concept for the Raman measurements to be implemented at-line or on-line in small pilot plants or large-scale production sites. For known reactions that are repetitively performed, or for continuous on-line process monitoring of reactions, the present approach provides a convenient solu-tion for process understanding as well as a basis for future implementation.

Thomas Padlo and Katherine Bakeev


Raman spectroscopy is a laser-based form of molecular spectroscopy that pro-vides specificity and sensitivity for quali-tative and quantitative analysis of sub-stances from their molecular vibrations. It is used in many different environments as an analytical tool for the study of solids, liquids, and gases. Many Raman instru-ments are interfaced with a fiber-coupled probe, which gives the versatility and flexibility for measurements to be made in different places, including in situ as is often the requirement for the monitor-ing of processes. More than a decade ago, hardware developments that lead to an increase in the adoption of Raman spec-troscopy were recognized as the develop-ment of compact lasers, introduction of spectroscopic-grade charge-coupled de-vice detectors, improvements in sampling

optics (including fiber-optic probes), and the advances in powerful personal com-puters and associated software to collect and analyze volumes of data (3). These developments, as well as the advancement of technology in laser and spectrometer miniaturization and improved filters for laser light rejection and fiber-optic probes, have allowed for the development of por-table Raman spectroscopy systems that can be deployed and transported to differ-ent locations for uses that include process analysis. There are other complementary spectroscopic techniques to Raman such as Fourier transform infrared (FT-IR) and near-infrared (NIR) spectroscopy. How-ever, because of Raman spectroscopy’s flexible sampling interface, high sam-pling rate, and high spectral specificity it is an invaluable tool for qualitative and

0

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30,000

40,000

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Raman shift (cm-1)

Reactant 1

Reactant 2

Product

2-Aminopyridine 2-Phenylimidazo[1,2-a]pyridine 2-Bromoacetophenone

Rela

tive In

ten

sity

1000 1200 1400 1600 1800

Figure 1: Overlay of the Raman spectra of the reactants and product for the first synthesis: green = 2-aminopyridine (reactant), red = 2-bromoacetophenone (reactant), and blue = final product: 2-phenylimidazo[1,2-a]pyridine.


quantitative analysis of chemical systems for reaction monitoring and end-point detection for chemical synthesis and po-lymerization reactions, hydrogenation, hydrolysis, and polymorphic character-ization (1,3,5).

In the development of a process, there are many parameters that may be evalu-ated and experiments performed to opti-mize the yield, purity, and cycle time. To understand the impact of process changes on the process and product, in situ mea-surements can be made that relate the process parameters to product and pro-cess properties. In choosing measure-ment tools in process development, the needs of the chemical system in terms of “purpose, specificity, sensitivity, cycle time, on-line–off-line, qualitative–quan-titative, accuracy, precision” must be de-fined and the proper technology that can fulfill the defined criteria must be chosen (4). In early phases of process develop-ment, qualitative information on reaction progress may be sufficient to gauge reac-

tion completion. The trending of reactant- and product-relative concentrations based on peak height or area in a spectrum can be used to follow a reaction, even as other reaction parameter (such as solvent and other reagents or temperature) are changed from one reaction to another. In reactions where stoichiometric control is required, a quantitative measure of reac-tant concentrations may be needed. Off-line measurements by high performance liquid chromatography (HPLC) can be made and quantitative calibration mod-els developed, but such models are matrix dependent and often require updating or redevelopment when the reaction matrix is changed.

In this study, the goal was to determine the end point of the chemical reaction for the 6-methyl-2-phenylimidazo[1,2-a]pyridine synthesis and its derivative. The synthesis of this compound was under de-velopment for medicinal chemistry, and traditional thin-layer chromatography (TLC) was used to verify reaction comple-

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Figure 2: (a) Real-time peak trending of the two product peaks during the course of the reaction and (b) univariate peak trending plots using the (second derivative *-1 to remove any variation that might be coming from the baseline): red = 2-bromoacetophenone (reactant 1), green = 2-aminopyridine (reactant 2), and blue = final product: 2-phenylimidazo[1,2-a]pyridine synthesis.


tion, but very few aliquots are taken at the small-scale reactions, and the timing for sampling for the end point is based on the visual precipitation in the reaction mix-ture followed by the TLC test. Informa-tion on reaction progress and possible for-mation of intermediates and side products is possible only when Raman spectra are collected regularly throughout the course of the reaction. Monitoring the reaction with in situ Raman spectroscopy reduces the need for removing samples for analy-sis and also provides real-time informa-tion on reaction progress.

ExperimentalThe experimental setup consisted of an i-Raman Plus portable Raman spectrom-eter from B&W Tek with a 785-nm, 300-mW laser excitation source connected to a back-thinned charge-coupled device (CCD) array detector covering a spectral range of 65–3200 cm-1. The sampling interface was a fiber-optical bundle with an immersion probe to allow for in situ measurements during the reactions. The Raman instrument was used to monitor the small molecular synthesis of 6-methyl-2-phenylimidazo[1,2-a]pyridine synthesis

and its derivative 2-phenylimidazo[1,2-a]pyridine. The immersion probe was in-serted into the round-bottom flask for di-rect measurement throughout the course of the reaction. The starting reagents were suspended into acetonitrile, and a small amount of sodium bicarbonate was added to neutralize the hydrobromic acid that formed during the process. The reac-tion was placed under argon atmosphere and heated to 80 °C for approximately 2 h. Raman measurements were collected every minute during the reaction with an integration time of 3 s averaged over 10 spectra.

Two similar Sn2 reactions were moni-tored to understand the reactions and determine their reaction end point. In these chemical reactions for development of these medicinal chemistry compounds, the goal of the process monitoring was to have a qualitative trend of the reaction progress and a means to detect the reac-tion end point. The first reaction is 2-ami-nopryidine reacted with 2-bromoaceto-phenone to form 2-phenylimidazo[1,2-a]pyridine. For this reaction, a univariate approach of monitoring the reactant and product peaks was conducted using B&W

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Tek’s BWSP software. For the second re-action of 2-amino-5-methyl-pyridine and 2-bromoacetophenone to form 6-methyl-2-phenylimidazo[1,2-a]pyridine, a multi-variate analysis method was applied using the Unscrambler software (CAMO Soft-ware). This approach also provided quali-tative information, because there was no requirement of quantitation, but rather a real-time measure of reaction end point in place of previously used off-line TLC.

Results and DiscussionFor the first reaction of the synthesis of 2-phenylimidazo[1,2-a]pyridine, a spec-trum was taken for each of the starting materials and the final product to identify the Raman peaks to monitor during the course of the reaction process. As shown in Figure 1, the three regions of interest are 847 cm-1 (2-aminopyridine, reac-tant 1), 1547 cm-1 (final product), and broad dual peaks between 1684–1702 cm-1 (2-bromoacetophenone, reactant 2). Because of the specificity of the Raman spectrum and the fact that these identi-

fied peaks do not have any overlap with other Raman peaks of solvent or other reaction components, univariate analysis can be effectively used. During the pro-gression of the reaction the two product peaks at 1547 cm-1 and 1603 cm-1, respec-tively, were monitored as shown in Figure 2a. The region with a significant dip in the real-time trending plot of relative intensity as a function of time was caused by probe fouling. This fouling can occur during in situ monitoring because material may adhere to the probe, and for full process monitoring implementations a measure should be taken to minimize it, includ-ing better control of stirring or automated probe cleaning procedures. Figure 2b is the peak trending plot of the reactants and product peaks created post-reaction with the probe fouling data removed. The data plotted was preprocessed using a second order derivative Savitzky-Golay filter to minimize baseline effects; the data were then multiplied by -1 to reverse the trend in the plot to show the reactants decreasing and product peaks increasing.

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The Raman spectra collected throughout the reaction monitoring are shown in Figure 3, with the reactant peaks visibly diminishing while the product peak is si-multaneously increasing as the reaction progresses. The univariate analysis pro-vided supportive information into the end point of the reaction as shown in Figure 4a where the overlay of the first and last spectra from the reaction illustrate the complete consumption of the reactants. Based on the peak trending plots of the reactants and product (Figure 2b), along with the overlay the first and last spectra collected (Figure 4a), the reaction appears to finish within 2 h. On a closer observa-tion of the overlay regions of interest,

shown in Figure 4b, the data indicates the reaction could have come to completion sooner because the peaks for the reactants are not visible in the raw data before the last collected spectrum.

For the second reaction of the synthesis of 6-methyl-2-phenylimidazo[1,2-a]pyri-dine, the spectra of each starting material and final product were collected and the peaks of interest were identified for the reaction monitoring. The peaks were the same as the previous reaction, 847 cm-1 (2-amino-5-methyl-pyridine, reactant 1), 1547 cm-1 (final product), and broad dual peaks between 1684–1702 cm-1 (2-bro-moacetophenone, reactant 2), as shown in Figure 5. The reaction proceeded for

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Figure 5: Overlay of reactants and product for the MCR approach: red = 2-amino-5-methyl-pyridine (reactant), blue = 2-bromoacetophenone (reactant), and purple = final product 6-methyl-2-phenylimidazo[1,2-a]pyridine.

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around 2 h with data collected through-out the course of the reaction at 1 min intervals. The peak trending during the course of the reaction showed similar profiles as those generated for the first reaction and gave insight into reaction progress. After the reaction, further data analysis was performed on the collected data using exploratory multivariate analy-sis. A multivariate curve resolution alter-nating least squares (MCR-ALS) algo-rithm was applied using the Unscrambler software over the regions of interest that include the product and reactant peaks. By this analysis, a two-component con-centration and two-component spectra were determined, shown in Figure 6. The two component spectra were indicative to the product and reactant spectra. They are consistent with the reference spectra collected of the starting materials and product. The computed profiles plateau about midway through the reaction data collection, indicating that it was actually over in half the time, around 1 h. The experiments were repeated and validated using TLC to confirm the reaction com-pletion in 1 h. The Raman spectroscopic monitoring of the reaction provided in-formation on the reaction progress, and resulted in reducing the reaction cycle time by half.

ConclusionsRaman spectroscopy for process analyt-ics is an invaluable tool for understanding reactions that have significant benefits for the chemical, pharmaceutical, and other industries. The aim of this contribution was to demonstrate the ability for por-table Raman spectroscopy coupled with univariate and multivariate analysis tools in the process development stage to gain insight and process understanding of chemical reactions, specifically in deter-

mining reaction end points, on a labo-ratory-scale setup. The Raman spectral data was valuable to monitor the reaction qualitatively. These experiments serve as a proof of concept for further development with Raman measurements to move for-ward with at-line or on-line implementa-tion as the chemical process is scaled up to pilot and manufacturing scales. This work demonstrates the versatility and ability of portable Raman spectrometers and their utility in process development and understanding.

References(1) Process Analytical Technology:

Spectroscopic Tools and Implementation

Strategies for the Chemical and

Pharmaceutical Industries, 2nd Edition, K.A.

Bakeev, Ed. (John Wiley & Sons, Ltd., West

Sussex UK, 2010).

(2) US Food and Drug Administration,

Guidance for Industry: PAT — A

Framework for Innovative Pharmaceutical

Development, Manufacturing, and Quality

Assurance (FDA, Rockville, Maryland, 2004).

(3) J.B. Slater, J.M. Tedesco, R. Fairchild, and

I.R. Lewis, in Handbook of Raman

Spectroscopy, I.R. Lewis and H.G.M.

Edwards, Eds. (CRC Press, New York, New

York, 2001).

(4) G.L. Reid et al., Am. Pharm. Rev. June 20

(2012).

(5) X. Chen et al., Org. Process Res. Dev. 19(8),

995–1003 (2015).

Thomas Padlo and Katherine Bakeev are with B&W Tek in Newark, Delaware. Direct correspondence to: [email protected] and [email protected] ◾





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Reusable shop towels are a com-mon practice in the automo-tive industry. The automotive

shops typically use a laundry ser-vice to clean their soiled towels. This commonly used article can pose an unknown health hazard. Previous studies have shown that towels can retain metals even after laundering. Long-term exposure to certain metals, such as lead, can potentially result in health issues to employees using the towels. Laun-dered shop towels were collected from local automotive shops and analyzed to assess the ability of a portable handheld X-ray f luores-cence (XRF) instrument to measure harmful contaminants, such as lead, in the towels. The handheld XRF proved to be a valuable tool because

of its portability, speed, ease of use, and nondestructive nature.

ExperimentalFor this study, 45 laundered shop towels were collected from 16 local automotive shops in the Atlanta, Georgia, area. New unused Utopia auto shop towels were purchased through Amazon for control pur-poses. The samples were analyzed using a Thermo Scientif ic Niton XL3t 970 GOLDD+ handheld XRF analyzer for 60 s. The Consumer Goods internal calibration model that was provided with the spec-trometer was used for quantitative purposes. The towels were folded lengthwise and then tightly rolled into a tube and analyzed in three lo-cations. The same towels were then

Analysis for Lead in Laundered Shop Towels Using Handheld X-ray Fluorescence SpectroscopyMany automotive shops use a laundry service to clean their soiled shop towels. Previous studies have shown the towels can retain met-als even after laundering, and long-term exposure to certain met-als, such as lead, could potentially result in health issues to employ-ees using the towels. Laundered shop towels were collected from local automotive shops and analyzed to assess the ability of X-ray fluorescence (XRF) spectroscopy using a handheld system to mea-sure harmful metal contaminants, such as lead, in the towels.

Kyle W. Scott and Wade R. Thompson


reverse rolled to change the exposed surface of the towel and were ana-lyzed three more times for a total of six data points per towel. The re-ported results are an average of the six data points. Selective samples were also analyzed by inductively coupled plasma–optical emission spectrometry (ICP-OES) at Pro-Quality Labs in Cartersville, Geor-gia, to verify the results obtained with the handheld XRF analyzer.

Results and DiscussionReusable shop towels are a staple item in the automotive service in-dustry and other machinery-focused manufacturing and repair businesses. Shop towels (durable cloth wipers) are commonly used to spread clean-ers and absorb or remove oil, grease, and dirt. They are routinely used for a wide variety of applications in-cluding wiping engine or mechanical parts and for cleaning work surfaces. These types of industries commonly use a laundry service to clean their soiled shop towels. The laundering process can take shop towels from several different locations and indus-tries and combine them to wash the towels in bulk. The towels are then randomly redistributed back to their customers. If all the dirt and grime is not removed during the cleaning process, the residual contaminants from one industry can then end up on a laundered shop towel used by workers in a different industry. In ad-dition to the random redistribution of the towels, there is also the pos-sibility that as the towels are washed a portion of the dirt and grime is released and redeposited, cross-con-taminating the laundered towels. In

either case, commingling the shop towels from several different loca-tions and industries in the launder-ing process may result in hazardous heavy metals in one industry coming in contact with workers in another industry.

Although the ubiquitous red shop towel is a staple item in automotive shops, very little research has been done to characterize the trace met-als in the towels. A couple of stud-ies have investigated the hazards of using shop towels (1–3). The first two studies focused their efforts on estimating the oral intake of heavy metals from using the towels and the potential long-term exposure risks associated with using the towels. The third study measured the concen-tration of metals in synthetic sweat leachate from laundered shop towels. These measurements were used to do a risk assessment for the transfer of lead and other metals from the tow-els to the users.

The objective of this study was to better understand the scope of heavy metal contamination in laundered shop towels that are used in the au-tomotive industry using a portable handheld XRF analyzer. The hand-held XRF analyzer was chosen for this study for its portability, ease of use, and ability to rapidly analyze several samples with nominal sample preparation. The concept of using a handheld XRF spectrometer for this study was taken with some skepti-cism because of the porous nature of the shop towels. A denser substrate is typically desired because it provides a better signal from the sample, re-sulting in lower detection limits and better reproducibility. Consequently,


selective samples were also analyzed by ICP-OES to determine if the re-sults obtained by the handheld XRF were acceptable.

The laundered towels were col-lected from the 16 automotive shops, and although the towels had been

“cleaned” they were stained, con-tained metal and polymer fragments, and had a petroleum odor. Initial analysis of the towels identified nine metals at levels over 50 ppm includ-ing Ba, Fe, Sb, Cr, Ni, Ti, Cu, Pb, and Zn. Of these, lead, copper, and anti-mony were above what was reported to be a safe threshold limit based on previously published data (1,2). These previous studies concluded that lead posed the highest risk to an employee’s health because even at low concentrations in the blood it has been associated with impaired kidney function, high blood pressure, nervous system and neurobehavioral effects, and cognitive dysfunction later in life that might be caused by long-term, low-level exposure like that encountered with routine use of laundered shop towels (2). The majority of the antimony that was detected in the laundered shop tow-els most likely came from the fibers

used in the construction of the shop towels and was not from residual dirt and grime from prior use. Several of the laundered and new shop towels were analyzed by Fourier transform infrared (FT-IR) spectroscopy. These results confirmed the towels were a blend of cotton and polyester fibers. Antimony is commonly used as a catalyst in the production of poly-ester terephthalate and is the likely source of the antimony detected by the handheld XRF spectrometer. The source of the copper was attributed to the general use of the shop towels and likely represents residual metal particles in the towel because of use.

Figure 1 shows the XRF results for lead from the 48 towels that were analyzed. All of the laundered tow-els tested, except two, contained a detectable amount of lead. The amount of lead in the laundered tow-els ranged from nondetectable to in excess of 70 ppm. Coincidently, the two towels that did not test positive for any lead came from the same au-tomobile repair shop. All the towels from that particular location had significantly less lead than the tow-els from the other automotive shops and both towels looked relatively

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Figure 1: XRF data for lead from laundered automotive shop towels. Codes A–P = clean laundered shop towels from different automotive shops; Q = unused, nonlaundered shop towels; R = unused disposable Wypall shop towels; numbers = individual shop towels from respective automotive shops.


new. These two towels were not as threadbare as the towels obtained from the other repair shops. The new unlaundered shop towels and the unused disposable Wypall towels were also lead free. While inorganic lead does not readily enter the body through the skin, it can enter the body through accidental ingestion (eating, drinking, and smoking) via contaminated hands, clothing, and surfaces through hand to mouth transfer. Consequently, automotive mechanics and other people using similar laundered shop towels may inadvertently subject themselves to long-term exposure risks associated with lead and other heavy metals by using laundered shop towels (1).

The XRF data suggest that the lead contamination in the laundered shop towels is a ubiquitous contaminant and not shop dependent. A poten-tial source of the contamination could be cross-contamination dur-ing the laundering process. To verify this, lead-contaminated towels were

washed with new unused shop towels. Towels were impregnated with lead and lead oxide using a Mini Martin-dale abrasion and pilling tester. Ap-proximately 0.1 g of lead was rubbed into the towels using a random pat-tern and a cycle time of 50 with a 9-kPa pressure. Six towels containing the lead were washed with six new towels and six bath towels, which were used for bulk. An industrial laundry detergent that is designed to lift and suspend a wide range of metal particles, dirt, grease, and grime was used to clean the towels. The laundered towels were allowed to air dry before analysis. Approxi-mately 90% of the lead was removed from the contaminated towels by the laundering process. This implies that a significant amount of the lead particles were retained within the towels even after laundering. This lead retention was also evidenced by the discoloration of the towels after laundering. Lead analysis of the un-used laundered towels were found to

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contain 130–250 ppm of lead. Based on the levels of lead detected in the unused laundered towels, it is likely that more lead was used for this study than what is commonly trans-ferred to the towel during routine use. The data do, however, suggest that lead particles can transfer dur-ing the wash cycle, that not all the lead may be removed during wash-ing, and that cross contamination of heavy metals can occur during the laundering process.

To verify that the results obtained with the handheld XRF analyzer were consistent with a primary tech-nique commonly used for metals analysis, selective towels were ana-lyzed by ICP-OES. These results are summarized in Figure 2. Although the lead concentrations that were obtained with the handheld XRF analyzer are slightly, but consistently, overrepresented in the towels, the lead data obtained with the handheld XRF spectrometer were representa-tive of the sample sets.

ConclusionsLead was consistently detected on laundered shop towels from several different automotive shops using a handheld XRF spectrometer. The handheld XRF spectrometer pro-vided the ability to accurately assess the lead concentration in the laun-dered shop towels. The handheld XRF spectrometer also provided the ability to analyze several samples very quickly with little or no sample preparation and without destroying the sample. The presence of heavy metals in reusable shop towels is likely caused by residual material left behind even after the towels are laun-

dered. The data also suggest that the contaminants can transfer between the towels as they are washed.

It should be noted that laundered shop towels have been used for de-cades and have likely always con-tained heavy metal contaminants. Furthermore, there have been no reported adverse effects associated with using laundered shop towels (3). The data, however, confirm that heavy metals are present in the tow-els and the metal contaminates are not fully removed and can transfer to other towels during the launder-ing process. This lingering metal contamination could create the po-tential for unknown long-term ex-posure risks for workers using laun-dered shop towels.

References(1) L. Beyer, M. Seeley, and B. Beck, INJ.

Winter, 23–36 (2003).

(2) L. Beyer, G. Greeberg, and B. Beck.

Hum. Ecol. Risk Assess. 20, 111–136

(2013)

(3) K. Connor and B. Magee. Regul. Toxicol.

Pharmacol. 70, 125–137 (2014).

Kyle W. Scott is a product engi-neer with Kimberly-Clark Corporation in Neenah, Wisconsin. Wade R. Thompson is the Technical Leader with Kimberly-Clark Corporation in Roswell, Georgia. Direct correspondence to: [email protected] ◾





The GeoHeuristic Operational Strategies Testing (GHOST) project was developed to test

various rover science operational protocols, and determine a “best practice” site investigation method for future rover missions (1). Rather t ha n usi ng mecha nica l rovers or replica Mars instrumentation (for example, see reference 2), the

GHOST program uses human “rov-ers” and off-the-shelf field-portable instruments to isolate the variables of the scientific decision-making process. This approach eliminates the need for mission-specif ic in-strumentation, communication and data relays, and complex engineer-ing requirements, while still pro-viding the same basic information,

Field-Portable VNIR Spectrometry: Applications for Mars Rover Operational Strategies Testing at Terrestrial Analog SitesVisible–near infrared (VNIR) spectroscopy provides a wealth of compo-sitional information and is a valuable tool in planetary exploration. The 2016 GeoHeuristic Operational Strategies Testing (GHOST) program is a terrestrial analog rover simulation designed to refine Mars Rover operational strategies. The GHOST program used a handheld VNIR spec-trometer to simulate the function of the Mars Science Laboratory (MSL) ChemCam and Mars 2020 rover SuperCam. Commercially available instrumentation was used to eliminate engineering, communication, and mission-specific specifications, and allow the GHOST team to focus solely on investigative protocols. The portable spectrometer allowed for rapid data acquisition of in situ outcrops, similar to the data gathered by Mars rovers, and allowed the instrument operator to rapidly traverse the field site, maximizing the number of data points gathered for the science teams.

Sarah R. Black, R. Aileen Yingst, and Brian M. Hynek


such as mineralogy. The primary objective of the 2016 GHOST field season was to assess the efficiency and effectiveness of the traditional linear approach—a single path with no backtracking, commonly used by rover invest igat ions—versus a walkabout methodology, where the site is initially reconnoitered remotely and this information is used to choose sites for more de-tailed interrogation. Although it is potentially longer, the walkabout method allows the science team to conduct an initial assessment of the field site to determine the best pos-sible areas of interest for further in-depth analysis.

Field operations focused on a 2.6-km2 (1 mi2) region of the Utah desert, roughly 20 km southeast of Green River, Utah. The region pri-marily consists of sandstone, mud-stone, and limestone—representing

a groundwater-fed lake system (3)

that is similar to formation models proposed for the Mawrth Vallis re-gion of Mars (4–8). Limestone cap-ping units with preserved microbial mat structures are also present at the site (3). The location was chosen because of the presence of similar sedimentary deposits on Mars (4–8), as well as the preserved biological structures—the identif ication of which will be a primary goal of fu-ture Mars missions (see reference 9 for more information). Before the field campaign, the GHOST team used Mars analog remotely sensed datasets (Figure 1) to plan the rover t raverse pat h a nd prel iminar y waypoints for in situ investigation. Visible images were analogous to those collected by the Mars Recon-naissance Orbiter (MRO) Context Camera (CTX) and High Resolu-tion Imaging Science Experiment

Dolomite Sericite and (or) smectite

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(HiR ISE) cameras, with resolu-tions of 6 m/pixel and 30 cm/pixel, respectively (10,11). Visible–near infrared (VNIR) spectrometry data were analogous to MRO’s Compact Reconnaissance Imaging Spectrom-eter for Mars (CRISM) instrument, with a resolution of 18 m/pixel (12).

InstrumentationThe VNIR instrument used for the rover analog investigation was the TerraSpec Halo field-portable VNIR spectrometer from ASD/PANalyti-cal. The spectrometer is a contact probe that measures ref lected light from 0.35 mm to 2.5 mm with a 6 nm/band spectral resolution (13). It was deployed in this study as a field-portable stand-in for the Mars Science Laboratory (MSL) Chem-Cam and Mars 2020 SuperCam instruments . Bot h rover-based VNIR spectrometers sample select

windows were within the 0.35 mm to 2.5 mm range of terrestrial field VNIR spectrometers (14–16). The primary difference between the im-aging systems used on Mars and our experimental simulation is Chem-Cam and SuperCam’s ability to sam-ple a complete scene via the passive sampling function (14,16) (Figure 2a), while the field-portable spec-trometer operates solely as a con-tact probe—sampling an area with a diameter of 1 cm (Figure 2b). The MSL ChemCam instrument is also capable of point analysis through its laser-induced breakdown spectros-copy (LIBS) function. Although this method also returns compositional data, it operates by vaporizing the sample and sampling the emitted light as the excited electrons return to their ground state, rather than by measuring the ref lected light in the VNIR spectral window (14).

(a) (b)

Figure 2: (a) A ChemCam Remote Micro Imager (RMI) scene (CR0_439663426PRC_F0240312CCAM02475L1; NASA/JPL-Caltech/LANL) superimposed on a MastCam image (0475MR0018870000302888E02_DXXX; NASA/JPL-Caltech/MSSS) of the base of Mount Sharp, Gale Crater, acquired on sol 475 of the Curiosity Rover mission. VNIR reflectance of the area within the circle was sampled via ChemCam’s passive function. (b) Sampling site with the field-portable VNIR spectrometer. Sampled area, “Quinn,” is within the circle. Inset: The field-portable spectrometer in action.


MethodsOrbital data were used to develop hypotheses about the field site and plan notional traverses for both the walkabout and linear science teams. The two science operations teams (linear and walkabout) were given three days to remotely inves-tigate the field site by sending their respect ive human “rovers” and human-operated instruments out to gather data, while the science teams remained at mission control—the base camp where the science teams were sequestered—unable to see the field site in person. A third team of geologists also surveyed the field site as a control. The one field spectrometer deployed for this study gathered data for both rover science teams and the team of ge-ologists simultaneously. The spec-trometer was calibrated and white-referenced before each sample was gathered, and the resulting spectra

were checked before leaving the out-crop to ensure that high data quality was maintained throughout the in-vestigation. Upon returning to mis-sion control, data were downlinked and processed using ITT Exelis’ En-vironment for Visualizing Images (ENVI) software. Field spectra were matched to reference spectra from commonly used spectral libraries such as the United States Geologi-cal Survey (USGS) spectral library (17) and the CRISM Analysis Tool-kit (CAT) (18,19).

ResultsA tota l of 43 spectra were gath-ered over the course of the vari-ous science team investigations of the f ield site. Each team worked independently of the others, and collected spectra where they felt it was necessary or would aid them in their scientific interpretations. A total of 21 spectra were gathered

Wavelength (μm)

A

B

C

Va

lue

(O

ffse

t fo

r cl

ari

ty)

Amorphous silica

Calcite

Montmorillonite

Desert varnish

Hematite

0.5 1.0 1.5 2.0 2.5

Figure 3: A heavily weathered limestone outcrop, with spatially varied weathering products. Samples A, B, and C all contain montmorillonite, amorphous silica, and calcite. Spectral variations at the short wavelengths likely correspond to Fe, Mg, and Mn weathering products, such as hematite, desert varnish, and Mg-bearing smectites.


for the linear science team, 15 for the walkabout science team, five for the ground-truthing team of geolo-gists, and two additional spectra were shared between all groups. All samples were collected in situ, with roughly half of the spectra gathered on the first day, and the remainder on days 2 and 3. The instrument operator covered between 10.5 and 13.7 km each day by executing multiple deployments into the field and the subsequent data return to mission control. In locations with highly varied weathering patterns, the team simulated the scene sam-pl ing abi l it y of ChemCam and SuperCam by gathering multiple spectra within one outcrop—with a focus on color and texture varia-tions (Figure 3). Identified miner-alogy included montmoril lonite, kaolinite, illite, nontronite, general

smectite, calcite, dolomite, gypsum, amorphous silica, hematite, Fe3+, Fe2+, and Mn-oxide (desert varnish) (Figure 4). VNIR mineralogy was confirmed in locations where the science teams gathered additional chemical data via the portable X-ray diffraction (XRD) instrument that was also operated as part of this analog study.

DiscussionThe f ield-portable spectrometer was a valuable addition to this ter-restrial analog study of Mars rover science team methods. The instru-ment’s portability allowed the op-erator to rapidly traverse the field site for data collection and return to mission control. This swift turn-around gave the science teams the data they required to plan their next investigative steps, with minimal

Dolomite

In-situ sampling results:

Sericite and (or) smectite

Sericite + chlorite or Fe/Mg sericite

Hydrous silica

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1

2

3

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15 X

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X X

X

X

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X

X

X X X

X

X

X

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X

X

X

X

X

X

X

X

X

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X

Lo

cati

on

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ntm

ori

llo

nit

e

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ect

ite

No

ntr

on

ite

Illi

te

Ka

oli

nit

e

Ca

lcit

e

Do

lom

ite

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sum

Un

ide

nti

fie

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ulf

ate

He

ma

tite

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ati

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oate

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uart

z

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eth

ite

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rt v

arn

ish

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2+

Fe

3+

Am

orp

ho

us

SiO

2

XX

X X

X

XX

X

XXXX

X X X

0.25 0.5

Kilometers

1

Carbonate

Ferrous or coarse-grained ferric iron

Figure 4: In situ mineral identifications compared to the remotely sensed data available to the science teams before the mission. In situ data provide far greater detail and reveal more diverse mineralogy than what can be gathered from orbit.


wait time. Additionally, the instru-ment operator was able to scale large scree slopes and reach out-crops that a rover may not be able to physically reach, but could still in-terrogate with remote instruments. Where mineralogical variations oc-curred across an outcrop, multiple spectra were gathered in the same way that the current MSL team col-lects multiple ChemCam targets to sample compositional heterogeneity.

Although the science teams had access to remotely sensed VNIR data, the field-portable VNIR pro-vided a wealth of detail that could not be g leaned f rom the much coarser orbita l data (Figure 4). Orbita l VNIR shows carbonates (general carbonate and dolomite), various smectites, Fe2+, Fe3+, and hydrated silica throughout the re-gion. However, in situ sampling suggested a more diverse mineral-ogy with a wide variety of phyllosil-icates, Fe- and Mn-oxides, and sul-fates. Additionally, hydrated silica, various smectites, and calcite were shown to be ubiquitous throughout the field area rather than confined to specific units, as suggested by the orbital data. These mineralogical variations are significant because such geochemical differences con-tain clues regarding whether a geo-logic environment was habitable or capable of preserving evidence of prior life. Without the in situ VNIR data, the science teams would have missed critical information for their mission and interpretations.

Although data analysis requires a familiarity with VNIR spectra and processing software, the in-strument’s ease of use would allow

another minimally trained team member to gather spectra while the VNIR expert remained at mis-sion control to process returned samples. One instrument opera-tor gathering and processing data for three separate science teams simultaneously often led to a lag between each science team’s deploy-ment of the instrument and getting the mineralogical results. A two-person approach—with one instru-ment operator collecting spectra in the f ield and a separate VNIR expert processing returned data at mission control—would result in a faster sample turnaround time, and would allow the science teams to make more efficient use of their limited time at the field site.

ConclusionThe 2016 GHOST rover science teams were able to gather and use the desired data for their investiga-tion of the field site, and continued to refine their operational methods for future Mars rover exploration with minimal equipment delays. Additional ly, the dif ferences be-tween the terrestrial f ield instru-ment and the rover instrumentation did not negatively affect the science team operations or the investigation of operational methods. Although the f ield-portable spectrometer is not an exact replica of the MSL ChemCam or the Mars 2020 Super-Cam, the successful geologic inves-tigation of the field site conducted by the science teams suggests the f ield-portable VNIR instrument is a suitable stand-in for rover-mounted VNIR in this terrestrial analog study.


References(1) R.A. Yingst, B.A. Cohen, B.

Hynek, M.E. Schmidt, C.

Schrader, and A. Rodriguez, Acta

Astronaut. 99, 24–36 10.1016/j.

actaastro.2014.01.019 (2014).

(2) D. Eppler et al., Acta Astronaut.

90, 224–241 10.1016/j.

actaastro.2012.03.009 (2013).

(3) S. Potter-McIntyre, “Diagenetic

Alteration and Concretion Formation

in the Jurassic Brushy Basin Member

of the Morrison Formation, Colorado

Plateau, USA: An Analog Study for

the Yellowknife Bay Formation at

Gale Crater, Mars,” presented at

the Geological Society of America

Annual Meeting, Vancouver, British

Columbia, Canada, 2014.

(4) J.-P. Bibring et al., Science 312, 400–404, 10.1126/science.1122659

(2006).

(5) J.L. Bishop et al., Science

321(August), 830–834 (2008).

(6) W.H. Farrand, T.D. Glotch, J.W. Rice,

J.A. Hurowitz, and G.A. Swayze,

Icarus 204(2), 478–488, 10.1016/j.

icarus.2009.07.014 (2009).

(7) B.L. Ehlmann et al., Nature 479, 53–60 10.1038/nature10582

(2011).

(8) N.K. McKeown et al., J.

Geophys. Res. 114(E00D10)

10.1029/2008JE003301 (2009).

(9) K. Farley, “Mars 2020 Landing Site

Considerations,” presented at the

Mars 2020 Landing Site Workshop,

Washington D.C., 2014. (10) A.S. McEwen et al., J.

Geophys. Res. 112(E05S02),

10.1029/2005JE002605 (2007).

(11) M.C. Malin et al., J. Geophys.

Res. 112(E05S04),

10.1029/2006JE002808 (2007).

(12) S.L. Murchie et al., J.

Geophys. Res. 114(E00D07),

10.1029/2009JE003344 (2009).

(13) ASD Inc. TerraSpec HALO User

Manual: ASD Document; 2015.

(14) S. Maurice et al., Space Sci. Rev.

170(1–4), 95–166, 10.1007/s11214-

012-9912-2 (2012).

(15) S.M. Clegg et al., “Remote

Geochemical and Mineralogical

Analysis with SuperCam on

the Mars 2020 Rover and on

Earth” presented at the American

Geophysical Union, Fall Meeting,

San Francisco, 2014.

(16) J.R. Johnson et al., Icarus 249, 74–

92, 10.1016/j.icarus.2014.02.028

(2015).

(17) R.N. Clark et al., USGS digital

spectral library splib06a; U.S.

Geological Survey, Digital Data

Series 231, 2007.

(18) C.E. Viviano-Beck, MRO CRISM Type

Spectra Library, NASA Planetary

Data System, 2015.

(19) PDS Geosciences Node; CRISM

Spectral Library Working Group

CRISM Spectral Library, 2014.

Sarah R. Black and Brian M. Hynek are with the Department of Geological Sciences and the Laboratory for Atmospheric and Space Physics at the University of Colorado Boulder in Boulder, Colorado. R. Aileen Yingst is with the Planetary Science Institute in Tucson, Arizona. Direct correspondence to: [email protected] ◾





C arbon nanotubes (CNTs) are a relatively new class of nano-materials first discovered by

Iijima (1). The unique electrical and mechanical properties of CNTs make them one of the most attractive build-ing blocks for multiple nanomaterials designed for various applications. The typical applications include but are not limited to nanoelectronics and nanomechanics, ultrastrong yarns, interconnects, batteries and capaci-tors, and aerospace and defense (2,3).

As a result, the global CNTs and CNT-based product market is projected to reach 5.64 billion US dollars by 2020, with an annual growth rate (CAGR) of 20.1% (4).

Manufacturing, modif ication, functionalization, and customization of the CNT-based materials require bulk quantities of well-defined CNTs, with a high degree of structural purity. Since tight control of the CNT sizes, chirality, and structural quality still remains one of the major challenges

Carbon Nanotube Characterization and Quality Control Using Portable Raman: 532-nm Versus 785-nm Laser ExcitationRecent growth in commercial carbon nanotube (CNT) production resulted in a strong demand for analytical techniques capable of rapid characteriza-tion of these nanomaterials. The latest developments in portable Raman spectroscopy made this technique one of the most viable options for the task. This paper compares the relative performance of 532- and 785-nm portable Raman systems, as well as demonstrates an automated analytical methodol-ogy suitable for CNT characterization and quality control applications. Both 532- and 785-nm Raman spectra were used to directly analyze and compare important CNT structural parameters and properties including CNT diam-eters, diameter distributions, CNT structural quality (% of defects), CNT types, and other properties. The data indicate advantages in a number of areas for using 532- versus 785-nm excitation for CNT Raman measurements.

Aleksandr V. Mikhonin, Laurence A. Nafie, and Rina K. Dukor


during CNT fabrication (3), analytical techniques capable of thorough CNT characterization have become a criti-cal part of the manufacturing process.

Several techniques are currently used for CNT characterization, in-cluding electron microscopy, Raman spectroscopy, thermogravimetric analysis (TGA), and optical absorp-tion spectroscopy. However, Raman spectroscopy has become increasingly attractive for CNT characterization because of its intrinsic ability to detect even small changes in CNT structure, its low analysis cost, little or no sample preparation required, and dramati-

cally improved analysis efficiency due to recent engineering breakthroughs in Raman instrumentation.

CNT Raman spectra can provide information about the CNT diameter, chirality, structural quality, conductor or semiconductor character, crystal-linity, and degree of functionalization (5–8). A brief introduction to major Raman features of carbon nanotubes is given below.

The radial breathing mode (RBM) band represents radial expansion–contraction of a nanotube. This is a very important band because it is a marker of the presence of single-wall

Ram

an

in

ten

sity

(a.u

.)


RBM-band(s)D-band

0 500 15001000 2000 3000 40002500 3500

G-band

G’-band (D-band overtone)

Figure 1: 532-nm Raman spectrum of carbon nanotubes measured at 30-mW laser power. This spectrum contains all of the important CNT Raman bands, which can be used for CNT characterization (see text for detailed discussion).

Ram

an

in

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sity

(a.u

.)


RBM-band(s)

D-band

0 200 400 600 200 1200 1400 1600 18001000 2000 2200

G-band

Figure 2: 785-nm Raman spectrum of carbon nanotubes measured at 30-mW laser power. Low RBM/G-band intensity ratio combined with high D/G band intensity ratio may indicate that the 785-nm laser partially destroys CNTs at the laser power density used in this study (see the text for detailed discussion).


carbon nanotubes (absent in the spec-tra of essentially all other graphite-like materials). The RBM band frequency can also be directly related to CNT diameter (7,8).

The G-band is a major Raman band in graphite-like materials rep-resenting a tangential shear mode of carbon atoms. In CNTs, this mode has two major contributions, G+ and G- (7). The shape of the G-band and

its frequency profile can be used to es-timate whether the CNT is “metallic” or semi-conducting; or roughly esti-mate the nanotube diameter.

Because of Raman selection rules, the D-band is a forbidden Raman mode in ideal nanotubes and requires a defect to show up in the Raman spectra (7). Thus, the D/G band ratio can directly serve as a measure of CNT structural quality.

Ram

an

in

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sity

(a.u

.)

Raman shift (cm–1) Raman shift (cm–1)

Raman shift (cm–1)Raman shift (cm–1)

CNT diameter (nm)

CN

T d

iam

ete

r d

istr

ibu

tio

n (

%)

RB

M f

req

uen

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istr

ibu

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n (

%)

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(a.u

.)

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T d

iam

ete

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istr

ibu

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%)

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req

uen

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istr

ibu

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%)

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(a) (b)

(d)

(f)(e)

(c)

00.8

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55

1 1.2 0.8 1 1.2

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15

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250200

532 nm

532 nm

532 nm

785 nm

785 nm

785 nm

300250

200 300250 300250

400

600

800

200

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10000

5000

Figure 3: Deconvolutions of 532- and 785-nm Raman RBM band profiles to obtain population distributions of CNT diameters in a particular CNT sample. Calculated using Ram-NANO Software. (a) and (b) 532- and 785-nm RBM band profile of a CNT sample “as is;” (c) and (d) frequency deconvolutions of 532- and 785-nm RBM band profile using homogeneous linewidth (HWHH), Γ = 6 cm-1; (e) and (f) population distributions of CNT diameters as obtained from 532- and 785-nm Raman spectra, respectively. See the text for detailed discussion.


The G′-band is the first overtone of the D-band. However, unlike the D-band, it does not require a defect to show up in Raman spectra. The G′-band can be used, in particular, to probe CNT electronic structure and independently verify CNT chi-rality (7).

In this article, the relative perfor-mance of 532- and 785-nm portable Raman spectrometers was compared for CNT characterization and quality control applications. In addition, an analytical methodology is presented that enables the calculation of CNT structural parameters directly from measured Raman spectra.

InstrumentationCarbon nanotube samples were ex-amined using the RamTest-NANO and Mobile μ-Raman analyzers with Raman technology (both from BioTools, Inc.). The RamTest-NANO analyzer is a handheld unit with 532-nm laser excitation, ~120–4000 cm-1 spectral range, 4–6 cm-1 spectral reso-lution, and a maximum laser power of 30 mW. The Mobile-μ-Raman ana-lyzer is a portable unit with 785-nm excitation and variable laser power within a 0–100 mW range. A high resolution (HR) configuration of the Mobile μ-Raman analyzer was used in this study, with ~200–2000 cm-1 spec-tral range and ~4 cm-1 resolution to match the resolution of the RamTest unit.

The RamTest unit was run in au-tomated mode (requiring no prior knowledge of Raman spectroscopy), where all measurement parameters are automatically adjusted by the in-strument to optimize signal-to-noise ratio and minimize f luorescence,

with the remaining f luorescence background (if any) automatically subtracted.

The Mobile μ-Raman analyzer was run in a mode where all measurement parameters were manually set before the measurement. The laser power was set to 30 mW to match that of the RamTest unit.

Data AnalysisBioTools’ Ram-NANO sof tware add-on is capable of calculating the following CNT parameters or prop-erties: structural quality (percent of structural defects), CNT diameter distribution, type of nanotube, and several other parameters. This soft-ware add-on is directly applicable to both RamTest-NANO and Mobile μ-Raman data to enable automated CNT Raman data processing, and a PDF or Excel report generation.

Technical Results532 and 785 nm Raman

Spectra of Carbon Nanotubes

Figures 1 and 2 show 532- and 785-nm Raman spectra of CNTs recorded at identical laser powers of 30 mW at both wavelengths, respectively. There are four major Raman bands observed for the CNT samples measured dur-ing this study: RBM bands around 190–320 cm-1, D-band at ~1320–1370 cm-1, G-band at ~1580 cm-1, and G′-band at ~2700 cm-1 (out of instrument spectral range for the 785-nm Raman spectrum shown in Figure 2). The meaning of these CNT Raman bands has been extensively described in the literature (5–8).

There are three major observa-tions from Figures 1 and 2. First, the 532-nm Raman spectrum shows a


much greater RBM/G band intensity ratio than the 785-nm Raman spec-trum. Second, the D/G band intensity ratio is much smaller for the 532-nm Raman spectrum than that for the 785-nm Raman spectrum. Third, the G-band of the 532-nm Raman is fairly symmetric, whereas the 785-nm G-band shows a distinct high-frequency shoulder.

Since the RBM band is indicative of CNT presence and the D-band is a measure of fraction of structural defects in CNTs, these observations may indicate that a 785-nm laser par-tially destroys CNTs, at least at the laser power density used in this study. The increased heating or destruction of the CNTs by 785-nm light (versus 532-nm light at an identical power of 30 mW) can likely be explained by the increased absorption of the 785-nm light by these particular CNT samples. A further decrease of laser power helps to reduce the 785-nm laser-induced CNT damage (data not shown). However, it also results in reduction of the 785-nm Raman signal and, therefore, the necessity to increase measurement time.

Thus, the preliminary conclusion is that both 532- and 785-nm Raman can be used for CNT characteriza-tion. However, care must be taken to ensure that relatively delicate CNT samples are not being overheated or even destroyed during analysis.

Homogeneous Linewidth of the RBM

Raman Band of an Isolated CNT

The RBM band profile of CNT sam-ples measured in this study is fairly broad and spans the ~190–320 cm-1 spectral region (Figures 3a and 3b). Since the RBM frequency is related

to CNT diameter (7,8), the observed RBM frequency span is likely to re-sult from a subset of CNTs with dif-ferent diameters. Thus, if we knew the homogeneous linewidth of the CNT RBM band, we could directly estimate the CNT diameter population distri-butions directly from the RBM band profile.

Fortunately, Raman spectra of iso-lated single-wall CNTs were reported in the literature (8), and their RBM bandwidths are in the range from 9 to 14 cm-1 full width at half height (FWHH). These RBM bandwidths are wider than the resolution of both the 532- and 785-nm portable Raman instruments used in this study (~4 cm-1). Thus, for the sake of simplic-ity, we can take a “rough average” of the reported values, and estimate the RBM band homogeneous linewidth of an individual CNT with a particular diameter, Γ, as ~6 cm-1 half-width at half-height (HWHH).

Deconvolution of

RBM-Band Frequency

Using the ~6 cm-1 homogeneous line-width of a single isolated CNT with a particular diameter, and assum-ing that Raman cross-sections of all CNT conformations are independent of their frequencies, we can estimate the population distribution of each CNT conformation directly from the RBM band profiles using a method similar to that reported by Asher and colleagues (9). We assume that an inhomogeneously broadened RBM band profile, RBM(ν) is a sum of N Lorentzians with identical intensities and homogeneous linewidths Γ = 6 cm-1, occurring at different central frequencies ν = νi

0.


RBM(v) = π–1 \∑ P

i

\

+(v–v0i)2

N

i=1

Γ2

Γ2

[1]

where Pi is a probability for a band to occur at frequency νi

0.The resulted histograms of fre-

quency populations, Pi, are shown in Figures 3c and 3d for 532- and 785-nm Raman RBM bands, respectively.

CNT Diameter Distributions

Obtained Directly from

532- and 785-nm Raman Spectra

CNT diameter can be estimated from RBM band frequency using the fol-lowing relationship (8):

220d

CNT

νRBM +14=

∼ [2]

where νRBM is the RBM band fre-quency in cm-1 and dCNT is the CNT diameter in nm.

Using equation 2, we can con-vert the RBM frequency population distributions into CNT diameter population distributions, as shown in Figures 3e and 3f. These diameter distributions ref lect the percentage of CNTs with their diameters falling into a specified diameter interval and, thus, can be directly utilized for qual-ity control purposes.

Figure 3 demonstrates that RBM band profiles and the resulting CNT diameter distributions obtained from the 532-nm Raman spectra differ from those obtained from the 785-nm Raman spectra. This is despite the fact that both 532- and 785-nm Raman measurements were taken on the identical CNT samples, and using the same laser power of 30 mW on both 532- and 785-nm instruments.

Theoretically, the difference in di-ameter distributions obtained from

the 532- and 785-nm Raman spectra could have been explained by selec-tive resonance enhancement of dif-ferent subsets of CNTs by 532- and 785-nm light, because of the differ-ent resonance Raman conditions (5). However, as it was already discussed above, comparison of 532- and 785-nm relative Raman intensity ratios for RBM, D, and G bands indicates that, most likely, the 785-nm laser at 30-mW power partially destroys the CNTs (in contrast to 532-nm laser used at the same power). If this con-clusion is correct, then Figure 3 may indicate that the 785-nm laser se-lectively destroys CNTs with higher diameters.

ConclusionsBoth 532- and 785-nm portable Raman instruments can be used for CNT characterization and quality control applications. Specifically, CNT di-ameters, diameter distributions, and structural quality (percent of struc-tural defects), other important param-eters, and multiple controls plots can be directly obtained or generated from 532- and 785-nm Raman spectra using the automated analytical methodology demonstrated in this paper.

The differences observed between 532- and 785-nm Raman spectra of identical CNT samples can likely be explained by partial destruction of CNT samples by the 785-nm laser, at the laser power used in this study. Al-ternatively, these differences can be ex-plained by selective resonance Raman enhancement of different subsets of CNTs by 532- and 785-nm lasers, re-spectively (5).

Even though multiple Raman excita-tions are still preferable for complete


characterization of CNTs, the follow-ing techno-economic benefits are iden-tified for 532-nm as opposed to 785-nm portable Raman, if, for any reason, an end-user decides to use only one Raman wavelength for CNT analysis:t� A 532-nm laser at 30-mW power does

not readily destroy CNTs examined here, whereas the 785-nm laser must only be used at a significantly lower power. This can likely be explained by increased absorption of the 785-nm light by these particular CNT samples.t� The Raman signal at 532-nm excita-

tion is approximately fivefold stron-ger than that at 785 nm, per unit laser power. This is because Raman signal is ~(1/λEX)4, where λEX = 532 nm, 785 nm, and so forth (10). Thus, 532-nm portable Raman units are funda-mentally capable of approximately five times faster analysis or improved analysis quality. In addition, in the case of especially delicate samples, the 532-nm Raman analyzers can be used at up to approximately fivefold lower laser power than the 785-nm based units, respectively, without compro-mising spectral analysis quality. t� The 532-nm Raman technique

provides a superior combination of spectral resolution and spectral range (compared to the 785-nm Raman technique) to enable superior CNT structural characterization. This is because the use of 532-nm light provides an engineering ad-vantage in space-limited portable instruments.t� Quantum efficiencies of best in class

charge-coupled devices (CCD) detec-tors and optics are greater at 532 nm (compared to 785 nm) to even further increase the observed Raman signal in favor of 532-nm excitation.

t� The 532-nm Raman analyzer has a superior performance not only for the analysis of CNTs “as is,” but also for the analysis of CNT suspensions in aqueous solutions, because of the reduced absorption of 532-nm light by water versus that of 785-nm light.

References(1) S. Iijima, Nature 354, 56–58 (1991).

(2) M.F.L. De Volder et al., Science 339, 535–539 (2013).

(3) A. Barreiro et al., Carbon 45, 55–61

(2007).

(4) Data obtained from www.

marketsandmarkets.com.

(5) R. Saito and H. Kataura, in Carbon

nanotubes: Synthesis, structure, properties,

and applications, M.S. Dresselhaus,

G. Dresselhaus, and Ph. Avouris, Eds.

(Springer-Verlag, 2001), pp. 213–246.

(6) A.G. Souza Filho et al., Nanotechnology

14(10), 2043–2061 (2003).

(7) M.S. Dresselhaus et al., Carbon 40, 2043–2061 (2002).

(8) F. Hennrich et al., J. Phys. Chem. B 109, 10567–10573 (2005).

(9) S.A. Asher et al., J. Am. Chem. Soc. 126, 8433–8440 (2004).

(10) Handbook of Vibrational Spectroscopy,

J.M. Chalmers and P.R. Griffiths, Eds. (John

Wiley & Sons, Chichester, 2002).

Aleksandr V. Mikhonin and Rina K. Dukor are with BioTools Inc., in Jupiter, Florida. Laurence A. Nafie is with BioTools Inc., and the Department of Chemistry at Syracuse University in Syracuse, New York. Direct correspon-dence to: [email protected] ◾


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