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4 Raman Technology for Today’s Spectroscopists June 2015

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Articles 8 Raman Dynamic Imaging for the Examination of Chemical Reactions Richard A. Larsen

Raman imaging reveals the reaction process involved in curing cyanoacrylate adhesives. This technique can be applied to studying other surface reactions, from biological membranes to photovoltaic devices.

16 Techniques for Raman Analysis of Lithium-Ion Batteries Dick Wieboldt, Ines Ruff, and Matthias Hahn

In situ Raman imaging can show the spatial distribution of phase changes in electrodes over time, and ex situ Raman imaging provides results with a higher degree of confidence than studies using single-point measurements.

30 Raman Spectroscopy of Pharmaceutical Ingredients in a Humidity-Controlled Atmosphere Vincent Larat and Caroline Feltham

The combined use of Raman microspectroscopy and a controlled- humidity cell makes it possible to accurately follow the structural changes of pharmaceutical substances when exposed to environmental conditions.

36 Carbon Black At-Line Characterization Using Portable Raman Spectroscopy Dawn Yang

A look at how correlations between Raman spectra and the structure of carbon black materials can be established

44 How to Design a Miniature Raman Spectrometer Thomas Rasmussen, Michael Rasmussen, Poul Hansen, Ole Jespersen, Nicolai Rasmussen, and Bjarke Rose

Key factors that influence the overall size of a spectrometer are explained.

Cover image courtesy of Tetra Images/Getty Images; Johner Images/Getty Images; Vstock/Getty Images

Raman Technology for Today’s SpectroscopistsJune 2015 volume 30 issue s6




The use of “time-resolved” or “dy-namic imaging” methods for var-ious samples has been described

previously in various texts (1–7). To our knowledge, however, there has not been a comparison of the reaction rates for a macro observation of a chemical kinetics reaction to the same reaction observed on the microscale. It was of interest to us to review the chemical kinetics observed for the curing of a cyanoacrylate adhesive compound in both the macro- and microscales (Fig-ure 1) to see if the reaction rates would compare for both observation methods.

Figure 1 outlines the polymerization reaction of the cyanoacrylate adhesive as it cures. The reaction is initialized by an absorption of an OH group from atmospheric water by the adhesive and

the polymerization reaction proceeds continuously until the unpolymerized ethylcyanoacrylate molecules are de-pleted. The analysis of the polymeriza-tion reaction was determined by using Raman spectroscopy in a time-resolved mode. The spectra were collected as in-dividual macro- or microspectra of the adhesive in a time-based sequence. By collecting several sets of spectra of the adhesive until the completion of the curing reaction, for as long as 1300–1800 s, the degree of polymerization was observed in the Raman spectra as the reaction progressed.

For the macro experiments, a drop of the cyanoacrylate adhesive was spread on a gold mirror to obtain a uniform layer of the adhesive. Raman sample spectra were collected using a Jasco

Raman Dynamic Imaging for the Examination of Chemical Reactions

This article investigates Raman imaging for the study of the reaction process involved in the curing of cyanoacrylate adhesives. These adhesives cure within a relatively short time, often just a few min-utes, and while the chemical reaction that occurs is well understood, it is of interest to examine how the curing process occurs on a microscale across the surface. The reaction rates for “macro” single-point locations and data from micro-imaging areas are compared. Raman imaging of dynamic processes can then be extended to other surface reactions, ranging from biological membranes to photovol-taic devices and more.

Richard A. Larsen


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NRS-5100 instrument with a 5× refrac-tive objective to provide an analysis of a 20-µm area of the sample. Spectra were collected with 532-nm illumination; two accumulations with a 4-s exposure of

the charge-coupled device (CCD) were obtained per spectrum. A 1200-gr/mm grating was used, resulting in a spectral resolution of approximately 8.6 cm-1. A time resolution of 10 s between sample

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NN N

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OO

OO

O

O

O

O

O

O

C2H5

C2H5

C2H5

C2H5

C2H5

H2CH2O

CH2

OHCH5

OHCH2

Figure 1: Polymerization reaction sequence of ethylcyanoacrylate adhesive.

4000

3000

2000

1000

1900 1500 1000 500 100

0

Inte

nsi

ty 0 s

2260 s

Raman shift (cm-1)

841.2

18 c

m-1

1390.6

2 c

m-1

634.8

18 c

m-1

601.2

18 c

m-1

814.4

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m-1

Figure 2: Raman fingerprint spectra of ethylcyanoacrylate adhesive at 0 s (blue) and 2260 s (red).


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scans was used to obtain spectra for ap-proximately 4800 s (80 min).

For the microscopy experiments, the adhesive was prepared in the same manner, but discrete Raman microscope

spectra were collected of the adhesive in a “lattice mapping” mode. For the map-ping experiments, Raman spectra were collected with the NRS-5100 system and the 50× objective, and discrete spectra

863.372

800

600

400

200

161.329

Inte

nsi

ty

Time (s)

0 1000 2000 3000 4000 4697.85

Figure 3: Extracted Raman scattering intensity traces observed during the macro reaction for the C-H bend at 1391 cm-1 (blue) and the C-C stretch at 814 cm-1 (red) Raman peaks.

1400

1000

500

200

Inte

nsi

ty

Time (s)

0 5000 10,000 11,960

Figure 4: Extracted Raman peak intensity traces observed during the micro reaction for the 1391 cm-1 (C-H bend, red) and 814 cm-1 (C-C stretch, blue) Raman peaks.


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with a spatial resolution of approximately 2 µm were obtained. The 532-nm exci-tation laser was used with the 1200-gr/mm grating, obtaining spectra with a spectral resolution of approximately 8.6 cm-1. Mapping spectra of one ac-cumulation per spectrum with a 6-s exposure of CCD were obtained as the adhesive cured over a total area of 25 ×25 µm using a 3×3 lattice mapping mode. A time resolution of 60 s per sample map-ping was used for observations of the

mapping area during a total observation time of approximately 4900 s (82 min).

Results and DiscussionAs a result of the overwhelming inten-sity of the C-H stretch motions, Figure 2 outlines only the fingerprint region of the Raman spectra of the adhesive at the beginning of the reaction (blue trace) and near the end of the curing (red trace), with specific peaks labeled in the traces. The C-C stretch peak at 814 cm-1 and

Table I: Comparison of reaction rates for macro samples (black) versus micro-sampling (red)

Wavelength (cm-1) Time Constant Rate (s-1) Half-Life

6351035 0.00097 717

1016 0.00098 704

8411192 0.00084 826

1054 0.00095 730

1391946 0.00106 656

990 0.00100 687

814798 0.00125 553

675 0.00148 468

601730 0.00137 506

752 0.00133 522

700

600

400

200

0

Inte

nsi

ty

Time (s)

0 5000 10,000 11,960

Figure 5: Extracted Raman peak intensity traces observed during the micro reaction for the C-C stretch peak at 841 cm-1 (blue) and the C-C bend mode at 601 cm-1 (red) Raman peaks.


June 2015 Raman Technology for Today’s Spectroscopists 15

the C-C bend peak at 601 cm-1, are in-creasing during the reaction, indicating the increase in the number of the CH2-C-CH2 bonds as the polymerization proceeds. Conversely, the C-H bend at 1391 cm-1 and the C=C stretch and bend modes at 841 and 635 cm-1, respectively, are decreasing as bonds associated with the C=C group are rearranged during the curing reaction.

The changes in the vibrational modes were observed for multiple examples of the cyanoacrylate curing reaction and the reaction kinetics calculated for the increasing and decreasing peak inten-sity values in the Raman spectra for the 1391 cm-1 (C-H bend) and 814 cm-1 (C-C stretch) peaks (Figure 3) and the 841 (C-C stretch), 635 (C=C bend), and 601 cm-1

(C-C bend) peaks for the macro experi-ment. Additional plots of the 1391 cm-1

(C-H bend) and 806 cm-1 (C-C stretch) peaks (Figure 4) and the 841 cm-1 (C-C stretch) and 601 cm-1 (C-C bend) (Figure 5) peaks for the micro experiment are also displayed. These figures all show the increasing and decreasing intensity values for the vibrational motions out-lined earlier. The reaction kinetics val-ues are calculated for these peak intensity changes using the Reaction Rate Calcu-lation software available in the Spectra Manager software, and the results for these calculations are displayed as Table I.

As outlined in Table I, the macro- and microsampling methods provide reaction rate values for the time con-stant, reaction rate, and reaction half-life values calculated using the Reaction Rate Calculation software that agree well within each experiment.

Conclusions

The macro- and microsampling meth-ods for observation of an ethylcyano-

acrylate adhesive curing reaction can provide useful kinetics data for the adhesive curing. The reaction data calculated from the Raman kinetics spectra compares quite well with the micro observation method. These ex-periments demonstrate that additional information can be gained by using a Raman imaging method so that the reaction kinetics on a microscale can be investigated for other chemical systems. In this instance, a standard commercial Raman instrument was utilized for the imaging experiments and no special instrumentation or ac-cessories were required.

References

(1) C.A. Coutts-Lendon, N.A. Wright,

E.V. Miseo, and J.L. Koenig, J.

Con. Rel. 93, 223 (2003).

(2) G. Srinivasan and R. Bhargava,

Spectrosc. 22, 30 (2007).

(3) T. Tadokoro, T. Fukazawa, and H. Tori-

umi, Jpn. J. Appl. Phys. 36, 1207 (1997).

(4) R. Bhargave and I.W. Levin, Appl.

Spectrosc. 57, 357 (2003).

(5) R. Bhargava and I.W. Levin, Mac-

romolecules 36, 92 (2003).

(6) R. Bhargava and I.W. Levin, Appl.

Spectrosc. 58, 995 (2004).

(7) H. Sugiyama, J. Koshoubu, S. Kashi-

wabara, T. Nagoshi, R.A. Larsen, and K.

Akao, Appl. Spectrosc. 62, 17 (2008).

Richard A. Larsen is a spectroscopy

applications specialist with Jasco, Inc.,

in Easton, Maryland. Direct correspon-

dence to: [email protected] ◾

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



The use of Raman spectroscopy for the analysis of battery materials has been around for years. Dur-

ing the 1960s, researchers used Raman to elucidate many of the fundamental spectral features of the minerals and in-organic materials widely used in battery research today (1,2). Raman is a good fit for these materials because many of the characteristic vibrational and rotational modes occur in the low-wavenumber re-gion of the spectrum typically accessible only by far-infrared (IR) measurements. In that day and age, both Raman and far-IR measurements were time con-suming and difficult experiments.

Advances in instrumentation have greatly increased the ease-of-use of Raman making it a much more ap-proachable technique. New areas of ap-plication ensued such as the exploding interest in rechargeable lithium (Li)-ion batteries. Many researchers are involved

and have published careful studies of materials specifically related to Li-ion batteries as well as next generation batteries. A review article by Baddour-Hadjean (3) published in 2010 is an excel-lent resource for those wishing to get up to speed in this field. The focus of this article is on the in situ and ex situ ap-plications of Raman spectroscopy as it pertains to battery research.

Analysis Techniques: In Situ Versus Ex SituThe term in situ is used to describe experi-ments where the battery components are studied in an assembled cell under op-erating conditions. Think of in situ as a window on the case of a battery that lets you see the chemistry of what goes on when you charge and discharge a battery. There are very few commercially avail-able cell designs compatible with spec-troscopic measurements. Researchers

Techniques for Raman Analysis of Lithium-Ion Batteries

The needs of the lithium-ion battery customers can be segmented into in situ and ex situ modes of analysis. In situ analysis allows researchers to follow changes in a battery cell during its charge and discharge cycles. Recent improvements in Raman sensitivity enable these changes to be imaged on a dynamic time scale. The same tech-niques offer significant improvements for ex situ analysis of battery components by providing more thorough sampling than traditional single-point measurements. Examples are presented showing the advantages of both modes of analysis for lithium-ion electrodes.

Dick Wieboldt, Ines Ruff, and Matthias Hahn


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have resorted to building their own cells to meet the needs of their experimental apparatus. Examples of such designs have been published along with experimental results (4–10).

While in situ cells provide valuable in-formation, their use is generally targeted at research and development of batteries. After a formulation is designed, a can-didate battery is scaled up through pilot production to actual product samples. At this stage of development, researchers are most interested in characterizing failure modes and gaining a better understand-ing of performance differences. For ex-ample, they want to learn what makes one production run work better than another, and why one battery fails yet its siblings from the same batch work fine.

To answer these questions, research-ers carefully disassemble a battery cell so the individual components can be ex-amined. This type of analysis is termed ex situ because the battery components are removed from the operating battery cell. The goal is to prepare the samples for analysis in as close to a native state as possible.

Battery disassembly for ex situ analy-sis is carried out in an inert environ-ment such as an argon-filled glove box to protect the battery components from moisture and oxidation. For example, the anode, separator, and cathode sandwich must be carefully separated and rinsed to remove excess electrolytes.

After the samples are prepared, they must be kept in an inert environment to protect against changes during analysis. When space is available, instruments are installed inside the glove box so the samples can be analyzed. In most cases, the sample must be removed from the glove box and transferred to an external instrument for analysis. This is where an

ex situ transfer cell becomes a key com-ponent of the workflow. It preserves the inert environment around the sample so it can be studied.

From Single Point Measurements to Raman ImagingThe majority of published research on Li-ion battery in situ Raman work is based on single-point measurements acquired over time during charge–discharge cycles. An example is the excellent work done by Kostecki’s group at Lawrence Berkeley National Lab (11).

Single-point measurements can be misleading because there is no way of knowing if the sampled point is represen-tative of the entire electrode. It is impor-tant to make multiple measurements to be sure. Because the Raman signals are weak, it takes many minutes to generate enough signal-to-noise ratio at each measurement point. A complete, multipoint experiment can be quite time consuming to complete.

Today, Raman imaging is a viable al-ternative that lets you quickly make thou-sands of measurements over an area of the electrode rather than just single-point measurements. Each pixel in a Raman image is a complete Raman spectrum. This technique provides confidence in understanding if changes are heteroge-neous or hot spots.

The following experimental results demonstrate the f lexibility of using Raman spectroscopy for both in situ and ex situ analysis of Li-ion batteries and their components.

In Situ Example: Lithiation of GraphiteGraphite is widely used as an anode ma-terial for rechargeable Li-ion batteries.

During the Li-ion battery charging cycle, positively charged Li+ ions move




from the cathode through the electro-lyte, across a separator into the anode to balance the flow of electrons in the external circuit (Figure 1). This pro-cess of Li+ ions entering the graphitic structure of the anode is called inter-calation. Intercalation causes changes in the anode structure—primarily a swelling of the graphite structure.

ExperimentalThe experimental setup for this example consists of a DXRxi Raman imaging microscope (Thermo Scientific) and an ECC-Opto-Std optical electrochemical cell (EL-CELL). This cell enables the investigation of batteries in a so-called sandwich configuration where the work-ing electrode material is placed under a sapphire (Al2O3) window. Electrode ma-terial (graphite powder in this example) is spread onto a copper grid serving as the current collector. This working elec-trode is sandwiched from below, with a glass fiber separator soaked with the electrolyte solution and lithium metal as the counter electrode.

The Raman beam from the micro-scope objective impinges onto the back-side of the working electrode material through the sapphire window (Figure 2). The advantage of investigating the backside of the electrode is that the path-way for the Raman beam is minimized, allowing the use of high magnification objectives and optimizing the spectra quality. The drawback is the gradient of lithiation concentration along the depth of the electrode. Accordingly, the electrode must be charged very slowly to minimize this unwanted gradient.

Discharging ChargingCurrent Current

Cathode

Electrolyte Electrolyte

CathodeAnode Anode

Separator Separator

Figure 1: Movement of Li+ ions balance electrons during the charge and discharge cycles of a Li-ion battery.

Microscopeobjective

Currentcollector

Optical window Holed WE currentcollector

Workingelectrode

2-mmbananasocket

Spring-loadedCE piston

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Referenceelectrode

Workingelectrode

contactpin

Window

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CE

Figure 2: Experimental setup for the in situ example showing the electrochemical cell mounted on the stage of a Raman imaging microscope.


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The graphite electrode was cycled at a constant rate of approximately 0.06C. The C-rate is measure of how rapidly a battery is charged-discharged. This rate of 0.06C corresponds to 33 h for a full charge-discharge cycle between 1.5 and 0.005 V against Li/Li+. Raman imaging was carried out during the initial 480 min of the charging (lithiation) process only.

Raman spectra were collected over a 30 µm × 30 µm area at 1 µm pixel spac-ing using 2 mW of 532-nm laser exci-tation, a 0.01-s exposure time for each pixel, and 50 scans per image. Higher laser powers or longer exposure times resulted in burning of the graphite and boiling of the electrolyte.

ResultsA Raman image is a hyperspectral data set with each pixel in the image being a complete Raman spectrum. Using a va-riety of spectral processing techniques, this hyperspectral Raman data gener-ates image contrast pertaining to specific

chemical features. It is this capability that visualizes minute differences within a sampled area. By collecting a sequence of Raman images, we now have the abil-ity to monitor changes in both space and time. As mentioned earlier, a variety of chemical images can be created from each data set showing changes within the sampled area. Alternatively, the Raman spectral data within each data set can be averaged to produce a single spectrum for each time slice. In this mode, the Raman imaging data set is used as a means of homogenizing any differences in the electrode area. This average spectrum represents a single-point measurement yet each point represents a 30-µm square compared with the typical 1-µm sample area from a standard Raman microscope.

In Figure 3, the three-dimensional (3D) view (bottom left) shows changes in the Raman spectrum as a function of time over 8.3 h (1–500 min). During this time, the battery cell is in the charging (lithiation) process only. This portion of

Ramanband shifts10 cm-1

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the electrochemical cycle is shown in the lower right of Figure 3.

The spectrum of graphite exhibits a prominent peak at 1580 cm-1 attributed to the E2g2 mode (G band). At potentials between 0.42 and 0.31 V (specific charge 33 and 45 mAh/g), the band gradually disappears along with the simultaneous emergence of a peak centered at 1590 cm-1. This peak shift is attributed to the Li+ ions intercalated into the graphite structure. This is more easily seen in the center, two dimensional (2D) Raman image. The inset shows Raman spectra before and after the change.

Toward the end of the charge cycle at 8.3 h (496 min), where the voltage is less than 0.15 V (specific charge greater than 146 mAh/g), a strong Raman band centered at 154 cm-1 begins to appear. This Raman band has not been previ-ously reported so its assignment is not conclusive. Strong Raman bands in this region have been attributed to TiO2, Sb, and metal chlorides.

The type of views shown in Figure 3 are “spectrum-centric” because they show

changes in the Raman spectra captured at different times during a time-based analysis. Figure 4 shows another way of exploring the same Raman imaging data set from an alternative “image-centric” point-of-view. Here, we are not as inter-ested in the Raman spectrum itself, but rather its use as a tool to enhance differ-ences within the image (image contrast).

In Figure 4, Raman images are pre-sented in which the image contrast is generated by multivariate curve resolu-tion (MCR) analysis. In this case, MCR finds the differences not only within each image but also across the entire time sequence. A different color is as-signed to each resolved component. This is analogous to the use of dyes in biological fluorescence imaging, which tag different parts of a cell. Each image is from the same 30-µm-square por-tion of the anode. The blue MCR com-ponent is indicative of the 1580 cm-1

band; green is the 1590 cm-1 band; yellow is the 154 cm-1 band; and red represents carbon black, a conductivity enhancer.

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Figure 4: Raman images from different time slices in the graphite lithiation experiment.


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It can be challenging to visualize the information content with such a massive wealth of data. Figure 4 shows just three frames to demonstrate this type of analysis. The changes are eas-ier to grasp using a time lapse viewer of the complete time sequence (video clips are available at http://youtu.be/geq6mbYVARE and http://youtu.be/Ic0MFAB5U4M).

Ex Situ Example: Characterization of Li-Ion AnodesAfter safety concerns, a leading area of interest in Li-ion battery research is un-derstanding the cause of performance degradation over time. Research indicates the solid electrolyte interphase (SEI) layer formed on the surface of the electrode is key to performance. The SEI layer is formed by deposition of organic and inor-ganic compounds during the first several charge-discharge cycles (12). It stabilizes the electrode from further decomposition and promotes reversible capacity. Because of the complexity of the SEI layer, results from any and all analytical techniques

contribute to an incremental understand-ing of its behavior.

As you might expect, it is a messy business to extract electrodes from a used battery so the SEI can be stud-ied. It takes great care to prepare the sample and preserve its integrity for ex situ analysis. This is usually achieved by working in an argon-filled glove box to prevent sample degradation because of atmospheric exposure. A transfer cell with a window is used to seal the sample in the inert argon environment, and re-move it from the glove box for analysis using a Raman microscope.

For this example, anode samples from a disassembled Li-ion battery were cut and mounted in a transfer cell (Thermo Scientific) so that a cross-section of the anode could be imaged. Raman spec-tra were collected over a 76 µm × 160 µm area at spatial resolution of 1.0 µm per pixel using a DXRxi Raman imag-ing system (Thermo Scientific). Laser power at the sample was 2.0 mW at 532 nm with a 0.2-s exposure time and four image scans. A 50× long working

Anode material coating

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Figure 5: Cross-section view of a Li-ion battery anode. The Raman image indicates a difference in the anode coating on each side. Inset Raman spectra are color coded to the areas in the Raman image.



distance, 0.5 NA microscope objective (Olympus) was used to focus through the transfer cell window.

Figure 5 is a micrograph of the anode cross section. The copper current collec-tor is in the center with anode material coated on both surfaces. The Raman image created from the spectral differ-ences shown by the inset Raman spec-tra is superimposed. The Raman image clearly shows that the coating on one side of the copper current collector is domi-nated by carbon black (red) whereas the other side has a much greater density of the active graphite phase (blue).

This example demonstrated the ad-vantage of Raman imaging over tradi-tional single-point measurements. The major differences in the two coatings could easily have been missed by sin-gle-point measurements depending on where the points were measured.

Conclusions

The high sensitivity of Raman imaging is a benefit for Li-ion battery analysis. In situ Raman imaging techniques show the spatial distribution of phase changes in electrodes over time. This is a capability that was not possible with single-point measurements using traditional Raman microscopy. Ex situ Raman imaging measurements give re-sults with a higher degree of confidence compared to single points.

References

(1) P. Tarte, J. Inorg. Nucl. Chem.

29(4), 915Ð923 (1967).

(2) W.B. White and B.A. De Ange-

lis, Spectrochim. Acta, Part A

23(4), 985Ð995 (1967).

(3) R. Baddour-Hadjean and J.P. Pereira-

Ramos, Chem. Rev. (Washington, DC,

U. S.) 110(3), 1278Ð1319 (2010).

(4) T. Gross and C. Hess, J. Power

Sources 256, 220Ð225 (2014).

(5) P. Nov‡k, D. Goers, L. Hardwick, M. Holza-

pfel, W. Scheifele, J. Ufheil, and A. Wursig,

J. Power Sources 146, 15Ð20 (2005).

(6) C.M. Burba and R. Frech, Appl. Spec-

trosc. 60(5), 490Ð493 (2006).

(7) E. Markevich, V. Baranchugov, G. Salitra,

D. Aurbach, and M. Schmidt, J. Electro-

chem. Soc. 155(2), A132ÐA137 (2008).

(8) Y. Luo, W.B. Cai, X.K. Xing, and

D.A. Scherson, Electrochem. Solid-

State Lett. 7(1), E1ÐE5 (2004).

(9) T. Gross, L. Giebeler, and C.

Hess, Rev. Sci. Instrum. 84(7),

073109-1Ð073109-6 (2013).

(10) K. Hongyou, T. Hattori, Y. Nagai, T.

Tanaka, H. Nii, and K. Shoda, Power

Sources 243, 72Ð77 (2013).

(11) J. Lei, F. McLarnon, and R. Kostecki, J.

Phys. Chem. B 109(2), 952Ð957 (2005).

(12) A. Chagnes and J. Swiatowska in Lithium

Ion Batteries - New Developments, I.

Belharouak, Ed. (ISBN: 978-953-51-

0077-5, InTech, 2012). Available at:

http://www.intechopen.com/books/

lithium-ion-batteries-new-developments/

electrolyte-and-solid-electrolyte-inter-

phase-layer-in-lithium-ion-batteries.

Dick Wieboldt is a senior scientist in

the Research and Advanced Development

group at Thermo Fisher Scientific in

Madison, Wisconsin. Ines Ruff is an appli-

cation specialist for Central Europe in the

Molecular Spectroscopy division of Thermo

Fisher Scientific GmbH in Dreieich, Germany.

Matthias Hahn is with EL-CELL GmbH in

Hamburg, Germany. Direct correspondence

to: [email protected] ◾



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W ith the benefits of rapid, nondestructive, no sample preparation and focusing measurement

through packaging material, Raman spectroscopy is gaining more popularity in chemical analysis of various

Active Pharmaceutical Ingredient (API) Analysiswith 1064 nm Dispersive Raman SpectroscopyJack Qian, PhD, and Lynn Chandler, PhD, BaySpec, Inc.

Figure 1: Raman spectra of the Zantac® tablet collected through the coating with BaySpec’s Raman spectroscopy excited by 532 nm, 785 nm, and 1064 nm laser.

Figure 2: BaySpec’s Agility™ transportable Raman spectrometer. Quick-change sample options (liquid, solid, pill holder, and fber probe) for any sample type.

application areas, including pharmaceutical, biomedi-cal, forensic, and more.

However, fuorescence interference encountered in many pharmaceutical samples has limited the use of the Raman technique for API analysis. Often, fuorescence is much more intense when illuminated by short wave-lengths as in Figure 1. BaySpec’s new dispersive 1064 nm Raman spectrometer ofers users a turn-key solution that combines the speed, sensitivity, and rugged design of dispersive Raman instruments to suppress fuores-cence of any complex pharmaceutical substrates.

Discussion and Conclusion• Raman fingerprint information ensures unique for

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Before being introduced to the mar-ket, pharmaceutical products need to be fully analyzed; this includes

detailed characterization of the active ingredient in terms of physical and struc-tural properties. Active substances may exist as various polymorphic forms, or with different hydration levels, which may have an impact on the efficacy of the drug.

Characterization also involves stabil-ity studies of both the active substrate and

the entire formulation. Several factors may affect the stability such as tempera-ture, light, and humidity. The impact of the storage conditions of manufactured drugs must be thoroughly evaluated, con-sidering that some tropical countries are constantly exposed to warm and humid weather, which may alter the dosage form over time.

Raman spectroscopy is commonly used in the investigation of pharma-

Raman Spectroscopy of Pharmaceutical Ingredients in a Humidity-Controlled Atmosphere

Full and detailed characterization of pharmaceutical formulations is required before the release of new drugs. Vibrational spectroscopy, and in particular Raman spectroscopy, provides quantities of valuable informa-tion about the structural properties of such formulations. Polymorphism is the perfect example of the benefit brought by Raman spectroscopy; being highly chemically selective, Raman might be used to check the presence of the desired (or undesired) form of the active ingredient. When exposed to warm and humid weather over time, ingredients may undergo changes in their hydration level. This can, of course, greatly affect the properties or efficiency of the drug. Raman spectroscopy, combined with a temperature and humidity controller can mimic such harsh conditions and can be used to monitor the properties of the different ingredients. For example, the hydration of anhydrous lactose was found to be incomplete after 24 h exposure to 95 °C. In addition, Raman mapping can be performed over time in such a controlled environment to effectively spatially monitor the kinetics of hydration of an active ingredient.

Vincent Larat and Caroline Feltham



ceutical compounds, and its applica-tions have been extensively reviewed (1).

Raman spectroscopy is highly chemi-cally selective and offers the possibility to monitor the chemical and structural change of ingredients when exposed to harsh conditions. It can be combined with a temperature- and humidity-controlled cell to understand the mod-ifications induced by environmental changes.

Raman Spectroscopy Applied to Polymorphism CharacterizationPolymorphism expresses the possibil-ity of a given material to exist as dif-ferent forms of its crystalline structure. These different forms might be more or less stable at certain conditions of temperature and pressure. This is the case, for example, with quartz existing as α-quartz, which has a trigonal crys-tal system and is stable at room tem-perature, but undergoes a polymorphic change to obtain the β-quartz form at 570 °C with a hexagonal crystal system.

This type of changes in the crystal sym-metry and the unit cell will modify the resulting Raman spectrum.

Similarly, during the manufacturing of the active pharmaceutical ingredient (API), depending on the crystallization process, pseudo-polymorphs result-ing from the hydration or solvation of the crystals can be formed. Various examples describe the use of Raman spectroscopy to study the hydration of crystalline structures (2–4), either in situ or in the dosage form, and for both qualitative and quantitative char-acterization.

ExperimentalA LabRAM HR Evolution micro-Raman spectrometer from Horiba Scientific was used to perform the measurements, while the samples were maintained to a certain temper-ature and humidity level via the use of a commercially available humidity control cell from Linkam Scientific Instruments.

Inte

nsi

ty

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400 600 800 1000 1200 1400

Figure 1: Raman spectra of anhydrous lactose (blue) and lactose monohydrate (red).



Approximately 100 mg of each sample (anhydrous lactose and theophylline) were loaded into a 6-mm-diameter cru-cible and set into the hermetically closed chamber of the cell. The humidity within the cell was raised to and maintained at 95% relative humidity (RH) to create favorable conditions for hydration and polymorph change, and the temperature was set constant at 25 °C.

The Raman spectra measurements were performed by focusing the laser through the glass window of the cell onto the powder. • For the anhydrous lactose, a laser

emitting at 785 nm was used, with a power applied to the sample of ap-proximately 100 mW. The beam was focused via a ×50 long working dis-tance (10.6 mm) objective. Spectral data were acquired using a dispersive grating of 300 gr/mm; during 15-s times two accumulations were ob-tained in the fingerprint range, that is,

between 200 and 1700 cm-1, where the main spectral features of the lactose lie. • For the theophylline, a laser emit-

ting at 532 nm was used and focused through a ×10 objective. Approxi-mately 100 mW was applied on the sample and the acquisition time per spectrum was 0.15 s. Data were acquired within the 150–3300 cm-1

range, using a dispersive grating of 300 gr/mm.For the theophylline experiment,

the mappings were performed using a XY moving stage, acquiring data every 20 µm in both the x and y axis. A ma-crospot of 20 µm² was created via the use of fast rastering mirrors to enlarge the laser beam size and ensure that the overall area of the packed powder was fully covered.

Hydration of Anhydrous LactoseAnhydrous lactose is often found as an inactive ingredient in different medica-

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%)

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Figure 2: Scores (%) of anhydrous lactose (blue) and lactose monohydrate (red) obtained from CLS fitting, during a 24-h exposure of anhydrous lactose to a relative humidity of 95%.



tions. It is particularly useful because it contains no water, which means that it will not react with medications that are sensitive to moisture.

Lactose also exists as a monohydrate form, which can behave differently from the anhydrous form when formulated with other ingredients. It is therefore im-portant to understand the conditions at which anhydrous lactose converts into the monohydrated form. Anhydrous and monohydrate lactose have different Raman features as shown in Figure 1.

Anhydrous lactose was exposed during 24 h in the hermetic humidity controlled cell, set to a RH value of 95%, with a constant temperature of 25 °C. The spectra were measured every 1 min and the spectral features of the dataset were analyzed to determine if anhy-drous lactose underwent changes.

The spectra of both pure anhydrous and monohydrate forms were used with the classical least squares (CLS) fitting

algorithm to determine the proportion of each form during the experiment.

Figure 2 indicates that the anhydrous crystals gradually (and nonlinearly) ab-sorb water to form the monohydrate form. After 24 h the transformation is incomplete, and provided that both pure spectra of anhydrous lactose and lactose monohydrate were acquired with the same measurement parameters as the dataset, it gives a fair estimate of the conversion to the monohydrate form.

Hydration of TheophyllineTheophylline, also known as 1,3-di-methylxanthine, is a methylxanthine drug used in therapy for respiratory diseases. As a member of the xanthine family, it bears structural and pharma-cological similarity to caffeine.

Similar to lactose, theophylline ex-ists in different forms: monohydrated, anhydrous I, and anhydrous II. The anhydrous II form is stable at room

Raman shift (cm-1)

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ty

600 800 1000 1200

1166 cm-1

1183 cm-1

1400 1600 1800

Figure 3: Raman spectra of the anhydrous (blue) and monohydrated (red) forms of theophylline.



temperature, whereas anhydrous I isn’t. The solubility, and consequently the bioavailability, of theophylline is affected by hydration, and it is thus of importance to understand the kinetics of hydration.

Anhydrous theophylline was placed in the moisture-controlled cell main-tained at 25 °C and with a RH of 95% for 24 h. Figure 3 indicates that the anhydrous II and the monohydrated forms have rather different spectra, notably the peaks at 1166 and 1683 cm-1 are characteristic of the mono-hydrated form.

Spectra were taken every 30 s, and the dataset was analyzed with the CLS fitting algorithm using both pure an-hydrous II and monohydrate forms to evaluate the proportion of each form in each spectrum.

The profiles in Figure 4 suggest an instantaneous transformation of the anhydrous form after 6 h of expo-sure; however, this statement only ap-

plies within the few cubic micrometer sampling volume resulting from the microscope sampling geometry. For powders, made of grains varying in size and shape, such sampling is clearly not statistically viable, and the results in Figure 4 may not reflect the bulk behav-ior of the powder. To check if the con-version is homogeneous, various maps were taken in a repeated analysis of anhydrous theophylline submitted to a RH of 95%. An area of 1.6 × 1.6 mm of packed anhydrous powder in a crucible was mapped every 4 min over the 24-h experiment time. Each map comprised 400 data points, corresponding to a map size of 80 µm—the DuoScan macrospot option was used to match the laser spot size to this step, to ensure full coverage of the sample surface.

Spectral features of the monohydrate form were used to monitor its presence in each map.

The maps represented in Figure 5 reveal that the transformation to the

Time (h)

Sco

re (

%)

0 5 10 15 20

Figure 4: Scores (%) of anhydrous theophylline (blue) and theophylline monohydrate (red), obtained from CLS fitting, during a 24-h exposure of anhydrous theophylline to a relative humidity of 95%.



monohydrated form is indeed not ho-mogeneous over the whole area. Some spots start to convert after just a few hours and the conversion to monohy-drate spreads out to the rest of the area from these starting points. The trans-formation to monohydrate is complete after the 24 h test.

By taking the average spectra of each mapped area, and applying the CLS fit-ting algorithm, it is then possible to plot the conversion to monohydrate form over time (Figure 6) in a more comprehensive and rigorous way than by analyzing one single spot (Figure 4).

Y (

µm

)

Time = 0 s

Time = 43,200 s Time = 50,400 s Time = 57,000 s Time = 86,400 s

Time = 21,600 s Time = 28,800 s Time = 36,000 s

200 µm200 µm200 µm200 µm

200 µm 200 µm 200 µm 200 µm

X (µm) X (µm) X (µm) X (µm)

X (µm) X (µm) X (µm) X (µm)

Y (

µm

)

Y (

µm

)

Y (

µm

)

Y (

µm

)

Y (

µm

)

Y (

µm

)

Y (

µm

)

Figure 5: Raman images of the monohydrated form of the theophylline at different time of the exposure to 95% RH: 0 h, 6 h, 8 h, 10 h, 12 h, 14 h, 16 h, and 24 h.

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Figure 6: Scores (%) of the monohydrated form, obtained with the CLS fitting of the averaged spectra of each mapped area.

Continued on page 54



Carbon black material is mainly used as reinforcing filler in au-tomobile tires and other rubber

products. It is also used in pigments, paint, and carbon paper. The mor-phology study of carbon black mate-rial provides useful information about the property of the original materials and how it will affect the properties of an end user’s materials. For example, the particle size distribution of the carbon black is directly related to the degree of blackness, the degree of re-inforcement to the rubber, and the ma-terial’s capability of ultraviolet (UV) absorption.

Because of the complexity of the material structure and lack of defini-tive standard test methods, material

characterization of carbon black ma-terials is rather limited to conven-tional tests (1), which include surface area, particle size estimated from the surface area values, iodine adsorp-tion numbers for evaluation of car-bon black grades, and dibutyl phthal-ate absorption numbers to determine the relative amount of oil that carbon blacks can absorb. While there are techniques such as X-ray diffraction (XRD), high-performance imaging systems such as transmission electron microscopy (TEM), and micro-Raman spectroscopy and Raman imaging systems that can effectively depict the morphology of carbon black material structure, none of these approaches allow for the quick and convenient on-

Carbon Black At-Line Characterization Using Portable Raman Spectroscopy

Carbon black is a form of amorphous carbon. It is mainly used as rein-forcement filler in automobile tires and other rubber products, but it‘s also used in pigments, paint, and carbon paper. Raman spectroscopy is a very effective analytical technology for characterizing carbon materials. The Raman bands associated with different carbon bonds reveal details of the structure on the molecular level. Most of the research using Raman technology has been applied to carbon nanotubes and graphene. The fast characterization of carbon black material using Raman spectroscopy is discussed here to demonstrate that Raman technology is well suited for carbon black material characterization.

Dawn Yang


ENESTEDT.SE More coHAIRence

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line or at-line real-time testing on the molecular level that may help evaluate, control, and monitor the carbon black manufacturing process.

In this article, portable Raman spectroscopy for at-line characteriza-tion of carbon black is explored. Be-cause of the distinctive information contained in the peak ratio between the D-band and G-band of sp2 carbon material, Raman spectroscopic analy-sis can be an effective test to charac-terize carbon black material. Because of the highly selective, unique chemi-cal signatures contained in Raman spectra and the fact that the intensity of Raman peaks is proportional to the related compound concentration, Raman technology has been used as a molecular fingerprint to analyze molecular structures, to identify spe-cific molecules, and to predict the

chemical concentrations in mixtures. The peak intensity ratio between two Raman signature peaks can also pro-vide useful information about mate-rial crystallinity, phase transition, and material structure disorder. Because of the nondestructive nature and the fact that no sample preparation is re-quired for testing, Raman spectros-copy has become a go-to method of analysis.

Graphite Versus Carbon BlackThe carbon microstructure is highly Ra ma n ac t ive , ma k ing Ra ma n uniquely suited for analysis of car-bon materials in different crystal structures. Graphite contains hex-agonal planes of carbon atoms, with four carbon atoms in one unit cell. Figure 1 displays the graphite crys-tal structure with carbon atoms in perfect layer stacking structure. The different planes are connected by translations or rotations around the symmetry axis (2). For the single crystal graphite symmetric group D6h

4, one of the vibrational modes E2g is strongly Raman active and is associated with a Raman peak at 1582 cm-1 (G-band) (3). Figure 2 shows the graphite structure of the E2g mode within one layer, which is an in-plane vibration that is as-sociated with the G-band. Graph-ite with highly single crystallinity, under the name of highly ordered pyrolytic graphite (HOPG), only displays a Raman peak at 1582 cm-1. The Raman spectrum of HOPG also displays a broad Raman peak around 2710 cm-1. This is a second-order Raman peak of graphite with the name of G′ or 2D. G′ implies the peak is specific to graphite, while

Figure 1: Carbon atoms layer stacking in graphite. Image is taken from reference 8 with permission from public domain.

E2g

E2g

Figure 2: E2g vibrational mode of carbon atoms in one graphite layer.



2D implies the second-order nature of the peak.

Carbon black material is graphite in an amorphous structure, with a lower

degree of crystallite development than graphite. For carbon black ma-terial with amorphous microcrystal-line structures, another peak around

G=E2g

25,000

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Figure 3: Raman spectra of carbon black and graphite.

15,000

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C1 C2 C3

ONN C3 : Dark_Subtracted

D G=E2g

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G′or 2D

Figure 4: Raman spectra of three carbon black materials with D-band and G-band.



1350 cm-1 (D-band) will appear. It has been concluded that the peak at 1350 cm-1 is attributed to the structure dis-order near the edge of the microcrys-talline (hence the name D-band) that destructs the structure of the sym-metry (3). Therefore, the Raman peak intensity ratio of ID/IG can be used to characterize the degree of disorder of the graphite materials. Researchers also believe that the ID/IG is inversely proportional to the grain size of the carbon black materials for grain sizes larger than 2 nm (4). In addition to the peak ratios, the full width half maxi-mum (FWHM) of both the D-band and G-band provide information on

the development of carbon crystallites. For both the D-band and G-band, the FWHM of the Raman bands decreases (the band narrows) when a higher de-gree of crystallite develops (5).

ExperimentalCommercially available carbon black materials were characterized by Raman spectroscopy using the i-Ra-man Plus portable Raman spectrom-eter (B&W Tek, Inc.) with a 532-nm laser excitation and spectral resolution at 4.5 cm-1. A portable video micro-scope sampling system was coupled with the portable i-Raman Plus system to facilitate accurate laser focus on the

Rela

tive in

ten

sity


10,000

9000

8000

7000

6000

5000

4000

3000

2000

1000

0

1200 1300 1400 1500 1600 1700 1800

Figure 5: Raman spectrum of carbon black before and after the baseline correction.

Table I: Peak info of the D-band and G-band for three carbon black samples with 532-nm laser excitation

Sample D (cm–1) G (cm–1) ID IG ID/IGFWHM-D

(cm–1)FWHM-G

(cm–1)

C1 1337 1586 909.4 1120.5 0.81 108.0 76.0

C2 1337 1581 2763.1 2828.1 0.98 68.3 65.6

C3 1336 1574 4022.5 3263.2 1.23 52.4 57.6



sample surface. The Raman spectra were collected at room temperature with an integration time of 120 s using laser power around 40 mW.

The data analysis was done using BWSpec (B&W Tek, Inc.). Baseline correction, which is based on an al-gorithm called adaptive iteratively reweighted penalized least squares (airPLS) (6), was applied to the car-bon black spectrum to extract the peak intensities for both the D-band and G-band.

Results and DiscussionThe distinctive Raman peaks for car-bon-based material reveals the struc-ture difference of carbon materials. The G-band relates to an in-plane vibration that is associated graphite structure. The D-band is related to the level of disorder of the crystal struc-ture with reduced symmetry. Figure 3

shows the overlay of Raman spectra of graphite and carbon black where the Raman spectrum of graphite displays a strong and sharp G-band while the D-band is not present at all, whereas the Raman spectrum of carbon black shows a broader G-band and the exis-tence of the D-band that is associated with the structure disorder near the edge of the microcrystalline.

Figure 4 displays Raman spectra of three different carbon black samples with the noted D-band and G-band. Since the D-band is attributed to the structure disorder near the edge of the microcrystalline that destructs the structure of the symmetry of sp2 car-bon, the Raman peak intensity ratio of ID/IG is directly related the degree of disorder of the graphite materials. The overlay of the Raman spectra in Figure 4 reveals the different level of ID/IG for these three carbon black materials.

Rela

tive in

ten

sity


7000

6000

5000

4000

3000

1200 1300 1400 1500 1600 1700

C1-BC C2-BC C3-BC

2000

1000

0

Figure 6: Raman spectra of carbon black samples after the baseline correction with different ID/IG values: red is for C1, blue is for C2, and green is for C3.



The baseline correction was a cru-cial step in obtaining the accurate peak intensity which was then used to calculate peak ratio of ID/IG. Any fluorescence that may accompany the Raman signal had to be removed by the baseline correction. Figure 5 dem-onstrates the Raman spectrum of car-bon black before and after the airPLS baseline correction. After the baseline correction, the Raman peak positions, peak intensities, and FWHM of the peaks were obtained by using the peak analysis function of the software. The Raman peak intensity ratio of the D-band and G-band was then calculated.

Figure 6 shows the Raman spec-tra of three different carbon black materials after baseline correction. The D-band and G-band peak po-sitions, peak intensities, and peak ratios ID/IG of three samples are cal-culated and displayed in Table I. One feature of the D-band that makes it more interesting is that the D-band position is not independent of the excitation laser wavelength. It was reported that the D-band peak po-sition is shifted from 1360 cm-1 to 1330 cm-1 when the laser excitation changed from 488 nm to 647 nm (7). As demonstrated in the experiment, the D-band peak position is around 1337 cm-1 when using 532-nm laser excitation. For samples C1 and C2, the ID/IG ratios are less than one, indicating that these carbon black materials have some degree of dis-order, which is in the typical range of graphite rod (C1) (3) and graphite powder (C2) (3). For sample C3, the ID/IG ratio is larger than one, which indicates that the sample has a higher degree of disorder. The FWHM of both the D-band and G-band indi-

cates the degree of crystallite devel-opment of the samples. C3 has the highest degree of development of carbon crystallites among the three samples, while C1 has the lowest de-gree of carbon crystallites develop-ment. The fact that the G′ appears in the Raman spectrum of C3 is also an indication of the higher degree of crystallites. The development of carbon crystallite can be directly related to process parameters dur-ing the carbon black manufacturing process, such as temperature.

Conclusions

Correlations between the Raman spectra and the structure of carbon black materials can be established. The G-band represents the level of order of graphite in its single crystal-line form. The presence of a D-band is related to the level of disorder of the crystal structure with reduced sym-metry of the sp2 carbon. The ratio of ID/IG can be used to characterize car-bon black material in several aspects, including the degree of disorder of the carbon black, an estimation of the grain size of the carbon black materi-als, batch uniformity when multiple measurements are taken at different locations of the materials, and the de-gree of development of carbon crys-tallite, which can be directly related to the process parameters of the carbon black manufacturing.

Compared to large benchtop, lab-grade Raman spectrometers, which cannot be easily placed on-line or at-line for real-time analysis, portable Raman spectrometers with high sen-sitivity are an easier alternative for ob-taining quality spectra with detailed

Continued on page 54




Raman spectroscopy (1) has been around for many years and is being used as one of a multitude of

molecular spectroscopic techniques. The typical Raman spectrometer is a large and expensive benchtop instrument with very high performance. Such instruments are typically for general-purpose laboratory use and, therefore, need to be flexible in the choice of Raman shift range and reso-lution through configuration with differ-ent lasers and gratings. Also, the required resolution can be as low as 1 cm-1.

Recently, Raman instruments have been introduced that are dedicated to specific applications like security screen-ing for hazardous materials (2) and in-coming inspection of raw material in the pharmaceutical industry (3). These ap-plications have been enabled by the com-pact size, lower cost, and reduced power consumption of such dedicated Raman instruments.

In contrast to the benchtop, high per-formance, laboratory Raman systems, the dedicated instruments only need to

cover a fixed wavelength range, and the resolution requirement is often relaxed, since the spectral features to look for are known in advance. As an example, many systems use a 785-nm laser and a 800–1100 nm spectrometer, which will pro-vide a Raman shift range from 200 cm-1 to 3650 cm-1 with a resolution of 10–20 cm-1.

The main enabler for these compact Raman instruments has been the devel-opment of ultracompact, high-power, and low-cost semiconductor lasers (4). How-ever, miniaturization of the spectrometer is also an important factor.

In this article, we describe the key factors that influence the overall size of a spectrometer, such as the diffraction grating groove density and detector size. Furthermore, we demonstrate compact Raman spectrometer designs and share some experimental results.

Theory of Raman Spectrometer DesignBased on the key spectrometer param-eters defined in Figure 1, we will discuss

How to Design a Miniature Raman Spectrometer

In this article, we describe the key factors that influence the overall size of a spectrometer, such as the diffraction grating groove density and detec-tor size. Furthermore, we demonstrate compact Raman spectrometer designs as small as 30 mm × 30 mm in footprint by using highly disper-sive gratings and uncooled detectors.

Thomas Rasmussen, Michael Rasmussen, Poul Hansen, Ole Jespersen, Nicolai Rasmussen, and Bjarke Rose



how these parameters influence the overall size of the spectrometer. The basic theory for a spectrometer design can be found in Neumann’s book (5).

Light enters at the input slit and is collimated into a parallel bundle of rays by the collimation mirror. The grating diffracts the different wavelengths λ of the input light into different angles β(λ) as given by the grating equation:

β(�) = sin−1�

G− sin(α)

�

��

�

�� [1]

Where G is the grating groove density and α is the angle of incidence on the gratings. The focusing mirror focuses the different wavelengths in different positions on an array of photodetec-tors—often a charge-coupled device (CCD) or complementary metal–oxide–semiconductor (CMOS) type array.

Next, we use examples from practical limitations and requirements of Raman spectroscopy to demonstrate how ultra-compact spectrometers can be designed.

Focal LengthOne of the key parameters for the overall spectrometer size is the focal length of the two mirrors or lenses in the spectrometer. Equation 2 gives a good estimation of how the focal length of the focusing mirror (LF) is related to the length of the detector (LD), the grating groove density G, and the wavelength range (λ2 – λ1) that needs to be covered:

[2]LF=

LDcos �( )

G λ2−λ

1( )

From Figure 1 it can be seen that the size of the spectrometer scales with the focal length, so to obtain a compact spectrometer the focal length should be minimized. The wavelength range

is typically given by the application and is not a design parameter. For instance, if the laser wavelength is 785 nm and the fingerprint region 200–3650 cm-1 is to be measured, the wavelength range must be 800–1100 nm. From equation 1 it can be seen that the focal length can be minimized by choosing a grating with a high groove density G or a short detector length LD. β is the diffraction angle for the center wavelength (950 nm in the example above) and is dependent on G as seen from equation 1, however the cosine term cos(β) is a slowly vary-ing function of G. In practice, the com-mercially available detectors are typi-cally either 1/4-in., 1/2-in., or 1-in. wide. Grating groove densities typically vary from 300 lines per mm (l/mm) to 1800 l/mm for the wavelength range of interest.

In Figure 2 we have plotted the focal length of the focusing mirror as a function of the grating groove density for 1/4-in., 1/2-in., and 1-in. detectors

Diffractiongrating

Input slit

Focus mirror

Detector array

LF

LD

LC

Collimation mirror

Figure 1: Schematic diagram of diffractive spectrometer with definitions of relevant parameters: LC = focal length of collimation mirror; LF = focal length of focus mirror; LD = length of detector.



covering the 800–1100 nm wavelength range. The graph clearly shows that there are nearly two orders of magni-tude in difference between the largest spectrometer design (300 l/mm grat-ing and 1-in. detector) and the small-est spectrometer design (1800 l/mm grating and 1/4-in. detector).

Numerical ApertureAnother important parameter that in-fluences the size of a spectrometer is the numerical aperture or f-number. As shown in Figure 3, the numerical aper-ture of the spectrometer is the sine of the cone of angles that the spectrometer ac-

cepts as input. The larger the numerical aperture, the more light can be coupled into the spectrometer. However, the numerical aperture will also determine the width of the beam going through the spectrometer and thereby the size of all the optical elements inside. This means that a compromise often has to be made between compact size and high throughput.

ResolutionThe resolution of a spectrometer defines the ability to resolve closely spaced fea-tures in the spectral domain (measured in nanometer or wavenumber). Raman

1000

100

10

1

300 500 700 900 1100 1300

Grating groove density (I/mm)

1/1 inch

1/2 inch

1/4 inch

1500 1700

Fo

cal le

ng

th (

mm

)

Figure 2: Focal length of focus mirror (LF) as a function grating groove density and detector size. Wavelength range: 800–1100 nm.

Table I: Pixel resolution for 800–1100 nm spectrometer for different numbers of pixels in the detector

Number of Detector Pixels Pixel Resolution (800–1100 nm)

256 18 cm–1/1.2 nm

512 9.1 cm–1/0.6 nm

1024 4.5 cm–1/0.3 nm

2048 2.3 cm–1/0.15 nm



spectra often exhibit closely spaced peaks and for this reason spectrometers with high resolution are required. How-ever, the resolution will also influence the size of the spectrometer as will be explained in this section.

The resolution of the spectrometer is determined by the following two factors:• Pixelresolution: The number of pix-

els covering the spectral range.

• Opticalresolution: The image of the input slit on the detector.

Pixel Resolution

The more pixels the detector has, the better the resolution, as seen from the example in Table I. However, more pixels generally also means a wider detector, which again means a larger spectrometer as explained previously. Because the smallest pixel spacing

(a) θ; NA = sin(θ)

θ; NA = sin(θ)(b)

Figure 3: Size of spectrometer for the case of (a) a low numerical aperture and (b) a high numerical aperture.

Table II: Parameters for compact 800–1100 nm spectrometers with 966-l/mm and 1500-l/mm gratings

Grating 966 l/mm 1500 l/mm

Wavelength range 800–1100 nm 800–1100 nm

Resolution 0.6 nm 0.6 nm

Detector width 14 mm 14 mm

Number of pixels 1024 1024

Numerical aperture 0.11 0.11

Focal length 38 mm 22 mm

Slit size 10 µm 10 µm

Optical footprint 40 mm × 40 mm 30 mm × 30 mm



of scientific CCDs is typically in the 10–12 µm range, one can say—as a rule of thumb—that 2048 pixels corre-sponds to a 1-in. detector while a 512-pixel detector corresponds to a 1/4-in. detector. Typically, a peak needs to be sampled by a minimum of 2–3 pixels to determine the peak center accurately.

As mentioned in the introduction, pro-cess Raman typically requires ~10 cm-1

resolution, so with a 1024-pixel detector (4.5 cm-1/pixel sampling as seen from Table I) we should be able to obtain the required resolution. If the resolution re-quirement is 20–25 cm-1, we can even reduce the number of pixels to 512.

1.2

1

0.8

0.6

0.4

Reso

luti

on

(n

m)

Wavelength (nm)

0.2

0800 850 900 950 1000

Measured

Theory

1050 1100

Figure 5: Measured resolution of the 800–1100 nm spectrometer. The solid line is the theoretically calculated resolution.

Focus mirror Focus

mirror

Detector array

(a) (b)

Detector array

Diffractiongrating966 I/mm Diffraction

grating966 I/mm

Input slit (10 µm) Input slit (10 µm)

40 mm x 40 mmfootprint

30 mm x 30 mmfootprintCollimation

mirror

Collimationmirror

14 m

m

38 mm

22 mm

14 m

m

1100 nm

1100 nm

800 nm

800 nm

Figure 4: 800–1100 nm spectrometer designs: (a) 966-l/mm grating with a footprint of 40 mm × 40 mm and (b) 1500-l/mm grating with a footprint of 30 mm × 30 mm.



Optical Resolution

The optical resolution of the spec-trometer is determined by the input slit size and the optics inside the spec-trometer. The best possible resolution that can be obtained is the diffraction limit, which is the resolution obtained with an infinitely small input slit. The diffraction limit is related to the size of the beam width inside the spec-trometer. For instance, equation 3 determines how the beam width on the grating w

beam limits the resolution

ΔλFWHM:

[3]ΔλFWHM

= 0.84λ

Gwbeam

The wider the beam, the more grating lines the beam illuminates and, there-fore, the better the resolution. This is also referred to as the resolving power of the grating.

Signal-to-Noise Ratio

In Raman spectroscopy, the optical sig-nal levels are very weak. Since the CCD detectors are capacitive, the electrical signal level can be increased by integrat-ing for a long time—often several sec-onds. The drawback of this approach is that the noise of the CCD also increases with a long integration time. Therefore, it is quite common to cool the detector to lower the detector noise level.

For compact spectrometers, it is often desirable that they are battery operated, since the instrument should be portable. This requires that the electrical power consumption of the instrument is low, which can be ob-tained if the detector is uncooled.

As written above, if cooling is to be avoided, the integration time has to be short and thereby the signal level as high as possible. Therefore, it becomes

important to use few, yet efficient optical elements in the spectrometer to increase the optical throughput.

Experimental ResultsIn this section, we describe the results obtained using compact spectrom-eters that we have designed follow-ing the general guidelines described above. We wanted a spectrometer that covered 800–1100 nm with a resolu-tion of 0.6 nm, corresponding to 9 cm-1 at 800 nm. We also wanted to use a noncooled detector, so we chose to use transmission gratings with very high diffraction efficiency.

Table II lists the key parameters for two spectrometer designs we have made, and Figure 4 shows the optical designs on a relative scale. Both spectrometers have a numerical aperture of 0.11. To compare the relative size of the two spectrometers, we have indicated the footprint of the optical beam paths in Figure 4. As can be seen, the footprint is 40 mm × 40 mm with the 966-l/mm grating and reduces to only 30 mm × 30 mm with the 1500-l/mm grating. Obviously, the footprint will be 10–15 mm larger on each side to allow for the size of the mirrors and housing around the spectrometer.

To experimentally verify our design work, we built a spectrometer with the 966-l/mm transmission grating and tested it. From Table I, we chose to use a 14-mm-wide noncooled back-thinned (BT)-CCD detector with 1024 pixels of 14 µm pixel-to-pixel spacing, which has enough pixels to resolve a peak with a full width at half maximum (FWHM) of 0.6 nm. The mirrors were spherical mir-rors. The spectrometer was designed for a theoretical resolution of 0.5 nm using a 10-µm-wide slit, since we expected the



experimental resolution to be somewhat larger than the theory.

After assembling and aligning the spectrometer, we measured the resolu-tion versus wavelength. We used spectral lines from an argon lamp (see reference 6 for details), which all had very small linewidths, such that the measurements were not disturbed by the lamp itself.

The measured resolution is shown with diamond marks in Figure 5. As shown in the figure, the resolution is very close to the desired 0.6 nm across the full range except close to 1100 nm. The solid line in Figure 5 is the theoretical resolution calculated numerically using ray-tracing. The measurements are in good agreement with the theory, taking into account the tolerances of alignment and optical mir-ror curvatures. The overall footprint including mirrors and mounts for this spectrometer were approximately 60 mm × 60 mm × 20 mm.

SummaryIn summary, we have shown that the key parameters determining the size of a spectrometer are the grating groove density, the detector width, and the nu-merical aperture. To design a compact spectrometer, the grating groove den-sity should be as high as possible and the detector and numerical aperture as small as possible. We also discussed that the desired resolution of the spec-trometer will ultimately set a limit to how small the design can be. Also, the numerical aperture will likely have to be a compromise between high throughput and small size. We designed two spec-trometers, one with a 966-l/mm grating and one with a 1500-l/mm grating, and showed that the design can be as small as 30 mm × 30 mm. Furthermore, we built a spectrometer using a 966-l/mm

transmission grating. This spectrome-ter covered the full 800–1100 nm range, and we measured the resolution to be 0.6 nm across almost the complete range. Such a spectrometer could be as small as 60 mm × 60 mm × 20 mm and is applicable for a portable Raman system with a 785-nm laser, thereby providing coverage of the 200–3650 cm-1 range with better than 10 cm-1 resolution.

References(1) D.J. Gardiner, Practical Raman Spectroscopy

(Springer-Verlag, Berlin, Germany, 1989).

(2) P. Loeffen, G. Maskall, S. Brontron,

M. Bloomfield, and C. Tombling,

Proc. SPIE 8018, 80818E (2011).

(3) P. Matousek, F. Thorley, P. Chen, M.

Hargreaves, C. Tombling, P. Loef-

fen, and M. Bloomfield, Spec-

troscopy 26, 44–51 (2011).

(4) B.L. Volodin, S.V. Dolgy, E.D. Mel-

nik, E. Downs, J. Shaw, and V.S. Ban,

Opt. Lett. 29, 1891–1893 (2004).

(5) W. Neumann, Fundamentals of

Dispersive Optical Spectroscopy

Systems (SPIE, PM242, 2014).

(6) A. Kramida, Y. Ralchenko, J. Reader, and

NIST ASD Team. NIST Atomic Spectra

Database (Ver. 5.2), (online). Available at:

http://physics.nist.gov/asd (January 22,

2015), (National Institute of Standards and

Technology, Gaithersburg, Maryland, 2014).

Thomas Rasmussen, Michael

Rasmussen, Poul Hansen, Ole

Jespersen, Nicolai Rasmussen and

Bjarke Rose are with Ibsen Photonics in

Farum, Denmark. Direct correspondence to:

[email protected] ◾



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ADVERTISEMENT52 Molecular Spectroscopy

Raman spectroscopy is an ideal tool for char-acterizing carbon nanomaterials quickly and nondestructively. In graphene, several char-acteristic bands can be used to determine the number of graphene layers, purity and quality of the sample, but the cost of systems to do so can be prohibitive.

R esearch in carbon nanomaterials has surged in order to study their unique electrical, thermal,

chemical, and mechanical properties. Applications in-clude materials for aerospace, catalysis, and coatings. Microelectronics, sensors, and drug delivery also stand to benefit from these unique materials.

Graphene exhibits a number of characteristic bands in its Raman spectrum, which can be correlated to as-pects of its structure and quality. The G band observed

at ~1580 cm-1 is a good indicator of the number of gra-phene layers, as the band position shifts to lower energy as layer thickness increases, dependent on doping and strain. The G’ band (also referred to as the 2D band) at ~2700 cm-1 can also be used, albeit via a more com-plex relationship. The intensity of the D band at ~1350 cm-1 is related to the number of defects in the material (amorphous carbon), a marker of purity. It also there-fore increases in intensity with the number of graphene layers.

Options for Raman microscopy range from expensive systems emphasizing image quality over Raman signal collection to less expensive, inefficient systems based on fiber optic coupling. A new Raman microscope com-bines the best of both worlds, using an innovative free space design with co-localized Raman and imaging planes for cost-efficient, high performance.

Cost-Effcient Graphene CharacterizationYvette Mattley and Cicely Rathmell, Ocean Optics

9000

6000 Single Layer Graphene

Multilayer Graphene

3000

01000 1500 2000

Raman Shift (cm–1)

2500 3000

Inte

nsi

ty (

cou

nts

)

Single and Multilayer CVD Graphene on 285 nm Silicon Dioxide Wafers

Figure 1: Characteristic peaks in the Raman spectra of graphene can be used to quantify layer thickness and defect levels.


ADVERTISEMENT Molecular Spectroscopy 53

Experimental ConditionsTe IDRaman micro 532 was used to study two graphene samples with 532 nm laser excitation. One sample was a single layer, while the other was known to have an aver-age of four layers. Both samples were measured across the system’s full Raman shift range, 200–3200 cm-1. A peak cleaning function was used in the software to remove background signal, capturing high quality data from all three bands of interest.

ResultsA shift of 12 cm-1 to lower energy was observed for the G band of the single layer sample as compared to the multilayer sample. The width of the G’ band broadened from 39 cm-1 for the single layer sample to 60 cm-1 for the multilayer sample. Both spectral changes are consis-tent with an increase in the number of layers.

The single layer sample showed no evidence of the de-fect band, or D band at 1350 cm-1, as would be expected for a monolayer of graphene. The D band did appear in the multilayer sample, with an intensity of ~930 counts.

ConclusionsThe degree of G band shift and G’ band broadening demonstrated that a Raman microscope with 7 cm-1 resolution such as the IDRaman micro 532 should be fully capable of building an accurate model of layer thickness, even for graphene systems with less than five layers.

Additionally, the absence of the defect band in the monolayer sample and clear signal in the multilayer sys-tem shows dynamic range that is more than adequate for assessment of defect levels.

Reference(1) A. Jorio, “Raman Spectroscopy in Graphene Related

Systems,” Weinheim: Wiley-VCH, 2011.

Ocean Optics830 Douglas Avenue, Dunedin, FL tel. (727) 733-2447, fax (727) 733-3962Website: www.OceanOptics.com

ES621469_SpecRaman0615_053.pgs 05.26.2015 23:24 ADV blackcyan


ConclusionThe combined use of Raman microspec-troscopy and the controlled-humidity cell offers the possibility to accurately follow the structural changes of pharmaceuti-cal substances when exposed to given environmental conditions (RH and tem-perature). Such capabilities allow not only monitoring of sample hydration (as in the case of lactose and theophylline discussed in this article), but also characterization of polymorphic or phase transitions, and swelling or degradation induced by mois-ture. The use of the cell is also compatible with Raman imaging, and thus is ideally suited for the analysis of heterogeneous samples, for example, structural modi-fications in dosage forms such as tablets.

References(1) G. Fini, J. Raman Spectrosc. 35, 335 (2004).

(2) A. Amado, M. Nolasco, and P. Ribeiro-Claro, J.

Pharm. Sci. 96, 1366–1379 (2007).

(3) H. Wikström, C. Kakidas, and L. Taylor, J.

Pharm Biomed. Analysis 49, 247–252

(2009).

(4) P-F. Sung, Y-L. Hsieh, K. Angonese, D. Dunn,

R. King, R. Machbitz, A. Christianson, W.

Chappell, L. Taylor, and M. Harris, J. Pharm.

Sci. 100, 2920–2934 (2011).

Vincent Larat is a Raman application

scientist with HORIBA Jobin Yvon

S.A.S., in Villeneuve d’Ascq, France.

Caroline Feltham is a scientist with

Linkam Scientific Instruments Ltd., in

Surrey, United Kingdom.

Direct correspondence to:



Continued from page 35

spectroscopic information and distinc-tive D-bands and G-bands. This may benefit carbon black manufacturing for process control and monitoring.

References(1) D.T. Norman, http://www.

continentalcarbon.com/pdfs/

What_Is_Carbon_Black.pdf.

(2) S. Reich and C. Thomsen, Phil. Trans. R.

Soc. Lond. A 362, 2271–2288 (2004).

(3) Y. Wang, D.C. Alsmeyer, and R.L. McCre-

ery, Chem. Mater. 2, 557–563 (1990).

(4) F. Tuinstra and J.L. Koenig, J.

Chem. Phys. 53, 1126 (1970).

(5) K. Ishimaru, T. Hata, P. Bronsveld,

T. Nishzawa, and Y. Imamura, J.

Wood Sci. 53, 442–448 (2007).

(6) Z.-M. Zhang, S. Chen, and Y.-Z. Liang,

Analyst 135, 1138–1146 (2010).

(7) R.P. Vidano, D.B. Fischbach, L.J.

Willis, and T.M. Loehr, Solid State

Commun. 39, 341 (1981).

(8) Benjah-bmm27—Own work. Licensed

under Public Domain via Wikimedia

Commons, http://commons.wiki-

media.org/wiki/File:Graphite-layers-

side-3D-balls.png#mediaviewer/

File:Graphite-layers-side-3D-balls.png.

Dawn Yang is an applications manager

with B&W Tek, Inc., in Newark, Delaware.

Direct correspondence to:



Continued from page 42



www.horiba.com/raman

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