Fourier-transform near-infrared (FT-NIR) spectroscopy continues to be a rapidly growing process analytical technique, particularly in the pharmaceutical and biopharmaceutical industry. The technique offers a number of advantages for online applications, and most of the major NIR instrument vendors now compete in this segment of the market. 

 Biopharmaceutical and pharmaceutical process FT-NIR vendor share in 2010.			

   FT-NIR spectrometers make use of a simpler mechanical design than dispersive NIR instruments, and therefore provide a more rugged and reliable design that is advantageous in any industrial setting, including pharmaceutical and biopharmaceutical manufacturing. In addition, FT-NIR provides simultaneous analysis of all frequencies in the spectrum range, rather than scanning individual wavelengths. FT-NIR is also capable of much higher resolution than dispersive instruments, which is important in the pharmaceutical and biopharmaceutical industry. 

   FT-NIR is becoming increasingly popular for process analytical technology (PAT) and other online applications in the pharmaceutical and biopharmaceutical industry, such as monitoring drying and blending processes. The global market for process FT-NIR in the pharmaceutical and biopharmaceutical industry in 2010 was more than $21 million, and it is expected to continue to see annual growth in the mid-teens for the foreseeable future. At least a half dozen instrument vendors are significant competitors in the market. 

   The foregoing data were extracted from SDi's market analysis and perspectives report entitled 

Biotech & Pharmaceutical Process Analysis: PAT Instrumentation and More, March 2011

. For more information, contact Stuart Press, Vice President, Strategic Directions International, Inc., 6242 Westchester Parkway, Suite 100, Los Angeles, CA 90045, (310) 641-4982, fax: (310) 641-8851, 

Articles

Market Profile

Fourier-transform near-infrared (FT-NIR) spectroscopy continues to be a rapidly growing process analytical technique, particularly in the pharmaceutical and biopharmaceutical industry. The technique offers a number of advantages for online applications, and most of the major NIR instrument vendors now compete in this segment of the market.

Market Profile: Process FT-NIR for PAT in Pharma and Biopharma

2 Fundamentals of Digital Signal Processing and X-ray Detectors/Gold Camp

7–11 Advanced Tools in Environmental Biogeochemistry — Opportunities and Limitations

27–28 Introduction to Modern Mass Spectrometry

www.proed.acs.org/courses/search_results.cfm?categoryID=1&sbc=Y

27-28 Infrared Spectral Interpretation: A Systematic Approach

27-28 Structure Determination of Small Molecule Organic Compounds Using One and Two Dimensional NMR Spectroscopy

11–15 Applied Spectroscopy: Structure Elucidation of Organic Compounds (at EUROanalysis 16) 

11–15 Basics in Mass Spectrometry and Applications in the Pharmaceutical Industry (at EUROanalysis 16) 

21–22 Practical Applications of Mass Spectrometry for Small Molecules 

31 Advanced Impedance Spectroscopy for Fuel Cells (Electrochemical Society sponsored short course at the Fuel Cell Seminar) 

14 High-Throughput Drug Analysis by LC–MS 

14 Hands-on FTIR, NIR and Data Analysis — What Is the Right Tool to Solve Your Problem 

14 Introduction to Near-Infrared Spectroscopy: Applications in the Pharmaceutical and Biotech Industries 

14 Interpretation of Mass Spectra with Practical Solutions to Problems

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Short Courses

This is the second part of a multipart series on Maxwell's equations of electromagnetism. The ultimate goal is a definitive explanation of these four equations; readers will be left to judge how definitive it is. Please note that figures are being numbered sequentially throughout this series, which is why the first figure in this column is Figure 7. Another note: This is going to get a bit mathematical. It can't be helped: models of the physical universe, like Newton's second law 

, are based in math. So are Maxwell's equations.

James Clerk Maxwell (Figure 7) was born in 1831 in Edinburgh, Scotland. His unusual middle name derives from his uncle, who was the 6th Baronet Clerk of Penicuik (pronounced "penny-cook"), a town not far from Edinburgh. Clerk was, in fact, the original family name; Maxwell's father, John Clerk, adopted the surname Maxwell after receiving a substantial inheritance from a family named Maxwell. By most accounts, James Clerk Maxwell (hereafter referred to as simply "Maxwell") was an intelligent but relatively unaccomplished student.  

Figure 7: James Clerk Maxwell as a young man (holding a color wheel he invented) and as an older man. 

He began blossoming in his early teens, however, becoming interested in mathematics (especially geometry). He eventually attended the University of Edinburgh and, later, Cambridge University, where he graduated in 1854 with a degree in mathematics. He stayed on for a few years as a fellow, then moved to Marischal College in Aberdeen. When Marischal merged with another college to form the University of Aberdeen in 1860, Maxwell was laid off (an action for which the university should still be kicking itself, but who can foretell the future?) and he found another position at King's College London (later the University of London). He returned to Scotland in 1865, only to go back to Cambridge in 1871 as the first Cavendish Professor of Physics. He died of abdominal cancer in November 1879 at the relatively young age of 48; curiously, his mother died of the same ailment and at the same age, in 1839. 

Although he had a relatively short career, Maxwell was very productive. He made contributions to color theory and optics (indeed, the first photo in Figure 7 shows Maxwell holding a color wheel of his own invention) and actually produced the first true color photograph as a composite of three images. He made major contributions to the development of the kinetic molecular theory of gases, for which the "Maxwell-Boltzmann distribution" is named partially after him. He also made major contributions to thermodynamics, deriving the relations that are named after him and devising a thought experiment about entropy that was eventually called "Maxwell's demon." He demonstrated mathematically that the rings of Saturn could not be solid, but must instead be composed of relatively tiny (relative to Saturn, of course) particles — a hypothesis that was supported spectroscopically in the late 1800s but finally directly observed the first time when the Pioneer 11 and Voyager 1 spacecraft passed through the Saturnian system in the early 1980s (Figure 8).  

Figure 8: Maxwell proved mathematically that the rings of Saturn couldn't be solid objects, but were likely an agglomeration of smaller bodies. This image of a back-lit Saturn is a composite of several images taken by the Cassini spacecraft in 2006. Depending on the reproduction, you may be able to make out a tiny bluish dot in the 10 o'clock position just inside the second outermost diffuse ring. That's Earth. 

Maxwell also made seminal contributions to the understanding of electricity and magnetism, concisely summarizing their behaviors with four mathematical expressions known as Maxwell's equations of electromagnetism. He was strongly influenced by Faraday's experimental work, believing that any theoretical description of a phenomenon must be grounded in phenomenological observations. Maxwell's equations essentially summarize everything about classical electrodynamics, magnetism, and optics, and were only supplanted when relativity and quantum mechanics revised our understanding of the natural universe at certain limits. Far away from those limits, in the realm of classical physics, Maxwell's equations still rule just as Newton's equations of motion rule under normal conditions. 

Maxwell's laws are written in the language of calculus. Before we move forward with an explicit discussion of the first equation, here we deviate to a review of calculus and its symbols. 

Calculus is the mathematical study of change. Its modern form was developed independently by Isaac Newton and the German mathematician Gottfried Leibnitz in the late 1600s. Although Newton's version was used heavily in his influential 

 (in which Newton used calculus to express a number of fundamental laws of nature), it is Leibnitz's notations that are commonly used today. An understanding of calculus is fundamental to most scientific and engineering disciplines.  

Figure 9: A plot of a straight line, which has a constant slope m, given by Îy/Îx. 

Consider a car moving at constant velocity. Its distance from an initial point (arbitrarily set as a position of 0) can be plotted as a graph of distance from zero versus time elapsed. Commonly, the elapsed time is called the independent variable and is plotted on the 

 axis of a graph (called the abscissa) while distance traveled from the initial position is plotted on the 

 axis of the graph (called the ordinate). Such a graph is plotted in Figure 9. The slope of the line is a measure of how much the ordinate changes as the abscissa changes; that is, slope 

For the straight line shown in Figure 9, the slope is constant, so 

 has a single value for the entire plot. This concept gives rise to the general formula for any straight line in two dimensions, which is   

-intercept, which is where the plot would intersect with the 

 axis. Figure 9 shows a plot that has a positive value of 

, it would be going down, not up, as you go from left to right. A horizontal line has a value of 0 for 

; a vertical line has a slope of infinity. 

Many lines are not straight. Rather, they are curves. Figure 10 gives an example of a plot that is curved. The slope of a curved line is more difficult to define than that of a straight line because the slope is changing. That is, the value of the slope depends on the point (

) of the curve you're at. The slope of a curve is the same as the slope of the straight line that is tangent to the curve at that point (

). Figure 10 shows the slopes at two different points. Because the slopes of the straight lines tangent to the curve at different points are different, the slopes of the curve itself at those two points are different.  

Figure 10: A plot of a curve, showing (with the thinner lines) the different slopes at two different points. Calculus helps us determine the slopes of curved lines. 

Calculus provides ways of determining the slope of a curve, in any number of dimensions (Figure 10 is a two-dimensional plot, but we recognize that functions can be functions of more than one variable, so plots can have more dimensions, or variables, than two). We have already seen that the slope of a curve varies with position. That means that the slope of a curve is not a 

 itself. We are not concerned about the methods of determining the functions for the slopes of curves here; that information can be found in a calculus text. Instead, we are concerned with how they are represented. 

The word that calculus uses for the slope of a function is 

. The derivative of a straight line is simply 

, its constant slope. Recall that we mathematically defined the slope 

 above using "Δ" symbols, where Δ is the Greek capital letter delta. Δ is used generally to represent "change", as in Δ

 coordinate). For straight lines and other simple changes, the change is definite; in other words, it has a specific value. 

 because the slope of the curve is constantly changing. Thus, it is not proper to refer to a definite change because — to overuse a word — the definite change changes during the course of the change. What we have to do is a thought experiment: We have to imagine that the change is infinitesimally small over both the 

 coordinates. This way, the actual change is confined to an infinitesimally small portion of the curve: a 

, not a distance. The point involved is the point at which the straight line is tangent to the curve (Figure 10). 

Rather than using "Δ" to represent an infinitesimal change, calculus starts by using "d". Rather than using 

 to represent the slope, calculus puts a prime on the dependent variable as a way to represent a slope (which, remember, is a function and not a constant). Thus, for a curve we have for the slope 

as our definition for the slope of that curve. 

We hinted earlier that functions may depend on more than one variable. If that is the case, how do we define the slope? First, we define a 

 as the derivative of a multivariable function with respect to only one of its variables. We assume that the other variables are held constant. Instead of using a "d" to indicate a partial derivative, we use the lowercase Greek delta "δ". It is also common to explicitly list the variables being held constant as subscripts to the derivative, although this can be omitted because it is understood that a partial derivative is a one-dimensional derivative. Thus we have 

spoken as "the partial derivative of the function 

." Graphically, this corresponds to the slope of the multivariable function 

Figure 11: For a function of several variables, a partial derivative is a derivative in only one variable. The line represents the slope in the x direction. 

, is the sum of the partial derivatives in each dimension; that is, with respect to each variable individually. For a function of three variables, 

where each partial derivative is the slope with respect to each individual variable and d

 directions. The total derivative has as many terms as the overall function has variables. If a function is based in three-dimensional space, as is commonly the case for physical observables, then there are three variables and so three terms in the total derivative. 

When a function typically generates a single numerical value that is dependent on all of its variables, it is called a 

. An example of a scalar function might be 

), 4, is a scalar: it has magnitude but no direction.  

Figure 12: The definition of the unit vectors i, j, and k, and an example of how any vector can be expressed in terms of how many of each unit vector. 

 is a function that determines a vector, which is a quantity that has magnitude and direction. Vector functions can be easily expressed using unit vectors, which are vectors of length 

 along each dimension of the space involved. It is customary to use the representations 

 dimensions, respectively (Figure 12). Vectors are typically represented in print as boldfaced letters. Any random vector can be expressed as, or decomposed into, a certain number of 

 vectors as is demonstrated in Figure 12. A vector function might be as simple as 

in two dimensions, which is illustrated in Figure 13 for a few discrete points. Although only a few discrete points are shown in Figure 13, understand that the vector function is continuous. That is, it has a value at every point in the graph.  

Figure 13: An example of a vector function F = xi + yj. Each point in two dimensions defines a vector. Although only 12 individual values are illustrated here, in reality this vector function is a continuous, smooth function on both dimensions. 

One of the functions of a vector that we will have to evaluate is called a 

where |a| represents the magnitude (that is, length) of 

, and cosθ is the cosine of the angle between the two vectors. The dot product is sometimes called the scalar product because the value is a scalar, not a vector. The dot product can be thought of physically as how much one vector contributes to the direction of the other vector, as shown in Figure 14. A fundamental definition that uses the dot product is that for work, 

, which is defined in terms of the force vector 

 and the displacement vector of a moving object, 

, and the angle between these two vectors: 

Thus, if the two vectors are parallel (θ = 0° so cosθ = 1) the work is maximized, but if the two vectors are perpendicular to each other (θ = 90° so cosθ = 0), the object does not move and no work is done (Figure 15).

Figure 14: Graphical representation of the dot product of two vectors. The dot product gives the amount of one vector that contributes to the other vector. Understand that an equivalent graphical representation would have the b vector projected into the a vector. In both cases, the overall scalar results are the same. 

I hope you've followed so far — but so far, it's been easy. To truly understand Maxwell's first equation, we need to do a bit more advanced stuff. Don't worry — our job in "The Baseline" is to talk you through it. Unfortunately, we're going to have to wait until the next installment to pursue the more advanced stuff and get to the heart of Maxwell's first equation.

The Baseline

In the realm of classical physics, Maxwell's equations still rule, just as Newton's equations of motion rule under normal conditions.

Maxwell's Equations, Part II

Methyl yellow is an important azo dye that may be used as a pH indicator. In this study, the surface-enhanced Raman spectra of methyl yellow in silver colloids with various pH values were investigated. A relative intensity analysis indicated the intensities of the Raman bands changed primarily when the pH was lowered from 4 to 3. The enhancement of surface-enhanced Raman scattering (SERS) signals is determined mainly by surface plasmon resonance, charge-transfer resonance, and their combination. The varying intensities of SERS signals in solutions with different pH levels may result from the change in the contributions of their combined systems, such as the charge of methyl yellow molecules and the adsorption of molecules on the surface of silver.

Surface-enhanced Raman scattering (SERS) is a highly sensitive analytical tool for investing the adsorption of molecules and characterizing the structure and orientation of the molecules on rough metal surfaces (1–5). It is well known that some combination of three resonances (surface plasmon resonance, molecular resonance, and charge-transfer resonance) is responsible for the SERS enhancement (6). Surface plasmon resonance is mostly a property of the metal, such as the nature of the conduction band and nanoscale surface features, whereas molecular resonance is a property of the molecule. Charge-transfer resonance results from interaction between the molecule and the rough metal surface (7,8). Some information about the molecules may be obtained by analyzing the SERS spectra. Raman shift and relative intensity changes in the SERS spectra provide information about the adsorption of molecules on the metal surface (9,10). 

Azo dyes have attracted much attention in recent years (11–13), because they represent the largest class of dyes used in industry and are widely applied in analytical chemistry as acid–base, redox, and metallochromic indicators (14). Methyl yellow is an important azo dye and may be used as a pH indicator. Methyl yellow presents as the azo form (Figure 1a) in the organic phase, and as the hydrazo form (MYH

, Figure 1b) in the acidified aqueous phase. That structure transformation has been studied by the UV–vis spectrum and resonance Raman spectra (15). In those studies, however, the Raman spectra were observed only in a higher wavenumber range, 1100–1650 cm

. Moreover, there have been few systemic investigations of the methyl yellow structure transformation with variations in pH. In this study, the methyl yellow structure transformation in solutions with varied pH values was investigated by SERS. The structure transformation and adsorption of the methyl yellow molecule on a silver surface were studied by analyzing the relative intensity change in the Raman bands in the SERS spectra.  

Figure 1: Schematic structure of methyl yellow as (a) the azo-form in organic phase and as (b) the hydrazo-form in acidified aqueous phase.			

Methyl yellow was purchased from Aldrich Chemical Company (St. Louis, Missouri). All the chemicals used in the experiments were analytical grade.  

Silver colloids were prepared in an aqueous solution by the reduction of silver nitrate with sodium citrate following the method reported by Lee and Meisel (16). Transmission electron microscopy (TEM) examination of the silver particles revealed that the particles were homogeneous, with an average diameter of ~50 nm (17).  

The silver colloid was diluted with water in a ratio of 1:4 (v/v). The silver colloid solution was activated by the addition of 0.01 M NaCl solution to the colloid solution in a ratio of 1:5 (v/v). For SERS measurements, samples were prepared by incubating the methyl yellow solution (in ethanol) at a concentration of 5 × 10

  M with the NaCl-activated silver colloid at a ratio of 1:9, resulting in a final concentration of 5 × 10

  M. Diluted HCl was used to adjust the solution pH and pH paper was used to measure values. 

Raman and SERS spectra were recorded with a Raman microscopic spectrometer (Renishaw RM-1000, New Mills, United Kingdom) composed of an optical microscope, a CCD video camera, and a spectrometer. A 632.8-nm HeNe laser was used as the excitation source. The SERS spectra of the probed molecules were measured with an accumulation time of 10 s using a 20× object lens. The laser power focused on the sample was ~5 mW, and the spectral resolution was set to 3 cm

. The analysis of the spectra for all pH values was performed by nonlinear curve fitting using Origin 7.0 software (OriginLab Corporation, Northampton, Massachusetts). Curve fitting was carried out considering the band as a Lorentzian curve. 

Figure 2a shows the UV–vis absorption spectrum of the silver solution. The silver solution absorbs light at λ

 = 432 nm. The absorption spectrum of methyl yellow solution (in ethanol) is shown in Figure 2b. The absorption peak appears at λ

 = 403 nm, which is near the absorption peak of the silver solution. Figure 2c shows the absorption spectrum of the combined system of silver solution and methyl yellow. The addition of methyl yellow to the silver solution resulted in a decreased intensity of the absorption peak, along with an appearance of a new wide band in the range of 600–800 nm resulting from the aggregated silver particles. It was found that a significant enhancement of the band intensities may be obtained when aggregates of two or more nanoparticles oscillate collectively and these collective oscillations of nanoparticles extend the range of useful localized surface plasmon effects throughout the visible and near-infrared region of the spectrum (6). The excitation wavelength of the laser, 632.8 nm, is far away from the absorption peak of methyl yellow, thus there are no surface-enhanced Raman resonance scattering components in the SERS spectra in this study. The contribution of molecular resonance to the SERS enhancement is small.    

Figure 2: UVâvis absorption spectra of (a) silver solution, (b) methyl yellow solution, and (c) the combined system of silver solution and methyl yellow.			

Figure 3 shows the normal Raman spectrum of methyl yellow powder. Figures 4 and 5 show the SERS spectra of methyl yellow in silver colloid solution with varied pH values. We have recorded SERS spectra of methyl yellow by changing the pH values in one-unit increments from 1 to 7. However, in Figures 4 and 5 we have shown a few characteristic spectra. The Raman bands are labeled and the assignments of the Raman bands are listed in Table I (15,18–23).  

Figure 3: Normal Raman spectrum of methyl yellow powder in the ranges of (a) 200â950 cm-1 and (b) 950â1800 cm-1.			

Compared with the normal Raman spectrum, the Raman signals of the molecules have been significantly enhanced in SERS. Some new bands appear in the SERS spectra, including bands at 892, 997, 428, 474, 510, and 977 cm

  (Table I). According to a unified approach to SERS (6), the presence of non-totally symmetric bands in the excitation profile indicates the presence of charge-transfer contributions to the enhancement. Although these molecules do not have enough symmetry for a definitive test of the strong changes in spectral lines, the presence of the Raman lines mentioned above in the SERS spectra indicates strong charge-transfer effects between the molecules and the silver surface.  

Table I: Observed normal Raman and SERS bands of DAB in varied environments and their tentative assignments			

When the pH levels are varied from 4 to 7, no new Raman bands appear in the SERS spectra. At pH 2 and pH 3, however, new Raman bands appear at 892 cm

  (Figure 5a). Although the assignments of these bands are not clear, the presence of the bands could be attributed to the vibrational modes of hydrazo form (MYH

  is dominant when the pH of the solution is less than 3.3, the p

 of methyl yellow. The characteristic Raman bands of MYH

, were observed (Figure 5a) and are attributed to υ(C=C) and υ(N-N), respectively (18).  

Figure 4: SERS spectra of methyl yellow (5 Ã10-5 M) in 200â1100 cm-1 range at pH (a) 2, (b) 3, (c) 4, and (d) 7.			

The relative intensity changes of all the SERS Raman bands with pH variation were analyzed. Figure 6 shows the relative intensity variations of three representative SERS bands at 1140, 1408, and 824 cm

  with pH variations from 2 to 7. The intensities of the SERS Raman bands change slightly when the pH values change from 4 to 7 and from 2 to 3, respectively, whereas there is a large change in the intensities when the pH value is lowered from 4 to 3.  

Figure 5: SERS spectra of methyl yellow (5 Ã10-5 M) in 1100â1800 cm-1 range at pH (a) 2, (b) 3, (c) 4, and (d) 7.			

The azo form predominates when the pH is greater than 3.3, the p

of methyl yellow, and the hydrazo form predominates when the pH is lower than 3.3. This can be seen clearly by comparing the SERS from pH 2 to pH 7. A band is present at 276 cm

  at pH 7 (Figure 4d), whereas this band disappears at pH 2 (Figure 4a) and pH 3 (Figure 4b). It is reported that Ag-N vibration appears at very close frequencies in this range (21–23). It is reasonable to think that the methyl yellow molecule in azo form at high pH levels is adsorbed on the silver surface through the nonbonding electron of the N atom. On the other hand, at low pH levels, the dominant structure of methyl yellow is the hydrazo form, with a positively charged N atom, and there are more chlorinated sites on the silver surface as a result of the high Cl

  concentration. It is reasonable to suppose that the MYH

  molecule is adsorbed on the silver surface through the bridge of N

Figure 6: Relative intensity variation of methyl yellow SERS bands at (a) 1140, (b) 1408, and (c) 824 cm-1 with pH variation from 2 to 7.			

The Raman spectra provide vibrational information that is specific for the chemical bonds in molecules. The enhancement of SERS signals is determined by the surface plasmon resonance, molecular resonance, charge-transfer resonance, and their combination (6). The UV–vis absorption spectra shown in Figure 2 indicate that the contribution of molecular resonance to the SERS is small. The surface plasmon resonance is excited by laser radiation and may result from the aggregations of silver nanoscale particles. Some new lines appear in the SERS spectra, indicating strong charge-transfer effects between the molecules and the silver surface. Therefore, the enhancement of SERS signals is determined mainly by surface plasmon resonance, charge-transfer resonance, and their combination. The change in SERS intensities with variations in the pH may result from the change in the contributions of the various resonances. At high pH levels (pH 4–7), the azo form of methyl yellow molecules predominates, because these molecules adsorb onto the silver surface through Ag-N bonding. At low pH levels (pH 2–3), the hydrazo form predominates, because these molecules are adsorbed on silver surface through the bridge of N

. The adsorption change of the molecules may make the molecular orientation change, thus causing the intensities to change. This is consistent with the surface selection rules based on surface plasmon resonance (6). The interaction of molecules with a silver surface may change primarily as a result of the charge of the methyl yellow molecule (MYH

The SERS spectra of methyl yellow in varied pH solutions were studied. The enhancement of SERS signals is determined mainly by surface plasmon resonance, charge-transfer resonance, and their combination. A relative intensity analysis of the Raman bands indicated that the intensities change primarily when the pH changes from 4 to 3. This may be a result of a change in the contributions of their combined system, such as the charge of methyl yellow molecules and adsorption of molecules on the silver surface. 

 (1) M. Fleischman, P.J. Hendra, and A.J. McQuillian, 

 (2) K. Kneipp, H. Kneipp, V.B. Kartha, R. Manoharan, G. Deinum, I. Itzkan, R.R. Dasari, and M.S. Feld, 

 (5) P.W. Barber, R.K. Chang, and H. Massoudi, 

 (7) J.R. Lombardi, R.L. Brike, T. Lu, and J. Xu, 

 (8) J.F. Arenas, I. Lopez Tacon, M.S. Wooley, J. Cotero, and J.I. Marcos, 

 (9) A.K. Ojha, A. Singha, S. Dasgupta, R.K. Singh, and A. Roy, Chem. 

 (12) F.A. Pasha, M. Muddassar, H.W. Chung, S.J. Cho, and H. Cho, 

 (13) H. Destaillats, A.G. Turjanski, D.A. Estrin, and M.R. Hoffmann, 

Color Chemistry, Syntheses, Properties, and Applications of Organic Dyes and Pigments

 (17) Z.L. Zhang, Y.F. Yin, J.W. Jiang, and Y.J. Mo, 

 (18) M. Michl, B. Vlckova, and P. Mojzes, 

 (19) K. Machida, B.K. Kim, Y. Saito, K. Igarashi, and T. Uno, 

Article

Surface plasmon resonance, charge-transfer resonance, and their combination determine the enhancement of surface-enhanced Raman scattering signals, and the varying intensities of the signal at different pH levels may result from the change in contributions of the combined system.

The pH Dependence of the SERS Spectra of Methyl Yellow in Silver Colloid

Milestone's DMA-80 direct mercury analyzer is designed to require no sample preparation and deliver results comparable to traditional techniques such as cold vapor atomic absorption and ICP-MS. According to the company, the analyzer provides results in as little as 5 min per sample and enables operators to analyze for mercury in virtually any matrix — solids, liquids and gasses. 

The Nunavut InGaAs camera–detector from BaySpec is designed to enable low light spectroscopy in the near-infrared region. Optimal cooling of the detector array reportedly allows for improved low-light spectral measurements and offers an improvement of >250× signal/noise performance. The camera–detector  is available in 900–1700 nm, 1100–2200 nm, and 1250–2500 nm linear array configurations. 

Thermo Fisher Scientific's iCE 3300GF compact atomic absorption (AA) spectrometer for dedicated graphite furnace analysis is designed for analytical applications including environmental, academic, clinical, and food safety analyses. According to the company, it offers guaranteed deuterium background correction and automatic optical set-up. The device includes an integrated furnace autosampler for preparing standards and intelligently diluting over-range samples, and an Ebert optical system. 

Agilent Technologies' 500 ion-trap LC–MS system is designed for MS-MS analysis of chemicals, food- and product-safety testing, and other applications. According to the company, the instrument offers MS, MS-MS, and MS

  capabilities, and can be operated with the company's LC products. 

B&W Tek's preconfigured Glacier X miniature fiber-coupled spectrometers for designed for fluorescence and Raman spectroscopy applications. According to the company, the spectrometers' TE cooling allows the signal to be integrated for sufficient time to accumulate measurable signals for detection, without being saturated with dark noise. For fluorescence, spectrometers are configured with a 100-μm slit, a 350–1050 nm wavelength range, and a cylindrical lens. For Raman spectroscopy, the spectrometers reportedly include a 785-nm excitation source, have a wavelength range of 3215 cm

  from the Rayleigh line, and a resolution of ~10 cm

ChromaTOF-GC 4.40 software from Leco Corporation is designed for use with the company's separation science instruments. The software reportedly integrates acquisition, data processing, and reporting features and includes features for compliance with 21 CFR Part 11 through three separate software additions (User Permissions, Electronic Signatures, and Audit Trail) 

The Agilent 1290 Infinity LC system is available with the company's Intelligent System Emulation Technology (ISET) feature, which is designed to enable the LC system to emulate other LC systems for transfer of methods between LC systems, regardless of brand. According to the company, the feature allows users to emulate other HPLC and UHPLC instruments with a mouse click. Existing 1290 Infinity LC systems reportedly are fully compatible with the system emulation feature and can be upgraded. 

Schimadzu Scientific Instruments has completed a brochure, "Measurement of Transmission and Reflectance of PV Materials and Cells," for its UV-vis–NIR spectrophotometers. According to the company, the brochure explains the role of the company's SolidSpec-3700 and UV-3600 spectrophotometers in the manufacture of solar cell materials, cells, and modules. It also offers two application notes detailing the spectrophotometric analysis of photovoltaics. 

Hamamatsu Photonics' Quantaurus compact instruments are designed for measuring the properties of photoluminescent materials. According to the company, the Quantaurus-Tau instrument measures fluorescence lifetimes as short as 100 ps, and the Quantaurus-QY instrument performs absolute measurements and requires no known reference samples. The instruments reportedly can be used to analyze materials in thin film, powder, solid, or liquid form, and liquid samples can be cooled down to –196 °C. 

The GD-Profiler 2 RF glow discharge optical emission spectroscopy system from Horiba Scientific is designed to provide ultrafast elemental bulk, surface, and depth profile analysis. According to the company, the system utilizes controlled sputtering of a material by a 4-mm diameter plasma, analyzing all elements including gases (N, O, H, Cl) as a function of the depth in conductive or nonconductive layers and substrates. Samples of varying sizes and shapes can be measured.

Oxxius' SLIM-561-300 continuous wave diode-pumped solid-state (CW DPSS) laser is designed with optical characteristics that reportedly combine transverse mode (M

  less than 1.2) and pointing stability of less than 10 μrad over 3 °C. The 300-mW, 561-nm laser can be used in confocal Raman spectroscopy, SERS, holography, and laser Doppler velocimetry. The laser is available in an OEM version designed for integration and a "Plug & Play" CDRH-compliant version for laboratory use. 

Thermo Fisher Scientific's Antaris Industrial Gas System (IGS) is an FT-IR gas analyzer based on the company's Nicolet 6700 Fourier-transform interferometer and provides scan speeds as fast as 5 Hz at 0.5 cm

  resolution.The analyzer reportedly is capable of monitoring dozens of gases simultaneously while providing accurate and fast multicomponent gas analysis. Applications include industrial analyses such as landfill methane analysis. Precalibrated gas analysis methods are available. 

Andor Technology's iStar scientific-grade ICCDs are designed to combine rapid acquisitions rates and ultrahigh sensitivity down to a single photon. According to the company, the detectors include high quantum-efficiency image intensifiers, thermoelectric cooling to –40 °C, 500-kHz photocathode gating rates, and enhanced intensifier EBI noise reduction. The detectors reportedly provide low jitter, low insertion delay gating electronics and nanosecond-scale optical gating for timing accuracy down to few tens of picoseconds, allowing precise synchronization of complex experiments through various input–output triggering options. 

SPEX CertiPrep's consumer safety compliance standards are designed to help comply with new regulations being enforced to protect consumers from a variety of potentially dangerous chemicals and elements. According to the company, recent wordwide regulations restrict the levels of heavy metals in consumer products and waste electronics, as well as a variety of phthalates in children's products. 

Product Resources

Products

We continue to examine in detail the spectral behavior of three-component mixtures.

This column is the continuation of our discussion of the classical least squares approach to calibration (1–5). In this installment, as in the previous one (5), we will be dealing largely with figures, and the spectra therein. So far, we have looked at the spectra of the pure components comprising the ternary mixtures we are working with. We also have looked at the spectra of all the mixtures overlaid on each other. 

One important reason to perform this extensive exercise of examining spectra is to address, and hopefully clear up, a misconception some people have about near-infrared (NIR) spectra. Because of its history, NIR spectroscopy sometimes is believed to rely on, and require, the "magic" of chemometric analysis to obtain meaningful scientific results. But that's not always true. That perception has arisen because NIR measurements most often are made on samples that are powdered solids, part of a dynamic mechanism, or involve other difficult conditions that often make other types of spectral measurements impossible. 

This has created an impression that NIR exists in a universe of its own, disconnected from the rest of science. Therefore, we take this opportunity to emphasize (and maybe overemphasize) the fact that NIR is not some "magical" technique that is different from the rest of the universe, but in fact is the same spectroscopy we are all used to seeing in other spectral regions. When samples are presented to a spectrometer, they behave the same way in the NIR as they do in the UV, visible, mid-IR, or any other region.  

In the current experiment, we have a situation where clear liquid samples are used, and measured in a cuvette having plane parallel windows — the near-ideal conditions that are normally used for any spectral region. In this way, we can demonstrate that NIR spectra, under those conditions, do indeed behave the same way as spectra in other spectral regions do.  

Let us look at the three sets of two-component mixtures that the experimental design includes. These are presented in Figures 1–3. In these figures, we first present the full spectrum, and then for each mixture we present the spectra of the mixtures in the several subranges that contain the useful absorbance bands.  

Figure 1: Transmission spectra of mixtures of toluene and dichloromethane: (a) full spectrum from 4000 to 10,000 cm-1; (b) 4000â5000 cm-1; (c) 5000â6500 cm-1; (d) 6500â7500 cm-1; (e) 7500â9000 cm-1. 

Examining these spectra, we see some features that were partially obscured when the spectra from all the mixtures were plotted together. One effect that was hidden by the overlapping spectra was the presence of isosbestic points. Although Figure 5e from part V of this column series (5) shows a ternary isosbestic point, that is rare; for the most part isosbestic points are not seen in the previous presentations of the spectra because the other spectra in the set obscure the isosbestic points.  

Figure 2: Transmission spectra of mixtures of toluene and n-heptane: (a) 4500â10,000 cm-1; (b) 4000â5000 cm-1; (c) 5000â6500 cm-1; (d) 6500â7500 cm-1; (e) 7700â9000 cm-1. 

Examining the spectra of the two-component mixtures, however, reveals a plethora of isosbestic points. In fact, when we look at the expansions of the wavelength ranges, we find that all three sets of two-component mixtures contain multiple isosbestic points.  

Figure 3: Transmission spectra of mixtures of dichloromethane and n-heptane: (a) 4000 to 10,000 cm-1; (b) 4000â5000 cm-1; (c) 5000â6500 cm-1; (d) 6500â7500 cm-1; (e) 7600â9000 cm-1. 

Another phenomenon more easily seen in the spectra of the two-component mixtures is the presence of the expected nonlinearity of the transmission spectra with respect to composition, especially for the stronger absorbance bands. Because the strongest absorbance bands in the spectra generally fall in the range of 5000–6500 cm

, this nonlinearity is very visible in the bands of the two-component mixtures falling in this range. Examples where this effect is prominent include the band at 5700 cm

  in Figure 3c. In fact, the "clipping" that we previously observed in the 4000–5000 cm

  region now can be seen as simply an exaggerated version of this effect; the absorbance bands in that region are so strong that effectively no energy is left to come through the sample.  

The nonlinearity is not necessarily visible in all spectra, of course. For the compression of the spectra to become easily visible, simply having a part of the spectrum with high absorbance does not suffice. It is also necessary for the transmittance at the given wavelength to vary over an appreciable range in the different samples. If the transmittance is uniformly high, as is the case for the band around 7270 cm

  in Figure 1d, then the nonlinearity does not compress the spectra to the point where it is readily visible to the eye. Similarly, if the transmittance is low, but is uniformly low for all the samples of the set, we do not observe the nonlinearity because it would not be sufficient over a small range of transmission to be observed. An example of this is the band at 5700 cm

  in Figure 3c. This behavior of the absorbance bands, to not show the nonlinearity, is often associated with an isosbestic point, as in the case of the band in Figure 3c. 

It is well known this nonlinearity of transmittance versus composition in the 5000–6500 cm

  range, however, occurs because the transmission of energy through a clear but absorbing liquid decreases asymptotically, but exponentially, to zero. Therefore we see that although the composition varies in equal increments (of 25%, as described in the experimental section in part V of this column series [5]), the spectral differences do not show the same equal spacing, but are visibly compressed at the higher absorbance levels.  

Figure 4: Absorption spectra of mixtures of toluene and dichloromethane: (a) from 4500 to 10,000 cm-1; (b) 4500â5000 cm-1; (c) 4800â6300 cm-1; (d) 6500â7500 cm-1; (e) 8000â9000 cm-1. 

To achieve equal spacing of the spectra for equal changes in composition, the transmission spectra must be converted to absorbance spectra. A set of absorbance spectra paralleling the transmission spectra seen in Figures 1–3 is shown in Figures 4–6. As we did with the transmission spectra, we plotted these absorption spectra both at the full wavelength range and in each of the wavelength ranges of interest. We also have limited the lower end of the wavelength range to 4500 cm

  to prevent the "zero" transmission values from being converted to extremely high absorption values that would compress the bands at other wavelengths to the point of invisibility.  

Figure 5: Absorption spectra of mixtures of toluene and n-heptane: (a) from 4500 to 10,000 cm-1; (b) 4500â5000 cm-1; (c) 5000â6500 cm-1; (d) 6500â7500 cm-1; (e) 7700â9000 cm-1. 

We can compare the spacings between spectra in the various parts of Figures 4–6 with the spacings in the corresponding parts of Figures 1–3. Mostly we see that the spacings have tended to even out; this can be seen, for example, by comparing the band at 5700 cm

  in Figure 1c with the corresponding absorbance band in Figure 4c. Nevertheless, although the bands in Figure 4c show less compression in the higher absorbance spectra than the bands in Figure 1c, there is still a noticeable amount.  

Figure 6: Absorption spectra of mixtures of dichloromethane and n-heptane: (a) from 4500 to 10,000 cm-1; (b) 4500â5000 cm-1; (c) 5000â6500 cm-1; (d) 6500â7500 cm-1; (e) 7600â9000 cm-1. 

Surprisingly, an opposite effect sometimes occurs. For example, the band at roughly 5900 cm

  in those same two figures is nearly evenly spaced in the transmission display, but shows increasing spacing for the higher-absorbance spectra. 

We will discuss this further in a future column. 

The editors congratulate Howard Mark and Jerry Workman for 25 years of statistics and chemometrics columns in 

The detailed examination of the spectral behavior of three-component mixtures continues.

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