Spectral features

Electronic absorption spectra of a few typical transition-metal complexes are shown in Fig. 2-1. The following features are to be noted. 

Transition Metal ChemistryM. Gerloch, E. C. Constable Copyright 1994 VCH Verlagsgesellschaft mbH, Weinheim ISBN 3-527-29218-7 

X-ray Absorption Fine Structure (XAFS) spectroscopic studies of ceria-based catalysts concentrate frequently on the Ce Lm-edge, in which electrons from the Ce 

For Ce02, the XANES is dominated by four resolved resonances, named in Fig. 5.13 A, Bi, B2 and C following literature labeling [109] such large peaks at an edge jump are known as white lines for historical reasons. The double-peak white line structure of Ce02 arises from two possible configurations in the final state of the 2p3/2 5d5/2,3/2 transition the usual theory explains that this arises from the 

Another method of analysis makes use of a subtraction procedure and has been applied to investigate the oxidation state of a series of Ce-Zr mixed oxides during a temperature-programmed process [195]. A difference spectrum between the sample at a defined state and the calcined, fully oxidized material displays a positive peak at the position of feature Bo, characteristic of Ce and a negative peak at the position of feature C, characteristic of Ce. The overall peak-to-peak amplitude is then proportional to the average reduction degree of Ce in the sample. 

A statistical approach of the Factor Analysis class, especially adapted for XANES spectroscopy [196, 197], has been used to analyze spectra of Ce-containing catalysts, not for the cerium edge but for others corresponding to the active metals [61]. This method is particularly fruitful when applied to temperature-programmed experiments, which provide a relatively large number of spectra in a homogeneous series so that the advantages of a statistical technique are utilized at maximum. 

XAFS Studies of the Cerium Oxide Phase in Catalytic Materials 5.5.3.1. Structural and Electronic Details of the Cerium Oxide Materials 

Although we shall not be concerned with the mathematics of ligand field theory, it is important to comment upon it briefly since we shall be using ligand field stabilization energies (LFSEs) later in this chapter. 

Ligand field theory is an extension of crystal field theory which is freely parameterized rather than taking a localized field arising from point charge ligands. 

Charge transfer (CT) gives rise to intense absorptions, whereas d-d bands are much weaker. In some spectra, CT absorptions mask bands due to d-d transitions, although CT absorptions (as well as ligand-centred n- K and TT-TT bands) often occur at higher energies than d-d absorptions. 

MLCT = metal-to-ligand charge transfer LMCT = ligand-to-metal charge transfer 

Absorption bands in electronic spectra are usually broad the absorption of a photon of light occurs in sslO s whereas molecular vibrations and rotations occur more slowly. Therefore, an electronic transition is a snapshot of 

Values of ,ax range from close to zero (a very weak absorption) to 10000dm mor cm (an intense absorption). 

Some important points (for which explanations will be given later in the section) are that the electronic spectra of  

Charge transfer transitions are not restricted by the selection rules that govern d-d transitions (see later). The probability of these electronic transitions is therefore high, and the absorption bands are therefore intense (Table 21.8). 

Ligand-to-metal charge transfer may give rise to absorptions in the UV or visible region of the electronic spectrum. One of the most well-known examples is observed for KMn04. The deep purple colour of aqueous solutions of KMn04 arises from an intense LMCT absorption in the 

Explain why aqueous solutions of Mn04 are purple whereas those of Re04 are colourless. 

A characteristic feature of many 7-block metal complexes is their colours, which arise because they absorb light in the visible region (e.g. Fig. 20.4). Studies of electronic spectra of metal complexes provide information about structure and bonding, although interpretation of the spectra is not always straightforward. Absorptions arise from transitimis between electronic energy levels  

Electronic absorptirMi spectra and the notation for electronic transitirms were introduced in Section 4.7, along with the Beer—Lambert law which relates absorbance to the concentration of the solutiOTi. The molar extinction coefficient, max, is determined from the Beer—Lambert law (eq. 20.12) and indicates the intensity of an absorption. 

An absorption band is characterized by both the wavelength, Amax of the absorbed electromagnetic radiation and An absorption spectrum may be represented as a plot of absorbance (A) against wavelength (Fig. 20.16), e against 

The FTIR spectra of phosphorus-containing polysulfones usually show the following absorption bands  

The phosphoryl linkage stretching is found to absorb at 1320-1200 cm. However, the considerable overlap between absorptions bands originating from the 

P-O-Alkyl and P-O-Aryl group absorbs at 1030-1050 and 1190-1240 cm respectively. In a particular case of phosphorus bulky group substitute, the P-CH group presents a strong absorption at 910cm  

Both the H and NMR spectra alone are too complex for a detailed structure analysis of bulky aromatic phosphorus substitute of PSF [47] because of the large number of different aromatic protons and carbons. MQCgs-QNP-34 NMR and the NMR resolved these problems. The characteristic peaks associated to protons of -CH Cl group situated at 4.64 ppm in chloromethylated PSF spectrum disappeared and the characteristic peak associated to protons of -CH -P-bulky group was moved at 3.5 ppm. Aromatic domain in H- C-NMR spectra is quite different for chloromethylated and phosphorylated PSF caused by the additional number of protons and carbon atoms provided by phosphorus group structure. 

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Despite its success in reproducing the hydrogen atom spectmm, the Bolir model of the atom rapidly encountered difficulties. Advances in the resolution obtained in spectroscopic experiments had shown that the spectral features of the hydrogen atom are actually composed of several closely spaced lines these are not accounted for by quantum jumps between Bolir s allowed orbits. However, by modifying the Bolir model to... 

It is possible to identify particular spectral features in the modulated reflectivity spectra to band structure features. For example, in a direct band gap the joint density of states must resemble that of critical point. One of the first applications of the empirical pseudopotential method was to calculate reflectivity spectra for a given energy band. Differences between the calculated and measured reflectivity spectra could be assigned to errors in the energy band... 

As a final point, it should again be emphasized that many of the quantities that are measured experimentally, such as relaxation rates, coherences and time-dependent spectral features, are complementary to the thennal rate constant. Their infomiation content in temis of the underlying microscopic interactions may only be indirectly related to the value of the rate constant. A better theoretical link is clearly needed between experimentally measured properties and the connnon set of microscopic interactions, if any, that also affect the more traditional solution phase chemical kinetics. 

In addition to the many applications of SERS, Raman spectroscopy is, in general, a usefiil analytical tool having many applications in surface science. One interesting example is that of carbon surfaces which do not support SERS. Raman spectroscopy of carbon surfaces provides insight into two important aspects. First, Raman spectral features correlate with the electrochemical reactivity of carbon surfaces this allows one to study surface oxidation [155]. Second, Raman spectroscopy can probe species at carbon surfaces which may account for the highly variable behaviour of carbon materials [155]. Another application to surfaces is the use... 

Depending on the relative phase difference between these temis, one may observe various experimental spectra, as illustrated in figure Bl.5.14. This type of behaviour, while potentially a source of confiision, is familiar for other types of nonlinear spectroscopy, such as CARS (coherent anti-Stokes Raman scattering) [30. 31] and can be readily incorporated mto modelling of measured spectral features. 

Figure Cl.1.4. Photoelectron spectra of V, ,(A= 17, 27, 43, and 65) at 6.42 eV photon energy, compared to tire bulk photoelectron spectmm of V(100) surface at 21.21 eV photon energy. The cluster spectra reveal tire appearance of bulk features at and how tire cluster spectral features evolve toward tire bulk. The bulk spectmm is referenced to tire Fenni level. Wu H, Desai S R and Wang L S 1996 Phys. Rev. Lett. 77 2436, figure 2.

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Multivariate data analysis usually starts with generating a set of spectra and the corresponding chemical structures as a result of a spectrum similarity search in a spectrum database. The peak data are transformed into a set of spectral features and the chemical structures are encoded into molecular descriptors [80]. A spectral feature is a property that can be automatically computed from a mass spectrum. Typical spectral features are the peak intensity at a particular mass/charge value, or logarithmic intensity ratios. The goal of transformation of peak data into spectral features is to obtain descriptors of spectral properties that are more suitable than the original peak list data. 

Spectral features and their corresponding molecular descriptors are then applied to mathematical techniques of multivariate data analysis, such as principal component analysis (PCA) for exploratory data analysis or multivariate classification for the development of spectral classifiers [84-87]. Principal component analysis results in a scatter plot that exhibits spectra-structure relationships by clustering similarities in spectral and/or structural features [88, 89]. 

Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

In spectroscopy, the separation between two spectral features, such as absorption or emission lines. 

The electromagnetic spectrum is a quantum effect and the width of a spectral feature is traceable to the Heisenberg uncertainty principle. The mechanical spectrum is a classical resonance effect and the width of a feature indicates a range of closely related r values for the model elements. 

Figure 7.10 shows the 60-MHz spectra of poly (methyl methacrylate) prepared with different catalysts so that predominately isotactic, syndiotactic, and atactic products are formed. The three spectra in Fig. 7.10 are identified in terms of this predominant character. It is apparent that the spectra are quite different, especially in the range of 5 values between about 1 and 2 ppm. Since the atactic polymer has the least regular structure, we concentrate on the other two to make the assignment of the spectral features to the various protons. 

Transition Widths and Strengths. The widths and strengths of spectroscopic transitions determine the information that can be extracted from a spectmm, and are functions of the molecular parameters summarized in Table 2. Detectivity is deterrnined by spectral resolution and transition strength. Resolution, the abiUty to distinguish transitions of nearly equal wavelength, depends on both the widths of the spectral features and characteristics of the instmmentation. Unperturbed transitions have natural, Av widths owing to the intrinsic lifetimes of the states involved. The full width at... 

The much less sophisticated PPP approximation has been shown to well reproduce the electronic spectral features not only of the monocyclic furan, pyrrole, thiophene, selenophene and tellurophene but also many of the benzo fused derivatives as well (79MI30101, 68JPC3975, 68MI30100). 

The spectrum of Figure lb is a fingerprint of the presence of a CO molecule, since it is different in detail from that of any other molecule. UPS can therefore be used to identify molecules, either in the gas phase or present at surfaces, provided a data bank of molecular spectra is available, and provided that the spectral features are sufficiently well resolved to distinguish between molecules. By now the gas phase spectra of most molecules have been recorded and can be found in the literature. Since one is using a pattern of peaks spread over only a few eV for identification purposes, mixtures of molecules present will produce overlapping patterns. How well mixtures can be analyzed depends, obviously, on how well overlapping peaks can be resolved. For molecules with well-resolved fine structure (vibrational) in the spectra (see Figure lb), this can be done much more successfiilly than for the broad. 

The features of the spectrum are then converted into sample parameters using an appropriate model of the PL process. A sampling of some of the informadon derived from spectral features is given in Table 1. 

Gai Aljf As, Hgi Cd Te) alloys, and quaternary Ai B CyDi y (e.g., Ini jfGaj j) l j,) alloys. The spectral features in Figure 1, e.g., Fq and Ej vary with alloy composition. Modulation Spectroscopy thus can be employed conveniently for this purpose even at 300 K. 

Fig. 4.65. Different spectral features of tanf for a strong model oscillator at 1000 cm" on a metal substrate. The TO mode (1000 cm" ), Berreman effect (1050 cm" ), and excitation ofa surface wave (1090 cm" ) are seen for different 1150 thicknesses - 1, 5, 10, 50,100, 500, and 1000 nm.

We next address selected Raman scattering data collected on nanotubes, both in our laboratory and elsewhere. The particular method of tubule synthesis may also produce other carbonceoiis matter that is both difficult to separate from the tubules and also exhibits potentially interfering spectral features. With this in mind, we first digress briefly to discuss synthesis and purification techniques used to prepare nanotube samples. 

Kastner et al. [25] also reported Raman spectra of cathode core material containing nested tubules. The spectral features were all identified with tubules, including weak D-band scattering for which the laser excitation frequency dependence was studied. The authors attribute some of the D-band scattering to curvature in the tube walls. As discussed above, Bacsa et al. [26] reported recently the results of Raman studies on oxidatively purified tubes. Their spectrum is similar to that of Hiura et al. [23], in that it shows very weak D-band scattering. Values for the frequencies of all the first- and second-order Raman features reported for these nested tubule studies are also collected in Table 1. 

Considering all the spectra from nested tubule samples first, it is clear from Table 1 that the data from four different research groups are in reasonable agreement. The spectral features identified with tubules appear very similar to that of graphite with sample-dependent variation in the intensity in the D (disorder-induced) band near 1350 cm" and also in the second-order features associated with the D-band (i.e., 2 X D <= 2722 cm ) and -f- D 2950 cm . Sample-dependent D-band scattering may stem from the relative admixture of nanoparticles and nanotubes, or defects in the nanotube wall. 

Raman modes. Such a symmetry analysis will also be useful for identifying the chirality of CNTs. The spectral features in the intermediate frequency range may come from the finite length of CNTs. The resonant Raman intensity may reflect differences in the DOS between metallic and semiconducting CNTs. 

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