A Raman spectroscopic study of the uranyl mineral rutherfordine – revisited

The molecular structure of the uranyl mineral rutherfordine has been investigated by the measurement of the NIR and Raman spectra and complemented with infrared spectra including their interpretation. The spectra of the rutherfordine show the presence of both water and hydroxyl units in the structure as evidenced by IR bands at 3562 and 3465 cm-1 (OH) and 3343, 3185 and 2980 cm-1 (H2O). Raman spectra show the presence of four sharp bands at 3511, 3460, 3329 and 3151 cm-1. Corresponding molecular water bending vibrations were only observed in both Raman and infrared spectra of one of two studied rutherfordine samples. The second rutherfordine sample studied contained only hydroxyl ions in the equatorial uranyl plane and did not contain any molecular water. The infrared spectra of the (CO3)2- units in the antisymmetric stretching region show complexity with three sets of carbonate bands observed. This combined with the observation of multiple bands in the (CO3)2- bending region in both the Raman and IR spectra suggests that both monodentate and bidentate (CO3)2- units may be present in the structure. This cannot be exactly proved and inferred from the spectra; however, it is in accordance with the X-ray crystallographic studies. Complexity is also observed in the IR spectra of (UO2)2+ antisymmetric stretching region and is attributed to non-identical UO bonds. U-O bond lengths were calculated using wavenumbers of the 3 and 1 (UO2)2+ and compared with data from X-ray single crystal structure analysis of rutherfordine. Existence of solid solution having a general formula (UO2)(CO3)1-x(OH)2x.yH2O ( x, y  0) is supported in the crystal structure of rutherfordine samples studied.

Raman spectroscopic study of the arsenite minerals leiteite ZnAs2O4 , reinerite Zn3(AsO3)2 and cafarsite Ca5(Ti,Fe,Mn)7(AsO3)12.4H2O

The selected arsenite minerals leiteite, reinerite and cafarsite have been studied by Raman spectroscopy. DFT calculations enabled the position of AsO22- symmetric stretching mode at 839 cm-1, the antisymmetric stretching mode at 813 cm-1, and the deformation mode at 449 cm-1 to be calculated. The Raman spectrum of leiteite shows bands at 804 and 763 cm-1 assigned to the As2O42- symmetric and antisymmetric stretching modes. The most intense Raman band of leiteite is the band at 457 cm-1 and is assigned to the ν2 As2O42- bending mode. A comparison of the Raman spectrum of leiteite is made with the arsenite minerals reinerite and cafarsite.

Optical absorption, infrared, Raman and EPR spectral studies on natural Iowaite mineral

Natural iowaite, magnesium–ferric oxychloride mineral having light green color originating from Australia has been characterized by EPR, optical, IR, and Raman spectroscopy. The optical spectrum exhibits a number of electronic bands due to both Fe(III) and Mn(II) ions in iowaite. From EPR studies, the g values are calculated for Fe(III) and g and A values for Mn(II). EPR and optical absorption studies confirm that Fe(III) and Mn(II) are in distorted octahedral geometry. The bands that appear both in NIR and Raman spectra are due to the overtones and combinations of water and carbonate molecules. Thus EPR, optical, and Raman spectroscopy have proven most useful for the study of the chemistry of natural iowaite and chemical changes in the mineral.

Raman spectroscopic study of the antimony containing mineral partzite Cu2Sb2(O,OH)7

Raman spectroscopy of the mineral partzite Cu2Sb2(O,OH)7 complimented with infrared spectroscopy were studied and related to the structure of the mineral. The Raman spectrum shows some considerable complexity with a number of overlapping bands observed at 479, 520, 594, 607 and 620 cm-1 with additional low intensity bands found at 675, 730, 777 and 837 cm-1. Raman bands of partzite in the spectral region 590 to 675 cm-1 are attributable the ν1 symmetric stretching modes. The Raman bands at 479 and 520 cm-1 are assigned to the ν3 antisymmetric stretching modes. Raman bands at 1396 and 1455 cm-1 are attributed to SbOH deformation modes. A complex pattern resulting from the overlapping band of the water and OH units is found. Raman bands are observed at 3266, 3376, 3407, 3563, 3586 and 3622 cm-1. The first three bands are assigned to water stretching vibrations. The three higher wavenumber bands are assigned to the stretching vibrations of the OH units. It is proposed that based upon observation of the Raman spectra that water is involved in the structure of partzite. Thus the formula Cu2Sb2(O,OH)7 may be better written as Cu2Sb2(O,OH)7 •xH2O

Raman spectroscopic study of the multi-anion mineral dixenite CuMn2+14Fe3+(AsO3)5(SiO4)2(AsO4)(OH)6

The mixed anion mineral dixenite has been studied by Raman spectroscopy, complimented with infrared spectroscopy. The Raman spectrum of dixenite shows bands at 839 and 813 cm-1 assigned to the (AsO3)3- symmetric and antisymmetric stretching modes. The most intense Raman band of dixenite is the band at 526 cm-1 and is assigned to the ν2 AsO33- bending mode. DFT calculations enabled the position of AsO22- symmetric stretching mode at 839 cm-1, the antisymmetric stretching mode at 813 cm-1, and the deformation mode at 449 cm-1 to be calculated. Raman bands at 1026 and 1057 cm-1 are assigned to the SiO42- symmetric stretching vibrations and at 1349 and 1386 cm-1 to the SiO42- antisymmetric stretching vibrations. Both Raman and infrared spectra indicate the presence of water in the structure of dixenite. This brings into question the commonly accepted formula of dixenite as CuMn2+14Fe3+(AsO3)5(SiO4)2(AsO4)(OH)6. The formula may be better written as CuMn2+14Fe3+(AsO3)5(SiO4)2(AsO4)(OH)6•xH2O.

Thermal analysis of synthetic reevesite and cobalt substituted reevesite (Ni,Co)6Fe2(OH)16(CO3)•4H2O

The mineral reevesite and the cobalt substituted reevesite have been synthesised. The d(003) spacings of the minerals ranged from 7.54 to 7.95 Å. The maximum d(003) value occurred at around Ni:Co 0.4:0.6. This maximum in interlayer distance is proposed to be due to a greater number of carbonate anions and water molecules intercalated into the structure. The stability of the reevesite and cobalt doped reevesite was determined by thermogravimetric analysis. The maximum temperature of the reevesite occurs for the unsubstituted reevesite and is around 220°C. The effect of cobalt substitution results in a decrease in thermal stability of the reevesites. Four thermal decomposition steps are observed and are attributed to dehydration, dehydroxylation and decarbonation, decomposition of the formed carbonate and oxygen loss at ~807 °C. A mechanism for the thermal decomposition of the reevesite and the cobalt substituted reevesite is proposed.

Raman microscopy of haidingerite Ca(AsO3OH)•H2O and brassite Mg(AsO3OH)•4H2O

Raman spectroscopy has been used to study the arsenate minerals haidingerite Ca(AsO3OH)•H2O and brassite Mg(AsO3OH)•4H2O. Intense Raman bands in haidingerite spectrum observed at 745 and 855 cm-1 are assigned to the (AsO3OH)2- ν3 antisymmetric stretching and ν1 symmetric stretching vibrational modes. For brassite two similarly assigned intense bands are found at 809 and 862 cm-1. The observation of multiple Raman bands in the (AsO3OH)2- stretching and bending regions suggests that the arsenate tetrahedrons in the crystal structures of both minerals studied are strongly distorted. Broad Raman bands observed at 2842 cm-1 for haidingerite and 3035 cm-1 for brassite indicate strong hydrogen bonding of water molecules in the structure of these minerals. OH…O hydrogen bond lengths were calculated from the Raman spectra based on empiric relations.

Vibrational spectroscopic study of the antimonate mineral stibiconite Sb3+Sb5+2O6(OH)

The Raman and infrared spectrum of the antimonate mineral stibiconite Sb3+Sb5+2O6(OH) were used to define aspects of the molecular structure of the mineral. Bands attributable to water, OH stretching and bending and SbO stretching and bending were assigned. The mineral has been shown to contain both calcium and water and the formula is probably best written (Sb3+,Ca)ySb5+2-x(O,OH,H2O)6-7 where y approaches 1 and x varies from 0 to 1. Infrared spectroscopy complimented with thermogravimetric analysis proves the presence of water in the stibiconite structure. The mineral stibiconite is formed through replacement of the sulphur in stibnite. No Raman or infrared bands attributable to stibnite were identified in the spectra.

Synthesis and thermal analysis of Indium based hydrotalcites of formula Mg6In2(CO3)(OH)16•4H2O

Insight into the unique structure of layered double hydroxides has been obtained using a combination of X-ray diffraction and thermal analysis. Indium containing hydrotalcites of formula Mg4In2(CO3)(OH)12•4H2O (2:1 In-LDH) through to Mg8In2(CO3)(OH)18•4H2O (4:1 In-LDH) with variation in the Mg:In ratio have been successfully synthesised. The d(003) spacing varied from 7.83 Å for the 2:1 LDH to 8.15 Å for the 3:1 indium containing layered double hydroxide. Distinct mass loss steps attributed to dehydration, dehydroxylation and decarbonation are observed for the indium containing hydrotalcite. Dehydration occurs over the temperature range ambient to 205 °C. Dehydroxylation takes place in a series of steps over the 238 to 277 °C temperature range. Decarbonation occurs between 763 and 795 °C. The dehydroxylation and decarbonation steps depend upon the Mg:In ratio. The formation of indium containing hydrotalcites and their thermal activation provides a method for the synthesis of indium oxide based catalysts.

Analysis of distrubances in large interconnected power systems

World economies increasingly demand reliable and economical power supply and distribution. To achieve this aim the majority of power systems are becoming interconnected, with several power utilities supplying the one large network. One problem that occurs in a large interconnected power system is the regular occurrence of system disturbances which can result in the creation of intra-area oscillating modes. These modes can be regarded as the transient responses of the power system to excitation, which are generally characterised as decaying sinusoids. For a power system operating ideally these transient responses would ideally would have a “ring-down” time of 10-15 seconds. Sometimes equipment failures disturb the ideal operation of power systems and oscillating modes with ring-down times greater than 15 seconds arise. The larger settling times associated with such “poorly damped” modes cause substantial power flows between generation nodes, resulting in significant physical stresses on the power distribution system. If these modes are not just poorly damped but “negatively damped”, catastrophic failures of the system can occur. To ensure system stability and security of large power systems, the potentially dangerous oscillating modes generated from disturbances (such as equipment failure) must be quickly identified. The power utility must then apply appropriate damping control strategies. In power system monitoring there exist two facets of critical interest. The first is the estimation of modal parameters for a power system in normal, stable, operation. The second is the rapid detection of any substantial changes to this normal, stable operation (because of equipment breakdown for example). Most work to date has concentrated on the first of these two facets, i.e. on modal parameter estimation. Numerous modal parameter estimation techniques have been proposed and implemented, but all have limitations [1-13]. One of the key limitations of all existing parameter estimation methods is the fact that they require very long data records to provide accurate parameter estimates. This is a particularly significant problem after a sudden detrimental change in damping. One simply cannot afford to wait long enough to collect the large amounts of data required for existing parameter estimators. Motivated by this gap in the current body of knowledge and practice, the research reported in this thesis focuses heavily on rapid detection of changes (i.e. on the second facet mentioned above). This thesis reports on a number of new algorithms which can rapidly flag whether or not there has been a detrimental change to a stable operating system. It will be seen that the new algorithms enable sudden modal changes to be detected within quite short time frames (typically about 1 minute), using data from power systems in normal operation. The new methods reported in this thesis are summarised below. The Energy Based Detector (EBD): The rationale for this method is that the modal disturbance energy is greater for lightly damped modes than it is for heavily damped modes (because the latter decay more rapidly). Sudden changes in modal energy, then, imply sudden changes in modal damping. Because the method relies on data from power systems in normal operation, the modal disturbances are random. Accordingly, the disturbance energy is modelled as a random process (with the parameters of the model being determined from the power system under consideration). A threshold is then set based on the statistical model. The energy method is very simple to implement and is computationally efficient. It is, however, only able to determine whether or not a sudden modal deterioration has occurred; it cannot identify which mode has deteriorated. For this reason the method is particularly well suited to smaller interconnected power systems that involve only a single mode. Optimal Individual Mode Detector (OIMD): As discussed in the previous paragraph, the energy detector can only determine whether or not a change has occurred; it cannot flag which mode is responsible for the deterioration. The OIMD seeks to address this shortcoming. It uses optimal detection theory to test for sudden changes in individual modes. In practice, one can have an OIMD operating for all modes within a system, so that changes in any of the modes can be detected. Like the energy detector, the OIMD is based on a statistical model and a subsequently derived threshold test. The Kalman Innovation Detector (KID): This detector is an alternative to the OIMD. Unlike the OIMD, however, it does not explicitly monitor individual modes. Rather it relies on a key property of a Kalman filter, namely that the Kalman innovation (the difference between the estimated and observed outputs) is white as long as the Kalman filter model is valid. A Kalman filter model is set to represent a particular power system. If some event in the power system (such as equipment failure) causes a sudden change to the power system, the Kalman model will no longer be valid and the innovation will no longer be white. Furthermore, if there is a detrimental system change, the innovation spectrum will display strong peaks in the spectrum at frequency locations associated with changes. Hence the innovation spectrum can be monitored to both set-off an “alarm” when a change occurs and to identify which modal frequency has given rise to the change. The threshold for alarming is based on the simple Chi-Squared PDF for a normalised white noise spectrum [14, 15]. While the method can identify the mode which has deteriorated, it does not necessarily indicate whether there has been a frequency or damping change. The PPM discussed next can monitor frequency changes and so can provide some discrimination in this regard. The Polynomial Phase Method (PPM): In [16] the cubic phase (CP) function was introduced as a tool for revealing frequency related spectral changes. This thesis extends the cubic phase function to a generalised class of polynomial phase functions which can reveal frequency related spectral changes in power systems. A statistical analysis of the technique is performed. When applied to power system analysis, the PPM can provide knowledge of sudden shifts in frequency through both the new frequency estimate and the polynomial phase coefficient information. This knowledge can be then cross-referenced with other detection methods to provide improved detection benchmarks.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Swept frequency biompedance analysis for the determination of body water compartments

Bioelectrical impedance analysis, (BIA), is a method of body composition analysis first investigated in 1962 which has recently received much attention by a number of research groups. The reasons for this recent interest are its advantages, (viz: inexpensive, non-invasive and portable) and also the increasing interest in the diagnostic value of body composition analysis. The concept utilised by BIA to predict body water volumes is the proportional relationship for a simple cylindrical conductor, (volume oc length2/resistance), which allows the volume to be predicted from the measured resistance and length. Most of the research to date has measured the body's resistance to the passage of a 50· kHz AC current to predict total body water, (TBW). Several research groups have investigated the application of AC currents at lower frequencies, (eg 5 kHz), to predict extracellular water, (ECW). However all research to date using BIA to predict body water volumes has used the impedance measured at a discrete frequency or frequencies. This thesis investigates the variation of impedance and phase of biological systems over a range of frequencies and describes the development of a swept frequency bioimpedance meter which measures impedance and phase at 496 frequencies ranging from 4 kHz to 1 MHz. The impedance of any biological system varies with the frequency of the applied current. The graph of reactance vs resistance yields a circular arc with the resistance decreasing with increasing frequency and reactance increasing from zero to a maximum then decreasing to zero. Computer programs were written to analyse the measured impedance spectrum and determine the impedance, Zc, at the characteristic frequency, (the frequency at which the reactance is a maximum). The fitted locus of the measured data was extrapolated to determine the resistance, Ro, at zero frequency; a value that cannot be measured directly using surface electrodes. The explanation of the theoretical basis for selecting these impedance values (Zc and Ro), to predict TBW and ECW is presented. Studies were conducted on a group of normal healthy animals, (n=42), in which TBW and ECW were determined by the gold standard of isotope dilution. The prediction quotients L2/Zc and L2/Ro, (L=length), yielded standard errors of 4.2% and 3.2% respectively, and were found to be significantly better than previously reported, empirically determined prediction quotients derived from measurements at a single frequency. The prediction equations established in this group of normal healthy animals were applied to a group of animals with abnormally low fluid levels, (n=20), and also to a group with an abnormal balance of extra-cellular to intracellular fluids, (n=20). In both cases the equations using L2/Zc and L2/Ro accurately and precisely predicted TBW and ECW. This demonstrated that the technique developed using multiple frequency bioelectrical impedance analysis, (MFBIA), can accurately predict both TBW and ECW in both normal and abnormal animals, (with standard errors of the estimate of 6% and 3% for TBW and ECW respectively). Isotope dilution techniques were used to determine TBW and ECW in a group of 60 healthy human subjects, (male. and female, aged between 18 and 45). Whole body impedance measurements were recorded on each subject using the MFBIA technique and the correlations between body water volumes, (TBW and ECW), and heighe/impedance, (for all measured frequencies), were compared. The prediction quotients H2/Zc and H2/Ro, (H=height), again yielded the highest correlation with TBW and ECW respectively with corresponding standard errors of 5.2% and 10%. The values of the correlation coefficients obtained in this study were very similar to those recently reported by others. It was also observed that in healthy human subjects the impedance measured at virtually any frequency yielded correlations not significantly different from those obtained from the MFBIA quotients. This phenomenon has been reported by other research groups and emphasises the need to validate the technique by investigating its application in one or more groups with abnormalities in fluid levels. The clinical application of MFBIA was trialled and its capability of detecting lymphoedema, (an excess of extracellular fluid), was investigated. The MFBIA technique was demonstrated to be significantly more sensitive, (P<.05), in detecting lymphoedema than the current technique of circumferential measurements. MFBIA was also shown to provide valuable information describing the changes in the quantity of muscle mass of the patient during the course of the treatment. The determination of body composition, (viz TBW and ECW), by MFBIA has been shown to be a significant improvement on previous bioelectrical impedance techniques. The merit of the MFBIA technique is evidenced in its accurate, precise and valid application in animal groups with a wide variation in body fluid volumes and balances. The multiple frequency bioelectrical impedance analysis technique developed in this study provides accurate and precise estimates of body composition, (viz TBW and ECW), regardless of the individual's state of health.

The use of phase in complex spectrum subtraction for robust speech recognition

In this paper we propose a new method for utilising phase information by complementing it with traditional magnitude-only spectral subtraction speech enhancement through Complex Spectrum Subtraction (CSS). The proposed approach has the following advantages over traditional magnitude-only spectral subtraction: (a) it introduces complementary information to the enhancement algorithm; (b) it reduces the total number of algorithmic parameters, and; (c) is designed for improving clean speech magnitude spectra and is therefore suitable for both automatic speech recognition (ASR) and speech perception applications. Oracle-based ASR experiments verify this approach, showing an average of 20% relative word accuracy improvements when accurate estimates of the phase spectrum are available. Based on sinusoidal analysis and assuming stationarity between observations (which is shown to be better approximated as the frame rate is increased), this paper also proposes a novel method for acquiring the phase information called Phase Estimation via Delay Projection (PEDEP). Further oracle ASR experiments validate the potential for the proposed PEDEP technique in ideal conditions. Realistic implementation of CSS with PEDEP shows performance comparable to state of the art spectral subtraction techniques in a range of 15-20 dB signal-to-noise ratio environments. These results clearly demonstrate the potential for using phase spectra in spectral subtractive enhancement applications, and at the same time highlight the need for deriving more accurate phase estimates in a wider range of noise conditions.

Transition of chromium oxyhydroxide nanomaterials to chromium oxide : a hot stage Raman spectroscopic study

The transition of disc-like chromium hydroxide nanomaterials to chromium oxide nanomaterials has been studied by hot stage Raman spectroscopy. The structure and morphology of α-CrO(OH) synthesised using hydrothermal treatment was confirmed by X-ray diffraction and transmission electron microscopy. The Raman spectrum of α-CrO(OH) is characterised by two intense bands at 823 and 630 cm-1 attributed to ν1 CrIII-O symmetric stretching mode, bands at 1179 cm-1 attributed to CrIII-OH δ deformation modes. No bands are observed above 3000 cm-1. The absence of characteristic OH vibrational bands may be due to short hydrogen bonds in the α-CrO(OH) structure. Upon thermal treatment of α-CrO(OH), new Raman bands are observed at 599, 542, 513, 396, 344 and 304 cm-1, which are attributed to Cr2O3. This hot-stage Raman study shows that the transition of α-CrO(OH) to Cr2O3 occurs before 350 °C.