Structural models of the bimetallic subunit at the A-cluster of acetyl coenzyme a synthase/CO dehydrogenase: binuclear sulfur-bridged Ni-Cu and Ni-Ni complexes and their reactions with CO

The Ni(II)-dicarboxamido-dithiolato complexes (Et4N)2[Ni(NpPepS)] (1) and (Et4N)2[Ni(PhPepS)] (2) were used as Nid metallosynthons in the construction of higher nuclearity dinuclear Ni-Cu and Ni-Ni species to model the bimetallic Mp-Nid site of the A-cluster of acetyl coenzyme A synthase/CO dehydrogenase (ACS/CODH). Reaction of 1 with [Cu(neo)Cl] and [Ni(terpy)Cl2] in MeCN affords the dinuclear complexes (Et4N)[Cu(neo)Ni(NpPepS)] (3) and [Ni(terpy)Ni(NpPepS)] (4), respectively. Reaction of 2 with [Ni(dppe)Cl2] in MeCN yields [Ni(dppe)Ni(PhPepS)] (6). The Ni-Cu complex 3 exhibits no redox chemistry at the Nid site and no reaction with CO. In contrast, the Nip sites in 4 and 6 are readily reduced (characterized by their Ni(I) EPR spectra) and bind CO, exhibiting nuco bands at 2044 and 1997 cm-1, respectively, indicating terminal CO binding. The present Ni-Ni systems replicate the structural and chemical properties of the A-cluster site in ACS/CODH and support the presence of Ni at Mp in the catalytically active enzyme.     

A method for calibration and validation subset partitioning

This paper proposes a new method to divide a pool of samples into calibration and validation subsets for multivariate modelling. The proposed method is of value for analytical applications involving complex matrices, in which the composition variability of real samples cannot be easily reproduced by optimized experimental designs. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. The proposed technique is illustrated in a case study involving the prediction of three quality parameters (specific mass and distillation temperatures at which 10 and 90% of the sample has evaporated) of diesel by NIR spectrometry and PLS modelling. For comparison, PLS models are also constructed by full cross-validation, as well as by using the Kennard-Stone and random sampling methods for calibration and validation subset partitioning. The obtained models are compared in terms of prediction performance by employing an independent set of samples not used for calibration or validation. The results of F-tests at 95% confidence level reveal that the proposed technique may be an advantageous alternative to the other three strategies.     

Ensemble wavelet modelling for determination of wheat and gasoline properties by near and middle infrared spectroscopy

The wavelet transform has been shown to be a useful tool for multivariate calibration. However, the choice of wavelet transform settings (wavelet family, length and number of decomposition levels) for a given application is still an open problem. The present paper proposes an alternative approach, which consists of generating an ensemble model by combining individual models obtained with different wavelet transform settings. The advantages of the proposed method are demonstrated in two analytical problems, namely the determination of moisture and protein in wheat by near infrared spectroscopy and the determination of specific mass and three distillation temperatures (T10, T50, T90) in gasoline by middle infrared spectroscopy. In these problems, the results varied considerably among individual models, which underlines the risk associated to an inadequate choice of wavelet transform settings. In contrast, the ensemble model always provided adequate results in terms of prediction error and noise sensitivity. The proposed method can be seen as an advantageous alternative for multivariate calibration in the wavelet domain, as it frees the analyst from the need to choose a particular configuration for the wavelet transform.     

Calibration transfer employing univariate correction and robust regression

This paper proposes a new method for calibration transfer, which was specifically designed to work with isolated variables, rather than the full spectrum or spectral windows. For this purpose, a univariate procedure is initially employed to correct the spectral measurements of the secondary instrument, given a set of transfer samples. A robust regression technique is then used to obtain a model with low sensitivity with respect to the univariate correction residuals. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphthenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). The proposed method should be of a particular value for use with application-targeted instruments that monitor only a small set of spectral variables.     

A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents

This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet–visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense.     

Temperature dependence of the schottky barrier height in gallium arsenide

The Schottky barrier height is measured in Ni-low doped n-type gallium arsenide and found to decrease with increasing temperature. The temperature dependence of the barrier height is nearly equal to that of the energy gap in GaAs.     

Calculation of centrifugal distortion constants for diatomic molecules from RKR potentials

Expressions are developed for computing the centrifugal distortion constants D_v, H_v, and L_v directly from the Rydberg-Klein-Rees rotationless potential of a diatomic molecule. These expressions involve summations over integrals of the wavefunctions of all neighboring vibrational levels. Application is made to the X¹Σ⁺ state of CO and the $\text{X}{}^3\text{Σ}{}_{\mathrm{g}}{}^{\mathrm{?}}$ state of O₂. In these applications, we have neglected the contributions of the continuum wavefunctions. For higher vibrational levels, particularly those near the dissociation limit, this approximation would be expected to fail. For the lowest vibrational levels of an electronic state, this method gives the same results for D_v, H_v, and L_v as the Dunham relations. However, for intermediate vibrational levels the present method is an improvement since expressions for only a few coefficients of the Dunham expansion are available. The use of D_v and H_v values calculated from Rydberg-Klein-Rees potentials in an iterative improvement of the reduction of spectroscopic data is described.     

Resolution of the discrepancies concerning the optical and microwave values for B0 and D0 of the X 3Σg− state of O2

The discrepancies concerning the optical and microwave values of B₀ and D₀ for the X${}^3\text{Σ}{}_{\mathrm{g}}{}^{\mathrm{?}}$ state of O₂ have been removed by a nonlinear least-squares fit to all of the lines of the O₂, $\text{b}{}^1\text{Σ}{}_{\mathrm{g}}{}^{\mathrm{+}}\text{-X}{}^3\text{Σ}{}_{\mathrm{g}}{}^{\mathrm{?}}$ Red Atmospheric bands recorded by Babcock and Herzberg (Astrophys. J., 108, 167, 1948). The resulting values for B₀″ and D₀″ are in excellent agreement with the Raman and microwave values. Improved values are determined for B₁″, D₁″, γ₁″ (spin-rotation), and ?₁″ (spin-spin). Both γ_v″ and ?_v″ increase in magnitude from v″ = 0 to v″ = 1. Improved Dunham Y_i0 and Y_i1 expansion coefficients are determined for the $\text{b}{}^1\text{Σ}{}_{\mathrm{g}}{}^{\mathrm{+}}$ state, from which the Rydberg-Klein-Rees potential is constructed.     

A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers

This work presents a comparative study of calibration transfer among three near infrared spectrometers for determination of naphthenes and RON (Research Octane Number) in gasoline. Seven transfer methods are compared: direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), reverse standardization (RS), piecewise reverse standardization (PRS), slope and bias correction (SBC) and model updating (MU). Two pre-treatment procedures, namely standard normal variate (SNV) and multiplicative scatter correction (MSC), are also investigated. The choice of an appropriate number of transfer samples for each technique, as well as the effect of window size in PDS/PRS and OSC components, are discussed. A broad set of gasoline samples representative of the Northeastern states of Brazil is employed in the investigation. The results show that the use of calibration transfer yields prediction errors comparable to those obtained with complete recalibration of the secondary instrument. Overall, the results point to RS as the best method for the analytical problem under consideration. When storage and/or physical transportation of transfer samples are impractical, MU is more appropriate. The comprehensive investigation carried out in the present work will be of value for practitioners involved in networks of fuel monitoring.     

Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans

This paper proposes a novel wavelet denoising method, which exploits the statistics of individual scans acquired in the course of a coaveraging process. The proposed method consists of shrinking the wavelet coefficients of the noisy signal by a factor that minimizes the expected square error with respect to the true signal. Since the true signal is not known, a sub-optimal estimate of the shrinking factor is calculated by using the sample statistics of the acquired scans. It is shown that such an estimate can be generated as the limit value of a recursive formulation. In a simulated example, the performance of the proposed method is seen to be equivalent to the best choice between hard and soft thresholding for different signal-to-noise ratios. Such a conclusion is also supported by an experimental investigation involving near-infrared (NIR) scans of a diesel sample. It is worth emphasizing that this experimental example concerns the removal of actual instrumental noise, in contrast to other case studies in the denoising literature, which usually present simulations with artificial noise. The simulated and experimental cases indicate that, in classic denoising based on wavelet coefficient thresholding, choosing between the hard and soft options is not straightforward and may lead to considerably different outcomes. By resorting to the proposed method, the analyst is not required to make such a critical decision in order to achieve appropriate results.