Processing of signals from an ion-elective electrode array by a neural network

Neural network software is described for processing the signals of arrays of ion-selective electrodes. The performance of the software was tested in the simultaneous determination of calcium and copper(II) ions in binary mixtures of copper(II) nitrate and calcium chloride and the simultaneous determination of potassium, calcium, nitrate and chloride in mixtures of potassium and calcium chlorides and ammonium nitrate. The measurements for the Ca2+/Cu2+ determinations were done with a pH-glass electrode and calcium and copper ion-selective electrodes; results were accurate to ±8%. For the K+/Ca2+NO−3/Cl− determinations, the measurements were made with the relevant ion-selective electrodes and a glass electrode; the mean relative error was ±6%, and for the worst cases the error did not exceed 20%

Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K¿ and Kß lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges

Modelling the permeability of polymers: a neural network approach

In this short communication, the prediction of the permeability of carbon dioxide through different polymers using a neural network is studied. A neural network is a numeric-mathematical construction that can model complex non-linear relationships. Here it is used to correlate the IR spectrum of a polymer to its permeability. The underlying assumption is that the chemical information hidden in the IR spectrum is sufficient for the prediction. The best neural network investigated so far does indeed show predictive capabilities

On the unsteady behavior of turbulence models

Periodically forced turbulence is used as a test case to evaluate the predictions of two-equation and multiple-scale turbulence models in unsteady flows. The limitations of the two-equation model are shown to originate in the basic assumption of spectral equilibrium. A multiple-scale model based on a picture of stepwise energy cascade overcomes some of these limitations, but the absence of nonlocal interactions proves to lead to poor predictions of the time variation of the dissipation rate. A new multiple-scale model that includes nonlocal interactions is proposed and shown to reproduce the main features of the frequency response correctly

Thermoelectric performance of multiphase XNiSn (X = Ti, Zr, Hf) half-Heusler alloys

Quantitative X-ray powder diffraction analysis demonstrates that mixing Ti, Zr and Hf on the ionic site in the half-Heusler structure, which is a common strategy to lower the lattice thermal conductivity in this important class of thermoelectric materials, leads to multiphase behaviour. For example, nominal Ti0.5Zr0.5NiSn has a distribution of Ti1−xZrxNiSn compositions between 0.24 ≤ x ≤ 0.70. Similar variations are observed for Zr0.50Hf0.5NiSn and Ti0.5Hf0.5NiSn. Electron microscopy and elemental mapping demonstrate that the main compositional variations occur over micrometre length scales. The thermoelectric power factors of the mixed phase samples are improved compared to the single phase end-members (e.g. S2/ρ = 1.8 mW m−1 K−2 for Ti0.5Zr0.5NiSn, compared to S2/ρ = 1.5 mW m−1 K−2 for TiNiSn), demonstrating that the multiphase behaviour is not detrimental to electronic transport. Thermal conductivity measurements for Ti0.5Zr0.5NiSn0.95 suggest that the dominant reduction comes from Ti/Zr mass and size difference phonon scattering with the multiphase behaviour a secondary effect