The computerized determination of double-layer capacitance with the use of kalousek-type waveforms and its application in titrimetry

A method for the rapid determination of double-layer capacitance—potential curves of electrodes is described. An on-line computer is used to apply Kalousek-type waveforms to the electrochemical cell and to measure the accompanying current response. The capacitances are determined from the slope of the plots of log current against time. For 0.1 M KCl, the computerized method agrees well with the bridge method, except for the potential range of 0 to –0.15 V. The method is very useful for automating titrations with tensammetric detection of the end-point. The method is applied to the titration of barium with a macrocydic compound (kryptofix 222) and the titration of cetyl-trimethyl-ammonium bromide with bromocresol purple. The accuracy of the titrations is ±2%

A multivariate calibration procedure for the tensammetric determination of detergents

A multivariate calibration procedure based on singular value decomposition (SVD) and the Ho-Kashyap algorithm is used for the tensammetric determination of the cationic detergents Hyamine 1622, benzalkonium chloride (BACl), N-cetyl-N,N,N-trimethylammonium bromide (CTABr) and mixtures of CTABr and BACl. The sensitivity and accuracy depend strongly on the nature of the detergent. Acceptable accuracy is obtained with a two-step calculation procedure in which calibration constants for the total concentration range of interest are used to guide the choice of a more specific set of calibration constants which are valid for a much smaller concentration span. For Hyamine 1622, concentrations in the range 5 × 10−6−2 × 10−4 M could be determined with an accuracy of ± 10−6 M. For CTABr, these numbers were 3 × 10−6−2 × 10−4 M and ± 5 × 10−7 M; for BACl, they were 2 × 10−3−9 × 10−2 g l−1 and ± 1 × 10−3 g l−1. In the mixtures of CTABr and BACl, the accuracies were ± 3 × 10−6 M and × 1 × 10−3 g l−1, respectively

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

A novel reactor for determination of kinetics for solid catalyzed gas reactions

A novel perfectly mixed laboratory reactor for determining kinetics of heterogeneously catalyzed gas-phase reactions has been developed. Perfect mixing is achieved by circulating the gas in the reactor using an axial flow impeller in a well streamlined enclosure. Pellets are fixed in a rectangular opening in the blades of the impeller. They rotate with the impeller, thus realizing high particle velocities in the reactor. Interparticle mass transfer was studied experimentally by vaporization of naphthalene pellets. The mass-transfer coefficient in the novel reactor was found to depend on the velocity of a particle in the reactor. Mass-transfer coefficients in an internal recycle reactor at equal impeller tip speeds are 4-6 times lower than those in the novel reactor, and conditions can be chosen easily where at higher rotational speeds the mass- and heat-transfer rates are 8-10 times higher than in classical recycle reactors. The recycle flow rate in a recycle reactor was found to depend strongly on the resistance to flow caused by the catalyst bed itself. The novel reactor was tested under reacting conditions using the hydrogenation of ethene

Uses of National Accounts; History, International Standardization and Applications in the Netherlands

The national accounts is commonly known by its key-aggregates (e.g. GDP and saving) and their role in public debate and decision-making. However, the national accounts plays many different roles for many different uses. This paper provides an overview of the development of these roles and uses since the seventeenth century. Three periods are distinguished: the early estimates (1660-1930), revolutionary decades (1930-1950) and the era of the international guidelines (1950-present). The paper discusses these roles and uses also much more in detail for one country: the Netherlands, a country which played an important role in modern national accounting and where expert data users, like the CPB, SCP and the Dutch central bank, have developed several interesting applications of the national accounts.Uses of the national accounts, history of national accounting, history of taxation, economic growth, Dutch national accounts, relevance and reliability of the national accounts, Petty, King, Vauban, Quesnay, Keynes, Clark, Kuznets, Leontief, Tinbergen, Hicks, van Cleeff, Stone, Meade, guidelines on national accounting, European unification, macro-economic modeling and forecasting, CPB, SCP, Dutch central bank, fiscal policy, productivity analysis, performance management, national accounts and welfare, measurement in economics

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