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Multimedia courseware packages are considerably enhanced by the inclusion of moving images and animations. The cost of making such images is, however, very high. Rather than each group or country making its own images, i.e. “re-inventing the same wheel”, it makes better economic sense to re-cycle existing images. The work of a dual language project team towards designing and producing multimedia resources in a number of languages is described. For example, the series of twenty dual-language CD-ROMs entitled “Le bon geste pratique en Chimie/Practical Laboratory Chemistry”. The work opens up a wide range of possibilities for customizing resources from an “originating language” into any other language. [Chem. Educ. Res. Pract.: 2003, 4, 77-82]

The 13C NMR chemical shift of sp3 carbon atoms situated in the α position relative to the double bond in acyclic alkenes was estimated with multilayer feedforward artificial neural networks (ANNs) ...

The 13 C NMR chemical shift sum ( CSS ) of alkanes was estimated with multi-linear regression (MLR) and multi-layer feed-forward artificial neural networks (ANN), using as structural descriptors the number of paths of length 1, 2, 3, and 4. The CSS prediction ability of both the MLR and ANN models was tested by the “leave-20%-out” (L20%O) cross-validation method. Four activation functions were tested in the neural model: the hyperbolic tangent, a bell-shaped function, a linear function and the symmetric logarithmoid function. The linear and symmetric logarithmoid functions were used only for the output layer. All combinations of activation functions give close results both in calibration and cross-validation, with somewhat lower performances for the networks with a bell-shaped output function. The best results were exhibited by the networks with the symmetric logarithmoid output function, followed by the networks with a linear output function. Because the results were very close, from a statistical point of view one could not definitively choose a particular combination of activation functions. The neural model provides better calibration and cross-validation results than the MLR model.

The 13C NMR chemical shift of sp2 carbon atoms in acyclic alkenes was estimated with multilayer feedforward artificial neural networks (ANNs) and multilinear regression (MLR), using as structural descriptors a vector made of 12 components encoding the environment of the resonating carbon atom. The neural network quantitative model provides better results than the MLR model calibrated with the same data. The predictive ability of both the ANN and MLR models was tested by the leave-20%-out (L20%O) cross-validation method, demonstrating the superior performance of the neural model. The number of neurons in the hidden layer was varied between 2 and 7, and three activation functions were tested in the neural model: the hyperbolic tangent or a bell-shaped function for the hidden layer and a linear or a hyperbolic tangent function for the output layer. All four combinations of activation functions give close results in the calibration of the ANN model, while for the prediction a linear output function performs ...

The 13C NMR chemical shift of sp2 carbon atoms in acyclic alkenes was estimated with multilayer feedforward artificial neural networks (ANNs) and multilinear regression (MLR), using as structural descriptors a vector made of 12 components encoding the environment of the resonating carbon atom. The neural network quantitative model provides better results than the MLR model calibrated with the same data. The predictive ability of both the ANN and MLR models was tested by the leave-20%-out (L20%O) cross-validation method, demonstrating the superior performance of the neural model. The number of neurons in the hidden layer was varied between 2 and 7, and three activation functions were tested in the neural model: the hyperbolic tangent or a bell-shaped function for the hidden layer and a linear or a hyperbolic tangent function for the output layer. All four combinations of activation functions give close results in the calibration of the ANN model, while for the prediction a linear output function performs ...

Teaching chemistry students to deduce chemical structures from the results of spectroscopic methods suffers severe practical problems. Often the theoretical basis of the method lies beyond the student's comprehension. Although theoretical models allow the prediction of spectra of simple compounds from their structures, the models have limited utility for the inverse operation of determining structures from spectra. This later operation is primarily empirical and does not lend itself to a systematic teaching approach. The ability to elucidate chemical structures is built largely on implicit heuristics and informal experience accumulated by solving numerous examples.

A set of 100 exercises in organic structure determination from NMR, IR, and mass spectra are offered for online use.

In chromatographic studies polar effect constants σ*c, similar to those of Taft and Ingold, can be obtained from an empirical additivity relationship which takes into account three structural increments.

lComparison of stationary-phase classification scales Comparison of the stationary-phase classifications demonstrates their relation to the nature of the forces interacting between the solvent and the solute. The ?* factor, which expresses the intensity of the chromatographic interaction, seems to be the most satisfactory basis for classification at present.

Behaviour of ethers. VII. Principle of determination of the polar effect constants It is pointed out that the simplified linear extrathermodynamic relationship where retention data are plotted versus the polar factor and the number of ramifications located on the first carbon atom neighbouring the chemical function, leads to a satisfactory result. This relationship may be used for enlargement of the σ* scale, when applied to chromatographic phenomena. On the other hand, enlargement of this scale can be carried out by incrementation.

Behaviour of ethers. I. Working out a “Taft”-like equation founded on retention data of saturated ethers Variations in retention data of a series of saturated aliphatic ethers have been studied with a view to express them in a Taft-like equation. The linear relationships pointed out show the influence of the ramifications located on the atom neighbouring the oxygen. The numerical values of ϱ * are determined for six stationary phases. They express the selectivity of the stationary phases towards the ethers and they can be considered as an evaluation of the polarity of these substances.

L'etude de la variation du logarithme du temps de retention reduit relatif d'une serie d'allyloxyalcanes et de phenoxyalcanes est abordee a l'aide de la methode de topologie-information DARC, de sorte que les relations types obtenues tinnent compte de la contribution de chacun des atomes de carbone des groupements alkyles variables. De plus, ces relations elargissent le domaine de validite de la loi de James-Martin aux molecules ramifiees.

Application of a Taft-type equation to retention data. Additivity of substituent effects The previously established linear extrathermodynamic relationship can be applied to a chemical population the common functional group of which is characterized by an axis of symmetry. In all other cases it is possible to differentiate the particular contribution of each alkyl group Rl; consequently their effects must then be considered separately.

In order to predict retention data in gas-liquid chromatography, extension of the Taft scale of σ * polar effect constants is essential and can be carried out by means of the equation log t ′ R = ρ * σ * + h ( n H -3). Using retention data of alcohols, σ * of 42 substituents have been estimated, giving rise to a σ * c scale appropriated in the field of chromatography.