O. Moldes

Cyclodextrin-Surfactant Mixed Systems as Reaction Media

In recent years our reseach group has investigated the chemical behaviour of β-cyclodextrin (CD)/surfactant mixed systems and their characteristics as reaction media. The results have been interpreted in terms of a pseudophase model that takes into account the formation of both CD-surfactant and CD-substrate complexes and also, in some cases, the exchange of X- and OH- ions between the micellar and aqueous pseudophases. from the experimental results it was concluded that the presence of CD has no effect on existing micelles but raises the critical micellar concentration (cmc). on the other hand, at surfactant concentrations above the cmc, competition between the micellisation and complexation processes leads to the existence of a significant concentration of free CD in equilibrium with the micellar aggregates. The percentage of uncomplexed β-CD in equilibrium with the micellar system increases on increasing the hydrophobicity of the surfactant molecule. This behaviour was justified taking into account the existence of two simultaneous processes: complexation of surfactant monomers by CD and the process of self-assembly to form micellar aggregates. The autoaggregation of surfactant monomers is more important than the complexation process in this mixed system. Varying the hydrophobicity of the surfactant monomer enabled us to determine that the percentages of uncomplexed CD in equilibrium with the micellar system were in the range of 5-95%. When the surfactant self-assembly structure is a vesicle, the free CD in the CD/surfactant mixed system yields a percentage of 100%.

Esters flash point prediction using artificial neural networks

In this article, an artificial neural network to predict the flash point of 95 esters was implemented. Four variables were used for its development. A neural network with 4‐5‐8‐5‐1 topology was encountered to gain the best agreement of the experimental results with those predicted (square correlation coefficient (R2) and root mean square error were 0.99 and 5.46 K for the training phase and 0.96 and 13.02 K for the testing set).

Artificial Intelligence for Electrical Percolation of AOT-based Microemulsions Prediction

Abstract Different Artificial Neural Network architectures have been assayed to predict percolation temperature of AOT/i-C8/H2O microemulsions. A Perceptron Multilayer Artificial Neural Network with five entrance variables (W value of the microemulsions, additive concentration, molecular weight of the additive, atomic radii and ionic radii of the salt components) was used. Best ANN architecture was formed by five input neurons, two middle layers (with eleven and seven neurons respectively) and one output neuron. Root Mean Square Errors (RMSEs) are 0.18°C (R = 0.9994) for the training set and 0.64°C (R = 0.9789) for the prediction set.

Influence Prediction of Small Organic Molecules (Ureas and Thioureas) Upon Electrical Percolation of AOT-Based Microemulsions Using Artificial Neural Networks

Abstract In order to predict percolation temperature of AOT-Based microemulsions (AOT/iC8/H2O w/o microemulsions) in the presence of small organic molecules (ureas and thioureas), different Artificial Neural Network architectures (ANN) have been carried out using a Perceptron Multilayer Artificial Neural Network with three entrance variables (W = value of the microemulsion, additive concentration, logP value). Best ANN architecture consists in three input neurons, one middle layer (with two neurons) and one output neuron. Correlation values were R = 0.9251 for the training set and R = 0.9719 for the prediction set.

Alkaline fading of triarylmethyl carbocations in self-assembly microheterogeneous media

This review reports on the alkaline fading of crystal violet and other related carbocations in the presence of different microheterogeneous media (micelles, microemulsions and vesicles).

Percolation Threshold of AOT Microemulsions with n-Alkyl Acids as Additives Prediction by Means of Artificial Neural Networks

Abstract Different artificial neural networks architectures have been assayed to predict percolation temperature of AOT/iC8/H2O microemulsions in the presence of n-alkyl acids with a chain length between 0 and 24 carbons, using a multilayer perceptron with five easy-acquired entrance variables (number of carbons, log P, length of the hydrocarbon chain, pKa and acid concentration). The evaluation of the neural networks was carried out by means of RMSE and IDP, resulting that the architecture with better results consists in five input neurons, two middle layers (with five and ten neuron respectively) and one output neuron. Results prove that Artificial Neural Networks are a useful tool elaborating models to predict percolation temperature of microemulsion systems in the presence of additives.

Linear Polyethers as Additives for AOT-Based Microemulsions: Prediction of Percolation Temperature Changes Using Artificial Neural Networks

Abstract Predictive models based on artificial neural networks have been developed for the percolation threshold of AOT based microemulsions with addition of either glymes or polyethylene glycols. Models have been built according to the multilayer perceptron architecture, with five input variables (concentration, molecular mass, log P, number of C and O of the additive). Best model for glymes has a topology of five input neurons, five neurons in a single hidden layer and one output neuron. Polyethylene glycol models architecture consists in five input neurons, three hidden layers with eight neurons in both first two and five in the last, and a neuron in the last output layer. All of them have a good predictive power according to several quality parameters.

Predicting Critical Micelle Concentration Values of Non-Ionic Surfactants by Using Artificial Neural Networks

Abstract Critical Micelle Concentration is a fundamental property on studying behaviour of surfactants. In general terms it depends on temperature, pressure and on the existence and concentration of other surface-active substances and electrolytes. In this work it is presented a model based on Artificial Neural Networks to obtain predictive values of Critical Micelle Concentration (CMC) of some non-ionic surfactants. ANN model works using topological descriptors of the molecules involved together with already known CMC values and provides predictive values for new cases. It is proposed a specific architecture for ANN consisting of an input layer with seven neurons, one intermediate layer with fourteen neurons and one neuron in the output layer. This ANN model seems to be a good method for forecast CMC.

A critical review on the use of artificial neural networks in olive oil production, characterization and authentication

ABSTRACT Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterraneans economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.

A Critical Review on the Applications of Artificial Neural Networks in Winemaking Technology

ABSTRACT Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.