Publicacións nas que colabora con Óscar Moldes Figueiral (22)

2019

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

    Critical Reviews in Food Science and Nutrition, Vol. 59, Núm. 12, pp. 1913-1926

  2. Interaction of caffeic acid with SDS micellar aggregates

    Molecules, Vol. 24, Núm. 7

2017

  1. A critical review on the applications of artificial neural networks in winemaking technology

    Critical Reviews in Food Science and Nutrition, Vol. 57, Núm. 13, pp. 2896-2908

  2. Redispersion and Self-Assembly of C60 Fullerene in Water and Toluene

    ACS Omega, Vol. 2, Núm. 5, pp. 2368-2373

2016

  1. Electrical percolation of AOT-based microemulsions with n-alcohols

    Journal of Molecular Liquids, Vol. 215, pp. 18-23

2015

  1. Cleavage of carbofuran and carbofuran-derivatives in micellar aggregates

    Progress in Reaction Kinetics and Mechanism, Vol. 40, Núm. 2, pp. 105-118

  2. Density prediction of ternary mixtures of ethanol + water + ionic liquid using backpropagation artificial neural networks

    Ionic liquid-based surfactant science: formulation, characterization and applications (John Wiley & Sons (USA)), pp. 447-458

  3. Influence prediction of alkylamines upon electrical percolation of AOT-based microemulsions using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 52, Núm. 6, pp. 473-476

  4. Linear polyethers as additives for AOT-based microemulsions: Prediction of percolation temperature changes using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 52, Núm. 4, pp. 264-270

2014

  1. Percolative behavior models based on artificial neural networks for electrical percolation of AOT microemulsions in the presence of crown ethers as additives

    Tenside, Surfactants, Detergents, Vol. 51, Núm. 6, pp. 533-540

2013

  1. Esters flash point prediction using artificial neural networks

    Journal of Computational Chemistry, Vol. 34, Núm. 5, pp. 355-359

  2. Percolation threshold of AOT microemulsions with n-alkyl acids as additives prediction by means of artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 50, Núm. 5, pp. 360-368

  3. Predicting critical micelle concentration values of non-ionic surfactants by using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 50, Núm. 2, pp. 118-124

2012

  1. Influence prediction of small organic molecules (Ureas and Thioureas) upon Electrical percolation of AOT-based microemulsions using artificial neural networks

    Tenside, Surfactants, Detergents, Vol. 49, Núm. 4, pp. 316-320

2011

  1. Alkaline fading of triarylmethyl carbocations in self-assembly microheterogeneous media

    Progress in Reaction Kinetics and Mechanism, Vol. 36, Núm. 2, pp. 139-165

  2. Artificial intelligence for electrical percolation of aot-based microemulsions prediction

    Tenside, Surfactants, Detergents, Vol. 48, Núm. 6, pp. 477-483

  3. Basic degradation of 3-keto-carbofuran in the presence of non-ionic self-assembly colloids

    Fresenius Environmental Bulletin, Vol. 20, Núm. 2, pp. 354-357

  4. Influence of anionic and nonionic micelles upon hydrolysis of 3-hydroxy-carbofuran

    International Journal of Chemical Kinetics, Vol. 43, Núm. 8, pp. 402-408

  5. N-alkylamines-based micelles aggregation number determination by fluorescence techniques

    Journal of Solution Chemistry, Vol. 40, Núm. 12, pp. 2072-2081

2010

  1. Cyclodextrin-surfactant mixed systems as reaction media

    Progress in Reaction Kinetics and Mechanism, Vol. 35, Núm. 2, pp. 105-129