No abstract

ResumenSe estudió el potencial uso de un carbón activado de origen bituminoso como adsorbente de Cr(VI) en solución acuosa. Se aplicó la metodología de superficie de respuesta como herramienta para la optimización de algunas condiciones operacionales de la adsorción (temperatura, pH y la dosis de adsorbente) usando como variable de respuesta el porcentaje de remoción de Cr(VI). Se establecieron las siguientes condiciones óptimas del proceso (pH de 2, a 40°C y dosis de adsorbente de 6 g.L -1 ). En estas condiciones experimentales la eficiencia de remoción de Cr(VI) fue del 100%. El carbón activado mostró una alta capacidad de adsorción (16.53 mg.g -1 ), alcanzando el equilibrio de adsorción en menos de 60 minutos. Los parámetros termodinámicos (ΔH° = 65.05 kJ.mol -1 , ΔS° = 0.2446 kJ.mol -1 y -6.664 (a 293 K)  ΔG°  -11.557 (a 313 K)) indicaron la naturaleza endotérmica y espontánea del proceso. El mecanismo de adsorción de Cr(VI) fue interpretado como quimisorción con base en el alto valor de entalpia. El proceso de adsorción se ajustó a un modelo cinético de pseudo-segundo orden y al modelo de adsorción de Langmuir. Palabras clave: adsorción; carbón activado bituminoso; metodología superficie de respuesta; Cr(VI) AbstractThe potential application of an activated carbon, of bituminous origin, as an adsorbent of Cr(VI) in aqueous solution was studied. The response surface methodology was used as a tool to optimize some of the operating conditions of adsorption process (temperature, pH and adsorbent dose) using as a criterion Cr(VI) removal percentage (%RCr). The following optimal conditions were found: pH equaled to 2, temperature of 40°C and adsorbent dose of 6 g.L -1 . The %Rcr obtained experimentally was 100%. Activated carbon showed a high adsorption capacity (16,53 mg.g -1 ), reaching equilibrium adsorption in less than 60 minutes. The thermodynamic parameters (ΔH° = 65.05 kJ.mol -1 , ΔS° = 0.2446 kJ.mol -1 and -6.664 (at 293 K)  ΔG°  -11.557 (at 313 K)) indicated the endothermic and spontaneous nature of adsorption. Cr(VI) adsorption mechanism was interpreted as chemisorption on the basis of high value of enthalpy. The adsorption process followed a pseudo-second kinetic model and obeyed the Langmuir isotherm model. INTRODUCCIÓNLa contaminación del agua con cromo, específicamente con Cr(VI), es motivo de gran preocupación debido a su aplicación en diferentes industrias, entre ellas: la galvanoplastia, el curtido del cuero, la minería, el acabado de metales, las industrias textiles (Ezechi et al., 2016). En muchos países en desarrollo, es común encontrar este tipo de empresas que operan en pequeña y mediana escala. Estas unidades pueden generar una carga de contaminación considerable que, con frecuencia, se descarga directamente al sistema hídrico sin adecuado tratamiento previo. Se ha reportado que la concentración de Cr(VI) en aguas residuales industriales puede variar entre 0.5 hasta 220 mg.L -1 (Rai et al., 2016). Solucionar el problema de la contaminación del agua causada por Cr(VI) es una tarea desafi...

Supernovae Ia (SN) are among the brightest objects we can observe and can provide a unique window on the large scale structure of the Universe at redshifts where other observations are not available. The photons emitted by SNe are in fact affected by the density field between the source and the observer, and from the observed luminosity distance it is possible to solve the inversion problem (IP), i.e. to reconstruct the density field which produced those effects.So far the IP was only solved assuming some restrictions about the geometry of the problem, such as spherical symmetry for example, and the approach was based on solving complicated systems of differential equations which required smooth function as inputs, while observational data is not smooth, due to its discrete nature. In order to overcome these limitations we develop for the first time an inversion method which is not assuming any symmetry, and can be applied directly to observational data, without the need of any data smoothing procedure.The method is based on the use of convolutional neural networks (CNN) trained on simulated data, and it shows quite accurate results. The training data set is obtained by first generating random density and velocity profiles, and then computing their effects on the luminosity distance. The CNN is then trained to reconstruct the density field from the luminosity distance. The CNN is a modified version of U-Net to account for the tridimensionality of the data, and can reconstruct the density and velocity fields with a good level of accuracy.The use of neural networks to analyze observational data from future SNe catalogues will allow to reconstruct the large scale structure of the Universe to an unprecedented level of accuracy, at a redshift at which few other observations are available.

Supernovae Ia (SNe) can provide a unique window on the large scale structure (LSS) of the Universe at redshifts where few other observations are available, by solving the inversion problem (IP) consisting in reconstructing the LSS from its effects on the observed luminosity distance. So far the IP was solved assuming some restrictions about space–time, such as spherical symmetry for example, while we obtain for the first time solutions of the IP problem for arbitrary space–time geometries using deep learning. The method is based on the use of convolutional neural networks (CNN) trained on simulated data. The training data set is obtained by first generating random density and velocity fields, and then computing their effects on the luminosity distance. The CNN, based on an appriately modified version of U-Net to account for the tridimensionality of the data, is then trained to reconstruct the density and velocity fields from the luminosity distance. We find that the velocity field inversion is more accurate than the density field, because the effects of the velocity on the luminosity distance only depend on the source velocity, while in the case of the density it is an integrated effect along the line of sight, giving rise to more degeneracy in the solution of the IP. Improved versions of these neural networks, modified to accommodate the non uniform distribution of the SNe, can be applied to observational data to reconstruct the large scale structure of the Universe at redshifts at which few other observations are available.