Kinetic study of the selective hydrogenation of styrene over a Pd egg-shell composite catalyst

This is a study on the kinetics of the liquid-phase hydrogenation of styrene to ethylbenzene over a catalyst of palladium supported on an inorganic–organic composite. This support has a better mechanical resistance than other commercial supports, e.g. alumina, and yields catalysts with egg-shell structure and a very thin active Pd layer. Catalytic tests were carried out in a batch reactor by varying temperature, total pressure and styrene initial concentration between 353–393 K, 10–30 bar, and 0.26–0.60 mol L−1. Kinetic models were developed on the assumptions of dissociative hydrogen chemisorption and non-negligible adsorption of hydrogen and styrene. Final chemical reaction expressions useful for reactor design were obtained. The models that best fitted the experimental data were those ones that considered the surface reaction as the limiting step. In this sense, a two-step Horiuti–Polanyi working mechanism with half hydrogenation intermediates gave the best fit of the experimental data. The heats of adsorption of styrene and ethylbenzene were also estimated.The authors are gratefully indebted to CONICET, ANPCyT and Universidad Nacional del Litoral for financially sponsoring this research work

Fast and flexible analysis of direct dark matter search data with machine learning

We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture nonlinear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs, such as light and charge signal strengths. This technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable

Human plague: An old scourge that needs new answers

Yersinia pestis, the bacterial causative agent of plague, remains an important threat to human health. Plague is a rodent-borne disease that has historically shown an outstanding ability to colonize and persist across different species, habitats, and environments while provoking sporadic cases, outbreaks, and deadly global epidemics among humans. Between September and November 2017, an outbreak of urban pneumonic plague was declared in Madagascar, which refocused the attention of the scientific community on this ancient human scourge. Given recent trends and plague’s resilience to control in the wild, its high fatality rate in humans without early treatment, and its capacity to disrupt social and healthcare systems, human plague should be considered as a neglected threat. A workshop was held in Paris in July 2018 to review current knowledge about plague and to identify the scientific research priorities to eradicate plague as a human threat. It was concluded that an urgent commitment is needed to develop and fund a strong research agenda aiming to fill the current knowledge gaps structured around 4 main axes: (i) an improved understanding of the ecological interactions among the reservoir, vector, pathogen, and environment; (ii) human and societal responses; (iii) improved diagnostic tools and case management; and (iv) vaccine development. These axes should be cross-cutting, translational, and focused on delivering context-specific strategies. Results of this research should feed a global control and prevention strategy within a “One Health” approach

Fast and flexible analysis of direct dark matter search data with machine learning

We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture nonlinear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs, such as light and charge signal strengths. This technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable

Fast and flexible analysis of direct dark matter search data with machine learning

We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs such as light and charge signal strengths. This technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable

Total phenolic content and antioxidant activity of different streams resulting from pilot-plant processes to obtain Amaranthus mantegazzianus protein concentrates

Antioxidant properties of different pilot-plant process streams to obtain Amaranthus mantegazzianus protein concentrates (APC) were evaluated. Conventional process (CP) (alkaline extraction and isoelectric precipitation) and two alternative processes (APs): (1) acid pre-treatment stage combined with isoelectric precipitation and (2) acid pre-treatment stage combined with ultrafiltration were applied at pilot-plant scale to obtain APC. Methanol and water extracts of APC and other fractions obtained in the processes were evaluated by Folin-Ciocalteau method in order to determine total phenolic content and by DPPH radical scavenging activity method to determine antioxidant activity. Acid pre-treatment stage and ultrafiltration caused an effective removal of phenolic compounds yielding on the one hand APC with lower phenolic content than the ones obtained by CP. On the other hand, the acid extract and the whey obtained presented high phenolic content and antioxidant activity and could be used as additives to increased this parameters in food. Finally, evaluated processes could be used to obtain several products (concentrates, whey, extracts) with different phenolic content and antioxidant activity suitable for different applications in food industry. (C) 2013 Elsevier Ltd. All rights reserved.1226267Projects: CAI + D type IIUNL [PI 57-283]ANCYPT - UNL [PICTO 35831, PICTO 36237]UNL [PI 57-283]ANCYPT - UNL [PICTO 35831, PICTO 36237

Comparison between isoelectric precipitation and ultrafiltration processes to obtain Amaranth mantegazzianus protein concentrates at pilot plant scale

The aim of this study was to compare protein yield, protein concentration and physicochemical characteristics of Amaranth mantegazzianus protein concentrates (APC) obtained at pilot-scale by a conventional process (CP) (alkaline extraction and isoelectric precipitation) and two alternative processes (AP): (1) acid pre-treatment process combined with isoelectric precipitation and (2) acid pre-treatment process combined with ultrafiltration. Although AP resulted in higher protein concentration, protein yield was lower than in CP. SDS-PAGE and size-exclusion chromatography showed high molecular weight fractions only for isoelectric precipitation concentrates (obtained by CP and AP). The amino acids concentration, especially phenylalanine, isoleucine and methionine, increased in all protein concentrates respect to the amaranth flour. Particularly, the product obtained by ultrafiltration was rich in phenylalanine and lysine, and presented no limiting amino acid with respect to the recommendation of the Food and Agriculture Organization of the United Nations (FAO). In conclusion, process (2) improved protein concentration and nutritional quality (balanced amino acid composition) of A. mantegazzianus protein concentrates respect to CP and process (1), suggesting that the ultrafiltration process is a viable alternative to conventional process and a promising method for obtaining protein concentrates. (C) 2012 Elsevier Ltd. All rights reserved.112428829