Gas permeation through carbon membranes:Model development and experimental validation

With a growing interest in carbon membranes for gas separation, understanding their performance and behaviour is essential for proper design of the membrane separation. Currently not many models exist that correctly describe transport through carbon membranes due to its complex nature. This work attempts to implement a general modelling approach which describes several key transport phenomena inside carbon membranes. The approach assumes a membrane wall to be a bundle of pores with parallel transport mechanisms using the pore size distribution as a weight factor to sum the different transport phenomena. This work adapts this approach specifically for carbon membranes, additionally accounting for molecular sieving and pore blocking effects. Imposing realistic boundary conditions, the model is solved using global optimization algorithms. For testing, four different CMSMs have been produced with hydroquinone and novolac precursors. Pure- and mixed gas permeation tests are done for these CMSMs with H2, N2, and CO2 and the model is fit to this permeation data. Fitting results with pure gas measurements show the model is able to predict the contributions of different mass transport mechanism for the different membranes. This is validated by comparing these results to gas-pair permselectivity data. The model is furthermore fit to mixed gas data. Existence of multi-component effects shows that the model could be further improved. Overall, the model presented in this work is shown to be able to describe complex mass transport behaviour for various different carbon membranes.</p

Energy analysis of a power-to-jet-fuel plant

Sustainable aviation fuel (SAF) production from captured carbon dioxide and green hydrogen, is referred to as the key to decarbonize the hard-to-abate aviation sector. Fischer-Tropsch is a mature and reliable pathway for hydrocarbon synthesis, with a wide spectrum of technological options and high plant efficiency extending to more than 80 % of e-kerosene selectivity. In this work, an Aspen Hysys model, coupled with different Matlab simulations for Fischer-Tropsch, Hydrocracker and SOEC, was set up to estimate efficiency and selectivity. The results show that global efficiency is mainly linked to the efficiency of the production of H2. Energetic efficiency reaches 48.06 % using the already existing commercial electrolyte supported cell in a SOEC electrolyser, but it could increase to 65.74 % if cathode supported cell was considered.</p

Respondents of health survey powered by the innovative NURO app exhibit correlations between exercise frequencies and diet habits, and between stress levels and sleep wellness

Nurosene's NURO app (nurosene.com) is an innovative smartphone application that gathers and analyzes active self-report metrics from users, empowering them with data-driven health machine intelligence. We present the data collected and analyzed from the initial round of participants who responded to a 12-question survey on their life-style and health status. Exploratory results using a variational autoencoder (VAE) suggested that much of the variability of the 12 dimensional data could be accounted for by two approximately uncorrelated latent variables: one pertaining to stress and sleep, and the other pertaining to exercise and diet. Subsequent modeling of the data using exploratory and confirmatory factor analyses (EFAs and CFAs) found that optimal data fits consisted of four factors, namely exercise, diet, stress, and sleep. Covariance values were high between exercise and diet, and between stress and sleep, but much lower between other pairings of non-identical factors. Both EFAs and CFAs provided extra contexts to and quantified the more preliminary VAE observations. Overall, our results significantly reduce the apparent complexity of the response data. This reduction allows for more efficient future stratification and analyses of participants based on simpler latent variables. Our discovery of novel relationships between stress and sleep, and between exercise and diet suggests the possibility of applying predictive analytics in future efforts

The predictive role of biomarkers and genetics in childhood asthma exacerbations

Asthma exacerbations are associated with significant childhood morbidity and mortality. Recurrent asthma attacks contribute to progressive loss of lung function and can sometimes be fatal or near‐fatal, even in mild asthma. Exacerbation prevention becomes a primary target in the management of all asthmatic patients. Our work reviews current advances on exacerbation predictive factors, focusing on the role of non‐invasive biomarkers and genetics in order to identify subjects at higher risk of asthma attacks. Easy‐to‐perform tests are necessary in children; therefore, interest has increased on samples like exhaled breath condensate, urine and saliva. The variability of biomarker levels suggests the use of seriate measurements and composite markers. Genetic predisposition to childhood asthma onset has been largely investigated. Recent studies highlighted the influence of single nucleotide polymorphisms even on exacerbation susceptibility, through involvement of both intrinsic mechanisms and gene‐environment interaction. The role of molecular and genetic aspects in exacerbation prediction supports an individual‐shaped approach, in which follow‐up planning and therapy optimization take into account not only the severity degree, but also the risk of recurrent exacerbations. Further efforts should be made to improve and validate the application of biomarkers and genomics in clinical settings

Asthma and food allergy: Which risks?

Over the past few decades, an increase in the prevalence of asthma and food allergy has been observed in the pediatric population. In infants, food sensitization, particularly to egg, has increased the risk of developing allergic asthma. This is even more likely if sensitization to food allergens occurs early within the first few years of life. It is indeed known that both diseases may be present simultaneously in the pediatric population, but coexistence may negatively influence the severity of both conditions by increasing the risk of life-threatening asthmatic episodes as well as food-related anaphylaxis. Therefore, an accurate clinical and phenotype characterization of this high-risk group of children with both asthma and food allergy and a more aggressive management might lead to reducing related morbidity and mortality. The aim of this review is to provide an updated overview on the close link between food allergy and asthma and their negative mutual influence

Fluidized bed gasification of biomass from plant-assisted bioremediation: Fate of contaminants

Fluidized-bed gasification (FBG) of Phyto-assisted Bioremediation (PABR) biomass is analyzed focusing on the contaminants' dispersion. Poplar pruning coming from an area contaminated by polychlorinated biphenyls (PCBs) and heavy metals (HM) are considered. The biomass analysis showed relevant contents in HMs, especially Cd and Cr, and no significant PCB content. FBG process was analyzed to: a) track pollutants, b) detect contaminants in the FBG and c) investigate the HMs concentration in the produced streams. The results showed that most of the metals are concentrated in the ashes collected in the bottom of the reactor (Pb, Cd, Cu, Cr), or in the cyclone (B, Na, Mg, Al, K and Fe). Interestingly, metals are also released by the olivine bed (Mg, Fe, Ni and Al) and transported downstream. Consistent fractions of Zn and Fe (also Cu) were detected in the fugitive ashes. As for the Volatile Organic Compounds (VOC) concentration, we noted similarities between PABR and virgin biomass syngas streams. A reduced-scale process was carried out in TGA-DTA to investigate the potential of such technique in reproducing the main features of the FBG process. Comparable results were obtained, thus suggesting its possible application for small-scale preliminary assessment of FBG process