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Porous biochar for improving the CO<sub>2</sub> uptake capacities and kinetics of concrete
No full textInternational audienceCarbonation is a natural process in concrete where atmospheric CO2 diffuses into the pores of the material and reacts with cement hydrates to form calcium carbonate. Although this process can help to sequester atmospheric CO2 and mitigate rising levels in urban areas, it slows down over time, resulting in low CO2 uptake over the service life of concrete. This study proposes a sustainable method to improve carbonation kinetics and CO2 capture in cement materials by incorporating highly porous biochar. The biochar, derived from seaweed pyrolysis, has a highly developed surface area, including micropores optimised for CO2 adsorption, mesopores and macropores, as well as oxygen-rich surface groups. These properties allow the biochar to efficiently adsorb CO2 and retain water. The biochar particles embedded in the cement matrix act as reservoirs for water and CO2 , influencing hydration and carbonation. The addition of biochar increases water retention in the composite, which promotes the formation of capillary pores and enhances the carbonation process. Experimental data and numerical simulations show that the adsorption of CO2 in the micropores of biochar facilitates the flow of CO2 through the composite, allowing deeper carbonation. The interaction between biochar and cement matrix enhances CO2 diffusion and promotes calcium carbonate formation both within the biochar and at the biocharcement interface, further improving CO2 uptake. The study demonstrates that the incorporation of porous biochar into cement materials significantly increases their potential for CO2 capture, offering a promising approach to sustainable construction and carbon sequestration
Particulate transport in porous media at pore-scale. Part 1: Unresolved-resolved four-way coupling CFD-DEM
No full textInternational audienceComputational Fluid Dynamics - Discrete Element Method (CFD-DEM) is a powerful approach to simulate particulate flow in porous media at the pore-scale, and hence decipher the complex interplay between particle transport and retention. Two separate CFD-DEM approaches are commonly used in the literature: the unresolved (particle smaller than the grid cell size) and the resolved (particle bigger than the grid cell size) approach. In this paper, we propose a novel CFD-DEM coupling approach that combines both unresolved and resolved coupling. Our new modeling technique allows for the simulation of particulate flows in complex pore morphology characteristic of porous materials. It relies on an efficient searching strategy to find grid cells covered by the particles and on an appropriate calculation of the fluid-solid momentum exchange term. The robustness and efficiency of the computational model are demonstrated using cases for which reference solutions – analytical or experimental – exist. The new unresolved-resolved four-way coupling CFD-DEM is used to investigate pore-clogging and permeability reduction due to the sieving and bridging of particles
Entropy-based burn in time analysis and ranking for (A)MCMC algorithms in high dimension
No full textInternational audienceMany recent and often (Adaptive) Markov Chain Monte Carlo (A)MCMC methods are associated in practice to unknown rates of convergence. We propose a simulation-based methodology to estimate and compare MCMC’s performance in terms of shortest burn in time, using a Kullback divergence criterion requiring an estimate of the entropy of the algorithm densities at each iteration, computed from iid simulated chains. In previous works, we proved some consistency results in MCMC setup for an entropy estimate based on Monte Carlo integration of a kernel density estimate proposed by [18], and we investigate an alternative Nearest Neighbor (NN) entropy estimate from [24]. This estimate has been used mostly in univariate situations until recently when entropy estimation in higher dimensions has been considered in other fields like neuroscience or system biology. Unfortunately, in higher dimensions, both estimators converge slowly with a noticeable bias. The present work goes several steps further, with bias reduction and automatic (A)MCMC burn in time analysis in mind. First, for bias reduction, we apply in our situation a “crossed NN-type” nonparametric estimate of the Kullback divergence between two densities, based on iid samples from each, introduced by [39, 40]. We prove the consistency of these entropy estimates under recent uniform control conditions, for the successive densities of a generic class of MCMC algorithm to which most of the methods proposed in the recent literature belong. Secondly, we propose an original solution based on a PCA for reducing relevant dimension and bias in even higher dimensions whenever PCA is efficient. Our algorithms for MCMC simulation and entropy estimation are progressively added to the R package EntropyMCMC taking advantage of recent advances in high performance (parallel) computing
Enhanced Numerical Solutions for Fractional PDEs Using Monte Carlo PINNs Coupled with Cuckoo Search Optimization
No full textInternational audienceIn this study, we introduce an innovative approach for addressing fractional partial differential equations (fPDEs) by combining Monte Carlo-based Physics-Informed Neural Networks (PINNs) with the Cuckoo Search (CS) optimization algorithm, termed PINN-CS. There is a further enhancement in the application of quasi-Monte Carlo assessment that comes with high efficiency and computational solutions to estimates of fractional derivatives. By employing structured sampling nodes comparable to techniques used in finite-difference approaches on staggered or irregular grids, the proposed PINN-CS minimizes storage and computation costs while maintaining high precision in estimating solutions. This is supported by numerous numerical simulations to analyze various high-dimensional phenomena in various environments, comprising of two-dimensional space-fractional Poisson equations, two-dimensional time-space fractional diffusion equations and three-dimensional fractional Bloch-Torrey equations. The results demonstrate that PINN-CS achieves superior numerical accuracy and computational efficiency compared to traditional fPINN and Monte Carlo fPINN methods. Further, the extended use to problem areas with irregular geometries and difficult to define boundary conditions makes the method immensely practical. This research thus lays a foundation for more adaptive and accurate use of hybrid techniques in the development of the fractional differential equations, in computing science and engineering
For better or for worse: differential effects of the emotional valence of words on children’s recall
No full textInternational audienceRecent research has revealed the widespread effects of emotion on cognitivefunctions and memory. However, the influence of emotional valence on verbalshort-term memory remains largely unexplored, especially in children. This studymeasured the effect of emotional valence on word immediate serial recall in 4–6-year-old French children (N = 124). Results show a robust effect of emotionalvalence on recall performances and recall errors. More precisely, we observed afacilitating effect of the positive valence of words: it allows better performance andcauses few recall errors. On the contrary, the data indicated a disruptive impact ofnegative word valence: the latter causes very low recall performance and isassociated with a high proportion of recall errors. These findings add new evidenceof the influence of emotion on children’s verbal short-term memory. Our resultsare discussed in relation to current semantic and attentional explanations of theemotional enhancement of memory
Influence of the sequential purification of biomass-derived carbon dots on their colloidal and optical properties
No full textInternational audienceThe purification of photoluminescent carbon dots has a strong impact on their applications, making rigorous protocols necessary to isolate them. In this work, a sequential filtration process was implemented on photoluminescent carbon dots synthesized by a microwave-assisted hydrothermal treatment of an agricultural waste, achieving good effectiveness and higher yields compared to chromatographic purification approaches. Filtration membranes of different cut-offs were carefully selected considering the by-products from the biomass decomposition. The synthetic route allowed the preparation of carbon dots with average particle sizes of 3.5 nm, composed of a graphitic-like core decorated with oxygen and nitrogen moieties. Dynamic light scattering measurements of the suspensions revealed higher aggregation of the nanoparticles in the least purified samples. This also influences the photoluminescence emission properties due to self-absorbing and quenching effects. The thermal characterization of the solids recovered after each filtration step has shown (for the least purified samples) the presence of fragments assigned to molecules arising from an incomplete hydrothermal transformation of the precursor. The aqueous suspension of the carbon dots recovered after the most purified protocol displayed a notorious emission upon excitation at 375 nm, confirming that the carbon dots are responsible for the observed optical features. These results highlight the importance of an adequate purification process of carbon dots obtained from complex precursors (such as biomass), to avoid bias interpretation of their photoluminescence properties.</div
Postural Control Specificities According to Sports Expertise: A Comparison between Female Soccer Players, Horseback Riders and Non-Athletes
No full textImproved protocol for metabolite extraction and identification of respiratory quinones in extremophilic Archaea grown on mineral materials
No full textInternational audienceWe investigated the metabolome of the iron-and sulfur-oxidizing, extremely thermoacidophilic archaeon Metallosphaera sedula grown on mineral pyrite (FeS2 ). The extraction of organic materials from these microorganisms is a major challenge because of the tight contact and interaction between cells and mineral materials. Therefore, we applied an improved protocol to break the microbial cells and separate their organic constituents from the mineral surface, to extract lipophilic compounds through liquid-liquid extraction, and performed metabolomics analyses using MALDI-TOF MS and UHPLC-UHR-Q/TOF. Using this approach, we identified several molecules involved in central carbon metabolism and in the modified Entner-Doudoroff pathway found in Archaea, sulfur metabolism-related compounds, and molecules involved in the adaptation of M. sedula to extreme environments, such as metal tolerance and acid resistance. Furthermore, we identified molecules involved in microbial interactions, i.e., cell surface interactions through biofilm formation and cell-cell interactions through quorum sensing, which relies on messenger molecules for microbial communication. Moreover, we successfully extracted and identified different saturated thiophene-bearing quinones using software for advanced compound identification (MetaboScape). These quinones are respiratory chain electron carriers in M. sedula, with biomarker potential for life detection in extreme environmental conditions