Hydrogen recovery from steam methane reforming using the ITQ-12 zeolite

The aim of this work is to investigate the potential use of the ITQ-12 zeolite in a PSA process to obtain purified hydrogen from the SMR product stream using molecular simulation. Since the main components of the product stream are hydrogen and carbon dioxide, we put the focus on the separation of these two gases. The separation of hydrogen from the other components (methane, carbon monoxide and nitrogen) will be briefly touched upon as well. From inspection of the adsorption isotherms of carbon dioxide and hydrogen in ITQ-12 we found that the former gas dominates at industrially relevant conditions (311 K and 16 Pa). Breakthrough curves reveal that the ITQ-12 zeolite is likely to perform well in a PSA process for the separation of hydrogen from carbon dioxide. We found that in general higher column lengths and lower gas feed velocities are favorable for a good separation while higher gas feed velocities are favorable for the cleansing of carbon dioxide from the column. For the full mixture it is necessary to employ longer column lengths in order to separate hydrogen from the other components. This is because nitrogen and carbon monoxide exhibit retention times similar to hydrogen, though the latter still travels through the column the fastest. Alternatively the use of ITQ-12 could be combined with other separation techniques, where ITQ-12 is used to mainly remove CO and CH from the SMR product stream. These findings suggest that SMR could become an economically viable way to produce hydrogen by using ITQ-12 in the separation process

Equivariant Networks for Porous Crystalline Materials

Porous crystalline materials have the potential to play a key role in developing solutions for molecular storage, gas separation and carbon adsorption. For these solutions, we need to develop new materials with specific properties. Estimating the properties of such porous materials involves first principle simulation using classical molecular simulations. The computational complexity of these methods can be a barrier to high throughput screening of the potential materials as the space of possible materials is vast. Data-driven methods, specifically machine learning methods based on deep neural networks offer a significant opportunity to significantly scale the simulation of the behavior of these materials. However, to effectively achieve this the Deep Learning models need to utilize the symmetries present in the crystals. Crystals pose specific symmetries that are present in their space group. Existing methods for crystal property prediction either have symmetry constraints that are too restrictive or only incorporate symmetries between unit cells. In addition, these models do not explicitly model the porous structure of the crystal. In this paper, we develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure. We evaluate our model by predicting the heat of adsorption of CO$_2$ for different configurations of the Mordenite and ZSM-5 zeolites. Our results confirm that our method performs better than existing methods for crystal property prediction and that the inclusion of pores results in a more efficient model.Comment: Added additional figures as well as additional experiments for MF

Adsorption of Carbon Dioxide in Non-Löwenstein Zeolites

We investigated the effect of the aluminum distribution in the adsorption properties of carbon dioxide in the MFI, MOR, and ITW zeolites. Because of its lack of experimental evidence and theoretical validation, Löwenstein’s rule was not generally imposed, and special attention was paid to the effect of the Al-O-Al linkages. To this end, we first generalized an existing transferable force field for CO2 adsorption in non-Löwenstein zeolites. By means of molecular simulations based on this force field, we showed that the carbon dioxide adsorption efficiency in MFI is determined by the number of Al atoms, rather than by their distribution in the framework. This was attributed to the small size of the CO2 molecules compared to the 3D wide-channel topology of the structure. Conversely, we found that the Al distribution has a higher impact on the heat of adsorption in MOR. Although structures with a very high and very low number of non-Löwenstein bonds presented significant differences, the bonds themselves do not affect the heat of adsorption directly. Instead, we found that an homogeneous distribution of the Al atoms in the sites forming the C-channel is more favorable. Finally, the small-pore distribution of the ITW zeolite led to high values of the heat of adsorption and wide error bars, which made the study feasible just for low aluminum concentrations. In that case, we report a small dependency of the heat of adsorption on the Al distribution.</p

A simulation study of linker vacancy distribution and its effect on UiO-66 stability

In this work, we computationally investigate the impact of the distribution of linker vacancies on the relative stability of defected UiO-66 structures. Analysis of a significant number of defected structures reveals that higher defect numbers correlate with lower stability, but variations in missing linker distribution and orientation contribute to widely differing amorphization pressures. Our results suggest that structures with more evenly distributed vacancies exhibit a positive linear relationship between amorphization pressure and bulk modulus. Furthermore, we found structures with a disproportionate number of missing linkers with the same orientation and structures where a large volume of the framework remains pristine, display outlier behavior in this respect. Evaluation of anisotropic elastic moduli uncovers directional instability in structures with a high number of vacancies with the same orientation. These findings have important implications for designing and optimizing UiO-66-based materials, aiding in defect configuration selection for specific applications.</p

Carbon dioxide capture enhanced by pre-adsorption of water and methanol in UiO-66

The rapidly rising level of carbon dioxide in the atmosphere resulting from human activity is one of the greatest environmental problems facing our civilization today. Most technologies are not yet sufficiently developed to move existing infrastructure to cleaner alternatives. Therefore, techniques for capturing carbon dioxide from emission sources may play a key role at the moment. The structure of the UiO‐66 material not only meets the requirement of high stability in contact with water vapor but through the water pre‐adsorbed in the pores, the selectivity of carbon dioxide adsorption is increased. We successfully applied the recently developed methodology for water adsorption modelling. It allowed to elucidate the influence of water on CO(2) adsorption and study the mechanism of this effect. We showed that water is adsorbed in octahedral cage and stands for promotor for CO(2) adsorption in less favorable space than tetrahedral cages. Water plays a role of a mediator of adsorption, what is a general idea of improving affinity of adsorbate. On the basis of pre‐adsorption of methanol as another polar solvent, we have shown that the adsorption sites play a key role here, and not, as previously thought, only the interaction between the solvent and quadrupole carbon dioxide. Overall, we explained the mechanism of increased CO(2) adsorption in the presence of water and methanol, as polar solvents, in the UiO‐66 pores for a potential post‐combustion carbon dioxide capture application

The Boost of Toluene Capture in UiO-66 Triggered by Structural Defects or Air Humidity

This work aimed to investigate the adsorption of toluene in UiO-66 materials. Toluene is a volatile, aromatic organic molecule that is recognized as the main component of VOCs. These compounds are harmful to the environment as well as to living organisms. One of the materials that allows the capture of toluene is the UiO-66. A satisfactory representation of the calculated isotherm steep front and sorption capacity compared to the experiment was obtained by reducing the force field σ parameter by 5% and increasing ϵ by 5%. Average occupation profiles, which are projections of the positions of molecules during pressure increase, as well as RDFs, which are designed to determine the distance of the center of mass of the toluene molecule from organic linkers and metal clusters, respectively, made it possible to explain the mechanism of toluene adsorption on the UiO-66 material.</p

Entropy effects during sorption of alkanes in zeolites

Recent developments in Configurational-Bias Monte Carlo (CBMC) techniques allow the accurate calculation of the sorption isotherms for alkanes, and their mixtures, in various zeolites. The CBMC simulations give new insights into subtle entropy effects affecting mixture adsorption. Three types of entropy effects can be distinguished. (1) Size entropy effects favour the component with the smaller number of C atoms because the smaller molecule finds it easier to fill in the 'gaps' within the zeolite matrix at high molecular loadings. (2) Configurational entropy effects come into play for mixtures of alkanes that differ in the degree of branching. For a mixture of linear and branched alkanes with the same number of C atoms, configurational entropy effects favour the linear isomer because such molecules 'pack' more efficiently within, say, the intersecting channel topology of MFI zeolite. (3) Length entropy effects comes into force for sorption of linear and branched alkanes within the cylindrical channels of say AFI and MOR zeolites; here the double branched alkane has the shortest length and can be packed more efficiently within the channels. We demonstrate that CBMC simulations allow the efficient screening of zeolite structures for a given separation duty and aid the development of novel separation processes exploiting entropy effects

Multidimensional Characterization of the Atrial Activity to Predict Electrical Cardioversion Outcome of Persistent Atrial Fibrillation

[EN] European Society of Cardiology guidelines recommend electrical cardioversion (ECV) as a rhythm control strategy in persistent atrial fibrillation (AF). Although ECV initially restores sinus rhythm (SR) in almost every patient, mid- and long-term AF recurrence rates are high, so that additional research is needed to anticipate ECV outcome and rationalize the management of AF patients. Although indices characterizing fibrillatory (f -) waves from surface lead V1, such as dominant frequency (DF), amplitude (FWA), and entropy, have reported good results, they discard the spatial information from the remaining leads. Hence, this work explores whether a multidimensional characterization approach of these parameters can improve ECV outcome prediction. The obtained results have shown that multidimensional FWA reported more balanced values of sensitivity and specificity, although the discriminant ability was similar in both cases. For DF, a similar outcome was also obtained. In contrast, multivariate entropy overcome discriminant ability of its univariate version by 5%, rightly anticipating result in more than 80% of ECV cases. Therefore, multidimensional entropy analysis seems to be able to quantify novel dynamics in the f-waves, which lead to a better ECV outcome predictionThis research was funded by the projects DPI2017-83952C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla La Mancha" and AICO/2019/036 from "Generalitat Valenciana"Cirugeda, EM.; Calero, S.; Plancha, E.; Enero, J.; Rieta, JJ.; Alcaraz, R. (2020). Multidimensional Characterization of the Atrial Activity to Predict Electrical Cardioversion Outcome of Persistent Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.377S1

Refined Multiscale Entropy Predicts Early Failure in Electrical Cardioversion of Atrial Fibrillation

[EN] Electrical cardioversion (ECV) is a well-established strategy for atrial fibrillation (AF) management. Despite its high initial effectiveness, a high relapsing rate is also found. Hence, identification of patients at high risk of early AF recurrence is crucial for a rationale therapeutic strategy. For that purpose, a set of indices characterizing fibrillatory (f-) waves have been proposed, but they have not considered nonlinear dynamics present at different timescales within the cardiovascular system. This work thus explores whether a multiscale entropy (MSE) analysis of the f-waves can improve preoperative predictions of ECV outcome. Thus, two MSE approaches were considered, i.e., traditional MSE and a refined version (RMSE). Both algorithms were applied to the main f-waves component extracted from lead V1 and entropy values were computed for the first 20 time-scales. As a reference, dominant frequency (DF) and f-wave amplitude (FWA) were also computed. A total of 70 patients were analyzed, and all parameters but FWA showed statistically significant differences between those relapsing to AF and maintaining sinus rhythm during a follow-up of 4 weeks. RMSE reported the best results for the scale 19, improving predictive ability up to an 8% with respect to DAF and FWA. Consequently, investigation of nonlinear dynamics at large time-scales can provide useful insights able to improve predictions of ECV failureThis research was funded by the projects DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla La Mancha" and AICO/2019/036 from "Generalitat Valenciana".Cirugeda, EM.; Calero, S.; Hidalgo, VM.; Enero, J.; Rieta, JJ.; Alcaraz, R. (2020). Refined Multiscale Entropy Predicts Early Failure in Electrical Cardioversion of Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.369S1