Structural changes induced by dehydration in the crystalline layered silicate Na-RUB-18: a computational/experimental combined study

Na-RUB-18 layered silicate (Na(8)Si(32)O(64)(OH)(8) center dot 32H(2)O) is prepared following the procedure reported in the literature (ref. 1: K. Kosuge and A. Tsunashima, J. Chem. Soc., Chem. Commun., 1995, 2427) and its significant structural modifications induced by progressive removal of hydration water molecules are studied for the first time by a combined experimental (TGA and variable temperature XRD and FTIR) and theoretical approach. A novel structure of a partially de-hydrated Na-RUB-18 obtained upon heating at 75 degrees C under inert gas flow, which leads to a reversible phase transformation in agreement with XRD and FTIR results, is found. TGA indicates that at this stage, two of the four hydration water molecules per Na ion are lost. Theoretical results based on DFT calculations suggest that the apical water molecules of the solvated octahedrally coordinated sodium ion, forming weak H-bonds with the silicate oxygen atoms, are removed at this stage. As a consequence of the dehydration, the silicate layers get closer, and, similarly to that observed for zeolites, the sodium ions move towards lattice oxygen atoms, to restore their coordination shell. A combination of IR spectroscopy and computational models is used to describe the vibrational properties of silanol/silanolate (SiOH/SiO)(-1) bridges, which are responsible for the proton conduction of Na-RUB-18. The method described can be of general utility to refine structures which are not fully accessible to standard X-ray structure analysis.191726102617European CommunityRegione Piemont

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

<p>Abstract</p> <p>Background</p> <p>In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several <it>concerns </it>about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these <it>concerns </it>and identify means of enhancing the current models for higher operational use.</p> <p>Methods</p> <p>We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.</p> <p>Results</p> <p>While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.</p> <p>Conclusions</p> <p>To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.</p