943 resultados para Unit Cell And Indentation Models


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Left ventricular diastolic dysfunction leads to heart failure with preserved ejection fraction, an increasingly prevalent condition largely driven by modern day lifestyle risk factors. As heart failure with preserved ejection fraction accounts for almost one-half of all patients with heart failure, appropriate nonhuman animal models are required to improve our understanding of the pathophysiology of this syndrome and to provide a platform for preclinical investigation of potential therapies. Hypertension, obesity, and diabetes are major risk factors for diastolic dysfunction and heart failure with preserved ejection fraction. This review focuses on murine models reflecting this disease continuum driven by the aforementioned common risk factors. We describe various models of diastolic dysfunction and highlight models of heart failure with preserved ejection fraction reported in the literature. Strengths and weaknesses of the different models are discussed to provide an aid to translational scientists when selecting an appropriate model. We also bring attention to the fact that heart failure with preserved ejection fraction is difficult to diagnose in animal models and that, therefore, there is a paucity of well described animal models of this increasingly important condition.

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Calculations of synthetic spectropolarimetry are one means to test multidimensional explosion models for Type Ia supernovae. In a recent paper, we demonstrated that the violent merger of a 1.1 and 0.9 M⊙ white dwarf binary system is too asymmetric to explain the low polarization levels commonly observed in normal Type Ia supernovae. Here, we present polarization simulations for two alternative scenarios: the sub-Chandrasekhar mass double-detonation and the Chandrasekhar mass delayed-detonation model. Specifically, we study a 2D double-detonation model and a 3D delayed-detonation model, and calculate polarization spectra for multiple observer orientations in both cases. We find modest polarization levels (<1 per cent) for both explosion models. Polarization in the continuum peaks at ∼0.1–0.3 per cent and decreases after maximum light, in excellent agreement with spectropolarimetric data of normal Type Ia supernovae. Higher degrees of polarization are found across individual spectral lines. In particular, the synthetic Si II λ6355 profiles are polarized at levels that match remarkably well the values observed in normal Type Ia supernovae, while the low degrees of polarization predicted across the O I λ7774 region are consistent with the non-detection of this feature in current data. We conclude that our models can reproduce many of the characteristics of both flux and polarization spectra for well-studied Type Ia supernovae, such as SN 2001el and SN 2012fr. However, the two models considered here cannot account for the unusually high level of polarization observed in extreme cases such as SN 2004dt.

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Modern manufacturing systems should satisfy emerging needs related to sustainable development. The design of sustainable manufacturing systems can be valuably supported by simulation, traditionally employed mainly for time and cost reduction. In this paper, a multi-purpose digital simulation approach is proposed to deal with sustainable manufacturing systems design through Discrete Event Simulation (DES) and 3D digital human modelling. DES models integrated with data on power consumption of the manufacturing equipment are utilized to simulate different scenarios with the aim to improve productivity as well as energy efficiency, avoiding resource and energy waste. 3D simulation based on digital human modelling is employed to assess human factors issues related to ergonomics and safety of manufacturing systems. The approach is implemented for the sustainability enhancement of a real manufacturing cell of the aerospace industry, automated by robotic deburring. Alternative scenarios are proposed and simulated, obtaining a significant improvement in terms of energy efficiency (−87%) for the new deburring cell, and a reduction of energy consumption around −69% for the coordinate measuring machine, with high potential annual energy cost savings and increased energy efficiency. Moreover, the simulation-based ergonomic assessment of human operator postures allows 25% improvement of the workcell ergonomic index.

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This Licentiate Thesis is devoted to the presentation and discussion of some new contributions in applied mathematics directed towards scientific computing in sports engineering. It considers inverse problems of biomechanical simulations with rigid body musculoskeletal systems especially in cross-country skiing. This is a contrast to the main research on cross-country skiing biomechanics, which is based mainly on experimental testing alone. The thesis consists of an introduction and five papers. The introduction motivates the context of the papers and puts them into a more general framework. Two papers (D and E) consider studies of real questions in cross-country skiing, which are modelled and simulated. The results give some interesting indications, concerning these challenging questions, which can be used as a basis for further research. However, the measurements are not accurate enough to give the final answers. Paper C is a simulation study which is more extensive than paper D and E, and is compared to electromyography measurements in the literature. Validation in biomechanical simulations is difficult and reducing mathematical errors is one way of reaching closer to more realistic results. Paper A examines well-posedness for forward dynamics with full muscle dynamics. Moreover, paper B is a technical report which describes the problem formulation and mathematical models and simulation from paper A in more detail. Our new modelling together with the simulations enable new possibilities. This is similar to simulations of applications in other engineering fields, and need in the same way be handled with care in order to achieve reliable results. The results in this thesis indicate that it can be very useful to use mathematical modelling and numerical simulations when describing cross-country skiing biomechanics. Hence, this thesis contributes to the possibility of beginning to use and develop such modelling and simulation techniques also in this context.

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Thermoelectric materials are revisited for various applications including power generation. The direct conversion of temperature differences into electric voltage and vice versa is known as thermoelectric effect. Possible applications of thermoelectric materials are in eco-friendly refrigeration, electric power generation from waste heat, infrared sensors, temperature controlled-seats and portable picnic coolers. Thermoelectric materials are also extensively researched upon as an alternative to compression based refrigeration. This utilizes the principle of Peltier cooling. The performance characteristic of a thermoelectric material, termed as figure of merit (ZT) is a function of several transport coefficients such as electrical conductivity (σ), thermal conductivity (κ) and Seebeck coefficient of the material (S). ZT is expressed asκσTZTS2=, where T is the temperature in degree absolute. A large value of Seebeck coefficient, high electrical conductivity and low thermal conductivity are necessary to realize a high performance thermoelectric material. The best known thermoelectric materials are phonon-glass electron – crystal (PGEC) system where the phonons are scattered within the unit cell by the rattling structure and electrons are scattered less as in crystals to obtain a high electrical conductivity. A survey of literature reveals that correlated semiconductors and Kondo insulators containing rare earth or transition metal ions are found to be potential thermoelectric materials. The structural magnetic and charge transport properties in manganese oxides having the general formula of RE1−xAExMnO3 (RE = rare earth, AE= Ca, Sr, Ba) are solely determined by the mixed valence (3+/4+) state of Mn ions. In strongly correlated electron systems, magnetism and charge transport properties are strongly correlated. Within the area of strongly correlated electron systems the study of manganese oxides, widely known as manganites exhibit unique magneto electric transport properties, is an active area of research.Strongly correlated systems like perovskite manganites, characterized by their narrow localized band and hoping conduction, were found to be good candidates for thermoelectric applications. Manganites represent a highly correlated electron system and exhibit a variety of phenomena such as charge, orbital and magnetic ordering, colossal magneto resistance and Jahn-Teller effect. The strong inter-dependence between the magnetic order parameters and the transport coefficients in manganites has generated much research interest in the thermoelectric properties of manganites. Here, large thermal motion or rattling of rare earth atoms with localized magnetic moments is believed to be responsible for low thermal conductivity of these compounds. The 4f levels in these compounds, lying near the Fermi energy, create large density of states at the Fermi level and hence they are likely to exhibit a fairly large value of Seebeck coefficient.

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Human β-defensins (hBDs) are a family of cationic peptides able to directly kill a wide range of microorganisms including bacteria, fungi and viruses. In addition to their antimicrobial activities, defensins also contribute to the modulation of both the host innate and adaptive immunity. In this project, we demonstrate that the αCD3/28 co-stimulation of human CD4+ T cells in the presence of 10μg/ml hBD-2 or hBD-3 together causes an up-regulation in numbers of CD4+CD69+CD25+ and CD4+CD69-CD25+ T cell subsets, indicating that the treatment of hBD-2 and 3 enhances CD4+ T cell activation. Consistent with this finding, proliferation assay using CFSE suggests that hBD-2 and hBD-3 treatment in vitro induces the proliferation of CD4+ T cells following by 96hrs culture. Analysis of expression of the regulatory T cells (Tregs) specific marker, FoxP3, reveals a shift in the CD4+CD127-CD25+ Treg subset at 18hrs. However, at the later time point, we found that the percentage of FoxP3+cells decreased in the CD4+CD127-CD25+ Treg population, whereas the presence of the FoxP3+CTLA-4+ Treg subset increased. These data indicate that Treg suppressive function may be potentially defective following the co-incubation of purified T cells with either hBD-2 or hBD-3 for 42hrs in vitro due to the apparent loss of FoxP3 expression. We further characterise the role of hBD-2 and hBD-3 in driving human CD4+ T cells polarisation. Our in vitro data suggests that treatment with hBD-2 and hBD-3 can not only induces effector T cell (Teff) differentiation into RORγt+T-bet+ (Th17/Th1) cells, but can also trigger the differentiation of Treg expressing RORγt and T-bet rather than the master controller of Treg function, FoxP3. This apparent plasticity of T cell phenotype allows them to convert from Treg to Th1/17-like effector T cell phenotype following 18hrs in culture. By 42hrs in culture, treatment with hBD-2 and hBD-3 induced both Teff cell and Treg cell differentiation towards the Th17-like phenotype. Compared with the treatment with hBD-2, treatment with hBD-3 induced a more pronounced effect to increase levels of RORγt in CD4+ T cells. This elevated expression may, in turn, be responsible for the induction of higher IL-17A secretion. Consistent with this idea, it was found that treatment with hBD-3 but not hBD-2 was capable of inducing the higher level of secretion of IL-17A. Additionally, treatment with hBD-3 induced an increased expression of IL-6, which is capable of driving the differentiation of naïve T cells towards IL-17-producing Th17 cells. Functionally, using the Treg suppression assay, the data suggested that hBD-2 may dampen down Treg cell ability to induce suppression of Teff cell activity. Interestingly, co-culture with hBD-2 would also appear to increase Teff cell resistance to Treg immunoregulation in vitro. Further investigation using microarray gene analysis revealed chemokine C-C motif ligand 1 (CCL1) as potential genes responding to hBD-2 treatment. The blockade of CCL1 has been reported to inhibit Treg suppressive function. Thus, this study explored the function of these antimicrobial candidates in regulating CD4+ T cell plasticity which could result in hBD-2 and hBD-3 being able to regulate its own production, but also may regulate Treg and Teff cell development and function, thus strengthening the link between innate and adaptive immunity

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Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.

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We revisit the visibility problem, which is traditionally known in Computer Graphics and Vision fields as the process of computing a (potentially) visible set of primitives in the computational model of a scene. We propose a hybrid solution that uses a dry structure (in the sense of data reduction), a triangulation of the type Ja
 1 , to accelerate the task of searching for visible primitives. We came up with a solution that is useful for real-time, on-line, interactive applications as 3D visualization. In such applications the main goal is to load the minimum amount of primitives from the scene during the rendering stage, as possible. For this purpose, our algorithm executes the culling by using a hybrid paradigm based on viewing-frustum, back-face culling and occlusion models. Results have shown substantial improvement over these traditional approaches if applied separately. This novel approach can be used in devices with no dedicated processors or with low processing power, as cell phones or embedded displays, or to visualize data through the Internet, as in virtual museums applications.

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Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

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Cancer cells have been noted to have an altered metabolic phenotype for over ninety years. In the presence of oxygen, differentiated cells predominately utilise the tricarboxylic acid (TCA) cycle and oxidative phosphorylation to efficiently produce energy and the metabolites necessary for protein and lipid synthesis. However, in hypoxia, this process is altered and cells switch to a higher rate of glycolysis and lactate production to maintain their energy and metabolic needs. In cancer cells, glycolysis is maintained at a high rate, even in the presence of oxygen; a term described as “aerobic glycolysis”. Tumour cells are rapidly dividing and have a much greater need for anabolism compared to normal differentiated cells. Rapid glucose metabolism enables faster ATP production as well as a greater redistribution of carbons to nucleotide, protein, and fatty acid synthesis, thus maximising cell growth. Recently, other metabolic changes, driven by mutations in genes related to the TCA cycle, indicate an alternative role for metabolism in cancer, the “oncometabolite”. This is where a particular metabolite builds up within the cell and contributes to the tumorigenic process. One of these genes is isocitrate dehydrogenase (IDH) IDH is an enzyme that forms part of the tricarboxylic acid (TCA) cycle and converts isocitrate to α-ketoglutarate (α-KG). It exists in three isoforms; IDH1, IDH2 and IDH3 with the former present in the cytoplasm and the latter two in the mitochondria. Point mutations have been identified in the IDH1 and IDH2 genes in glioma which result in a gain of function by converting α-KG to 2-hydroxyglutarate (2HG), an oncometabolite. 2HG acts as a competitive inhibitor of the α-KG dependent dioxygenases, a superfamily of enzymes that are involved in numerous cellular processes such as DNA and histone demethylation. It was hypothesised that the IDH1 mutation would result in other metabolic changes in the cell other than 2HG production, and could potentially identify pathways which could be targeted for therapeutic treatment. In addition, 2HG can act as a potential competitive inhibitor of α-KG dependent dioxygenases, so it was hypothesised that there would be an effect on histone methylation. This may alter gene expression and provide a mechanism for tumourogenesis and potentially identify further therapeutic targets. Metabolic analysis of clinical tumour samples identified changes associated with the IDH1 mutation, which included a reduction in α-KG and an increase in GABA, in addition to the increase in 2HG. This was replicated in several cell models, where 13C labelled metabolomics was also used to identify a possible increase in metabolic flux from glutamate to GABA, as well as from α-KG to 2HG. This may provide a mechanism whereby the cell can bypass the IDH1 mutation as GABA can be metabolised to succinate in the mitochondria by GABA transaminase via the GABA shunt. JMJ histone demethylases are a subset of the α-KG dependent dioxygenases, and are involved in removing methyl groups from histone tails. Changes in histone methylation are associated with changes in gene expression depending on the site and extent of chemical modification. To identify whether the increase in 2HG and fall in α-KG was associated with inhibition of histone demethylases a histone methylation screen was used. The IDH1 mutation was associated with an increase in methylation of H3K4, which is associated with gene activation. ChiP and RNA sequencing identified an increase in H3K4me3 at the transcription start site of the GABRB3 subunit, resulting in an increase in gene expression. The GABRB3 subunit forms part of the GABA-A receptor, a chloride channel, which on activation can reduce cell proliferation. The IDH1 mutation was associated with an increase in GABA and GABRB3 subunit of the GABA-A receptor. This raises the possibility of GABA transaminase as a potential therapeutic target. Inhibition of this enzyme could reduce GABA metabolism, potentially reducing any beneficial effect of the GABA shunt in IDH1 mutant tumours, and increasing activation of the GABA-A receptor by increasing the concentration of GABA in the brain. This in turn may reduce cell proliferation, and could be achieved by using Vigabatrin, a GABA transaminase inhibitor licensed for use in epilepsy.

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Most cancer-related deaths are due to metastasis formation, the ability of cancer cells to break away from the primary tumor site, transmigrate through the endothelium, and form secondary tumors in distant areas. Many studies have identified links between the mechanical properties of the cellular microenvironment and the behavior of cancer cells. Cells may experience heterogeneous microenvironments of varying stiffness during tumor progression, transmigration, and invasion into the basement membrane. In addition to mechanical factors, the localization of RNAs to lamellipodial regions has been proposed to play an important part in metastasis. This dissertation provides a quantitative evaluation of the biophysical effects on cancer cell transmigration and RNA localization. In the first part of this dissertation, we sought to compare cancer cell and leukocyte transmigration and investigate the impact of matrix stiffness on transmigration process. We found that cancer cell transmigration includes an additional step, ‘incorporation’, into the endothelial cell (EC) monolayer. During this phase, cancer cells physically displace ECs and spread into the monolayer. Furthermore, the effects of subendothelial matrix stiffness and endothelial activation on cancer cell incorporation are cell-specific, a notable difference from the process by which leukocytes transmigrate. Collectively, our results provide mechanistic insights into tumor cell extravasation and demonstrate that incorporation into the endothelium is one of the earliest steps. In the next part of this work, we investigated how matrix stiffness impacts RNA localization and its relevance to cancer metastasis. In migrating cells, the tumor suppressor protein, adenomatous polyposis coli (APC) targets RNAs to cellular protrusions. We observed that increasing stiffness promotes the peripheral localization of these APC-dependent RNAs and that cellular contractility plays a role in regulating this pathway. We next investigated the mechanism underlying the effect of substrate stiffness and cellular contractility. We found that contractility drives localization of RNAs to protrusions through modulation of detyrosinated microtubules, a network of modified microtubules that associate with, and are required for localization of APC-dependent RNAs. These results raise the possibility that as the matrix environment becomes stiffer during tumor progression, it promotes the localization of RNAs and ultimately induces a metastatic phenotype.

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This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.

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Open-cell metal foams show promise as an emerging novel material for heat exchanger applications. The high surface-area-to-volume ratio suggests increased compactness and decrease in weight of heat exchanger designs. However, the metal foam structure appears conducive to condensate retention, which would degenerate heat transfer performance. This research investigates the condensate retention behavior of aluminum open-cell metal foams through the use of static dip tests and geometrical classification via X-ray Micro-Computed Tomography. Aluminum open-cell metal foam samples of 5, 10, 20, and 40 pores per inch (PPI), all having a void fraction greater than 90%, were included in this investigation. In order to model the condensate retention behavior of metal foams, a clearer understanding of the geometry was required. After exploring the ideal geometries presented in the open literature, X-ray Micro-Computed Tomography was employed to classify the actual geometry of the metal foam samples. The images obtained were analyzed using specialized software from which geometric information including strut length and pore shapes were extracted. The results discerned a high variability in ligament length, as well as features supporting the ideal geometry known as the Weaire-Phelan unit cell. The static dip tests consisted of submerging the metal foam samples in a liquid, then allowing gravity-induced drainage until steady-state was reached and the liquid remaining in the metal foam sample was measured. Three different liquids, water, ethylene glycol, and 91% isopropyl alcohol, were employed. The behaviors of untreated samples were compared to samples subjected to a Beomite surface treatment process, and no significant differences in retention behavior were discovered. The dip test results revealed two distinct regions of condensate retention, each holding approximately half of the total liquid retained by the sample. As expected, condensate retention increased as the pores sizes decreased. A model based on surface tension was developed to predict the condensate retention in the metal foam samples and verified using a regular mesh. Applying the model to both the ideal and actual metal foam geometries showed good agreement with the dip test results in this study.

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Crystalline acid-functionalized metal phosphonates are potential candidates as proton conducting electrolytes. Their frameworks can be chemically modified to contain proton carriers such as acidic groups (P-OH; -SO3H, -COOH,…) and guest molecules (H2O, NH3,…) that generates hydrogen bond networks stable in a wide range of temperature [1,2]. In this work, focus is laid on properties derived from the combination of lanthanide ions with the amino-sulfophosphonate ligand (H2O3PCH2)2-N-(CH2)2-SO3H. Hightrough-put screening was followed to reach the optimal synthesis conditions under solvothermal conditions at 140 ºC. Isolated polycrystalline solids, Ln[(O3PCH2)2-NH-(CH2)2-SO3H].2H2O (Ln= La, Pr and Sm), crystallize in the monoclinic (La) and orthorhombic (Pr and Sm) systems with unit cell volume of ~2548 Å3. Preliminary proton conductivity measurements for Sm derivative have been carried out between 25º and 80 ºC at relative humidity (RH) values of 70 % and 95 %. The sample exhibits enhanced conductivity at high RH and T (Figure 1) and constant activation energies of 0.4 eV, typical of a Grothuss mechanism of proton.