Thermal conductivity and phonon hydrodynamics in transition metal dichalcogenides from first-principles

We carry out a systematic study of the thermal conductivity of four single-layer transition metal dichalcogenides, MX$_2$ (M = Mo, W; X = S, Se) from first-principles by solving the Boltzmann Transport Equation (BTE). We compare three different theoretical frameworks to solve the BTE beyond the Relaxation Time Approximation (RTA), using the same set of interatomic force constants computed within density functional theory (DFT), finding that the RTA severely underpredicts the thermal conductivity of MS$_2$ materials. Calculations of the different phonon scattering relaxation times of the main collision mechanisms and their corresponding mean free paths (MFP) allow evaluating the expected hydrodynamic behaviour in the heat transport of such monolayers. These calculations indicate that despite of their low thermal conductivity, the present TMDs can exhibit large hydrodynamic effects, being comparable to those of graphene, especially for WSe$_2$ at high temperatures.Comment: 16 pages, 9 figure

L'expressió corporal a l'Educació Física

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Giant Electrophononic Response in PbTiO3 by Strain Engineering

We demonstrate theoretically how, by imposing epitaxial strain in a ferroelectric perovskite, it is possible to achieve a dynamical control of phonon propagation by means of external electric fields, which yields a giant electrophononic response, i.e., the dependence of the lattice thermal conductivity on external electric fields. Specifically, we study the strain-induced manipulation of the lattice structure and analyze its interplay with the electrophononic response. We show that tensile biaxial strain can drive the system to a regime where the electrical polarization can be effortlessly rotated and thus yield giant electrophononic responses that are at least one order of magnitude larger than in the unstrained system. These results derive directly from the almost divergent behavior of the electrical susceptibility at those critical strains that drive the polarization on the verge of a spontaneous rotation.Peer reviewe

Evaluating the benefits of key-value databases for scientific applications

The convergence of Big Data applications with High-Performance Computing requires new methodologies to store, manage and process large amounts of information. Traditional storage solutions are unable to scale and that results in complex coding strategies. For example, the brain atlas of the Human Brain Project has the challenge to process large amounts of high-resolution brain images. Given the computing needs, we study the effects of replacing a traditional storage system with a distributed Key-Value database on a cell segmentation application. The original code uses HDF5 files on GPFS through an intricate interface, imposing synchronizations. On the other hand, by using Apache Cassandra or ScyllaDB through Hecuba, the application code is greatly simplified. Thanks to the Key-Value data model, the number of synchronizations is reduced and the time dedicated to I/O scales when increasing the number of nodes.This project/research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Speci c Grant Agreement No. 720270 (Human Brain Project SGA1) and the Speci c Grant Agreement No. 785907 (Human Brain Project SGA2). This work has also been supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contract 2017-SGR-1414).Postprint (author's final draft

Stochastic modelling of a neuron with the Hodgkin-Huxley model and its equivalent Markov description

Neurons are excitable cells which display fundamentally stochastic dynamics. In this work, we provide insights into their behavior and the role of stochasticity in their functioning. Starting from a conductance-based model, namely the Hodgkin-Huxley model, we first describe and simulate the action potential and then introduce external noise to the neurons as a Gaussian variable. Then, we move on to a Markov chain model to describe the dynamics of the ionic channels in a more accurate way and identify the intrinsic noise of the neurons with the stochasticity of the aforementionned channels2022/202

Synthesis of fluorescent dendrimeric antigen efficiently internalized by human dendritic cells

A new fluorescent dendrimeric antigen (DeAn) based on a dendron with amoxicilloyl terminal groups has been synthetized. The synthesis implies a novel class of all-aliphatic polyamide dendrimer (BisAminoalkylPolyAmide Dendrimers, or BAPAD).[1] The introduction of a cystamine core allows the incorporation of this dendrons into a 1,8-naphthalimide fluorofore functionalized with a maleimide group. The fluorescence properties of this DeAn has been studied and compared with the properties of an equivalent dendron possessing amino-terminal groups. This DeAn has been used as a synthetic antigen in a biomedical assay that tests the amoxicillin sensitivity of dendritic cells (DC) from tolerant and allergic patients.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Estudi fases construcció d'una nau industrial i la seva modelització 3D

Aquest treball es focalitza en la recerca dels materials i les etapes de construcció de les naus d’elements prefabricats i metàl·liques. Addicionalment es procedeix al modelat 3D d’un cas d’estudi d’aquestes dues tipologies de naus. Desprès, es troba una comparativa entre les dues tipologies de naus anteriorment descrites. Tot seguit, es presenta un pressupost que el seu principal objectiu és quantificar el cost que ha suposat fer totes les tasques d’aquest TFG, a més d’incloure un resum del cost que suposaria construir les dues tipologies de naus descrites per l’estudiant. A continuació, es veurà quina relació té aquest treball amb les implicacions socials i ambientals. També, es presentaran una sèrie de conclusions relacionades amb els objectius del treball, i per últim, es mostraran les referències consultades per aquest treball

The OTree: multidimensional indexing with efficient data sampling for HPC

Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.Peer ReviewedPostprint (author's final draft