Divergence of the Thermal Conductivity in Uniaxially Strained Graphene

We investigate the effect of strain and isotopic disorder on thermal transport in suspended graphene by equilibrium molecular dynamics simulations. We show that the thermal conductivity of unstrained graphene, calculated from the fluctuations of the heat current at equilibrium is finite and converges with size at finite temperature. In contrast, the thermal conductivity of strained graphene diverges logarithmically with the size of the models, when strain exceeds a relatively large threshold value of 2%. An analysis of phonon populations and lifetimes explains the divergence of the thermal conductivity as a consequence of changes in the occupation of low-frequency out-of-plane phonons and an increase in their lifetimes due to strain.Comment: 6 pages, 7 figures. Accepted for publication in Physical Review

Mechanical Tuning of Thermal Transport in a Molecular Junction

Understanding and controlling heat transport in molecular junctions would provide new routes to design nanoscale coupled electronic and phononic devices. Using first principles full quantum calculations, we tune thermal conductance of a molecular junction by mechanically compressing and extending a short alkane chain connected to graphene leads. We find that the thermal conductance of the compressed junction drops by half in comparison to the extended junction, making it possible to turn on and off the heat current. The low conductance of the off state does not vary by further approaching the leads and stems from the suppression of the transmission of the in--plane transverse and longitudinal channels. Furthermore, we show that misalignment of the leads does not reduce the conductance ratio. These results also contribute to the general understanding of thermal transport in molecular junctions.Comment: 12 pages, 6 figure

Microscopic Mechanism and Kinetics of Ice Formation at Complex Interfaces: Zooming in on Kaolinite

Most ice in nature forms thanks to impurities which boost the exceedingly low nucleation rate of pure supercooled water. However, the microscopic details of ice nucleation on these substances remain largely unknown. Here, we have unraveled the molecular mechanism and the kinetics of ice formation on kaolinite, a clay mineral playing a key role in climate science. We find that the formation of ice at strong supercooling in the presence of this clay is twenty orders of magnitude faster than homogeneous freezing. The critical nucleus is substantially smaller than that found for homogeneous nucleation and, in contrast to the predictions of classical nucleation theory (CNT), it has a strong 2D character. Nonetheless, we show that CNT describes correctly the formation of ice at this complex interface. Kaolinite also promotes the exclusive nucleation of hexagonal ice, as opposed to homogeneous freezing where a mixture of cubic and hexagonal polytypes is observed

Infrastructure Exposure, Extreme Weather Events & Climate Change - SF Bay - Napoli

Big Data analysis and computer modeling to compare the Mediterranean-type climate coastlands of San Francisco Bay, California (USA), and Naples Bay, southern Italy, prone to extreme weather events, sea storms and tsunami, climate change and sea level rise

Canonical sampling through velocity-rescaling

We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. We illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent on the thermostat parameter also with regard to the dynamic properties

Assetto geomorfologico dell’area marina di Sinuessa ed ipotesi di fruizione sostenibile

Studio geomorfologico dell’area costiera di Sinuessa (Golfo di Gaeta) che ha consentito di individuare l’approdo di epoca romana di Sinuessa; la ricostruzione dell’evoluzione geomorfologica e tettonica recente dell’area ha reso possibile l’individuazione delle cause della sommersione dell’approdo. L’intenso sviluppo insediativo che oggi caratterizza il tratto di litorale prospiciente l’area spinge a sviluppare un sistema di gestione integrato volto alla valorizzazione dell’area

Development and implementation of a smart greenhouse

A smart greenhouse was developed at Laboratorio de Informatica Aplicada with the department of Agronomic Engineering at the University of Rio Negro, it must provide real-time measurements of parameters such as humidity, temperature and luminosity; it also must allow manual and automatic control for actuators such as heaters, sprinklers and fans based on user input. In order to fulfill the aforementioned requirements the following actions were performed: (a) Design and implementation of a webpage to communicate with and control the greenhouse and, (b) Development: at first a model using Object Oriented Programming was implemented in an Arduino Mega board equipped with an ethernet shield; posteriorly, given that Arduino could not fulfill the necessary tasks, it was decided to develop a second prototype using a Raspberry Pi 2 Model B+ board along with completely new software programmed in Python 3.X Workshop Procesamiento de Señales y Sistemas de Tiempo Real.Red de Universidades con Carreras en Informátic

Development and implementation of a smart greenhouse

A smart greenhouse was developed at Laboratorio de Informatica Aplicada with the department of Agronomic Engineering at the University of Rio Negro, it must provide real-time measurements of parameters such as humidity, temperature and luminosity; it also must allow manual and automatic control for actuators such as heaters, sprinklers and fans based on user input. In order to fulfill the aforementioned requirements the following actions were performed: (a) Design and implementation of a webpage to communicate with and control the greenhouse and, (b) Development: at first a model using Object Oriented Programming was implemented in an Arduino Mega board equipped with an ethernet shield; posteriorly, given that Arduino could not fulfill the necessary tasks, it was decided to develop a second prototype using a Raspberry Pi 2 Model B+ board along with completely new software programmed in Python 3.X Workshop Procesamiento de Señales y Sistemas de Tiempo Real.Red de Universidades con Carreras en Informátic

Thermal Transport Across Graphene Step Junctions

Step junctions are often present in layered materials, i.e. where single-layer regions meet multi-layer regions, yet their effect on thermal transport is not understood to date. Here, we measure heat flow across graphene junctions (GJs) from monolayer to bilayer graphene, as well as bilayer to four-layer graphene for the first time, in both heat flow directions. The thermal conductance of the monolayer-bilayer GJ device ranges from ~0.5 to 9.1x10^8 Wm-2K-1 between 50 K to 300 K. Atomistic simulations of such GJ device reveal that graphene layers are relatively decoupled, and the low thermal conductance of the device is determined by the resistance between the two dis-tinct graphene layers. In these conditions the junction plays a negligible effect. To prove that the decoupling between layers controls thermal transport in the junction, the heat flow in both directions was measured, showing no evidence of thermal asymmetry or rectification (within experimental error bars). For large-area graphene applications, this signifies that small bilayer (or multilayer) islands have little or no contribution to overall thermal transport

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields