Sandia National Laboratories

About: Sandia National Laboratories is a facility organization based out in Livermore, California, United States. It is known for research contribution in the topics: Laser & Combustion. The organization has 21501 authors who have published 46724 publications receiving 1484388 citations. The organization is also known as: SNL & Sandia National Labs.

Metal‐Organic Frameworks: A Rapidly Growing Class of Versatile Nanoporous Materials

Abstract: Metal-organic frameworks (MOFs) represent a new class of hybrid organic-inorganic supramolecular materials comprised of ordered networks formed from organic electron donor linkers and metal cations. They can exhibit extremely high surface areas, as well as tunable pore size and functionality, and can act as hosts for a variety of guest molecules. Since their discovery, MOFs have enjoyed extensive exploration, with applications ranging from gas storage to drug delivery to sensing. This review covers advances in the MOF field from the past three years, focusing on applications, including gas separation, catalysis, drug delivery, optical and electronic applications, and sensing. We also summarize recent work on methods for MOF synthesis and computational modeling.

Real-time, portable genome sequencing for Ebola surveillance

Abstract: A nanopore DNA sequencer is used for real-time genomic surveillance of the Ebola virus epidemic in the field in Guinea; the authors demonstrate that it is possible to pack a genomic surveillance laboratory in a suitcase and transport it to the field for on-site virus sequencing, generating results within 24 hours of sample collection. This paper reports the use of nanopore DNA sequencers (known as MinIONs) for real-time genomic surveillance of the Ebola virus epidemic, in the field in Guinea. The authors demonstrate that it is possible to pack a genomic surveillance laboratory in a suitcase and transport it to the field for on-site virus sequencing, generating results within 24 hours of sample collection. The Ebola virus disease epidemic in West Africa is the largest on record, responsible for over 28,599 cases and more than 11,299 deaths1. Genome sequencing in viral outbreaks is desirable to characterize the infectious agent and determine its evolutionary rate. Genome sequencing also allows the identification of signatures of host adaptation, identification and monitoring of diagnostic targets, and characterization of responses to vaccines and treatments. The Ebola virus (EBOV) genome substitution rate in the Makona strain has been estimated at between 0.87 × 10−3 and 1.42 × 10−3 mutations per site per year. This is equivalent to 16–27 mutations in each genome, meaning that sequences diverge rapidly enough to identify distinct sub-lineages during a prolonged epidemic2,3,4,5,6,7. Genome sequencing provides a high-resolution view of pathogen evolution and is increasingly sought after for outbreak surveillance. Sequence data may be used to guide control measures, but only if the results are generated quickly enough to inform interventions8. Genomic surveillance during the epidemic has been sporadic owing to a lack of local sequencing capacity coupled with practical difficulties transporting samples to remote sequencing facilities9. To address this problem, here we devise a genomic surveillance system that utilizes a novel nanopore DNA sequencing instrument. In April 2015 this system was transported in standard airline luggage to Guinea and used for real-time genomic surveillance of the ongoing epidemic. We present sequence data and analysis of 142 EBOV samples collected during the period March to October 2015. We were able to generate results less than 24 h after receiving an Ebola-positive sample, with the sequencing process taking as little as 15–60 min. We show that real-time genomic surveillance is possible in resource-limited settings and can be established rapidly to monitor outbreaks.

A Multi-Level Algorithm For Partitioning Graphs

Abstract: The graph partitioning problem is that of dividing the vertices of a graph into sets of specified sizes such that few edges cross between sets. This NP-complete problem arises in many important scientific and engineering problems. Prominent examples include the decomposition of data structures for parallel computation, the placement of circuit elements and the ordering of sparse matrix computations. We present a multilevel algorithm for graph partitioning in which the graph is approximated by a sequence of increasingly smaller graphs. The smallest graph is then partitioned using a spectral method, and this partition is propagated back through the hierarchy of graphs. A variant of the Kernighan-Lin algorithm is applied periodically to refine the partition. The entire algorithm can be implemented to execute in time proportional to the size of the original graph. Experiments indicate that, relative to other advanced methods, the multilevel algorithm produces high quality partitions at low cost.

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

Abstract: There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. The Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.