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Institution

Sandia National Laboratories

FacilityLivermore, California, United States
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.


Papers
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Journal ArticleDOI
TL;DR: An extensive review of the literature in V&V in computational fluid dynamics (CFD) is presented, methods and procedures for assessing V &V are discussed, and a relatively new procedure for estimating experimental uncertainty is given that has proven more effective at estimating random and correlated bias errors in wind-tunnel experiments than traditional methods.

948 citations

Journal ArticleDOI
TL;DR: P porous nanoparticle-supported lipid bilayers (protocells) that synergistically combine properties of liposomes and nanoporous particles are reported that allow a single protocell loaded with a drug cocktail to kill a drug-resistant HCC cell, representing a 106-fold improvement over comparable liposome.
Abstract: Encapsulation of drugs within nanocarriers that selectively target malignant cells promises to mitigate side effects of conventional chemotherapy and to enable delivery of the unique drug combinations needed for personalized medicine. To realize this potential, however, targeted nanocarriers must simultaneously overcome multiple challenges, including specificity, stability and a high capacity for disparate cargos. Here we report porous nanoparticle-supported lipid bilayers (protocells) that synergistically combine properties of liposomes and nanoporous particles. Protocells modified with a targeting peptide that binds to human hepatocellular carcinoma exhibit a 10,000-fold greater affinity for human hepatocellular carcinoma than for hepatocytes, endothelial cells or immune cells. Furthermore, protocells can be loaded with combinations of therapeutic (drugs, small interfering RNA and toxins) and diagnostic (quantum dots) agents and modified to promote endosomal escape and nuclear accumulation of selected cargos. The enormous capacity of the high-surface-area nanoporous core combined with the enhanced targeting efficacy enabled by the fluid supported lipid bilayer enable a single protocell loaded with a drug cocktail to kill a drug-resistant human hepatocellular carcinoma cell, representing a 10(6)-fold improvement over comparable liposomes.

944 citations

Journal ArticleDOI
TL;DR: Comparing H2 versus CO2 Selective Polymeric Membranes 4103 and Disadvantages of Carbon Membrane 4098 5.2.1.
Abstract: 3.2. Preparation 4090 3.3. Intermediate Layers 4090 3.4. Support 4090 3.5. Modification 4091 3.5.1. Silica Membrane Modification 4091 3.5.2. Membrane Structure Modification 4092 3.6. Operational Stability 4092 4. Zeolite Membranes 4092 4.1. Membrane Growth Methods 4093 4.2. Permeation and Gas Transport 4093 4.3. Defect Site Diffusion/Nonzeolitic Pores 4094 4.4. Thin Films 4094 4.5. Zeolite Membrane Modification 4094 4.6. CO2 Sequestration in H2 Separations 4095 4.7. Manufacturing 4095 5. Carbon-Based Membranes 4096 5.1. Carbon Membrane Preparations 4097 5.2. Carbon Membrane Post-treatment 4097 5.3. Carbon Membrane Module Construction 4097 5.4. Selective Surface Flow Membranes 4097 5.5. Disadvantages of Carbon Membranes 4098 5.6. Molecular Sieving Carbon Membranes 4098 5.7. Carbon Nanotubes 4099 6. Polymer Membranes for H2 Separations 4100 6.1. Dense Polymeric Membranes 4100 6.2. Hydrogen Selective Polymeric Membranes 4101 6.3. H2 versus CO2 Selective Polymeric Membranes 4103

937 citations

Journal ArticleDOI
TL;DR: A method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample to confirm the validity of this approach.
Abstract: This paper presents a method for combining classifiers that uses estimates of each individual classifier's local accuracy in small regions of feature space surrounding an unknown test sample. An empirical evaluation using five real data sets confirms the validity of our approach compared to some other combination of multiple classifiers algorithms. We also suggest a methodology for determining the best mix of individual classifiers.

937 citations

Journal ArticleDOI
23 Mar 1999-Nature
TL;DR: In this article, the authors report a rapid, aerosol process that results in solid, completely ordered spherical particles with stable hexagonal, cubic, or vesicular mesostructures, which relies on evaporation-induced interfacial self-assembly confined to a spherical aerosol droplet.
Abstract: Nanostructured particles exhibiting well-defined pore sizes and pore connectivities (1-, 2-, or 3-dimensional) are of interest for catalysis, chromatography, controlled release, low dielectric constant fillers, and custom-designed pigments and optical hosts During the last several years considerable progress has been made on controlling the macroscopic forms of mesoporous silicas prepared by surfactant and block copolymer liquid crystalline templating procedures Typically interfacial phenomena are used to control the macroscopic form (particles, fibers, or films), while self-assembly of amphiphilic surfactants or polymers is used to control the mesostructure To date, although a variety of spherical or nearly-spherical particles have been prepared, their extent of order is limited as is the range of attainable mesostructures They report a rapid, aerosol process that results in solid, completely ordered spherical particles with stable hexagonal, cubic, or vesicular mesostructures The process relies on evaporation-induced interfacial self-assembly (EISA) confined to a spherical aerosol droplet The process is simple and generalizable to a variety of materials combinations Additionally, it can be modified to provide the first aerosol route to the formation of ordered mesostructured films

937 citations


Authors

Showing all 21652 results

NameH-indexPapersCitations
Lily Yeh Jan16246773655
Jongmin Lee1502257134772
Jun Liu13861677099
Gerbrand Ceder13768276398
Kevin M. Smith114171178470
Henry F. Schaefer111161168695
Thomas Bein10967742800
David Chandler10742452396
Stephen J. Pearton104191358669
Harold G. Craighead10156940357
Edward Ott10166944649
S. Das Sarma10095158803
Richard M. Crooks9741931105
David W. Murray9769943372
Alán Aspuru-Guzik9762844939
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202340
2022245
20211,510
20201,580
20191,535
20181,514