Lawrence Berkeley National Laboratory

About: Lawrence Berkeley National Laboratory is a facility organization based out in Berkeley, California, United States. It is known for research contribution in the topics: Catalysis & Laser. The organization has 28217 authors who have published 66584 publications receiving 4111321 citations. The organization is also known as: LBNL & LBL.

Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections

Abstract: We report re-optimization of a recently proposed long-range corrected (LC) hybrid density functional [J.-D. Chai and M. Head-Gordon, J. Chem. Phys., 2008, 128, 084106] to include empirical atom–atom dispersion corrections. The resulting functional, ωB97X-D yields satisfactory accuracy for thermochemistry, kinetics, and non-covalent interactions. Tests show that for non-covalent systems, ωB97X-D shows slight improvement over other empirical dispersion-corrected density functionals, while for covalent systems and kinetics it performs noticeably better. Relative to our previous functionals, such as ωB97X, the new functional is significantly superior for non-bonded interactions, and very similar in performance for bonded interactions.

Room-temperature ultraviolet nanowire nanolasers

Abstract: Room-temperature ultraviolet lasing in semiconductor nanowire arrays has been demonstrated The self-organized, oriented zinc oxide nanowires grown on sapphire substrates were synthesized with a simple vapor transport and condensation process These wide band-gap semiconductor nanowires form natural laser cavities with diameters varying from 20 to 150 nanometers and lengths up to 10 micrometers Under optical excitation, surface-emitting lasing action was observed at 385 nanometers, with an emission linewidth less than 03 nanometer The chemical flexibility and the one-dimensionality of the nanowires make them ideal miniaturized laser light sources These short-wavelength nanolasers could have myriad applications, including optical computing, information storage, and microanalysis

Semiconductor Nanocrystals as Fluorescent Biological Labels

Abstract: Semiconductor nanocrystals were prepared for use as fluorescent probes in biological staining and diagnostics. Compared with conventional fluorophores, the nanocrystals have a narrow, tunable, symmetric emission spectrum and are photochemically stable. The advantages of the broad, continuous excitation spectrum were demonstrated in a dual-emission, single-excitation labeling experiment on mouse fibroblasts. These nanocrystal probes are thus complementary and in some cases may be superior to existing fluorophores.

Structure, function and diversity of the healthy human microbiome

Abstract: The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

Abstract: Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.