Ultralow-loading platinum-cobalt fuel cell catalysts derived from imidazolate frameworks.

Achieving high catalytic performance with the lowest possible amount of platinum is critical for fuel cell cost reduction. Here we describe a method of preparing highly active yet stable electrocatalysts containing ultralow-loading platinum content by using cobalt or bimetallic cobalt and zinc zeolitic imidazolate frameworks as precursors. Synergistic catalysis between strained platinum-cobalt core-shell nanoparticles over a platinum-group metal (PGM)-free catalytic substrate led to excellent fuel cell performance under 1 atmosphere of O2 or air at both high-voltage and high-current domains. Two catalysts achieved oxygen reduction reaction (ORR) mass activities of 1.08 amperes per milligram of platinum (A mgPt -1) and 1.77 A mgPt -1 and retained 64% and 15% of initial values after 30,000 voltage cycles in a fuel cell. Computational modeling reveals that the interaction between platinum-cobalt nanoparticles and PGM-free sites improves ORR activity and durability.

The atomic simulation environment-a Python library for working with atoms.

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Using GIS in government: an overview of the VHA's Healthcare Atlas, FY-2000.

The amount of VA data available for analysis can be overwhelming to individuals who need to translate these data into usable information. The Atlas, using current GIS technology, was funded to provide data in a comprehensive guide. Patients were identified using a disease classification scheme based on Kaiser Permanente methodology and the Clinical Classifications Software (AHRQ). Utilization data were extracted from the Medical SAS Datasets. Cost data were obtained from the HERC. GIS tools were used to create the Atlas. The Atlas overviews the location of VA hospitals; profiles veteran, VA enrollee and patient populations; examines overall utilization; depicts patterns in healthcare use by specific disease cohorts; and examines geographic variations in costs. This product will enhance knowledge of VA's enrolled patient population and their healthcare needs, and provide background information that will improve the formulation of specific research questions to address those needs.

A primer and comparative review of major US mortality databases.

PURPOSE: Mortality data are important tools for research requiring vital status information. We reviewed the major mortality databases and mortality ascertainment services available in the United States, including the National Death Index (NDI), the Social Security Administration (SSA) files, and the Department of Veterans Affairs databases. METHODS: The content, reliability, and accuracy of mortality sources are described and compared. We also describe how investigators can gain access to these resources and provide further contact information. RESULTS: We reviewed the accuracy of major mortality sources. The sensitivity (i.e., the proportion of the true number of deaths) of the NDI ranged from 87.0% to 97.9%, whereas the sensitivity for the VA Beneficiary Identification and Records Locator System (BIRLS) ranged between 80.0% and 94.5%. The sensitivity of SSA files ranged between 83.0% and 83.6%. Sensitivity for the VA Patient Treatment File (PTF) was 33%. CONCLUSIONS: While several national mortality ascertainment services are available for vital status (i.e., death) analyses, the NDI information demonstrated the highest sensitivity and, currently, it is the only source at the national level with a cause of death field useful for research purposes. Researchers must consider methods used to ascertain vital status as well as the quality of the information in mortality databases.