22,863 research outputs found

    The NASA SBIR product catalog

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    The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

    Distributed environmental monitoring

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    With increasingly ubiquitous use of web-based technologies in society today, autonomous sensor networks represent the future in large-scale information acquisition for applications ranging from environmental monitoring to in vivo sensing. This chapter presents a range of on-going projects with an emphasis on environmental sensing; relevant literature pertaining to sensor networks is reviewed, validated sensing applications are described and the contribution of high-resolution temporal data to better decision-making is discussed

    Predicting the mass spectrum of polymerizing linoleates using weighted random graph modeling

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    Biopolymers and biopolymer networks that form via autoxidation, like in drying of oil paint or fat degradation in food components, contain a large variety of monomeric building blocks. While the monomer variety complicates the modeling itself, obtaining experimental validation of infinite polymer networks is inherently difficult as well. A new model is developed, where an automated reaction network generation (ARNG) procedure is used to automatically generate the monomer components, structures and masses, and their reactions. This methodology is combined with random graph (RG) modeling to predict global polymer properties: distributions of numbers of monomer units and molar masses, gel point and gel fraction. This computational framework is applied to two model systems for linseed oil paint binder: the polymerization of ethyl linoleate (EL) and methyl linoleate (ML). A novel method was constructed to deal with the variability of monomer masses that complicates inferring molar mass from monomer number distribution. By modeling the polymer as a weighted random graph where the nodes contain information about the monomer masses in the system, the total weight of the finite connected components is computed. The predicted mass spectrum of finite connected components is used for validation with experimental data. A size exclusion chromatography (SEC) trace of ML is employed, which after calibration using the proposed framework, proves consistency between model and SEC data. The model provides a practical approach to both characterize complex biopolymers as polymers in terms of molar mass distribution and gel point, while preserving the information down to the level of monomeric units.</p

    The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

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    We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production

    Triazolinediones as highly enabling synthetic tools

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    Triazolinediones (TADs) are unique reagents in organic synthesis that have also found wide applications in different research disciplines, in spite of their somewhat "exotic" reputation. In this review, we offer two case studies that demonstrate the possibilities of these versatile and reliable synthetic tools, namely, in the field of polymer science as well as in more recently emerging applications in the field of click chemistry. As the general use of triazolinediones has always been hampered by the limited commercial and synthetic availability of such reagents, we also offer a review of the available TAD reagents, together with a detailed discussion of their synthesis and reactivity. This review thus aims to serve as a practical guide for researchers that are interested in exploiting and further developing the exceptional click -like reactivity of triazolinediones in various applications
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