Heterogenized Catalysis on Metals Impregnated Mesoporous Silica

Ghaida, Fatima Abi; Clément, Sébastien; Mehdi, Ahmad

doi:10.1002/9781119156253.ch10

Cited by 2 publications

(3 citation statements)

References 96 publications

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“…Each a nr for a specific r is called a community or component. The concept of a temporal index having strengths of community membership may seem odd; it is perhaps more easily understood as the amount of community activity at a certain time (Gauvin et al 2014).…”

Section: Tensor Decompositionsmentioning

confidence: 99%

“…Different types of methods and algorithms have been used, for example: the Louvain algorithm (Aynaud and Guillaume 2010), statistical null models (Bassett et al 2013;Sarzynska et al 2016), algorithms which exploit the historic community structure of the network (He et al 2017;He and Chen 2015), Markov models (Rosvall et al 2014), semidefinite programming (Tantipathananandh and Berger-Wolf 2011), gravitational relationship between nodes (Yin et al 2017), and temporal matrix factorisation (Yu et al 2017), amongst others. Machine learning techniques (Savić et al 2019;Xin et al 2017), genetic algorithms (Folino and Pizzuti 2014), consensus clustering (Aynaud and Guillaume 2010) and tensor factorisation (Araujo et al 2014;Gauvin et al 2014) have only recently been used for the detection of communities in temporal networks.…”

Section: Introductionmentioning

confidence: 99%

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Evolution of communities of software: using tensor decompositions to compare software ecosystems

2019

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Modern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software "ecosystems" are complex socio-technical systems which can be represented as a multilayer dynamic network. Many of these libraries and software packages are open-source and developed in the open on sites such as GitHub, so there is a large amount of data available about these networks. Studying these networks could be of interest to anyone choosing or designing a programming language. In this work, we use tensor factorisation to explore the dynamics of communities of software, and then compare these dynamics between languages on a dataset of approximately 1 million software projects. We hope to be able to inform the debate on software dependencies that has been recently re-ignited by the malicious takeover of the npm package event-stream and other incidents through giving a clearer picture of the structure of software dependency networks, and by exploring how the choices of language designers-for example, in the size of standard libraries, or the standards to which packages are held before admission to a language ecosystem is granted-may have shaped their language ecosystems. We establish that adjusted mutual information is a valid metric by which to assess the number of communities in a tensor decomposition and find that there are striking differences between the communities found across different software ecosystems and that communities do experience large and interpretable changes in activity over time. The differences between the elm and R software ecosystems, which see some communities decline over time, and the more conventional software ecosystems of Python, Java and JavaScript, which do not see many declining communities, are particularly marked.

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Section: Tensor Decompositionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolution of communities of software: using tensor decompositions to compare software ecosystems

2019

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“…An elegant way to replace the organic volatile carboxylic acid by recoverable objects could be the use of a solid reagent with COOH pending functions [ 39 , 40 , 41 , 42 ]. For this, it was interesting to use the possibility of the functionalization of silica—using trialkoxysilane precursors—to obtain pending acidic functions on silica [ 43 , 44 , 45 , 46 ]. Silica was employed previously for different uses, especially to graft, in a covalent way, polydentate ligands and related complexes for catalyzed reactions, or to trap heavy metals for depollution concerns.…”

Section: Introductionmentioning

confidence: 99%

Replacement of Volatile Acetic Acid by Solid SiO2@COOH Silica (Nano)Beads for (Ep)Oxidation Using Mn and Fe Complexes Containing BPMEN Ligand

et al. 2021

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Mn and Fe BPMEN complexes showed excellent reactivity in catalytic oxidation with an excess of co-reagent (CH3COOH). In the straight line of a cleaner catalytic system, volatile acetic acid was replaced by SiO2 (nano)particles with two different sizes to which pending carboxylic functions were added (SiO2@COOH). The SiO2@COOH beads were obtained by the functionalization of SiO2 with pending nitrile functions (SiO2@CN) followed by CN hydrolysis. All complexes and silica beads were characterized by NMR, infrared, DLS, TEM, X-ray diffraction. The replacement of CH3COOH by SiO2@COOH (100 times less on molar ratio) has been evaluated for (ep)oxidation on several substrates (cyclooctene, cyclohexene, cyclohexanol) and discussed in terms of activity and green metrics.

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Heterogenized Catalysis on Metals Impregnated Mesoporous Silica

Cited by 2 publications

References 96 publications

Evolution of communities of software: using tensor decompositions to compare software ecosystems

Evolution of communities of software: using tensor decompositions to compare software ecosystems

Replacement of Volatile Acetic Acid by Solid SiO2@COOH Silica (Nano)Beads for (Ep)Oxidation Using Mn and Fe Complexes Containing BPMEN Ligand

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