Adsorbate-Induced Segregation of Cobalt from PtCo Nanoparticles: Modeling Au Doping and Core AuCo Alloying for the Improvement of Fuel Cell Cathode Catalysts

Platinum, when used as a cathode material for the oxygen reduction reaction, suffers from high overpotential and possible dissolution, in addition to the scarcity of the metal and resulting cost. Although the introduction of cobalt has been reported to improve reaction kinetics and decrease the precious metal loading, surface segregation or complete leakage of Co atoms causes degradation of the membrane electrode assembly, and either of these scenarios of structural rearrangement eventually decreases catalytic power. Ternary PtCo alloys with noble metals could possibly maintain activity with a higher dissolution potential. First-principles-based theoretical methods are utilized to identify the critical factors affecting segregation in Pt–Co binary and Pt–Co–Au ternary nanoparticles in the presence of oxidizing species. With a decreasing share of Pt, surface segregation of Co atoms was already found to become thermodynamically viable in the PtCo systems at low oxygen concentrations, which is assigned to high charge transfer between species. While the introduction of gold as a dopant caused structural changes that favor segregation of Co, creation of CoAu alloy core is calculated to significantly suppress Co leakage through modification of the electronic properties. The theoretical framework of geometrically different ternary systems provides a new route for the rational design of oxygen reduction catalysts

Antioxidant, antimicrobial and anticancer activity of the lichens Cladonia furcata, Lecanora atra and Lecanora muralis

<p>Abstract</p> <p>Background</p> <p>The aim of this study is to investigate in vitro antioxidant, antimicrobial and anticancer activity of the acetone extracts of the lichens <it>Cladonia furcata, Lecanora atra </it>and <it>Lecanora muralis</it>.</p> <p>Methods</p> <p>Antioxidant activity was evaluated by five separate methods: free radical scavenging, superoxide anion radical scavenging, reducing power, determination of total phenolic compounds and determination of total flavonoid content. The antimicrobial activity was estimated by determination of the minimal inhibitory concentration by the broth microdilution method against six species of bacteria and ten species of fungi. Anticancer activity was tested against FemX (human melanoma) and LS174 (human colon carcinoma) cell lines using MTT method.</p> <p>Results</p> <p>Of the lichens tested, <it>Lecanora atra </it>had largest free radical scavenging activity (94.7% inhibition), which was greater than the standard antioxidants. Moreover, the tested extracts had effective reducing power and superoxide anion radical scavenging. The strong relationships between total phenolic and flavonoid contents and the antioxidant effect of tested extracts were observed. Extract of <it>Cladonia furcata </it>was the most active antimicrobial agent with minimum inhibitory concentration values ranging from 0.78 to 25 mg/mL. All extracts were found to be strong anticancer activity toward both cell lines with IC₅₀values ranging from 8.51 to 40.22 μg/mL.</p> <p>Conclusions</p> <p>The present study shows that tested lichen extracts demonstrated a strong antioxidant, antimicrobial and anticancer effects. That suggest that lichens may be used as as possible natural antioxidant, antimicrobial and anticancer agents to control various human, animal and plant diseases.</p

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers’ connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains

Selected isotope ratio measurements of light metallic elements (Li, Mg, Ca, and Cu) by multiple collector ICP-MS

The unique capabilities of multiple collector inductively coupled mass spectrometry (MC-ICP-MS) for high precision isotope ratio measurements in light elements as Li, Mg, Ca, and Cu are reviewed in this paper. These elements have been intensively studied at the Geological Survey of Israel (GSI) and other laboratories over the past few years, and the methods used to obtain high precision isotope analyses are discussed in detail. The scientific study of isotopic fractionation of these elements is significant for achieving a better understanding of geochemical and biochemical processes in nature and the environment

Quantitative measurement of olivine composition in three dimensions using helical-scan X-ray micro-tomography

Olivine is a key constituent in the silicate Earth; its composition and texture informs petrogenetic understanding of numerous rock types. Here we develop a quantitative and reproducible method to measure olivine composition in three dimensions without destructive analysis, meaning full textural context is maintained. The olivine solid solution between forsterite and fayalite was measured using a combination of three-dimensional (3D) X-ray imaging techniques, 2D backscattered electron imaging, and spot-analyses using wavelength-dispersive electron probe microanalysis. The linear attenuation coefficient of natural crystals across a range of forsterite content from ∼73–91 mol% were confirmed to scale linearly with composition using 53, 60, and 70 kV monochromatic beams at I12-JEEP beamline, Diamond Light Source utilizing the helical fly-scan acquisition. A polychromatic X-ray source was used to scan the same crystals, which yielded image contrast equivalent to measuring the mol% of forsterite with an accuracy of 3 mm domains within a large crystal of San Carlos forsterite that varies by ∼2 Fo mol%. This offers a solution to an outstanding question of inter-laboratory standardization, and also demonstrates the utility of 3D, non-destructive, chemical measurement. To our knowledge, this study is the first to describe the application of XMT to quantitative chemical measurement across a mineral solid solution. Our approach may be expanded to calculate the chemistry of other mineral systems in 3D, depending upon the number, chemistry, and density of end-members

Exploring some of the physico-chemical properties of the LAMMER protein kinase DOA of Drosophila.

International audienc

Chromium isotope variations (delta(53)/(52)Cr) in mantle-derived sources and their weathering products: Implications for environmental studies and the evolution of delta(53)/(52)Cr in the Earth's mantle over geologic time

Abstract not availableJuraj Farkaš, Vladislav Chrastný, Martin Novák, Eva Čadkova, Jan Pašava, Ramananda Chakrabarti, Stein B. Jacobsen, Lukáš Ackerman, Thomas D. Bulle