With an increasing number of new scientific papers being released, it becomes harder for researchers to be aware of recent articles in their field of study. Accurately classifying papers is a first ...step in the direction of personalized catering and easy access to research of interest. The field of Density Functional Theory (DFT) in particular is a good example of a methodology used in very different studies, and interconnected disciplines, which has a very strong community publishing many research articles. We devise a new unsupervised method for classifying publications, based on topic modeling, and use a DFT-related selection of documents as a use case. We first create topics from word analysis and clustering of the abstracts from the publications, then attribute each publication/paper to a topic based on word similarity. We then make interesting observations by analyzing connections between the topics and publishers, journals, country or year of publication. The proposed approach is general, and can be applied to analyze publication and citation trends in other areas of study, beyond the field of Density Function Theory.
Ultrathin passive films effectively prevent the chemical attack of stainless steel grades in corrosive environments; their stability depends on the interplay between structure and chemistry of the ...constituents iron, chromium, and molybdenum (Fe-Cr-Mo). Carbon (C), and eventually boron (B), are also important constituents of steels, although in small quantities. In particular, nanoscale inhomogeneities along the surface can have an impact on material failure but are still poorly understood. Addressing a stainless-type glass-forming Fe 50 Cr 15 Mo 14 C 15 B 6 alloy and using a combination of complementary high-resolution analytical techniques, we relate near-atomistic insights into increasingly inhomogeneous nanostructures with time- and element-resolved dissolution behavior. The progressive elemental partitioning on the nanoscale determines the degree of passivation. A detrimental transition from Cr-controlled passivity to Mo-controlled breakdown is dissected atom by atom, demonstrating the importance of nanoscale knowledge for understanding corrosion.
For the first time methanol tolerant Platinum Group Metal-free (PGM-free) Oxygen Reduction Reaction (ORR) electrocatalyst commercially available on market is integrated into the cathodic layer of ...highly performed Direct Methanol Fuel Cell (DMFC). The intrinsic ORR activity of Fe–N–C electrocatalyst is studied by Rotating Disk Electrode (RDE) technique with and with no methanol added, confirming material inactivity towards Methanol Oxidation Reaction (MOR). The electrocatalyst is tested in DMFC conditions where such important parameters as catalyst:ionomer ratio, concentration of methanol and operational temperature are varied and optimized for highest performance. The obtained activity and methanol tolerance in RDE measurements of commercial Fe–N–C catalyst is found to be comparable with previously reported state-of-the-art PGM-free cathodic materials. Furthermore, this material, used at the cathode compartment of a DMFC, allows reaching the highest power density recorded for PGM-free catalysts in a DMFC until now under similar conditions.
•Commercial PGM-free catalyst was integrated into the cathodic layer of a DMFC.•A high PGM-free catalyst loading helped to counteract the increased crossover rate.•The DMFC reached the highest power density recorded for PGM-free catalysts.
Hydrogen is a promising energy carrier for transportation, domestic and industrial applications. Nowadays hydrogen is consumed basically by the chemical industry, but in long term its demand is ...expected to grow significantly due to emerging markets. Hence production of hydrogen with sustainable methods is a relevant issue. This work presents a review of the different CSP- aided thermochemical processes for hydrogen and syngas production. For each process, some relevant solar-tested reactor prototypes are described. In a second part, the developed solar furnaces for investigation of thermochemical process are also discussed. In addition, relevant research on hydrogen or syngas production in solar tower installations is presented. Finally the current challenges of the technology and the process for its future commercialization are also analyzed.
The PyProcar Python package plots the band structure and the Fermi surface as a function of site and/or s,p,d,f - projected wavefunctions obtained for each k-point in the Brillouin zone and band in ...an electronic structure calculation. This can be performed on top of any electronic structure code, as long as the band and projection information is written in the PROCAR format, as done by the VASP and ABINIT codes. PyProcar can be easily modified to read other formats as well. This package is particularly suitable for understanding atomic effects into the band structure, Fermi surface, spin texture, etc. PyProcar can be conveniently used in a command line mode, where each one of the parameters define a plot property. In the case of Fermi surfaces, the package is able to plot the surface with colors depending on other properties such as the electron velocity or spin projection. The mesh used to calculate the property does not need to be the same as the one used to obtain the Fermi surface. A file with a specific property evaluated for each k-point in a k−mesh and for each band can be used to project other properties such as electron–phonon mean path, Fermi velocity, electron effective mass, etc. Another existing feature refers to the band unfolding of supercell calculations into predefined unit cells.
Program Title: PyProcar
Program Files doi:http://dx.doi.org/10.17632/d4rrfy3dy4.1
Licensing provisions: GPLv3
Programming language: Python
Nature of problem: To automate, simplify and serialize the analysis of band structure and Fermi surface, especially for high throughput calculations.
Solution method: Implementation of a Python library able to handle, combine, parse, extract, plot and even repair data from density functional calculations. PyProcar uses color maps on the band structures or Fermi surfaces to give a simple representation of the relevant characteristics of the electronic structure.
Additional comments: Features: PyProcar can produce high-quality figures of band structures and Fermi surfaces (2D and 3D), projection of atomic orbitals, atoms, and/or spin components.
Restrictions: Only the VASP package is currently fully supported, the latest version of Abinit is partially supported (it will be fully supported in the Abinit versions 9.x). The PROCAR file format can easily be implemented within any DFT code.
Honeybees are important pollinators, having an essential role in the ecology of natural and agricultural environments. Honeybee colony losses episodes reported worldwide and have been associated with ...different pests and pathogens, pesticide exposure, and nutritional stress. This nutritional stress is related to the increase in monoculture areas which leads to a reduction of pollen availability and diversity. In this study, we examined whether nutritional stress affects honeybee gut microbiota, bee immunity, and infection by Nosema ceranae, under laboratory conditions. Consumption of Eucalyptus grandis pollen was used as a nutritionally poor-quality diet to study nutritional stress, in contraposition to the consumption of polyfloral pollen. Honeybees feed with Eucalyptus grandis pollen showed a lower abundance of Lactobacillus mellifer and Lactobacillus apis (Firm-4 and Firm-5, respectively) and Bifidobacterium spp. and a higher abundance of Bartonella apis, than honeybees fed with polyfloral pollen. Besides the impact of nutritional stress on honeybee microbiota, it also decreased the expression levels of vitellogenin and genes associated to immunity (glucose oxidase, hymenoptaecin and lysozyme). Finally, Eucalyptus grandis pollen favored the multiplication of Nosema ceranae. These results show that nutritional stress impacts the honeybee gut microbiota, having consequences on honeybee immunity and pathogen development. Those results may be useful to understand the influence of modern agriculture on honeybee health.
Advances in parallel computing, GPU technology and deep learning facilitate the tools for processing complex images. The purpose of this research was focused on a review of the state of the art, ...related to the performance of pre-trained models for the detection of objects in order to make a comparison of these algorithms in terms of reliability, ac-curacy, time processed and Problems detected The consulted models are based on the Python programming language, the use of libraries based on TensorFlow, OpenCv and free image databases (Microsoft COCO and PAS-CAL VOC 2007/2012). These systems are not only focused on the recognition and classification of the objects in the images, but also on the location of the objects within it, drawing a bounding box around the appropriate way. For this research, different pre-trained models were re-viewed for the detection of objects such as R-CNN, R-FCN, SSD (single-shot multibox) and YOLO (You Only Look Once), with different extractors of characteristics such as VGG16, ResNet, Inception, MobileNet. As a result, it is not prudent to make direct and parallel analyzes between the different architecture and models, because each case has a particular solution for each problem, the purpose of this research is to generate an approximate notion of the experiments that have been carried out and conceive a starting point in the use that they are intended to give.
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