Boosting Heterogeneous Catalyst Discovery by Structurally Constrained Deep Learning Models

The discovery of new catalysts is one of the significant topics of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources. Recently developed deep learning approaches such as graph neural networks (GNNs) open new opportunity to significantly extend scope for modelling novel high-performance catalysts. Nevertheless, the graph representation of particular crystal structure is not a straightforward task due to the ambiguous connectivity schemes and numerous embeddings of nodes and edges. Here we present embedding improvement for GNN that has been modified by Voronoi tesselation and is able to predict the energy of catalytic systems within Open Catalyst Project dataset. Enrichment of the graph was calculated via Voronoi tessellation and the corresponding contact solid angles and types (direct or indirect) were considered as features of edges and Voronoi volumes were used as node characteristics. The auxiliary approach was enriching node representation by intrinsic atomic properties (electronegativity, period and group position). Proposed modifications allowed us to improve the mean absolute error of the original model and the final error equals to 651 meV per atom on the Open Catalyst Project dataset and 6 meV per atom on the intermetallics dataset. Also, by consideration of additional dataset, we show that a sensible choice of data can decrease the error to values above physically-based 20 meV per atom threshold

The Aluminum-Ion Battery: A Sustainable and Seminal Concept?

The expansion of renewable energy and the growing number of electric vehicles and mobile devices are demanding improved and low-cost electrochemical energy storage. In order to meet the future needs for energy storage, novel material systems with high energy densities, readily available raw materials, and safety are required. Currently, lithium and lead mainly dominate the battery market, but apart from cobalt and phosphorous, lithium may show substantial supply challenges prospectively, as well. Therefore, the search for new chemistries will become increasingly important in the future, to diversify battery technologies. But which materials seem promising? Using a selection algorithm for the evaluation of suitable materials, the concept of a rechargeable, high-valent all-solid-state aluminum-ion battery appears promising, in which metallic aluminum is used as the negative electrode. On the one hand, this offers the advantage of a volumetric capacity four times higher (theoretically) compared to lithium analog. On the other hand, aluminum is the most abundant metal in the earth's crust. There is a mature industry and recycling infrastructure, making aluminum very cost efficient. This would make the aluminum-ion battery an important contribution to the energy transition process, which has already started globally. So far, it has not been possible to exploit this technological potential, as suitable positive electrodes and electrolyte materials are still lacking. The discovery of inorganic materials with high aluminum-ion mobility—usable as solid electrolytes or intercalation electrodes—is an innovative and required leap forward in the field of rechargeable high-valent ion batteries. In this review article, the constraints for a sustainable and seminal battery chemistry are described, and we present an assessment of the chemical elements in terms of negative electrodes, comprehensively motivate utilizing aluminum, categorize the aluminum battery field, critically review the existing positive electrodes and solid electrolytes, present a promising path for the accelerated development of novel materials and address problems of scientific communication in this field

ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network) II: protocol for case based antimicrobial resistance surveillance

Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest

New Quasicrystal Approximant in the Sc\u2013Pd System: From Topological Data Mining to the Bench

Intermetallics contribute signi\ufb01cantly to our current demand for high-performance functional materials. However, understanding their chemistry is still an open and debated topic, especially for complex compounds such as approximants and quasicrystals. In this work, targeted topological data mining succeeded in (i) selecting all known Mackay-type approximants, (ii) uncovering the most important geometrical and chemical factors involved in their formation, and (iii) guiding the experimental work to obtain a new binary Sc 12Pd 1/1 approximant for icosahedral quasicrystals containing the desired cluster. Single-crystal X-ray di\ufb00raction data analysis supplemented by electron density reconstruction using the maximum entropy method, showed \ufb01ne structural peculiarities, that is, smeared electron densities in correspondence to some crystallographic sites. These characteristics have been studied through a comprehensive density functional theory modeling based on the combination of point defects such as vacancies and substitutions. It was con\ufb01rmed that the structural disorder occurs in the shell enveloping the classical Mackay cluster, so that the real structure can be viewed as an assemblage of slightly di\ufb00erent, locally ordered, four shell nanoclusters. Results obtained here open up broader perspectives for machine learning with the aim of designing novel materials in the fruitful \ufb01eld of quasicrystals and their approximants. This might become an alternative and/or complementary way to the electronic pseudogap tuning, often used before explorative synthesis

Современные представления о влиянии земной и космической погоды на здоровье человека (обзор)

Известно, что земная и космическая погода могут оказывать значительное влияние на функциональное состояние организма, в т. ч. и в форме метеотропных реакций, развития острых или обострения хронических заболеваний. Анализ источников научной литературы, в которых установлены причинно-следственные связи между природно-климатическими факторами и заболеваемостью, необходим для определения вклада в риск для здоровья населения природных факторов, выбора приоритетных показателей с целью совершенствования социально-гигиенического мониторинга. Рассматривались научные работы, опубликованные в 2000–2021 годах. По результатам поиска в открытых источниках информации были отобраны 153 полнотекстовые публикации, из которых 56 полностью соответствовали критериям включения в систематический обзор. Установлено, что наиболее часто изучаемые природно-климатические факторы – температура и влажность атмосферного воздуха, атмосферное давление, скорость движения воздушных масс, солнечная активность, атмосферное электричество, вариации естественного магнитного поля Земли, в т. ч. их периодичность и сочетанное воздействие. Объектами исследований в основном являлись пациенты с болезнями органов кровообращения: артериальной гипертензией, ишемической болезнью сердца, нарушениями мозгового кровообращения, аневризмами аорты. При этом результаты рассмотренных работ не позволяют однозначно связать возникновение или развитие указанных заболеваний ни с одним из факторов. Сложившаяся ситуация может быть объяснена различиями климатических условий, в которых выполнялись исследования, перекрестным воздействием метеогелиогеофизических факторов, половозрастными различиями исследованных групп, влиянием таких конфаундинговых факторов, как характер питания, образ жизни, социально-экономическое положение, наличие вредных привычек. В связи с этим важен комплексный подход к анализу накопленных данных для углубления знаний о воздействии земной и космической погоды на человека. С целью эффективной профилактики и прогноза развития метеотропных нарушений здоровья назревает необходимость включения приоритетных природных факторов в систему социально-гигиенического мониторинга

RD50 Status Report 2008 - Radiation hard semiconductor devices for very high luminosity colliders

The objective of the CERN RD50 Collaboration is the development of radiation hard semiconductor detectors for very high luminosity colliders, particularly to face the requirements of a possible upgrade scenario of the LHC.This document reports the status of research and main results obtained after the sixth year of activity of the collaboration

Search for high-mass exclusive γγ\gamma\gamma→\to WW and γγ\gamma\gamma→\to ZZ production in proton-proton collisions at s\sqrt{s} = 13 TeV

A search is performed for exclusive high-mass $\gamma\gamma$$\to$ WW and $\gamma\gamma$$\to$ ZZ production in proton-proton collisions using intact forward protons reconstructed in near-beam detectors, with both weak bosons decaying into boosted and merged jets. The analysis is based on a sample of proton-proton collisions collected by the CMS and TOTEM experiments at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 100 fb$^{−1}$. No excess above the standard model background prediction is observed, and upper limits are set on the pp → pWWp and pp → pZZp cross sections in a fiducial region defined by the diboson invariant mass m(VV) > 1 TeV (with V = W, Z) and proton fractional momentum loss 0.04 < $ξ$ < 0.20. The results are interpreted as new limits on dimension-6 and dimension-8 anomalous quartic gauge couplings.[graphic not available: see fulltext

Measurement of pseudorapidity distributions of charged particles in proton-proton collisions at sqrt(s) = 8 TeV by the CMS and TOTEM experiments

Pseudorapidity ( $\eta$ ) distributions of charged particles produced in proton–proton collisions at a centre-of-mass energy of 8 $~\text {TeV}$ are measured in the ranges $|\eta | < 2.2$ and $5.3 < |\eta | < 6.4$ covered by the CMS and TOTEM detectors, respectively. The data correspond to an integrated luminosity of $\mathcal {L} = 45 \mu {\mathrm {b}}^{-1}$ . Measurements are presented for three event categories. The most inclusive category is sensitive to 91–96 % of the total inelastic proton–proton cross section. The other two categories are disjoint subsets of the inclusive sample that are either enhanced or depleted in single diffractive dissociation events. The data are compared to models used to describe high-energy hadronic interactions. None of the models considered provide a consistent description of the measured distributions