Showing papers by "Karlsruhe Institute of Technology published in 2022"

Global Carbon Budget 2021

Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Global Carbon Budget 2022

Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Ultrahigh surface density of Co-N2C single-atom-sites for boosting photocatalytic CO2 reduction to methanol

Abstract: Cobalt species as active sites for photocatalytic reduction of CO2 to valuable products such as methanol have received increasing attention, however, it remains a huge challenge to achieve the high activity. Herein, a pyrolysis-induced-vaporization strategy was successfully employed to fabricate Co/g-C3N4 single-atom catalysts (Co/g-C3N4 SACs) with surface Co atom loading up to 24.6 wt%. Systematic investigation of Co/g-C3N4 SACs formation process disclosed that concentrated-H2SO4 exfoliation of g-C3N4 nanosheets (g-C3N4 NSs) as the substrate followed by a two-step calcination process is essential to achieve ultrahigh metal loading. It was found that the ultrahigh-density of Co single-atom sites were anchored on the g-C3N4 substrate surface and coordinated with two nitrogen and one carbon atoms (Co-N2C). These single dispersed Co-N2C sites on the g-C3N4 surface were found to act not only as electron gathering centers but also as the sites of CO2 adsorption and activation, subsequently, boosting the photocatalytic methanol generation during light irradiation. As a result, the methanol formation rate at 4 h (941.9 μmol g−1) over Co/g-C3N4-0.2 SAC with 24.6 wt% surface Co loading was 13.4 and 2.2 times higher than those of g-C3N4 (17.7 μmol g−1) and aggregated CoOx/g-C3N4-0.2 (423.9 μmol g−1), respectively. Simultaneously, H2 (18.9 μmol g−1 h−1), CO (2.9 μmol g−1 h−1), CH4 (3.4 μmol g−1 h−1), C2H4 (1.1 μmol g−1 h−1), C3H6 (1.4 μmol g−1 h−1), and CH3OCH3 (3.3 μmol g−1 h−1) products were detected over Co/g-C3N4-0.2 SAC. Besides, the photocatalytic activity of the Co/g-C3N4-0.2 SAC for the reduction of CO2 to methanol was stable within 12-cycle experiments (~48 h). This work paves a strategy to boost the photoreduction CO2 activity via loading ultrahigh surface density single atomically dispersed cobalt active sites.

Atmospheric blocking and weather extremes over the Euro-Atlantic sector – a review

Abstract: Abstract. The physical understanding and timely prediction of extreme weather events are of enormous importance to society due to their associated impacts. In this article, we highlight several types of weather extremes occurring in Europe in connection with a particular atmospheric flow pattern, known as atmospheric blocking. This flow pattern effectively blocks the prevailing westerly large-scale atmospheric flow, resulting in changing flow anomalies in the vicinity of the blocking system and persistent conditions in the immediate region of its occurrence. Blocking systems are long-lasting, quasi-stationary and self-sustaining systems that occur frequently over certain regions. Their presence and characteristics have an impact on the predictability of weather extremes and can thus be used as potential indicators. The phasing between the surface and the upper-level blocking anomalies is of major importance for the development of the extreme event. In summer, heat waves and droughts form below the blocking anticyclone primarily via large-scale subsidence that leads to cloud-free skies and, thus, persistent shortwave radiative warming of the ground. In winter, cold waves that occur during atmospheric blocking are normally observed downstream or south of these systems. Here, meridional advection of cold air masses from higher latitudes plays a decisive role. Depending on their location, blocking systems also may lead to a shift in the storm track, which influences the occurrence of wind and precipitation anomalies. Due to these multifaceted linkages, compound events are often observed in conjunction with blocking conditions. In addition to the aforementioned relations, the predictability of extreme events associated with blocking and links to climate change are assessed. Finally, current knowledge gaps and pertinent research perspectives for the future are discussed.

Tough, Transparent, 3D‐Printable, and Self‐Healing Poly(ethylene glycol)‐Gel (PEGgel)

Abstract: Polymer gels, such as hydrogels, have been widely used in biomedical applications, flexible electronics, and soft machines. Polymer network design and its contribution to the performance of gels has been extensively studied. In this study, the critical influence of the solvent nature on the mechanical properties and performance of soft polymer gels is demonstrated. A polymer gel platform based on poly(ethylene glycol) (PEG) as solvent is reported (PEGgel). Compared to the corresponding hydrogel or ethylene glycol gel, the PEGgel with physically cross-linked poly(hydroxyethyl methacrylate-co-acrylic acid) demonstrates high stretchability and toughness, rapid self-healing, and long-term stability. Depending on the molecular weight and fraction of PEG, the tensile strength of the PEGgels varies from 0.22 to 41.3 MPa, fracture strain from 12% to 4336%, modulus from 0.08 to 352 MPa, and toughness from 2.89 to 56.23 MJ m-3 . Finally, rapid self-healing of the PEGgel is demonstrated and a self-healing pneumatic actuator is fabricated by 3D-printing. The enhanced mechanical properties of the PEGgel system may be extended to other polymer networks (both chemically and physically cross-linked). Such a simple 3D-printable, self-healing, and tough soft material holds promise for broad applications in wearable electronics, soft actuators and robotics.

A meta-analysis of residential PV adoption: the important role of perceived benefits, intentions and antecedents in solar energy acceptance

Abstract: The adoption of residential photovoltaic systems (PV) is seen as an important part of the sustainable energy transition. To facilitate this process, it is crucial to identify the determinants of solar adoption. This paper follows a meta-analytical structural equation modeling approach, presenting a meta-analysis of studies on residential PV adoption intention, and assessing four behavioral models based on the theory of planned behavior to advance theory development. Of 653 initially identified studies, 110 remained for full-text screening. Only eight studies were sufficiently homogeneous, provided bivariate correlations, and could thus be integrated into the meta-analysis. The pooled correlations across primary studies revealed medium to large correlations between environmental concern, novelty seeking, perceived benefits, subjective norm and intention to adopt a residential PV system, whereas socio-demographic variables were uncorrelated with intention. Meta-analytical structural equation modeling revealed a model (N = 1,714) in which adoption intention was predicted by benefits and perceived behavioral control (R2 = .280), and benefits in turn could be explained by environmental concern, novelty seeking, and subjective norm (R2 = .641). Our results imply that measures should primarily focus on enhancing the perception of benefits. Based on obstacles we encountered within the analysis, we suggest guidelines to facilitate the future aggregation of scientific evidence, such as the systematic inclusion of key variables and reporting of bivariate correlations.

The Emergence of Aqueous Ammonium‐Ion Batteries

Abstract: Aqueous ammonium-ion (NH4+ ) batteries (AAIB) are a recently emerging technology that utilize the abundant electrode resources and the fast diffusion kinetics of NH4+ to deliver an excellent rate performance at a low cost. Although significant progress has been made on AAIBs, the technology is still limited by various challenges. In this Minireview, the most recent advances are comprehensively summarized and discussed, including cathode and anode materials as well as the electrolytes. Finally, a perspective on possible solutions for the current limitations of AAIBs is provided.

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**

Abstract: Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web-tool on https://mof-synthesis.aimat.science.

Advances in air quality research – current and emerging challenges

Abstract: Abstract. This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

Assembly techniques for proton exchange membrane fuel cell stack: A literature review

Abstract: The assembly of proton exchange membrane fuel cell (PEMFC) is a complex process. The joint action of the assembly load and the working temperature causes an uneven stress distribution among the components and produces a certain degree of deformation. In addition, the assembly process is often related to load transfer, material transfer, energy exchange, multi-phase flow and electrochemical reaction. At present, the manufacturing cost of fuel cells is extremely high, and assembly cost accounts for a large proportion. Assembly techniques with low efficiency and accuracy further increase manufacturing costs. Therefore, there is an urgent need for further research on stack assembly theory and techniques to improve stack assembly efficiency and reduce costs. Based on a survey of the literature, the research status of assembly load, assembly load optimisation, assembly mechanism, and automatic assembly are summarised. Based on comprehensive analyses, this review proposes the development trends of assembly key techniques for fuel cell stacks, and provides a helpful reference for researchers and producers. In the future, the research of assembly technique should pay more attention to the assembly load of fuel cell under on-line conditions and the joint influence of assembly load and other stresses. The assembly mechanism should be designed according to the application scenario of fuel cell and be more convenient to dynamically adjust the assembly load. The review also points out that automation is the development direction of assembly technique. The design, manufacture and assembly of fuel cells need to be better coordinated.

Investigation on performance of RGB point cloud and thermal information data fusion for 3D building thermal map modeling using aerial images under different experimental conditions

Abstract: Three-dimensional thermal mapping provides many benefits for auditing building energy performance when compared with two-dimensional thermal images that provide only limited representation. However, the current thermal mapping approaches have accuracy and efficiency tradeoffs when modeling large areas using aerial images . Particularly, low-resolution thermal images make it harder to obtain a high-quality thermal mapping model. To avoid using low-definition thermal images to reconstruct building thermal models and improve the performance of large-area thermal mapping, we propose a thermal and RGB data fusion framework for thermal mapping. This paper aims to evaluate how different experimental conditions on the proposed fusion approach affect the results. The evaluated conditions include different camera altitudes (60 m and 35 m), distinct camera angles (45° and 30°), diverse flight path designs (mesh grid and Y path), and various building styles (university campus buildings and buildings in a central city area). The results demonstrate that different performances of conducting the proposed data fusion approach under different conditions are observed, and the study provides suggestions for using this approach in different conditions. In addition, this study provides strategies for improving the accuracy of the proposed framework.

COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors

Abstract: Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).

SpeciesRax: A Tool for Maximum Likelihood Species Tree Inference from Gene Family Trees under Duplication, Transfer, and Loss

Abstract: Species tree inference from gene family trees is becoming increasingly popular because it can account for discordance between the species tree and the corresponding gene family trees. In particular, methods that can account for multiple-copy gene families exhibit potential to leverage paralogy as informative signal. At present, there does not exist any widely adopted inference method for this purpose. Here, we present SpeciesRax, the first maximum likelihood method that can infer a rooted species tree from a set of gene family trees and can account for gene duplication, loss, and transfer events. By explicitly modeling events by which gene trees can depart from the species tree, SpeciesRax leverages the phylogenetic rooting signal in gene trees. SpeciesRax infers species tree branch lengths in units of expected substitutions per site and branch support values via paralogy-aware quartets extracted from the gene family trees. Using both empirical and simulated data sets we show that SpeciesRax is at least as accurate as the best competing methods while being one order of magnitude faster on large data sets at the same time. We used SpeciesRax to infer a biologically plausible rooted phylogeny of the vertebrates comprising 188 species from 31,612 gene families in 1 h using 40 cores. SpeciesRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax and on BioConda.

Representative volume elements for matrix-inclusion composites - a computational study on the effects of an improper treatment of particles intersecting the boundary and the benefits of periodizing the ensemble

Abstract: We investigate volume-element sampling strategies for the stochastic homogenization of particle-reinforced composites and show, via computational experiments, that an improper treatment of particles intersecting the boundary of the computational cell may affect the accuracy of the computed effective properties. Motivated by recent results on a superior convergence rate of the systematic error for periodized ensembles compared to taking snapshots of ensembles, we conduct computational experiments for microstructures with circular, spherical and cylindrical inclusions and monitor the systematic errors in the effective thermal conductivity for snapshots of ensembles compared to working with microstructures sampled from periodized ensembles. We observe that the standard deviation of the apparent properties computed on microstructures sampled from the periodized ensembles decays at the scaling expected from the central limit theorem. In contrast, the standard deviation for the snapshot ensembles shows an inferior decay rate at high filler content. The latter effect is caused by additional long-range correlations that necessarily appear in particle-reinforced composites at high, industrially relevant, volume fractions. Periodized ensembles, however, appear to be less affected by these correlations. Our findings provide guidelines for working with digital volume images of material microstructures and the design of representative volume elements for computational homogenization.

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

Abstract: In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under different RCP scenarios (2.6, 4.5, 8.5). We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. While less pronounced and fewer significant trends can be found under RCP2.6 and RCP4.5, we detect significantly declining trends of groundwater levels for most of the sites under RCP8.5, revealing a spatial pattern of stronger decreases, especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

A review of the importance of mineral nitrogen cycling in the plant-soil-microbe system of permafrost-affected soils—changing the paradigm

Abstract: Abstract The paradigm that permafrost-affected soils show restricted mineral nitrogen (N) cycling in favor of organic N compounds is based on the observation that net N mineralization rates in these cold climates are negligible. However, we find here that this perception is wrong. By synthesizing published data on N cycling in the plant-soil-microbe system of permafrost ecosystems we show that gross ammonification and nitrification rates in active layers were of similar magnitude and showed a similar dependence on soil organic carbon (C) and total N concentrations as observed in temperate and tropical systems. Moreover, high protein depolymerization rates and only marginal effects of C:N stoichiometry on gross N turnover provided little evidence for N limitation. Instead, the rather short period when soils are not frozen is the single main factor limiting N turnover. High gross rates of mineral N cycling are thus facilitated by released protection of organic matter in active layers with nitrification gaining particular importance in N-rich soils, such as organic soils without vegetation. Our finding that permafrost-affected soils show vigorous N cycling activity is confirmed by the rich functional microbial community which can be found both in active and permafrost layers. The high rates of N cycling and soil N availability are supported by biological N fixation, while atmospheric N deposition in the Arctic still is marginal except for fire-affected areas. In line with high soil mineral N production, recent plant physiological research indicates a higher importance of mineral plant N nutrition than previously thought. Our synthesis shows that mineral N production and turnover rates in active layers of permafrost-affected soils do not generally differ from those observed in temperate or tropical soils. We therefore suggest to adjust the permafrost N cycle paradigm, assigning a generally important role to mineral N cycling. This new paradigm suggests larger permafrost N climate feedbacks than assumed previously.

Stellar evolution confronts axion models

Abstract: Abstract Axion production from astrophysical bodies is a topic in continuous development, because of theoretical progress in the estimate of stellar emission rates and, especially, because of improved stellar observations. We carry out a comprehensive analysis of the most informative astrophysics data, revisiting the bounds on axion couplings to photons, nucleons and electrons, and reassessing the significance of various hints of anomalous stellar energy losses. We confront the performance of various theoretical constructions in accounting for these hints, while complying with the observational limits on axion couplings. We identify the most favorable models, and the regions in the mass/couplings parameter space which are preferred by the global fit. Finally, we scrutinize the discovery potential for such models at upcoming helioscopes, namely IAXO and its scaled versions.

Electronic structure and bonding in endohedral Zintl clusters

Abstract: Endohedral Zintl clusters-multi-metallic anionic molecules in which a d-block or f-block metal atom is enclosed by p-block (semi)metal atoms-are very topical in contemporary inorganic chemistry. Not only do they provide insight into the embryonic states of intermetallic compounds and show promise in catalytic applications, they also shed light on the nature of chemical bonding between metal atoms. Over the past two decades, a plethora of endohedral Zintl clusters have been synthesized, revealing a fascinating diversity of molecular architectures. Many different perspectives on the bonding in them have emerged in the literature, sometimes complementary and sometimes conflicting, and there has been no concerted effort to classify the entire family based on a small number of unifying principles. A closer look, however, reveals distinct patterns in structure and bonding that reflect the extent to which valence electrons are shared between the endohedral atom and the cluster shell. We show that there is a much more uniform relationship between the total valence electron count and the structure and bonding patterns of these clusters than previously anticipated. All of the p-block (semi)metal shells can be placed on a ladder of total valence electron count that ranges between 4n+2 (closo deltahedra), 5n (closed, three-bonded polyhedra) and 6n (crown-like structures). Although some structural isomerism can occur for a given electron count, the presence of a central metal cation imposes a preference for rather regular and approximately spherical structures which maximise electrostatic interactions between the metal and the shell. In cases where the endohedral metal has relatively accessible valence electrons (from the d or f shells), it can also contribute its valence electrons to the total electron count of the cluster shell, raising the effective electron count and often altering the structural preferences. The electronic situation in any given cluster is considered from different perspectives, some more physical and some more chemical, in a way that highlights the important point that, in the end, they explain the same situation. This article provides a unifying perspective of bonding that captures the structural diversity across this diverse family of multimetallic clusters.

A geometric multiscale model for the numerical simulation of blood flow in the human left heart

Abstract: We present a new computational model for the numerical simulation of blood flow in the human left heart. To this aim, we use the Navier-Stokes equations in an Arbitrary Lagrangian Eulerian formulation to account for the endocardium motion and we model the cardiac valves by means of the Resistive Immersed Implicit Surface method. To impose a physiological displacement of the domain boundary, we use a 3D cardiac electromechanical model of the left ventricle coupled to a lumped-parameter (0D) closed-loop model of the remaining circulation. We thus obtain a one-way coupled electromechanics-fluid dynamics model in the left ventricle. To extend the left ventricle motion to the endocardium of the left atrium and to that of the ascending aorta, we introduce a preprocessing procedure according to which an harmonic extension of the left ventricle displacement is combined with the motion of the left atrium based on the 0D model. To better match the 3D cardiac fluid flow with the external blood circulation, we couple the 3D Navier-Stokes equations to the 0D circulation model, obtaining a multiscale coupled 3D-0D fluid dynamics model that we solve via a segregated numerical scheme. We carry out numerical simulations for a healthy left heart and we validate our model by showing that meaningful hemodynamic indicators are correctly reproduced.