Influence of AlO_x Interlayers on LeTID Kinetics in Ga-Doped Cz-Si

Light and elevated temperature-induced degradation (LeTID) is causing a reduction in efficiency especially in p-type silicon based solar cells. It is assumed to be strongly influenced by the hydrogen content in the bulk material. The presented work focuses on the impact of differently thick (5-25 nm) atomic layer-deposited aluminum oxide (AlOx) interlayers underneath the hydrogen-rich silicon nitride (SiNy:H) capping layer. The interlayer acts as a diffusion barrier for H during the firing step. It is demonstrated that the AlOx interlayer has a comparable effect on the LeTID kinetics in Ga-doped Cz-Si (Cz-Si:Ga) as it is observed in B-doped Cz-Si (Cz-Si:B). Additionally, it substantially minimizes lifetime degradation in the Cz-Si:Ga sample. With a determined ratio of electron to hole capture cross sections k=26(3), the degradation phenomena are attributed to the LeTID kinetics. Deposition of AlOx barrier layers exceeding 10 nm in thickness does not yield additional positive effects. Resistivity measurements revealed that the change in hole concentration correlates with the defect density for varying AlOx layer thicknesses. The doping concentration seems to influence the change in maximum defect density for varying AlOx layer thicknesses.publishe

Attosekunden-Elektronenmikroskopie

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Education increases patience : Evidence from a change in a compulsory schooling law

I investigate the causal effect of education on time preferences. To deal with the endogeneity of education, I exploit exogenous variation in education imposed by a Turkish school reform that raised compulsory education from five to eight years. I find that education causes individuals to make more patient inter-temporal choices but does not induce them to report being more patient. I also provide evidence that the effect of education on patient inter-temporal choices does not operate through changes in financial well-being.publishe

Drug-Target identification in COVID-19 disease mechanisms using computational systems biology approaches

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms.Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue-or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.publishe

Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis

We propose to use optimally ordered orthogonal neighbor-joining (O 3 NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.publishe

Organized Labor Versus Robots? : Evidence from Micro Data

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Between Beveridge and Bismarck : Preferences for Redistribution through Public Pensions

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Interactive Learning with iPads and Augmented Reality : A Sustainability-Oriented Approach to Teaching Plastics Chemistry

As the use of handheld devices continues to proliferate in both private and educational sectors, critical questions emerge concerning the end-of-life management of materials and strategies to curtail waste generation. Augmented reality (AR) technology presents novel avenues for engaging students in science education. This paper presents a novel didactic methodology through a tablet-based, digitally enriched learning scenario that focuses on the properties, synthesis, substitution, and recycling of plastics, particularly in the context of iPads. The scenario utilizes AR technology to provide new perspectives on plastics’ chemistry, fostering interest and understanding. Additionally, the present study employs quantitative methods to investigate the impact on situational interest and understanding concerning learning with iPads and learning about plastics used in iPads on students. The analysis also includes an examination of attitudes toward learning experiences based on AR. A total of 65 secondary students participated in the study. The findings contribute to the ongoing debate on context-based learning and its impact on students’ interest and engagement in science education.publishe

Does raising awareness about inequality decrease support for school closures? : An information treatment survey experiment during the COVID-19 pandemic

The increase in inequalities during the ongoing COVID-19 pandemic has been the topic of intense scholarly and public debate. School closures are one of the containment measures that have been debated most critically in this regard. What drives support for closures of schools and pre-school services (daycare/kindergarten) during a public health crisis such as the current COVID-19 pandemic? More specifically, does inequality awareness affect this support? Theoretically, we assume that providing information on current levels of inequality can change policy preferences, as it increases awareness of their consequences for inequality. Moreover, we assume that the strength of the association between information provision and policy support varies across individuals—depending on their exposure to these policies, and the political attitudes that they hold. To identify causal linkages between awareness of inequalities and support for school and daycare/kindergarten closures, we use a survey experiment with information treatment, in which we randomly assign information designed to prime the respondents to think about either education inequality, gender inequality, or both. The experiment, involving more than 3,000 respondents, was conducted in the spring of 2021 at the end of a prolonged lockdown in Germany when a new piece of legislation was enacted, enabling or restricting school reopenings based on local infection rates. Using Probit Regression models for dichotomous dependent variables, we show that raising awareness of education inequality and gender inequality via an information treatment is associated with decreasing support for preschool and primary school closures. We also find that past exposure to school-closure policies strengthens the effects of information treatments, whereas previous political attitudes do not moderate the association between information treatments and support for preschool and school closures.publishe

Common principles for odour coding across vertebrates and invertebrates

The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.publishe