SrAlSi4N7:Eu2+

The new nitridoalumosilicate phosphor SrAlSi4N7:Eu2+ has been synthesized under nitrogen atmosphere at temperatures up to 1630°C in a radio-frequency furnace starting from Sr metal, α-Si3N4, AlN, and additional Eu metal. The crystal structure of the host compound SrAlSi4N7 has been solved and refined on the basis of single-crystal and powder X-ray diffraction data. In the solid, there is a network structure of corner-sharing SiN4 tetrahedra incorporating infinite chains of all edge-sharing AlN4 tetrahedra running along [001] (SrAlSi4N7: Pna21 (No. 33), Z = 8, a = 11.742(2) Å, b = 21.391(4) Å, c = 4.966(1) Å, V = 12.472(4) Å3, 2739 reflections, 236 refined parameters, R1 = 0.0366). The Eu2+-doped compound SrAlSi4N7:Eu2+ shows typical broadband emission originating from dipole-allowed 4f6(7FJ)5d1 → 4f7 (8S7/2) transitions in the orange-red spectral region (λmax = 632 nm for 2% Eu doping level, 450 nm excitation) with a spectral width of FWHM = 2955 (± 75) cm−1 and a Stokes shift ΔS = 4823 (± 100) cm−1. The luminescence properties make the phosphor an attractive candidate material as red component in trichromatic warm white light LEDs with excellent color rendition properties

Privacy as a Part of the Preference Structure of Users App Buying Decision

Information privacy and personal data in information systems are referred to as the ‘new oil’ of the 21st century. The mass adoption of smart mobile devices, sensor-enabled smart IoT-devices, and mobile applications provide virtually endless possibilities of gathering users’ personal information. Previous research suggests that users attribute very little monetary value to their information privacy. The current paper assumes that users are not able to monetize their value of privacy due to its abstract nature and non-transparent context. By defining privacy as a crucial product attribute of mobile applications the authors provide an approach to measure the importance of privacy as part of users’ preference structure. The results of the conducted choice-based conjoint Analysis emphasize the high relevance of privacy in users’ preference structure when downloading an app and provide an interesting contribution for theory and practice

Single-stage gradient-based stellarator coil design: stochastic optimization

We extend the single-stage stellarator coil design approach for quasi-symmetry on axis from [Giuliani et al, 2020] to additionally take into account coil manufacturing errors. By modeling coil errors independently from the coil discretization, we have the flexibility to consider realistic forms of coil errors. The corresponding stochastic optimization problems are formulated using a risk-neutral approach and risk-averse approaches. We present an efficient, gradient-based descent algorithm which relies on analytical derivatives to solve these problems. In a comprehensive numerical study, we compare the coil designs resulting from deterministic and risk-neutral stochastic optimization and find that the risk-neutral formulation results in more robust configurations and reduces the number of local minima of the optimization problem. We also compare deterministic and risk-neutral approaches in terms of quasi-symmetry on and away from the magnetic axis, and in terms of the confinement of particles released close to the axis. Finally, we show that for the optimization problems we consider, a risk-averse objective using the Conditional Value-at-Risk leads to results which are similar to the risk-neutral objective

Direct stellarator coil optimization for nested magnetic surfaces with precise quasisymmetry

We present a robust optimization algorithm for the design of electromagnetic coils that generate vacuum magnetic fields with nested flux surfaces and precise quasisymmetry. The method is based on a bilevel optimization problem, where the outer coil optimization is constrained by a set of inner least-squares optimization problems whose solutions describe magnetic surfaces. The outer optimization objective targets coils that generate a field with nested magnetic surfaces and good quasisymmetry. The inner optimization problems identify magnetic surfaces when they exist, and approximate surfaces in the presence of magnetic islands or chaos. We show that this formulation can be used to heal islands and chaos, thus producing coils that result in magnetic fields with precise quasisymmetry. We show that the method can be initialized with coils from the traditional two stage coil design process, as well as coils from a near axis expansion optimization. We present a numerical example where island chains are healed and quasisymmetry is optimized up to surfaces with aspect ratio 6. Another numerical example illustrates that the aspect ratio of nested flux surfaces with optimized quasisymmetry can be decreased from 6 to approximately 4. The last example shows that our approach is robust and a cold-start using coils from a near-axis expansion optimization

RNApredator: fast accessibility-based prediction of sRNA targets

Bacterial genomes encode a plethora of small RNAs (sRNAs), which are heterogeneous in size, structure and function. Most sRNAs act as post-transcriptional regulators by means of specific base pairing interactions with the 5′-untranslated region of mRNA transcripts, thereby modifying the stability of the target transcript and/or its ability to be translated. Here, we present RNApredator, a web server for the prediction of sRNA targets. The user can choose from a set of over 2155 genomes and plasmids from 1183 bacterial species. RNApredator then uses a dynamic programming approach, RNAplex, to compute putative targets. Compared to web servers with a similar task, RNApredator takes the accessibility of the target during the target search into account, improving the specificity of the predictions. Furthermore, enrichment in Gene Ontology terms, cellular pathways as well as changes in accessibilities along the target sequence can be done in fully automated post-processing steps. The predictive performance of the underlying dynamic programming approach RNAplex is similar to that of more complex methods, but needs at least three orders of magnitude less time to complete. RNApredator is available at http://rna.tbi.univie.ac.at/RNApredator

Field-induced Conductance Switching by Charge-state Alternation in Organometallic Single-Molecule Junctions

Charge transport through single molecules can be influenced by the charge and spin states of redox-active metal centres placed in the transport pathway. These molecular intrinsic properties are usually addressed by varying the molecules electrochemical and magnetic environment, a procedure that requires complex setups with multiple terminals. Here we show that oxidation and reduction of organometallic compounds containing either Fe, Ru or Mo centres can solely be triggered by the electric field applied to a two-terminal molecular junction. Whereas all compounds exhibit bias-dependent hysteresis, the Mo-containing compound additionally shows an abrupt voltage-induced conductance switching, yielding high to low current ratios exceeding 1000 at voltage stimuli of less than 1.0 V. DFT calculations identify a localized, redox active molecular orbital that is weakly coupled to the electrodes and closely aligned with the Fermi energy of the leads because of the spin-polarised ground state unique to the Mo centre. This situation opens an additional slow and incoherent hopping channel for transport, triggering a transient charging effect of the entire molecule and a strong hysteresis with unprecedented high low-to-high current ratios.Comment: 9 pages, 4 figure

Vorbereitung auf das Unterrichten mit digitalen Medien: szenarien-basierte Selbsteinschätzung in der Aus- und Weiterbildung von Lehrkräften

Teachers’ technology-related skills are often measured with self-assessments. However, self-assessments are often criticised for being inaccurate and biased. Scenario-based self-assessment is a promising approach to make self-assessment more accurate and less biased. In this study with N = 552 inservice and student teachers, we validated a scenario-based self-assessment instrument IN.K19+ for teachers. The instrument enables scenario-based self-assessment of instrumental and critical digital skills and technology-related teaching skills for teachers. In a confirmatory factor analysis, we show that the instrument has sufficient factorial validity. To test the predictive validity of the instrument, we examined the instruments’ relationship to the frequency of technology use during teaching and teacher-initiated student learning activities involving digital technologies. Results from structural equation modelling show that instrumental digital skills and technology-related teaching skills are positively related to the frequency of digital technology use during teaching, while critical digital skills are not. In terms of the initiation of student learning activities, instrumental and critical digital skills show relationships with initiating student learning activities that include lower cognitive engagement. Technology-related teaching skills are related to initiating learning activities that indicate higher cognitive engagement. The results show that instrumental and critical digital skills play an important role with respect to the basic use of digital technologies in the classroom, while technology-related teaching skills turn out to be crucial for more complex scenarios of digital technology use. This pattern of findings supports the predictive validity of the IN.K19+ instrument