Exceptionally strong magnetism in 4d perovskites RTcO3 (R=Ca,Sr,Ba)

The evolution of the magnetic ordering temperature of the 4d3 perovskites RTcO3 (R=Ca,Sr,Ba) and its relation with its electronic and structural properties has been studied by means of hybrid density functional theory and Monte Carlo simulations. When compared to the most widely studied 3d perovskites the large spatial extent of the 4d shells and their relatively strong hybridization with oxygen weaken the tendency to form Jahn-Teller like orbital ordering. This strengthens the superexchange interaction. The resulting insulating G-type antiferromagnetic ground state is characterized by large superexchange coupling constants (26-35 meV) and Neel temperatures (750-1200 K). These monotonically increase as a function of the R ionic radius due to the progressive enhancement of the volume and the associated decrease of the cooperative rotation of the TcO6 octahedra.Comment: 4 pages, 3 figure

Screened hybrid functional applied to 3d^0-->3d^8 transition-metal perovskites LaMO3 (M=Sc-Cu): influence of the exchange mixing parameter on the structural, electronic and magnetic properties

We assess the performance of the Heyd-Scuseria-Ernzerhof (HSE) screened hybrid density functional scheme applied to the perovskite family LaMO3 (M=Sc-Cu) and discuss the role of the mixing parameter alpha (which determines the fraction of exact Hartree-Fock exchange included in the density functional theory (DFT) exchange-correlation functional) on the structural, electronic, and magnetic properties. The physical complexity of this class of compounds, manifested by the largely varying electronic characters (band/Mott-Hubbard/charge-transfer insulators and metals), magnetic orderings, structural distortions (cooperative Jahn-Teller like instabilities), as well as by the strong competition between localization/delocalization effects associated with the gradual filling of the t_2g and e_g orbitals, symbolize a critical and challenging case for theory. Our results indicates that HSE is able to provide a consistent picture of the complex physical scenario encountered across the LaMO3 series and significantly improve the standard DFT description. The only exceptions are the correlated paramagnetic metals LaNiO3 and LaCuO3, which are found to be treated better within DFT. By fitting the ground state properties with respect to alpha we have constructed a set of 'optimum' values of alpha from LaScO3 to LaCuO3: it is found that the 'optimum' mixing parameter decreases with increasing filling of the d manifold (LaScO3: 0.25; LaTiO3 & LaVO3: 0.10-0.15; LaCrO3, LaMnO3, and LaFeO3: 0.15; LaCoO3: 0.05; LaNiO3 & LaCuO3: 0). This trend can be nicely correlated with the modulation of the screening and dielectric properties across the LaMO3 series, thus providing a physical justification to the empirical fitting procedure.Comment: 32 pages, 29 figure

Characterizing Interactive Communications in Computer-Supported Collaborative Problem-Solving Tasks: A Conditional Transition Profile Approach

Communication in a collaborative problem-solving activity plays a pivotal role in the success of the collaboration in both academia and the workplace. Computer-supported collaboration makes it possible to collect large-scale communication data to investigate the process at a finer granularity. In this paper, we introduce a conditional transition profile (CTP) to characterize aspects of each team member's communication. Based on the data from a large-scale empirical study, we found that participants in the same team tend to show similar CTP compared to participants from different teams. We also found that team members who showed more “negotiation” after the partner “shared” information tended to show more improvement after the collaboration while those who continued sharing ideas while their partners were negotiating tended to improve less

Cs⁺ incorporation into CH₃NH₃PbI₃ perovskite:substitution limit and stability enhancement

In this study we systematically explored the mixed cation perovskite Csx(CH3NH3)1-xPbI3. We exchanged the A-site cation by dipping MAPbI3 films into a CsI solution, thereby incrementally replacing the MA+ in a time-resolved dipping process and analysed the resulting thin-films with UV-Vis, XRD, EDAX, SEM and optical depth-analysis in a high-throughput fashion. Additional in situ UV-Vis and time-resolved XRD measurements allowed us to look at the kinetics of the formation process. The results showed a discontinuity during the conversion. Firstly, small amounts of Cs+ are incorporated into the structure. After a few minutes, the Cs content approaches a limit and grains of δ-CsPbI3 occur, indicating a substitution limit. We compared this cation exchange to a one-step crystallisation approach and found the same effect of phase segregation, which shows that the substitution limit is an intrinsic feature rather than a kinetic effect. Optical and structural properties changed continuously for small Cs incorporations. Larger amounts of Cs result in phase segregation. We estimate the substitution limit of CsxMA1-xPbI3 to start at a Cs ratio x = 0.13, based on combined measurements of EDAX, UV-Vis and XRD. The photovoltaic performance of the mixed cation perovskite shows a large increase in device stability from days to weeks. The initial efficiency of mixed CsxMA1-xPbI3 devices decreases slightly, which is compensated by stability after a few days.</p

Nondestructive Evaluation of Ceramic Matrix Composite Combustor Components

Combustor liners fabricated from a SiC/SiC composite (silicon carbide fibers in a silicon carbide matrix) were nondestructively interrogated before and after combustion rig testing by x-ray, ultrasonic, and thermographic techniques. In addition, mechanical test results were obtained from witness coupons, representing the as-manufactured liners, and from coupons machined from the components after combustion exposure. Thermography indications correlated with reduced material properties obtained after rig testing. The thermography indications in the SiC/SiC liners were delaminations and damaged fiber tows, as determined through microstructural examinations

Oxygen Vacancy Formation Energy in Metal Oxides: High Throughput Computational Studies and Machine Learning Predictions

The oxygen vacancy formation energy ($\Delta E_{vf}$) governs defect dynamics and is a useful metric to perform materials selection for a variety of applications. However, density functional theory (DFT) calculations of $\Delta E_{vf}$ come at a greater computational cost than the typical bulk calculations available in materials databases due to the involvement of multiple vacancy-containing supercells. As a result, available repositories of direct calculations of $\Delta E_{vf}$ remain relatively scarce, and the development of machine learning models capable of delivering accurate predictions is of interest. In the present, work we address both such points. We first report the results of new high-throughput DFT calculations of oxygen vacancy formation energies of the different unique oxygen sites in over 1000 different oxide materials, which together form the largest dataset of directly computed oxygen vacancy formation energies to date, to our knowledge. We then utilize the resulting dataset of $\sim$2500 $\Delta E_{vf}$ values to train random forest models with different sets of features, examining both novel features introduced in this work and ones previously employed in the literature. We demonstrate the benefits of including features that contain information specific to the vacancy site and account for both cation identity and oxidation state, and achieve a mean absolute error upon prediction of $\sim$0.3 eV/O, which is comparable to the accuracy observed upon comparison of DFT computations of oxygen vacancy formation energy and experimental results. Finally, we demonstrate the predictive power of the developed models in the search for new compounds for solar-thermochemical water-splitting applications, finding over 250 new AA$^{\prime}$BB$^{\prime}$O$_6$ double perovskite candidates

ArborZ: Photometric Redshifts Using Boosted Decision Trees

Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of Boosted Decision Trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single "best estimate" and error, and also provides a photo-z quality figure-of-merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.Comment: 10 pages, 13 figures, submitted to Ap

Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes

Extracellular electrode arrays can reveal the neuronal network correlates of behavior with single-cell, single-spike, and sub-millisecond resolution. However, implantable electrodes are inherently invasive, and efforts to scale up the number and density of recording sites must compromise on device size in order to connect the electrodes. Here, we report on silicon-based neural probes employing nanofabricated, high-density electrical leads. Furthermore, we address the challenge of reading out multichannel data with an application-specific integrated circuit (ASIC) performing signal amplification, band-pass filtering, and multiplexing functions. We demonstrate high spatial resolution extracellular measurements with a fully integrated, low noise 64-channel system weighing just 330 mg. The on-chip multiplexers make possible recordings with substantially fewer external wires than the number of input channels. By combining nanofabricated probes with ASICs we have implemented a system for performing large-scale, high-density electrophysiology in small, freely behaving animals that is both minimally invasive and highly scalable