Synergistic Role of Water and Oxygen Leads to Degradation in Formamidinium-Based Halide Perovskites

Mixed-cation metal halide perovskites have shown remarkable progress in photovoltaic applications with high power conversion efficiencies. However, to achieve large-scale deployment of this technology, efficiencies must be complemented by long-term durability. The latter is limited by external factors, such as exposure to humidity and air, which lead to the rapid degradation of the perovskite materials and devices. In this work, we study the mechanisms causing Cs and formamidinium (FA)-based halide perovskite phase transformations and stabilization during moisture and air exposure. We use in situ X-ray scattering, X-ray photoelectron spectroscopy, and first-principles calculations to study these chemical interactions and their effects on structure. We unravel a surface reaction pathway involving the dissolution of FAI by water and iodide oxidation by oxygen, driving the Cs/FA ratio into thermodynamically unstable regions, leading to undesirable phase transformations. This work demonstrates the interplay of bulk phase transformations with surface chemical reactions, providing a detailed understanding of the degradation mechanism and strategies for designing durable and efficient perovskite materials

Inter-comparison of melt pond products from optical satellite imagery

Given the importance that melt ponds have on the energy balance of summer sea ice, there have been several efforts to develop pan-Arctic datasets using satellite data. Here we intercompare three melt pond data sets that rely on multi-frequency optical satellite data. Early in the melt season, the three data sets have similar spatial patterns in melt pond fraction, but this agreement weakens as the melt season progresses despite relatively high interannual correlations in pond fractions between the data products. Most of the data sets do not exhibit trends towards increased melt pond fractions from 2002 to 2011 despite overall Arctic warming and earlier melt onset. Further comparisons are made against higher resolution optical data to assess relative accuracy. These comparisons reveal the challenges in retrieving melt ponds from coarse resolution satellite data, and the need to better discriminate between leads, small open water areas and melt ponds. Finally, we assess melt pond data sets as a function of ice type and how well they correlate with surface albedo. As expected, melt pond fractions are negatively correlated with surface albedo, though the strength of the correlation varies across products and regions. Overall, first-year ice has larger melt pond fractions than multi-year ice

Landfast sea ice monitoring using multisensor fusion in the Antarctic

Landfast sea ice (fast ice) means sea ice that is attached to the shoreline with little or no motion in contrast to pack ice which drifts on the sea. As fast ice plays an important role in the environmental and biological systems of the Antarctic, it is crucial to accurately monitor the spatiotemporal distribution of fast ice. Previous studies on fast ice using satellite remote sensing were mostly focused on the Arctic and near-Arctic areas, whereas few studies were conducted over the Antarctic, especially the West Antarctic region. This research mapped fast ice using multisensor data from 2003 to 2008 based on machine learning approaches - decision trees (DTs) and random forest (RF). A total of seven satellite-derived products, including Advanced Microwave Scanning Radiometer for the Earth observing system brightness temperatures and sea ice concentration, Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) and Special Sensor Microwave/Imager ice velocity, were used as input variables for identifying fast ice. RF resulted in better performance than that of DT for fast ice classification. Visual comparison of the fast ice classification results with 250-m MODIS images for selected areas also revealed that RF outperformed DT. Ice velocity and IST were identified as the most contributing variables to classify fast ice. Spatiotemporal variations of fast ice in the East and West Antarctic were also examined using the time series of the fast ice maps produced by RF. The residence time of fast ice was much shorter in the West Antarctic than in the East. ??? 2015 Taylor & Francisclose0

A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron’s spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385