Electronic structure origin of conductivity and oxygen reduction activity changes in low-level Cr-substituted (La, Sr)MnO3

The electronic structure of the (La0.8Sr0.2)(0.98)Mn1-xCrxO3 model series (x = 0, 0.05, or 0.1) was measured using soft X-ray synchrotron radiation at room and elevated temperature. O K-edge near-edge X-ray absorption fine structure (NEXAFS) spectra showed that low-level chromium substitution of (La, Sr)MnO3 resulted in lowered hybridisation between O 2p orbitals and M 3d and M 4sp valance orbitals. Mn L-3-edge resonant photoemission spectroscopy measurements indicated lowered Mn 3d-O 2p hybridisation with chromium substitution. Deconvolution of O K-edge NEXAFS spectra took into account the effects of exchange and crystal field splitting and included a novel approach whereby the pre-peak region was described using the nominally filled t(2g) up arrow state. 10% chromium substitution resulted in a 0.17 eV lowering in the energy of the t(2g) up arrow state, which appears to provide an explanation for the 0.15 eV rise in activation energy for the oxygen reduction reaction, while decreased overlap between hybrid O 2p-Mn 3d states was in qualitative agreement with lowered electronic conductivity. An orbital-level understanding of the thermodynamically predicted solid oxide fuel cell cathode poisoning mechanism involving low-level chromium substitution on the B-site of (La, Sr)MnO3 is presented. (C) 2015 AIP Publishing LLC.

Activity Recognition in Egocentric Life-Logging Videos

With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to life-logging, which is expected to be an important application for egocentric videos. In this work, we first conduct a comprehensive survey of existing egocentric video datasets. We observe that existing datasets do not emphasize activities relevant to the life-logging scenario. We build an egocentric video dataset dubbed LENA (Life-logging EgoceNtric Activities) (http://people.sutd.edu.sg/similar to 1000892/dataset) which includes egocentric videos of 13 fine-grained activity categories, recorded under diverse situations and environments using the Google Glass. Activities in LENA can also be grouped into 5 top-level categories to meet various needs and multiple demands for activities analysis research. We evaluate state-of-the-art activity recognition using LENA in detail and also analyze the performance of popular descriptors in egocentric activity recognition.