Charge compensation and Ce3+ formation in trivalent doping of the CeO2(110) surface: The key role of dopant ionic radius

In this paper, we use density functional theory corrected for on-site Coulomb interactions (DFT + U) and hybrid DFT (HSE06 functional) to study the defects formed when the ceria (110) surface is doped with a series of trivalent dopants, namely, Al3+, Sc3+, Y3+, and In 3+. Using the hybrid DFT HSE06 exchange-correlation functional as a benchmark, we show that doping the (110) surface with a single trivalent ion leads to formation of a localized MCe / + O O • (M = the 3+ dopant), O- hole state, confirming the description found with DFT + U. We use DFT + U to investigate the energetics of dopant compensation through formation of the 2MCe ′ +VO ̈ defect, that is, compensation of two dopants with an oxygen vacancy. In conjunction with earlier work on La-doped CeO2, we find that the stability of the compensating anion vacancy depends on the dopant ionic radius. For Al3+, which has the smallest ionic radius, and Sc3+ and In3+, with intermediate ionic radii, formation of a compensating oxygen vacancy is stable. On the other hand, the Y3+ dopant, with an ionic radius close to that of Ce4+, shows a positive anion vacancy formation energy, as does La3+, which is larger than Ce4+ (J. Phys.: Condens. Matter 2010, 20, 135004). When considering the resulting electronic structure, in Al3+ doping, oxygen hole compensation is found. However, Sc 3+, In3+, and Y3+ show the formation of a reduced Ce3+ cation and an uncompensated oxygen hole, similar to La3+. These results suggest that the ionic radius of trivalent dopants strongly influences the final defect formed when doping ceria with 3+ cations. In light of these findings, experimental investigations of these systems will be welcome.

Hybrid density functional theory description of N- and C-doping of NiO

The large intrinsic bandgap of NiO hinders its potential application as a photocatalyst under visible-light irradiation. In this study, we have performed first-principles screened exchange hybrid density functional theory with the HSE06 functional calculations of N- and C-doped NiO to investigate the effect of doping on the electronic structure of NiO. C-doping at an oxygen site induces gap states due to the dopant, the positions of which suggest that the top of the valence band is made up primarily of C 2p-derived states with some Ni 3d contributions, and the lowest-energy empty state is in the middle of the gap. This leads to an effective bandgap of 1.7 eV, which is of potential interest for photocatalytic applications. N-doping induces comparatively little dopant-Ni 3d interactions, but results in similar positions of dopant-induced states, i.e., the top of the valence band is made up of dopant 2p states and the lowest unoccupied state is the empty gap state derived from the dopant, leading to bandgap narrowing. With the hybrid density functional theory (DFT) results available, we discuss issues with the DFT corrected for on-site Coulomb description of these systems.

Education - research - practice

Through this paper we will look at links between architecture education, research and practice, using a current project as a vehicle to cover aspects of building, pilot and live project. The first aspect, the building project consists of the refurbishment and extension of a Parnell Cottage for a private client and is located near Cloyne, in East Cork, Ireland. The pilot project falls within the NEES Project, investigating the use of materials and services based on natural or recycled materials to improve the energy performance of new and existing buildings. The live project aims to hold a series of on site workshops and seminars for students of Architecture, Architects and interested parties, demonstrating the integration of the NEES best practice materials and techniques within the built project. The workshops, seminars and key project documents will be digitally recorded for dissemination through a web based publication. The small scale of the building project allowed for flexibility in the early conceptual design stages and the integration of the research and educational aspects.

Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.