Theoretical insights into the hydrophobicity of low index CeO2 surfaces

The hydrophobicity of CeO2 surfaces is examined here. Since wettability measurements are extremely sensitive to experimental conditions, we propose a general approach to obtain contact angles between water and ceria surfaces of specified orientations based on density functional calculations. In particular, we analysed the low index surfaces of this oxide to establish their interactions with water. According to our calculations, the CeO2 (111) surface was the most hydrophobic with a contact angle of {\Theta} = 112.53{\deg} followed by (100) with {\Theta} = 93.91{\deg}. The CeO2 (110) surface was, on the other hand, mildly hydrophilic with {\Theta} = 64.09{\deg}. By combining our calculations with an atomistic thermodynamic approach, we found that the O terminated (100) surface was unstable unless fully covered by molecularly adsorbed water. We also identified a strong attractive interaction between the hydrogen atoms in water molecules and surface oxygen, which gives rise to the hydrophilic behaviour of (110) surfaces. Interestingly, the adsorption of water molecules on the lower-energy (111) surface stabilises oxygen vacancies, which are expected to enhance the catalytic activity of this plane. The findings here shed light on the origin of the intrinsic wettability of rare earth oxides in general and CeO2 surfaces in particular and also explain why CeO2 (100) surface properties are so critically dependant on applied synthesis methods

Tailoring cations in a perovskite cathode for proton-conducting solid oxide fuel cells with high performance

© 2019 The Royal Society of Chemistry. A rational design of a high-performance cathode for proton-conducting solid oxide fuel cells (SOFCs) is proposed in this study with the aim of improving the hydration properties of conventional perovskite cathode materials, thus leading to the development of new materials with enhanced proton migration. Herein, potassium is used to dope traditional Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF), which is demonstrated to be a beneficial way for improving hydration, both experimentally and theoretically. The theoretical study was needed to overcome practical limits that hindered direct hydrogen mobility measurements. The novel material Ba0.4K0.1Sr0.5Co0.8Fe0.2O3-δ (BKSCF) shows a lower overall proton migration energy compared with that of the sample without K, suggesting that K-doping enhances proton conduction, which shows an improved performance by extending the catalytic sites to the whole cathode area. As a result, a fuel cell built with the novel BKSCF cathode shows an encouraging fuel cell performance of 441 and 1275 mW cm-2 at 600 and 700 °C, respectively, which is significantly higher than that of the cell using the pristine BSCF cathode. This study provides a new and rational way to design a perovskite cathode for proton-conducting SOFCs with high performance

First-principles investigation of quantum emission from hBN defects

© 2017 The Royal Society of Chemistry. Hexagonal boron nitride (hBN) has recently emerged as a fascinating platform for room-temperature quantum photonics due to the discovery of robust visible light single-photon emitters. In order to utilize these emitters, it is necessary to have a clear understanding of their atomic structure and the associated excitation processes that give rise to this single photon emission. Here, we performed density-functional theory (DFT) and constrained DFT calculations for a range of hBN point defects in order to identify potential emission candidates. By applying a number of criteria on the electronic structure of the ground state and the atomic structure of the excited states of the considered defects, and then calculating the Huang-Rhys (HR) factor, we found that the CBVN defect, in which a carbon atom substitutes a boron atom and the opposite nitrogen atom is removed, is a potential emission source with a HR factor of 1.66, in good agreement with the experimental HR factor. We calculated the photoluminescence (PL) line shape for this defect and found that it reproduces a number of key features in the experimental PL lineshape

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

© 2020 Wiley-VCH GmbH The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super-lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy

Muon spin motion at the crossover regime between Gaussian and Lorentzian distribution of magnetic fields

The muon spin relaxation method is a powerful microscopic tool for probing the electronic states of materials by observing local magnetic field distributions on the muon. It often happens that a distribution of local magnetic fields shows an intermediate state between Gaussian and Lorentzian shapes. In order to generally describe these intermediate field distributions, we considered the convolution of two isotropic distributions in three dimensions and derived exact muon-spin relaxation functions which can be applied to all crossover regimes between Gaussian and Lorentzian

Cerium Oxide Nanoparticles Protect Cardiac Progenitor Cells from Oxidative Stress

Cardiac progenitor cells (CPCs) are a promising autologous source of cells for cardiac regenerative medicine. However, CPC culture in vitro requires the presence of microenvironmental conditions (a complex array of bioactive substance concentration, mechanostructural factors, and physicochemical factors) closely mimicking the natural cell surrounding in vivo, including the capability to uphold reactive oxygen species (ROS) within physiological levels in vitro. Cerium oxide nanoparticles (nanoceria) are redox-active and could represent a potent tool to control the oxidative stress in isolated CPCs. Here, we report that 24 h exposure to 5, 10, and 50 !g/mL of nanoceria did not a!ect cell growth and function in cardiac progenitor cells, while being able to protect CPCs from H2O2-induced cytotoxicity for at least 7 days, indicating that nanoceria in an e!ective antioxidant. Therefore, these "ndings con"rm the great potential of nanoceria for controlling ROS-induced cell damage

The economic impact of moderate stage Alzheimer's disease in Italy: Evidence from the UP-TECH randomized trial

Background: There is consensus that dementia is the most burdensome disease for modern societies. Few cost-of-illness studies examined the complexity of Alzheimer's disease (AD) burden, considering at the same time health and social care, cash allowances, informal care, and out-of-pocket expenditure by families. Methods: This is a comprehensive cost-of-illness study based on the baseline data from a randomized controlled trial (UP-TECH) enrolling 438 patients with moderate AD and their primary caregiver living in the community. Results: The societal burden of AD, composed of public, patient, and informal care costs, was about �20,000/yr. Out of this, the cost borne by the public sector was �4,534/yr. The main driver of public cost was the national cash-for-care allowance (�2,324/yr), followed by drug prescriptions (�1,402/yr). Out-of-pocket expenditure predominantly concerned the cost of private care workers. The value of informal care peaked at �13,590/yr. Socioeconomic factors do not influence AD public cost, but do affect the level of out-of-pocket expenditure. Conclusion: The burden of AD reflects the structure of Italian welfare. The families predominantly manage AD patients. The public expenditure is mostly for drugs and cash-for-care benefits. From a State perspective in the short term, the advantage of these care arrangements is clear, compared to the cost of residential care. However, if caregivers are not adequately supported, savings may be soon offset by higher risk of caregiver morbidity and mortality produced by high burden and stress. The study has been registered on the website www.clinicaltrials.org (Trial Registration number: NCT01700556). Copyright � International Psychogeriatric Association 2015