A novel ionic diffusion strategy to fabricate high-performance anode-supported solid oxide fuel cells (SOFCs) with proton-conducting Y-doped BaZrO3 films

A stable Y-doped BaZrO3 electrolyte film, which showed a good performance in proton-conducting SOFCs, was successfully fabricated using a novel ionic diffusion strategy.

Thermal properties and thermal shock resistance of γ-Y 2Si2O7

Thermal properties, namely, Debye temperature, thermal expansion coefficient, heat capacity, and thermal conductivity of γ-Y 2Si2O7, a high-temperature polymorph of yttrium disilicate, were investigated. The anisotropic thermal expansions of γ-Y2Si2O7 powders were examined using high-temperature X-ray diffractometer from 300 to 1373 K and the volumetric thermal expansion coefficient is (6.68±0.35) × 10-6 K-1. The linear thermal expansion coefficient of polycrystalline γ-Y2Si2O7 determined by push-rod dilatometer is (3.90±0.4) × 10-6 K-1, being very close to that of silicon nitride and silicon carbide. Besides, γ-Y2Si2O7 displays a low-thermal conductivity, with a κ value measured below 3.0 W·(m·K) -1 at the temperatures above 600 K. The calculated minimum thermal conductivity, κmin, was 1.35 W·(m·K) -1. The unique combination of low thermal expansion coefficient and low-thermal conductivity of γ-Y2Si2O7 renders it a very competitive candidate material for high temperature structural components and environmental/thermal-barrier coatings. The thermal shock resistance of γ-Y2Si2O7 was estimated by quenching dense materials in water from various temperatures and the critical temperature difference, ΔTc, was determined to be 300 K.

Kinetics and mechanism of hot corrosion of γ-Y 2Si 2O 7 in thin-film Na 2SO 4 molten salt

γ-Y 2Si 2O 7 is a promising candidate material both for hightemperature structural applications and as an environmental/thermal barrier coating material due to its unique properties such as high melting point, machinability, thermal stability, low linear thermal expansion coefficient (3.9×10 -6/K, 200°-1300°C), and low thermal conductivity (<3.0 W/ṁK above 300°C). The hot corrosion behavior of γ-Y 2Si 2O 7 in thin-film molten Na 2SO 4 at 850°-1000°C for 20 h in flowing air was investigated using a thermogravimetric analyzer (TGA) and a mass spectrometer (MS). γ-Y 2Si 2O 7 exhibited good resistance against Na 2SO 4 molten salt. The kinetic curves were well fitted by a paralinear equation: the linear part was caused by the evaporation of Na2SO4 and the parabolic part came from gas products evolved from the hotcorrosion reaction. A thin silica film formed under the corrosion scale was the key factor for retarding the hot corrosion. The apparent activation energy for the corrosion of γ-Y 2Si 2O 7 in Na 2SO 4 molten salt with flowing air was evaluated to be 255 kJ/mol.

Developing an Unmanned Aerial Vehicle (UAV) multi-sensor payload carrier for air quality monitoring

Emissions of gases and particles from sea-faring ships have been shown to impact on the atmospheric chemistry and climate. To efficiently monitor and report these emissions found from a ship’s plume, the concept of using a multi-rotor or UAV to hover inside or near the exhaust of the ship to actively record the data in real time is being developed. However, for the required sensors obtain the data; their sensors must face into the airflow of the ships plume. This report presents an approach to have sensors able to read in the chemicals and particles emitted from the ship without affecting the flight dynamics of the multi-rotor UAV by building a sealed chamber in which a pump can take in the surrounding air (outside the downwash effect of the multi-rotor) where the sensors are placed and can analyse the gases safely. Results show that the system is small, lightweight and air-sealed and ready for flight test.

Green Falcon IV: An Unmanned Aerial System and an integrated Wireless Sensor Network for remote sensing tasks

This report summarises the development of an Unmanned Aerial System and an integrated Wireless Sensor Network (WSN), suitable for the real world application in remote sensing tasks. Several aspects are discussed and analysed to provide improvements in flight duration, performance and mobility of the UAV, while ensuring the accuracy and range of data from the wireless sensor system.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.