Structural study in Highly Compressed BiFeO3 Epitaxial Thin Films on YAlO3

We report a study on the thermodynamic stability and structure analysis of the epitaxial BiFeO3 (BFO) thin films grown on YAlO3 (YAO) substrate. First we observe a phase transition of MC-MA-T occurs in thin sample (<60 nm) with an utter tetragonal-like phase (denoted as MII here) with a large c/a ratio (~1.23). Specifically, MII phase transition process refers to the structural evolution from a monoclinic MC structure at room temperature to a monoclinic MA at higher temperature (150oC) and eventually to a presence of nearly tetragonal structure above 275oC. This phase transition is further confirmed by the piezoforce microscopy measurement, which shows the rotation of polarization axis during the phase transition. A systematic study on structural evolution with thickness to elucidate the impact of strain state is performed. We note that the YAO substrate can serve as a felicitous base for growing T-like BFO because this phase stably exists in very thick film. Thick BFO films grown on YAO substrate exhibit a typical "morphotropic-phase-boundary"-like feature with coexisting multiple phases (MII, MI, and R) and a periodic stripe-like topography. A discrepancy of arrayed stripe morphology in different direction on YAO substrate due to the anisotropic strain suggests a possibility to tune the MPB-like region. Our study provides more insights to understand the strain mediated phase co-existence in multiferroic BFO system.Comment: 18 pages, 6 figures, submitted to Journal of Applied Physic

A Deep Learning Approach to Radar-based QPE

In this study, we propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013-2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.Comment: 22 pages, 11 figures. Published in Earth and Space Scienc

Multi-Segment Foam Flow Field in Ambient Pressure Polymer Exchange Membrane Fuel Cell

In order to produce low-cost flow field plates for polymer electrolyte membrane fuel cells, we used nickel foam in this study rather than conventional flow field. Nickel foam has high electron conductivity, thermal conductivity, and mechanical strength. Electrochemical impedance spectrum analysis is carried out to evidence the use on flow field plates of nickel foam. From the impedance fitting results, the nickel foam cases showed the lower contact resistance than the serpentine. However, such plates have poor performance at low temperatures and ambient pressure. In order to overcome this, a multi-segment foam flow field is designed in this study. This increased the performance of the polarization curve by 70% from 162 to 275.5 mw cm-2 than the original nickel foam design. Also, the mass transfer resistance was reduced, and the Warburg impedance value of the operation voltage decreased by 0.4 V. The numerical analysis results demonstrate that increased segment numbers can increase the performance of the multi-segment foam flow field

Tsengwen Reservoir Watershed Hydrological Flood Simulation Under Global Climate Change Using the 20 km Mesh Meteorological Research Institute Atmospheric General Circulation Model (MRI-AGCM)

Severe rainstorms have occurred more frequently in Taiwan over the last decade. To understand the flood characteristics of a local region under climate change, a hydrological model simulation was conducted for the Tsengwen Reservoir watershed. The model employed was the Integrated Flood Analysis System (IFAS), which has a conceptual, distributed rainfall-runoff analysis module and a GIS data-input function. The high-resolution rainfall data for flood simulation was categorized into three terms: 1979 - 2003 (Present), 2015 - 2039 (Near-future), and 2075 - 2099 (Future), provided by the Meteorological Research Institute atmospheric general circulation model (MRI-AGCM). Ten extreme rainfall (top ten) events were selected for each term in descending order of total precipitation volume. Due to the small watershed area the MRI-AGCM3.2S data was downsized into higher resolution data using the Weather Research and Forecasting Model. The simulated discharges revealed that most of the Near-future and Future peaks caused by extreme rainfall increased compared to the Present peak. These ratios were 0.8 - 1.6 (Near-future/Present) and 0.9 - 2.2 (Future/Present), respectively. Additionally, we evaluated how these future discharges would affect the reservoir¡¦s flood control capacity, specifically the excess water volume required to be stored while maintaining dam releases up to the dam¡¦s spillway capacity or the discharge peak design for flood prevention. The results for the top ten events show that the excess water for the Future term exceeded the reservoir¡¦s flood control capacity and was approximately 79.6 - 87.5% of the total reservoir maximum capacity for the discharge peak design scenario

Bioavailable insulin-like growth factor-I as mediator of racial disparity in obesity-relevant breast and colorectal cancer risk among postmenopausal women

Bioavailable insulin-like growth factor (IGF)-I interacts with obesity and exogenous estrogen in a racial disparity in obesity-related cancer risk, yet their interconnected pathways are not fully characterized. We investigated whether circulating bioavailable IGF-I acted as a mediator of the racial disparity in obesity-related cancers such as breast and colorectal (CR) cancers and how obesity and estrogen use regulate this relationship