Rapid Gas-Phase Synthesis of the Perovskite-Type BaCe0.7Zr0.1Y0.1Yb0.1O3-δ Proton-Conducting Nanocrystalline Electrolyte for Intermediate-Temperature Solid Oxide Fuel Cells.

Perovskite-type proton-conducting materials, such as BaCe0.7Zr0.1Y0.1Yb0.1O3-δ (BCZYYb), are very attractive for the next-generation equipment of electrochemical energy conversion and storage owing to their excellent conductivity in the intermediate-temperature range (300-750 °C), as well as good thermo-chemical stability, coking resistance, and sulfur tolerance. However, the lack of a reliable and cost-effective synthesis method for such multi-component co-doping oxides limits their large-scale application. In this study, for the first time, we successfully synthesize BCZYYb electrolyte nanopowders by using a rapid, scalable flame-based gas-phase synthesis method with two different barium precursors Ba(NO3)2 and Ba(CH3COO)2, named as BCZYYb (N) and BCZYYb (CA). The as-synthesized nanoparticles exhibit good crystallinity of the pure orthorhombic perovskite BCZYYb phase. BCZYYb (CA) shows more uniform doping with the element ratio of 1:0.74:0.12:0.08:0.1, which is very close to the theoretical value. The shrinkage and surface SEM (scanning electron microscope) results indicate that the flame-made powders have superior sinterability compared to the sol-gel-made powders because of the smaller primary particle size (∼20 nm). Electrochemical impedance spectroscopy tests show that BCZYYb (CA) sintered at 1450 °C has the highest protonic conductivity of 1.31 × 10-2 S cm-1 in wet H2 when operating at 600 °C and still maintains a high-level conductivity of 1.19 × 10-2 S cm-1 even when the sintering temperature is reduced to 1350 °C, which is mainly attributed to uniform doping and good sinterability. The activation energy for the conductivity of BCZYYb (CA) is also significantly lower than that of conventional electrolytes, which suggests much better conductivity in the intermediate (∼600 °C) and even lower operating temperature. The excellent conductivity performance combined with the high-throughput production capability makes the swirling spray flame a promising synthesis method for promoting the BCZYYb electrolytes from lab to industrial-scale solid oxide fuel cells.

Sharing personal ECG time-series data privately.

OBJECTIVE: Emerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information, posing new privacy challenges. In this work, we study the applicability of a novel privacy model to enable individual-level time-series data sharing while maintaining the usability for data analytics. METHODS AND MATERIALS: We propose a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data, which leverages dimensional reduction technique and random sampling to achieve provable privacy protection. We show that our solution provides strong privacy protection against an informed adversarial model while enabling useful aggregate-level analysis. RESULTS: We conduct our evaluations on 2 real-world ECG datasets. Our empirical results show that the privacy risk is significantly reduced after sanitization while the data usability is retained for a variety of clinical tasks (eg, predictive modeling and clustering). DISCUSSION: Our study investigates the privacy risk in sharing individual-level ECG time-series data. We demonstrate that individual-level data can be highly unique, requiring new privacy solutions to protect data contributors. CONCLUSION: The results suggest our proposed privacy-protection method provides strong privacy protections while preserving the usefulness of the data.

Dual-Wavelength Excited Intense Red Upconversion Luminescence from Er3+-Sensitized Y2O3 Nanocrystals Fabricated by Spray Flame Synthesis.

Er3+-sensitized upconversion nanoparticles (UCNPs) have attracted great attention due to their tunable upconversion (UC) emissions, low cytotoxicity, high resistance to photobleaching and especially multiple effective excitation wavelengths. However, detailed energy conversion between Er3+ and Tm3+ ions in Y2O3 UCNPs is still a problem, especially under multi-wavelength and variable pulse width excitation. In this work, we successfully fabricated a series of Er3+-sensitized Y2O3 nanocrystals by a spray flame synthesis method with a production rate of 40.5 g h-1. The as-prepared UCNPs are a pure cubic phase with a mean size of 14 nm. Excited by both 980 and 808 nm lasers, the tunable upconversion luminescence (UCL) from Er3+ ions was achieved by increasing the Er3+ doping concentration, co-doping Tm3+ ions and extending excitation pulse-width. The investigations of the lifetimes and the laser power dependence of UC emissions further support the proposed mechanism, which provides guidance for achieving effective color control in anticounterfeiting and multiplexed labeling applications. In addition, the red UC emission at about 5 mm beneath the tissue surface was observed in an ex vivo imaging experiment under the excitation of 808 nm laser, indicating that the Y2O3:Er3+/Tm3+ UCNPs have great prospects in further biological applications.

Neutronic performance characteristics of different LEU fuels in a proposed NIST research reactor.

As a potential replacement for the National Bureau Standards Reactor (NBSR) at the U.S. National Institute of Standards and Technology (NIST), a conceptual design of a new reactor with a horizontally-split core has recently been studied using low-enriched uranium (LEU) silicide dispersion (U3Si2/Al) fuel. In this paper, the neutronics calculations of the proposed NIST reactor with other two low-enriched U-Mo fuels (U-10Mo monolithic fuel and U-7Mo/Al dispersion fuel) were performed, and the results were compared to that of the U3Si2/Al fuel, with the objective of identifying the best fuel candidate for the reactor cycle length and maximum cold neutron production. To make consistent comparisons, fuel inventories for multi-cycle equilibrium cores were produced for each fuel based on a 30 d reactor cycle at 20 MW thermal power. With its very high uranium density, the potential to load more uranium in the core with U-10Mo monolithic fuel was explored with test cases using an alternate fuel management scheme, a higher power level (30 MW), or a longer cycle (45 d). The research results indicate similar neutronics performance characteristics of the three LEU fuel options in the proposed NIST reactor with the same power level. However, the ability to load more fuel in the reactor with the U-10Mo option allows additional flexibility in the reactor design and could lead to other optimizations that maximize cold neutron production.

Hybrid biasing approaches for global variance reduction.

A new variant of Monte Carlo-deterministic (DT) hybrid variance reduction approach based on Gaussian process theory is presented for accelerating convergence of Monte Carlo simulation and compared with Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) approach implemented in the SCALE package from Oak Ridge National Laboratory. The new approach, denoted the Gaussian process approach, treats the responses of interest as normally distributed random processes. The Gaussian process approach improves the selection of the weight windows of simulated particles by identifying a subspace that captures the dominant sources of statistical response variations. Like the FW-CADIS approach, the Gaussian process approach utilizes particle importance maps obtained from deterministic adjoint models to derive weight window biasing. In contrast to the FW-CADIS approach, the Gaussian process approach identifies the response correlations (via a covariance matrix) and employs them to reduce the computational overhead required for global variance reduction (GVR) purpose. The effective rank of the covariance matrix identifies the minimum number of uncorrelated pseudo responses, which are employed to bias simulated particles. Numerical experiments, serving as a proof of principle, are presented to compare the Gaussian process and FW-CADIS approaches in terms of the global reduction in standard deviation of the estimated responses.