Search for: All records

Abstract Designing highly active and robust catalysts for the oxygen evolution reaction is key to improving the overall efficiency of the water splitting reaction. It has been previously demonstrated that evaporation induced self‐assembly (EISA) can be used to synthesize highly porous and high surface area cerate‐based fluorite nanocatalysts, and that substitution of Ce with 50% rare earth (RE) cations significantly improves electrocatalyst activity. Herein, the defect structure of the best performing nanocatalyst in the series are further explored, Nd₂Ce₂O₇, with a combination of neutron diffraction and neutron pair distribution function analysis. It is found that Nd^{3 +}cation substitution for Ce in the CeO₂fluorite lattice introduces higher levels of oxygen Frenkel defects and induces a partially reduced RE_1.5Ce_1.5O_{5 +x}phase with oxygen vacancy ordering. Significantly, it is demonstrated that the concentration of oxygen Frenkel defects and improved electrocatalytic activity can be further enhanced by increasing the compositional complexity (number of RE cations involved) in the substitution. The resulting novel compositionally‐complex fluorite– (La_0.2Pr_0.2Nd_0.2Tb_0.2Dy_0.2)₂Ce₂O₇is shown to display a low OER overpotential of 210 mV at a current density of 10 mAcm⁻²in 1M KOH, and excellent cycling stability. It is suggested that increasing the compositional complexity of fluorite nanocatalysts expands the ability to tailor catalyst design. 

Polar nanoregions (PNRs) are believed to play a decisive role in the local and macroscopic polarization in relaxor ferroelectrics. The limited microscopic understanding of the structure and dynamics of PNRs hampers the rational design of new lead-free materials. Here, the local structure of A-site disordered Bi 0.5 K 0.5 TiO 3 (BKT) is investigated using synchrotron x-ray and neutron pair distribution function (PDF) analysis and density functional theory (DFT) optimized special quasirandom structures (SQSs). DFT-relaxed SQS with a 4 × 4 × 4 supercell size can reproduce the experimental PDFs of disordered BKT, as well as the partial PDFs and total polarization, with comparable results to those reported from a combined analysis of x-ray and neutron PDF data with large-box reverse Monte Carlo methods. We find that small Bi 3+ -rich polar clusters are likely to be the microscopic origin of relaxor behavior in disordered BKT, and that the existence of large polar nanoregions (PNRs) is not necessary to explain the relaxor properties. Our results also highlight the great potential of the SQS approach to gain a nanoscale-to-microscopic understanding of other relaxor solid solutions. 

Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, systems researchers have recently started to provide support for serverless-based training. However, the ability to train DL models on serverless platforms is hindered by the resource limitations of today's serverless infrastructure and DL models' explosive requirement for memory and bandwidth. This paper describes FuncPipe, a novel pipelined training framework specifically designed for serverless platforms that enable fast and low-cost training of DL models. FuncPipe is designed with the key insight that model partitioning can be leveraged to bridge both memory and bandwidth gaps between the capacity of serverless functions and the requirement of DL training. Conceptually simple, we have to answer several design questions, including how to partition the model, configure each serverless function, and exploit each function's uplink/downlink bandwidth. In particular, we tailor a micro-batch scheduling policy for the serverless environment, which serves as the basis for the subsequent optimization. Our Mixed-Integer Quadratic Programming formulation automatically and simultaneously configures serverless resources and partitions models to fit within the resource constraints. Lastly, we improve the bandwidth efficiency of storage-based synchronization with a novel pipelined scatter-reduce algorithm. We implement FuncPipe on two popular cloud serverless platforms and show that it achieves 7%-77% cost savings and 1.3X-2.2X speedup compared to state-of-the-art serverless-based frameworks. 

Steady-state fluorescence spectroscopy has a central role not only for sensing applications, but also in biophysics and imaging. Light switching probes, such as ruthenium dipyridophenazine complexes, have been used to study complex systems such as DNA, RNA, and amyloid fibrils. Nonetheless, steady-state spectroscopy is limited in the kind of information it can provide. In this paper, we use time-resolved spectroscopy for studying binding interactions between amyloid-β fibrillar structures and photoluminescent ligands. Using time-resolved spectroscopy, we demonstrate that ruthenium complexes with a pyrazino phenanthroline derivative can bind to two distinct binding sites on the surface of fibrillar amyloid-β, in contrast with previous studies using steady-state photoluminescence spectroscopy, which only identified one binding site for similar compounds. The second elusive binding site is revealed when deconvoluting the signals from the time-resolved decay traces, allowing the determination of dissociation constants of 3 and 2.2 μM. Molecular dynamic simulations agree with two binding sites on the surface of amyloid-β fibrils. Time-resolved spectroscopy was also used to monitor the aggregation of amyloid-β in real-time. In addition, we show that common polypyridine complexes can bind to amyloid-β also at two different binding sites. Information on how molecules bind to amyloid proteins is important to understand their toxicity and to design potential drugs that bind and quench their deleterious effects. The additional information contained in time-resolved spectroscopy provides a powerful tool not only for studying excited state dynamics but also for sensing and revealing important information about the system including hidden binding sites.