Does Heterogeneous Strain Act as a Control on Seismic Anisotropy in Earth’s Lower Mantle?
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At extreme scale, the frequency of silent errors – a class of errors that remain undetected by low-level error detection mechanisms – increases significantly with the computational complexity of the application and the scale of the computing infrastructure. As hardware and software advances are made to usher in the next scientific era of computing, developing new approaches to mitigate the impact of silent errors remains a challenging problem. In this work, we propose an energy-aware fault-tolerance model, referred to diffReplication to overcome silent errors. In the proposed model, the main process is associated with one replica that executes at the same rate as the main process, and one diffReplica that is executed at a fraction of the main process' execution rate. If the main and its replica reach consensus at the end of a computation phase, the state of the diffReplica is updated and computation is resumed. If the synchronization attempt results in a disagreement, however, the diffReplica increases its execution speed to complete the computation and quickly reach the synchronization barrier. Assuming a single error over any given synchronization interval, a majority voting is used to reach consensus and tolerate silent errors. To further enhance its performance, diffReplication is augmented with speculative execution, whereby the main or its fast replica is selected to continue execution without waiting for the diffReplica. The selection process is based on the previous behaviour of the main and its replica. A performance analysis study is carried out to assess the performance of diffReplication, in terms of the energy saving and time-to-completion reduction achieved by the diffReplication scheme. The experiment shows that speculative execution reduces the time to completion with additional energy, and dynamic decision-making balances the energy consumption and time to completion.
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Our ability to generate data from simulations and experimental platforms is generally outpacing our ability to analyze those data efficiently and effectively. There are a growing number and variety of possible visualization display modalities, from commodity head-mounted displays to large-scale immersive environments, from desktop and touch displays to high-resolution display walls. Many of these visualization technologies and visual environments are rapidly evolving and could revolutionize analysis for certain classes of complex scientific and engineering data. However, the choice of what technology or combination of technologies to use has historically been largely ad hoc and lacks a carefully validated understanding of how to best utilize different visualization technologies to advance the science. To maximize the impact of our data and analyses, it is imperative that we use the most effective visualization environments for the analysis task at hand.
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Freestanding complex oxide membranes enable the release and transfer of epitaxial films, offering new design freedoms for next‐generation electronics. While the LaAlO 3 /SrTiO 3 (LAO/STO) heterostructure exhibits remarkable tunable conductivity at its interface, the active interface remains buried beneath the substrate, limiting access to this functionality. Here, we demonstrate how the LAO/STO heterostructure, in membrane form, can be flipped and precisely positioned on silicon and other platforms using polymer‐free micromanipulation. The transferred membranes preserve atomically smooth surfaces, high crystallinity, and key electronic properties. Through the 44‐nm insulating STO layer, ultra‐low‐voltage electron‐beam lithography (ULV‐EBL) writes conductive nanostructures at the now‐accessible STO/LAO interface, offering the potential to function as programmable local gates that modulate charge carriers in the underlying silicon. The platform establishes a general strategy for integrating complex oxide heterostructures with semiconductors, quantum materials, and flexible substrates, enabling new architectures for reprogrammable nanoelectronic devices.
Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium-ion cells. To facilitate the establishment of microstructure-resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure-resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium-ion cells are presented.
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Abstract The silylium‐like surface species [ i Pr 3 Si][(R F O) 3 Al−OSi≡)] activates (N^N)Pd(CH 3 )Cl (N^N=Ar−N=CMeMeC=N−Ar, Ar=2,6‐bis(diphenylmethyl)‐4‐methylbenzene) by chloride ion abstraction to form [(N^N)Pd−CH 3 ][(R F O) 3 Al−OSi≡)] ( 1 ). A combination of FTIR, solid‐state NMR spectroscopy, and reactions with CO or vinyl chloride establish that 1 shows similar reactivity patterns as (N^N)Pd(CH 3 )Cl activated with Na[B(Ar F ) 4 ]. Multinuclear 13 C{ 27 Al} RESPDOR and 1 H{ 19 F} S‐REDOR experiments are consistent with a weakly coordinated ion‐pair between (N^N)Pd−CH 3 + and [(R F O) 3 Al−OSi≡)]. 1 catalyzes the polymerization of ethylene with similar activities as [(N^N)Pd−CH 3 ] + in solution and incorporates up to 0.4 % methyl acrylate in copolymerization reactions. 1 produces polymers with significantly higher molecular weight than the solution catalyst, and generates the highest molecular weight polymers currently reported in copolymerization reactions of ethylene and methylacrylate.
Abstract Al(OC(CF 3 ) 3 )(PhF) reacts with silanols present on partially dehydroxylated silica to form well‐defined ≡SiOAl(OC(CF 3 ) 3 ) 2 (O(Si≡) 2 ) ( 1 ). 27 Al NMR and DFT calculations with a small cluster model to approximate the silica surface show that the aluminum in 1 adopts a distorted trigonal bipyramidal coordination geometry by coordinating to a nearby siloxane bridge and a fluorine from the alkoxide. Fluoride ion affinity (FIA) calculations follow experimental trends and show that 1 is a stronger Lewis acid than B(C 6 F 5 ) 3 and Al(OC(CF 3 ) 3 )(PhF) but is weaker than Al(OC(CF 3 ) 3 ) and i Pr 3 Si + . Cp 2 Zr(CH 3 ) 2 reacts with 1 to form [Cp 2 ZrCH 3 ][≡SiOAl(OC(CF 3 ) 3 ) 2 (CH 3 )] ( 3 ) by methide abstraction. This reactivity pattern is similar to reactions of organometallics with the proposed strong Lewis acid sites present on Al 2 O 3 .
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Abstract Al(OC(CF 3 ) 3 )(PhF) reacts with silanols present on partially dehydroxylated silica to form well‐defined ≡SiOAl(OC(CF 3 ) 3 ) 2 (O(Si≡) 2 ) ( 1 ). 27 Al NMR and DFT calculations with a small cluster model to approximate the silica surface show that the aluminum in 1 adopts a distorted trigonal bipyramidal coordination geometry by coordinating to a nearby siloxane bridge and a fluorine from the alkoxide. Fluoride ion affinity (FIA) calculations follow experimental trends and show that 1 is a stronger Lewis acid than B(C 6 F 5 ) 3 and Al(OC(CF 3 ) 3 )(PhF) but is weaker than Al(OC(CF 3 ) 3 ) and i Pr 3 Si + . Cp 2 Zr(CH 3 ) 2 reacts with 1 to form [Cp 2 ZrCH 3 ][≡SiOAl(OC(CF 3 ) 3 ) 2 (CH 3 )] ( 3 ) by methide abstraction. This reactivity pattern is similar to reactions of organometallics with the proposed strong Lewis acid sites present on Al 2 O 3 .
Single-site molybdenum dioxo catalysts, fullerenol/MoO 2 , are prepared via grafting precursor (DME)MoO 2 Cl 2 onto a highly polyhydroxylated fullerene (ful) and an isomerically-pure and well-defined fullerene (ful*). These catalyst structures are characterized by ICP-OES, XPS, XANES, EXAFS, DRIFT, Raman, and NMR spectroscopy, and DFT. Mo 3d 5/2 XPS and Mo K-edge XANES assign the oxidation state as Mo(VI). Mo EXAFS data fitting reveals two Mo=O double and two Mo–O single bonds at distances of 1.7 and 1.9 Å, respectively, while an Mo=O stretchingl mode is observed at ~950 cm –1 by DRIFT and Raman spectroscopy. These data align well with DFT computational results, supporting the proposed catalyst structure as Fullerene(-μ-O-) 2 M(=O) 2 . Additionally, DFT provides insight into the energetically favorable grafting sites for an isomerically pure fullerenol. The scope of fullerenol/MoO 2 mediated alkene epoxidation includes abiotic alkenes, natural occurring terpenes, and conjugated olefins. For cyclooctene the rate law is first-order in [Mo], near first order in [olefin] and zero-order in [t-butyl hydroperoxide]. A plausible reaction mechanism involves peroxide addition first and then cyclooctene addition directly across the peroxo bond forming the epoxide product, consistent with DFT computation. Overall, fullerenol/MoO 2 shows promise as a sustainable and structurally well-defined system with versatile catalytic activity and good epoxidation recyclability.
Redox mediators are attractive solutions for addressing the stringent kinetic stipulations required for efficient energy conversion processes. In this work, we compare the electrochemical properties of four vanadium complexes, namely [V(acac) 3 ], [V 6 O 7 (OMe) 12 ], [ n Bu 4 N] 3 [V 6 O 13 (TRIS NO2 ) 2 ], and [ n Bu 4 N] 5 [V 18 O 46 (NO 3 )] in non-aqueous solutions on glassy carbon electrodes. The goal of this study is to investigate the electron transfer kinetics and diffusivity of these compounds under identical experimental conditions to develop an understanding of structure-function relationships that dictate the physicochemical properties of vanadium oxide assemblies. Complex selection was dictated by two criteria – (1) nuclearity of the transition metal complexes (2) distribution of electron density in the native electronic configuration. In conclusion, our analyses establish that electronic communication between metal centers significantly impacts charge transfer kinetics of these vanadium-based compounds.