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Clusters of nitrogen- and carbon-coordinated transition metals dispersed in a carbon matrix (e. g., Fe−N−C) have emerged as an inexpensive class of electrocatalysts for the oxygen reduction reaction (ORR). Here, it was shown that optimizing the interaction between the nitrogen-coordinated transition metal clusters embedded in a more stable and corrosion-resistant carbide matrix yielded an ORR electrocatalyst with enhanced activity and stability compared to Fe−N−C catalysts. Utilizing first-principles calculations, an electrostatics-based descriptor of catalytic activity was identified, and nitrogen-coordinated iron (FeN4) clusters embedded in a TiC matrix were predicted to be an efficient platinum-group metal (PGM)-free ORR electrocatalyst. Guided by theory, selected catalyst formulations were synthesized, and it was demonstrated that the experimentally observed trends in activity fell exactly in line with the descriptor-derived theoretical predictions. The Fe−N−TiC catalyst exhibited enhanced activity (20 %) and durability (3.5-fold improvement) compared to a traditional Fe−N−C catalyst. It was posited that the electrostatics-based descriptor provides a powerful platform for the design of active and stable PGM-free electrocatalysts and heterogenous single-atom catalysts for other electrochemical reactions.

We present Grapple, a new and powerful framework for explicit-state model checking on GPUs. Grapple is based on swarm verification (SV), a model-checking technique wherein a collection or swarm of small, memory- and time-bounded verification tests (VTs) are run in parallel to perform state-space exploration. SV achieves high state-space coverage via diversification of the search strategies used by constituent VTs. Grapple represents a swarm implementation for the GPU. In particular, it runs a parallel swarm of internally-parallel VTs, which are implemented in a manner that specifically targets the GPU architecture and the SIMD parallelism its computing cores offer. Grapple also makes effective use of the GPU shared memory, eliminating costly inter-block communication overhead. We conducted a comprehensive performance analysis of Grapple focused on the various design parameters, including the size of the queue structure, implementation of guard statements, and nondeterministic exploration order. Tests are run with multiple hardware configurations, including on the Amazon cloud. Our results show that Grapple performs favorably compared to the SPIN swarm and a prior non-swarm GPU implementation. Although a recently debuted FPGA swarm is faster, the deployment process to the FPGA is much more complex than Grapple's.

A bstract We present a search for the charged lepton-flavor-violating decays ϒ(1 S ) → ℓ ± ℓ ′ ∓ and radiative charged lepton-flavour-violating decays ϒ(1 S ) → γ ℓ ± ℓ ′ ∓ [ ℓ , ℓ ′ = e, μ, τ ] using the 158 million ϒ(2 S ) sample collected by the Belle detector at the KEKB collider. This search uses ϒ(1 S ) mesons produced in ϒ(2 S ) → π + π − ϒ(1 S ) transitions. We do not find any significant signal, so we provide upper limits on the branching fractions at the 90% confidence level.

A bstract We present the first measurement of the branching fraction of the singly Cabibbo-suppressed (SCS) decay $$ {\Lambda}_c^{+} $$ Λ c + → pη ′ with η ′ → ηπ + π − , using a data sample corresponding to an integrated luminosity of 981 fb − 1 , collected by the Belle detector at the KEKB e + e − asymmetric-energy collider. A significant $$ {\Lambda}_c^{+} $$ Λ c + → pη ′ signal is observed for the first time with a signal significance of 5.4 σ . The relative branching fraction with respect to the normalization mode $$ {\Lambda}_c^{+} $$ Λ c + → pK − π + is measured to be $$ \frac{\mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)}{\mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right)}=\left(7.54\pm 1.32\pm 0.73\right)\times {10}^{-3}, $$ B Λ c + → pη ′ B Λ c + → pK − π + = 7.54 ± 1.32 ± 0.73 × 10 − 3 , where the uncertainties are statistical and systematic, respectively. Using the world-average value of $$ \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) $$ B Λ c + → pK − π + = (6 . 28 ± 0 . 32) × 10 − 2 , we obtain $$ \mathcal{B}\left({\Lambda}_c^{+}\to p\eta^{\prime}\right)=\left(4.73\pm 0.82\pm 0.46\pm 0.24\right)\times {10}^{-4}, $$ Bmore »Λ c + → pη ′ = 4.73 ± 0.82 ± 0.46 ± 0.24 × 10 − 4 , where the uncertainties are statistical, systematic, and from $$ \mathcal{B}\left({\Lambda}_c^{+}\to {pK}^{-}{\pi}^{+}\right) $$ B Λ c + → pK − π + , respectively.« less