A direct method to extract strain energy release rates using XFEM and Irwin’s integral

An analytical formulation based on Irwin’s integral and combined with the extended finite element method is proposed to extract mixed-mode components of strain energy release rates in linear elastic fracture mechanics. High-order crack tip enrichment functions in XFEM allow for evaluation of integral quantities in closed form, resulting in a simple, accurate, and efficient method. Hence, SIFs can directly be obtained upon solution of the XFEM discrete system. Several benchmark examples on pure and mixed mode problems are studied, investigating the effects of the order of the enrichment, mesh refinement, and the length of crack extension. The numerical results show that high accuracy can be achieved on structured as well as unstructured meshes. Examples of a crack approaching a hole and two cracks approaching each other are also investigated. The latter illustrate the advantage of this method over a J-integral class of methods, as SIFs can still be calculated when cracks are in close proximity and no remeshing is required. Hence potentially this method can address crack coalescence and branching more rigorously

N‑Doped Carbon Nanotube Shell Encapsulating the NiFe Metal Core for Enhanced Catalytic Stability in Methanol Oxidation Reaction by the Structural Cooperation Mechanism

Exploitation of high-efficiency catalysts toward methanol oxidation is a pivotal step to promote the commercialization of direct methanol fuel cells. Herein, a strategy is demonstrated to prepare nitrogen-doped carbon nanotubes with NiFe metal particles (NiFe@N-CNT) as the carrier material of Pt nanoparticles. Combining SEM and TEM, NiFe metal particles are fully encapsulated in N-CNTs, and they form the metal core and carbon nanotube shell structure based on the structural cooperation mechanism. Surprisingly, the as-prepared Pt/NiFe@N-CNT catalyst shows superior catalytic activity (1023 mA mg–1Pt) compared to commercial Pt/C (392 mA mg–1Pt), Pt/Ni@N-CNT (331 mA mg–1Pt), and Pt/Fe@N-CNT (592 mA mg–1Pt). After 1000 cycles, Pt/NiFe@N-CNT maintains the optimal catalytic activity (588 mA mg–1Pt), and its mass activity loss is 42.5%, which is better than those of commercial Pt/C (64.0%), Pt/Ni@N-CNT (67.7%), and Pt/Fe@N-CNT (59.6%) catalysts, indicating that the Pt/NiFe@N-CNT catalyst achieves excellent catalytic activity and stability, which stems chiefly from the homodispersed Pt nanoparticles and the generation of the metal core–carbon nanotube shell based on the structural cooperation mechanism. This study reports the facile construction of a metal core–carbon nanotube shell structure, which intrinsically ameliorates structural collapse of carrier material, thereby improving the catalytic stability of the Pt-based catalyst and broadening the view for design of other desire catalysts in methanol oxidation

Efficient position decoding methods based on fluorescence calcium imaging in the mouse hippocampus

Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigation tasks. Development of efficient neural decoding methods for reconstructing the animal's position in real or virtual environments can provide a fast readout of spatial representations in closed-loop neuroscience experiments. Here, we develop an efficient strategy to extract features from fluorescence calcium imaging traces and further decode the animal's position. We validate our spike inference-free decoding methods in multiple in vivo calcium imaging recordings of the mouse hippocampus based on both supervised and unsupervised decoding analyses. We systematically investigate the decoding performance of our proposed methods with respect to the number of neurons, imaging frame rate, and signal-to-noise ratio. Our proposed supervised decoding analysis is ultrafast and robust, and thereby appealing for real-time position decoding applications based on calcium imaging.Published versio