Binary ligand strategy toward interweaved encapsulation-nanotubes structured electrocatalyst for proton exchange membrane fuel cell

Hierarchically porous architecture of ir on-nitroge n-carb on(Fe-N-C)for oxyge n reducti on reaction(ORR)is highly desired towards efficient mass transfer in the fuel cell device manner.Herein,we reported a binary ligand strategy to prepare zeolitic imidazolate frameworks(ZIFs)-derived precursors,wherein the addition of secondary ligand endows precursors with the capabilities to transform into porously interweaved encapsulation-nanotubes structured composites after calcination.The optimal catalyst,i.e.,termed as Fe_(6)-M/C-3,exhibits excellent durability with 88.8%current retention after 50,000 seconds in 0.1 M HClO_(4)solution by virtue of nanoparticles-encapsulation features,which is more positive than the benchmark commercial 20 wt%Pt/C catalyst.Moreover,a promising maximum power density of Fe_(6)-M/C-3 as cathode catalyst was also dem on strated in proton exchange membrane fuel cells(PEMFCs)measurements.Therefore,this binary ligand approach to the fabrication of hierarchically porous structures would also have significant implications for various other electrochemical reactions.

Performance analysis of cognitive radio networks using channel-prediction-probabilities and improved frame structure

This paper focuses on potential issues related to the random selection of a sensing channel that occurs after the prediction phase in a Cognitive Radio Network (CRN).A novel approach (Approach-l)for improved selection is proposed,which relies on the probabilities of channels by which they are predicted idle.Further,closed-form expressions are derived for the throughput of Cognitive Users (CUs)in the conventional and proposed approaches. In addition to this,a fimdamental approach for computing the prediction probabilities is also proposed.Moreover, a new challenging issue named "sense and stuck"was observed in the conventional approach.The proposed approach is validated by comparing the results achieved with the results of the conventional approach.However, to achieve the prediction probabilities,the pre-channel-state-information is a prerequisite,but it may be unavailable for particular scenarios;therefore,a modified selection method is introduced to avoid the sense and stuck problem.An algorithm to evaluate the throughput using the random,improved,and modified selection methods is presented with its space and time complexities.Furthermore,for additional improvement in the throughput of the CU,a new frame structure is introduced,in which the spectrum prediction and sensing periods are exploited for simultaneous transmission of data via the underlay spectrtun access technique (Approach-2).The simulated results of Approach-2 are compared with our pre-obtalned results of Approach-I,which confirm significant improvement in the throughput.

A novel image encryption scheme based on Keplers third law and random Hadamard transform

In this paper, a novel image encryption scheme based on Keplers third law and random Hadamard transform is proposed to ensure the security of a digital image. First, a set of Kepler periodic sequences is generated to permutate image data, which is characteristic of the plain-image and the Keplers third law. Then, a random Hadamard matrix is constructed by combining the standard Hadamard matrix with the hyper-Chen chaotic system, which is used to further scramble the image coefficients when the image is transformed through random Hadamard transform. In the end, the permuted image presents interweaving diffusion based on two special matrices, which are constructed by Kepler periodic sequence and chaos system. The experimental results and performance analysis show that the proposed encrypted scheme is highly sensitive to the plain-image and external keys, and has a high security and speed, which are very suitable for secure real-time communication of image data.

Reflections on Observing ESL Reading and Writing Course at University of Ottawa

This article is about some reflections after observing an ESL Reading and Writing Course at the University of Ottawa.Compared to the English teaching situation at my university which is facing colossal challenges of reformation, four aspects are highlighted as the enlightenments: Use the online platform as a scaffold to help students climb up English rather than an arbitrary monitor of the students' after-class English study; Reading and writing interweave, with writing taking priority to reading in Reading and Writing Course; The teacher respect students very much in class rather than behave in a ruling attitude; Grammar matters than expected.

Enhanced interface strength in steel-nickel bimetallic component fabricated using wire arc additive manufacturing with interweaving deposition strategy

Realizing improved strength in composite metallic materials remains a challenge using conventional welding and joining systems due to the generation and development of brittle intermetallic compounds caused by complex thermal profiles during solidification.Here,wire arc additive manufacturing(WAAM)process was used to fabricate a steel-nickel structural component,whose average tensile strength of 634 MPa significantly exceeded that of feedstock materials(steel,537 MPa and nickel,455 MPa),which has not been reported previously.The as-fabricated sample exhibited hierarchically structural heterogeneity due to the interweaving deposition strategy.The improved mechanical response during tensile testing was due to the inter-locking microstructure forming a strong bond at the interface and solid solutions strengthening from the intermixing of the Fe and Ni increased the interface strength,beyond the sum of parts.The research offers a new route for producing high-quality steel-nickel dissimilar structures and widens the design opportunities of monolithic components,with site-specific properties,for specific structural or functional applications.

Precise segmentation of densely interweaving neuron clusters using G-Cut

With the support by the National Natural Science Foundation of China,the research team led by Associate Prof.Zhang JunSong(张俊松)at the Department of Cognitive Science,School of Informatics,Xiamen University,and Prof.Dong HongWei at Keck School of Medicine of USC,University of Southern California,recently reported a powerful automatic reconstruction method for automatically reconstructing densely interweaving neuron clusters into individual neurons,which was published in Nature Communications(2019,10:1549)