Transition-metal and nitrogen-codoped carbon-based (TM-N/C) catalysts are promising candidates for catalyzing the oxygen reduction reaction (ORR). However, TM-N/C catalysts suffer from insufficient ORR activity, unclear active site structure, and poor durability, particularly in acidic solution. Herein, we report single Co atom and N codoped carbon nanofibers (Co–N/CNFs) catalyst with high durability and desirable ORR activity in both acidic and alkaline solutions. The half-wave potential of the ORR shows a negligible decrease after a 10 000-cycle accelerated durability test. The high ORR durability is originated from the structural stability of the atomically dispersed Co-based active site, as revealed by probing analysis and density functional theory calculations. A passive direct methanol fuel cell with the Co–N/CNFs cathode delivers a maximal power density of 16 mW cm–2 and a remarkable stability during a 200 h test, demonstrating the application potential of Co–N/CNFs. The breakthrough of the highly durable TM-N/C ORR catalyst could open an avenue for affordable and durable fuel cells.
The inner leaves of crop canopy are covered by outer branches and leaves, leading to the information loss of the occluded canopy structure in modern crop perception, and it has restricted the development of phenotype observation and precision agriculture. In this paper, we propose a neural network method to reconstruct the occluded structures of crop canopies with an RGB-D sensor. Taking the cotton plant as the object of study, we propose a novel Cascade Leaf Segmentation and Completion Network (CLSCN) to reconstruct the occluded leaf images and propose a Fragmental Leaf Point–cloud Reconstruction Algorithm (FLPRA) to complete the missing point clouds. By combining the Instance Segmentation Network (ISN), Generative Adversarial Network (GAN) and Point-cloud Reconstruction Algorithm (PRA), the three-dimensional models of cotton plants with both completed internal and external structures of the canopy are smoothly reconstructed. Firstly, a great number of images and point clouds of leaves are captured with an RGB-D sensor from the top view of cotton canopies, and a cotton leaf VOC dataset is created for manual data division and annotation. Secondly, a network named CLSCN is cascading constructed with an Instance Segmentation Network (ISN) and a Generative Adversarial Network (GAN), and the two parts of CLSCN are separately trained with our constructed dataset to output complete cotton leaves. Thirdly, with the fusion of the completed RGB images output by cascaded network segmentation and the point clouds captured by RGB-D sensor, the proposed FLPRA is used to filter, reconstruct, fuse and register the cotton canopy leaf point clouds, and to obtain the whole cotton canopy point-clouds with inner occluded structure recovery. Finally, the CLSCN and FLPRA are validated using the validation dataset of cotton leaf. The test results show that: the front-end ISN of the proposed CLSCN can output high-quality cotton leaf masks, of which the mIoU can be up to 84.65%, and the back-end GAN of CLSCN can complete the occluded leaves with the accuracy over 94%; the reconstruction accuracy of the final three-dimensional model of the cotton canopy is up to 82.7%. Therefore, the proposed neural network and algorithm effectively solve the problem of incomplete canopy point cloud caused by the occlusion of outer leaves and provide an effective way to recover the complete three-dimensional structure of crop canopy with internal occlusion. It is a meaningful theoretical and technical support to realize real-time crop status observation and precise field management in agriculture production.
In order to ensure network secure and reliable and provide better and more qualified service,a reputation model was proposed to isolate bad peer. The mathematical model and algorithm to resolve reputation were given,and the factors which influences reputation were introduced.Simulating and analysis shows that the reputation model can isolate bad peer effectively
The photoelectrochemical (PEC) water splitting performance of BiVO4 is partially hindered by insufficient photoresponse in the spectral region with energy below the band gap. Here, we demonstrate that the PEC water splitting efficiency of BiVO4 electrodes can be effectively enhanced by decorating Pd nanoparticles (NPs) and nanorods (NRs). The results indicate that the Pd NPs and NRs with different surface plasmon resonance (SPR) features delivered an enhanced PEC water splitting performance in the visible and near-infrared (NIR) regions, respectively. Considering that there is barely no absorption overlap between Pd nanostructures and BiVO4 and the finite-difference time domain (FDTD) simulation indicating there are substantial energetic hot electrons in the vicinity of Pd nanostructures, the enhanced PEC performance of Pd NP-decorated BiVO4 and Pd NR-decorated BiVO4 could both benefit from the hot electron injection mechanism instead of the plasmon resonance energy transfer process. Moreover, a combination of Pd NPs and NRs decorated on the BiVO4 electrodes leads to a broad-band enhancement across visible-NIR region.
A Pd/Ni bimetallic nanostructured electrocatalyst was fabricated via a two-step reduction route. Owing to an epitaxial growth of Pd atoms on the surface of Ni nanoparticles, heterostructured Pd/Ni nanocomposites were formed and verified by high resolution transmission electron microscopy combined with energy-dispersion X-ray spectroscopy. X-ray diffraction confirmed that the as-prepared Pd/Ni nanocomposites possessed a single face-centered-cubic (fcc) Pd structure, probably due to a weaker diffraction intensity of metallic Ni and/or overlapping by that of Pd. The intrinsic catalytic activity on the Pd/Ni is higher than that on the Pd. Moreover, the durability of formic acid oxidation on the Pd/Ni was much enhanced over the Pd nanoparticles. The change in electronic structure of the surface coordination unsaturated Pd atoms and the possible dissolution of Ni species from the Pd/Ni heterostructure may account for such an improved durability for formic acid oxidation.
Variable transmission ratio racks show great potential in rice transplanters as a key component of variable transmission ratio steering to balance steering portability and sensitivity. The objective of this study was to develop a novel geometrical design method to achieve quick, high-quality modeling of the free curvilinear tooth profile of a variable transmission ratio rack. First, a discrete envelope motion 3D model was established between the pinion-sector and the variable transmission ratio rack blank based on the mapping relationship between the rotation angle of the pinion-sector and the displacement of the rack, according to the variable transmission ratio function. Based on the loop Boolean subtraction operation, which removed the pinion-sector from the rack blank during all moments of the discrete motion process, the final complex changing tooth shape of the variable transmission ratio rack was enveloped. Then, since Boolean cutting residues made the variable ratio tooth surface fluctuant and eventually affected the precision of the model, this study proposed a modification method for establishing a smooth and continuous tooth profile. First, a novel fitting algorithm used approximate variable ratio tooth profile points extracted from the Boolean cutting marks and generated a series of variable ratio tooth profiles by utilizing B-spline with different orders. Next, based on a transmission stability simulation, the variable ratio tooth profile with optimal dynamic performance was selected as the final design. Finally, tests contrasting the transmission stability of the machining samples of the initial variable ratio tooth profile and the final variable ratio tooth profile were conducted. The results indicated that the final variable ratio tooth profile is more effective than the initial variable ratio tooth profile. Therefore, the proposed variable ratio tooth profile modeling and modification method for eliminating Boolean cutting residues and improving surface accuracy is proved to be feasible. Keywords: rice transplanter, steering, variable ratio tooth profile, variable ratio curve, Boolean subtraction operation, transmission stability DOI: 10.25165/j.ijabe.20201305.4884 Citation: Niu Z R, Li J L, Xin S, Zou L L, Li Y H, Hou J L, et al. Geometrical design of variable ratio tooth profile based on Boolean subtraction operation and a novel modification method. Int J Agric & Biol Eng, 2020; 13(5): 125–133.
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