Boosting Catalytic Oxidative Desulfurization Performance over Yolk-Shell Nickel Molybdate Fabricated by Defect Engineering.

Developing catalysts with optimized surface properties is significant for advanced catalysis. Herein, a rational architectural design is proposed to successfully synthesize yolk-shell nickel molybdate with abundant oxygen vacancies (YS-VO-NMO) via an acid-assisted defect engineering strategy. Notably, YS-VO-NMO with the yolk-shell structure shows complex nanoconfined interior space, which is beneficial to the mass transfer and active sites exposure. Moreover, the defect engineering strategy is of great importance to modulate the surface electronic structure and atomic composition, which contributes to the enrichment of oxygen vacancies. Benefiting from these features, the higher hydrogen peroxide activation is achieved by YS-VO-NMO to produce more hydroxyl radicals compared with untreated nickel molybdate. Consequently, the defect-engineered YS-VO-NMO not only features superior catalytic activity (99.5%) but also retains high desulfurization efficiency after recycling eight times. This manuscript provides new inspiration for designing more promising defective materials via defect engineering and architecture for different applications besides oxidative desulfurization.

Modulating Electronic Characteristics of Nickel Molybdate via an Effective Manganese-Doping Strategy to Enhance Oxidative Desulfurization Performance.

Modulating the electronic characteristics of catalysts plays a significant role in optimizing their catalytic activity. Herein, Mn-doped nickel molybdate (MNMO) nanorods are synthesized via replacing the partial Ni sites by the Mn element, engineering a bimetallic synergistic effect to enhance the activation of oxygen (O2). Compared with the extremely low catalytic activity of pristine nickel molybdate (NiMoO4), complete desulfurization can be achieved by MNMO under the same reaction conditions. Characterization results show that the electronic structure and surface atomic composition of pure NiMoO4 will be modulated owing to the introduction of Mn atoms, leading to the enhancement of the oxygen vacancy content and stronger O2 activation capacity. Besides, the optimized catalyst MNMO-20 also displays satisfactory cycle performance, and the sulfur removal of dibenzothiophene still maintains 96.1% after six times of recycling. The distinctive engineering strategy and simple synthesis method provide a new insight in designing and developing oxidative desulfurization catalysts with high stability and effectivity.

Light-induced pyroelectric property of self-powered photodetectors based on all-inorganic perovskite quantum dots.

All-inorganic cesium lead bromine (CsPbBr3) perovskites quantum dots (QDs) are one of the most photoelectric materials due to their high absorption coefficient, pronounced quantum-size effect, tunable optical property. Here, a self-powered PD based on all-inorganic CsPbBr3perovskites QDs is fabricated and demonstrated. The light-induced pyroelectric effect is utilized to modulate the optoelectronic processes without the external power supply. The working mechanism of the PD is carefully investigated upon 532 nm laser illumination and the minimum recognizable response time of the self-powered PD is 1.5µs, which are faster than those of most previously reported wurtzite nanostructure PDs. Meanwhile, the frequency and temperature independence of the self-powered PD are experimented and summarized. The self-powered PD with high performance is expected to have extensive applications in solar cell, energy harvesting, resistive random access memory.

A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.

Atrial scar quantification via multi-scale CNN in the graph-cuts framework.

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.

Molecular mapping of the new blast resistance genes Pi47 and Pi48 in the durably resistant local rice cultivar Xiangzi 3150.

The indica rice cultivar Xiangzi 3150 (XZ3150) confers a high level of resistance to 95% of the isolates of Magnaporthe oryzae (the agent of rice blast disease) collected in Hunan Province, China. To identify the resistance (R) gene(s) controlling the high level of resistance in this cultivar, we developed 286 F(9) recombinant inbred lines (RILs) from a cross between XZ3150 and the highly susceptible cultivar CO39. Inoculation of the RILs and an F(2) population from a cross between the two cultivars with the avirulent isolate 193-1-1 in the growth chamber indicated the presence of two dominant R genes in XZ3150. A linkage map with 134 polymorphic simple sequence repeat and single feature polymorphism markers was constructed with the genotype data of the 286 RILs. Composite interval mapping (CIM) using the results of 193-1-1 inoculation showed that two major R genes, designated Pi47 and Pi48, were located between RM206 and RM224 on chromosome 11, and between RM5364 and RM7102 on chromosome 12, respectively. Interestingly, the CIM analysis of the four resistant components of the RILs to the field blast population revealed that Pi47 and Pi48 were also the major genetic factors responsible for the field resistance in XZ3150. The DNA markers linked to the new R genes identified in this study should be useful for further fine mapping, gene cloning, and marker-aided breeding of blast-resistant rice cultivars.