In this work, a new composite materials of graphene oxide (GO)-incorporated metal-organic framework (MOF)(UiO-66-NH2/GO) were in-situ synthesized, and were found to exhibit enhanced high performances for CO2 capture. X-ray diffraction (XRD), scanning electron microscope (SEM), N2 physical adsorption, and thermogravimetric analysis (TGA) were applied to investigate the crystalline structure, pore structure, thermal stability, and the exterior morphology of the composite. We aimed to investigate the influence of the introduction of GO on the stability of the crystal skeleton and pore structure. Water, acid, and alkali resistances were tested for physical and chemical properties of the new composites. CO2 adsorption isotherms of UiO-66, UiO-66-NH2, UiO-66/GO, and UiO-66-NH2/GO were measured at 273 K, 298 K, and 318 K. The composite UiO-66-NH2/GO exhibited better optimized CO2 uptake of 6.41 mmol/g at 273 K, which was 5.1% higher than that of UiO-66/GO (6.10 mmol/g). CO2 adsorption heat and CO2/N2 selectivity were then calculated to further evaluate the CO2 adsorption performance. The results indicated that UiO-66-NH2/GO composites have a potential application in CO2 capture technologies to alleviate the increase in temperature of the earth’s atmosphere.

ABSTRACT-The computing paradigm of "HPC in the Cloud" has gained a surging interest in recent years, due to its merits of cost-efficiency, flexibility, and scalability. Cloud is designed on top of distributed file systems such as Google file system (GFS). The capability of running HPC applications on top of data-intensive file systems is a critical catalyst in promoting Clouds for HPC. However, the semantic gap between data-intensive file systems and HPC imposes numerous challenges. For example, N-1 (N to 1) is a widely used data access pattern for HPC applications such as checkpointing, but cannot perform well on data-intensive file systems. In this study, we propose the CHunk-Aware I/O (CHAIO) strategy to enable efficient N-1 data access on data-intensive distributed file systems. CHAIO reorganizes I/O requests to favor data-intensive file systems and avoid possible access contention. It balances the workload distribution and promotes data locality. We have tested the CHAIO design over the Kosmos file system (KFS). Experimental results show that CHAIO achieves a more than two-fold improvement in I/O bandwidth for both write and read operations. Experiments in large-scale environment confirm the potential of CHAIO for small and irregular requests. The aggregator selection algorithm works well to balance the workload distribution. CHAIO is a critical and necessary step to enable HPC in the Cloud.

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In this article, we propose a monocular vision-based approach that can simultaneously recognize an object and estimate the distance to the target in package classification. Calibration is necessary due to lack of depth information in a single RGB image, and template matching makes it possible to estimate the distance of an irregular object without measurable parameters. First of all, capture images of the particular object as templates at set distances. Then, simplify the feature extraction to abandon the scale invariance. By exploiting a nonparametric estimation, the relationship between local feature correspondence and the similarity of two images is theoretically explored. Finally, the object will be recognized and the scale grade of it will be determined at the same time based on two-stage template matching. Experimental results have proved the high accuracy of our approach that has then been successfully applied to a real-time automatic package sorting line.

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