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We propose an approach to improve the performance of graphene-based gas sensors by the integration of defective graphene with pristine graphene. The defect density of defective graphene is controlled by the fluence of Si+ implantation, and an H2 etching process is utilized to tune defect size. As defects are able to adsorb target gas efficiently, the response of graphene-based sensors was improved remarkably with the controllable defect density. The response sensitivity of a defective-graphene-based sensor to concentrations of NO2 at 100 ppm can be as high as 248%, 13 times higher than that of a sensor built using pristine graphene. In addition, defective-graphene-based sensors exhibit high response and recovery rates at room temperature, which is comparable to those of pristine graphene-based sensors and faster than conventional defect-decorated graphene sensors. Most importantly, defective-graphene-based gas sensors exhibit excellent reproducibility, stability, and selectivity. Our study suggests a simple and effective strategy for the mass production of high-performance graphene-based gas sensors for NO2 gas detection. [ABSTRACT FROM AUTHOR]

Two dimensional electron systems (2DES) usually show the weak localization behavior in consequence of electron interaction in the limited dimension. Distinct from other 2DES, the monolayer graphene, due to the chirality, exhibits unique weak localization behavior sensitive to not only inelastic but also elastic carrier scattering. Grain boundaries, which usually exist in monolayer graphene, are apparently related to the elastic carrier scattering process, thus affecting the weak localization behavior. However, their effect is scarcely studied due to the lack of an ideal platform. Here, a complementary system consisting of both single-crystalline graphene grown on Ge (110) and poly-crystalline graphene grown on Ge (111) is constructed. From the comparison of magnetoresistivity measurements, the weak localization effect is found to be greatly enhanced for the poly-crystalline graphene on Ge(111) compared to the single-crystalline graphene on Ge(110). The degraded transport performance in graphene/Ge(111) is due to the presence of grain boundary in poly-crystalline graphene, which results in the enhanced elastic intervalley scattering. In addition, the inelastic scattering originating from the strong electron-electron interaction at low temperature also contributes to weak localization of poly-crystalline graphene/Ge(111). [ABSTRACT FROM AUTHOR]

The research on the three-dimensional counting algorithm of suspension cells is very important for the online counting of cells. Suspension cells are usually observed and counted by taking out cell samples under the microscope. This method cannot realize the online counting of cells. At the same time, there will be weak movement of suspended cells in three-dimensional space, making space counting more difficult. Aiming at the above difficulties, a three-dimensional counting algorithm of suspended cells based on deep learning is proposed. The Faster regions with convolutional neural network (R-CNN) framework is used to identify the target cells, the feature pyramid structure is introduced to reduce the feature loss in the fusion process, and the soft non-maximum suppression algorithm is used to remove the bounding box to improve the detection effect. To reduce the manual labeling workload, an automatic labeling method is proposed. The improved Faster RCNN framework achieves the F1-score of more than 98% after identifying cells, and our method is more accurate than other advanced methods. In terms of three-dimensional counting algorithms, the cells are spatially tracked. Cell matching and correction are carried out by extracting multiple feature information of cells, and adhesion cells are extracted separately for priority processing. The algorithm carried out a tracking experiment on the image sequences of five groups of suspended cells, and the final tracking accuracy of the experiment reached more than 95%. The tracking accuracy is better than other advanced algorithms. Experimental results show that the algorithm achieves high-precision three-dimensional tracking and counting of suspended cells. [ABSTRACT FROM AUTHOR]

We propose an approach to improve the performance of graphene-based gas sensors by the integration of defective graphene with pristine graphene. The defect density of defective graphene is controlled by the fluence of Si+ implantation, and an H2 etching process is utilized to tune defect size. As defects are able to adsorb target gas efficiently, the response of graphene-based sensors was improved remarkably with the controllable defect density. The response sensitivity of a defective-graphene-based sensor to concentrations of NO2 at 100 ppm can be as high as 248%, 13 times higher than that of a sensor built using pristine graphene. In addition, defective-graphene-based sensors exhibit high response and recovery rates at room temperature, which is comparable to those of pristine graphene-based sensors and faster than conventional defect-decorated graphene sensors. Most importantly, defective-graphene-based gas sensors exhibit excellent reproducibility, stability, and selectivity. Our study suggests a simple and effective strategy for the mass production of high-performance graphene-based gas sensors for NO2 gas detection. [ABSTRACT FROM AUTHOR]

Two magnetic-field-induced quantum critical behaviors were recently discovered in two dimensional electron gas (2DEG) at LaTiO 3 /SrTiO 3 interface and interpreted by disordered superconducting puddles coupled through 2DEG. In this scenario, the 2DEG is proposed to undergo a spontaneous phase separation and breaks up into locally superconducting puddles in a metallic matrix. However, as the inhomogeneous superconducting 2DEG is only illative, this proposal still lacks the direct experimental demonstration. Here, we artificially construct superconducting puddles-2DEG hybrid system by depositing tin nanoislands array on single crystalline monolayer graphene, where the two quantum critical behaviors are reproduced. Through the finite-size scaling analysis on magnetoresistivity, we show that the two quantum critical behaviors result from the intra-island and inter-island phase coherence, respectively, which are further illustrated by the phase diagram. This work provides a platform to study superconducting quantum phase transitions in a 2D system and helps to integrate superconducting devices into semiconductor technology.