B-GQDs@GSH as a Highly Selective and Sensitive Fluorescent Probe for the Detection of Fe^(3+) in Water Samples and Intracellular

Owing to the excellent stability,biocompatibility and photoluminescence property,graphene quantum dots(GQDs)are emerging as a kind of potential materials to be applied in a series of fields ranging from sensor to drug delivery.As the growing concern for human and environmental safety,selective detection of metal ions has been paid more and more attention.GQDs,as nanoparticles with superior optical properties,have been attracting growing attention in the field of metal ions detection.In this work,glutathione(GSH)functionalized boron doped graphene quantum dots(B-GQDs@GSH)were successfully synthesized with stable bright blue fluorescence and has been used for the detection of Fe^(3+).A good linear relationship between 1/(F_(0)-F)and 1/c with the concentration ranging from 0.70 to 53μmol/L was obtained with a detection limit of 5.5 nmol/L.The application of B-GQDs@GSH for Fe^(3+)detection in water samples was demonstrated and the quenching mechanism was further explored.Due to low cytotoxicity and favorable biocompatibility,B-GQDs@GSH were successfully applied for cell fluorescence imaging and intracellular determination of Fe^(3+).

Rapid thin-layer WS_(2) detection based on monochromatic illumination photographs

The thickness of two-dimensional(2D)nanomaterials shows a significant effect on their optical and electrical properties.Therefore,a rapid and automatic detection technology of 2D nanomaterials with desired layer-number is required to extend their practical application in optoelectronic devices.In this paper,an image recognition technology was proposed for rapid and reliable identification of thin-layer WS_(2) samples,which combining a layer-thickness identification criterion and a novel image segmentation algorithm.The criterion stemmed from optical contrast study of monochromatic illumination photographs,and the algorithm was based on Canny operator and edge connection iteration.This optical identification method can seek out thin-layer WS_(2) samples on complex surfaces,which provides a promising approach for automatic search of thin-layer nanomaterials.

False Negative Sample Detection for Graph Contrastive Learning

Recently,self-supervised learning has shown great potential in Graph Neural Networks (GNNs) through contrastive learning,which aims to learn discriminative features for each node without label information. The key to graph contrastive learning is data augmentation. The anchor node regards its augmented samples as positive samples,and the rest of the samples are regarded as negative samples,some of which may be positive samples. We call these mislabeled samples as “false negative” samples,which will seriously affect the final learning effect. Since such semantically similar samples are ubiquitous in the graph,the problem of false negative samples is very significant. To address this issue,the paper proposes a novel model,False negative sample Detection for Graph Contrastive Learning (FD4GCL),which uses attribute and structure-aware to detect false negative samples. Experimental results on seven datasets show that FD4GCL outperforms the state-of-the-art baselines and even exceeds several supervised methods.