Graphene quantum dots (GQDs) which are flat zero-dimensional nanomaerials, have attracted increasing interest because of their exceptional chemicophysical properties and novel applications in energy conversion and storage, electro-/photo-/chemical catalysis, flexible devices, sensing, display, imaging, and theranostics. In this article, we summarize the significant advances in the recent years with comparative and balanced discussion. We emphasize the difference between GQDs and other nanomaterials including their nanocarbon cousins, and highlight the unique advantages of GQDs for specific applications. The current challenges and outlook of this growing field are also discussed.

Carbon nanotubes are promising materials for various applications. In recent years, progress in manufacturing and functionalizing carbon nanotubes has been made to achieve the control of bulk and surface properties including the wettability, acid-base properties, adsorption, electric conductivity and capacitance. In order to gain the optimal benefit of carbon nanotubes, comprehensive understanding on manufacturing and functionalizing carbon nanotubes ought to be systematically developed. This review summarizes methodologies of manufacturing carbon nanotubes via arc discharge, laser ablation and chemical vapor deposition and functionalizing carbon nanotubes through surface oxidation and activation, doping of heteroatoms, halogenation, sulfonation, grafting, polymer coating, noncovalent functionalization and nanoparticle attachment. The characterization techniques detecting the bulk nature and surface properties as well as the effects of various functionalization approaches on modifying the surface properties for specific applications in catalysis including heterogeneous catalysis, photocatalysis, photoelectrocatalysis and electrocatalysis are highlighted.

Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In this paper, we revisit the problem of solving multiple instance learning problems using neural networks. Neural networks are appealing for solving multiple instance learning problem. The multiple instance neural networks perform multiple instance learning in an end-to-end way, which take a bag with various number of instances as input and directly output bag label. All of the parameters in a multiple instance network are able to be optimized via back-propagation. We propose a new multiple instance neural network to learn bag representations, which is different from the existing multiple instance neural networks that focus on estimating instance label. In addition, recent tricks developed in deep learning have been studied in multiple instance networks, we find deep supervision is effective for boosting bag classification accuracy. In the experiments, the proposed multiple instance networks achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, e.g., it takes only 0.0003 second to predict a bag and a few seconds to train on a MIL datasets on a moderate CPU.Index Terms-Multiple instance learning, neural networks, end-to-end learning.

Graphene quantum dots (GQDs), which is the latest addition to the nanocarbon material family, promise a wide spectrum of applications. Herein, we demonstrate two different functionalization strategies to systematically tailor the bandgap structures of GQDs whereby making them snugly suitable for particular applications. Furthermore, the functionalized GQDs with a narrow bandgap and intramolecular Z-scheme structure are employed as the efficient photocatalysts for water splitting and carbon dioxide reduction under visible light. The underlying mechanisms of our observations are studied and discussed.