ABSTRACT
In this paper, we prepared the supramolecular polymers (MWCNT-APP-s) with a dual energy storage mechanism as the electrode materials by the coordination of four transition metal ions with the small molecule chelator (APP) and functionalized carbon nanotubes, respectively. Among four MWCNT-APP-s, MWCNT-APP-Fe has the characteristics of moderate micropore/mesopore, significant hydrophobicity, redox property and functional groups. Interestingly, the redox reaction of Fe3+/Fe2+ and -CN-/-CN- transformation give MWCNT-APP-Fe an energy storage basis of pseudocapacitance, while MWCNTs and the micro/mesopore structure in MWCNT-APP-Fe provide a double-layer energy storage platform. As expected, on base of the dual energy storage mechanism, the symmetric supercapacitor assembled with MWCNT-APP-Fe has a higher specific capacity (Cs, 421 F g-1 at 1 mV s-1) as well as a long-lasting stability of 94.8% capacity retention with 99% Coulombic efficiency after 10,000 cycles at 20 mV s-1. More notably, the relevant aqueous Zn2+ hybrid supercapacitor provides a high capacity (Cm) of 191 mAh g-1 at 0.5 A g-1 and a long duration of over 2000 cycles at 50 A g-1, with a capacity retention of 92.4%. In summary, MWCNT-APP-Fe with a dual energy storage mechanism enables a potential application as an electrode material for high-performance supercapacitor.
ABSTRACT
The spatial-temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial-temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.
Subject(s)
Algorithms , Records , ReproductionABSTRACT
Microplastics (MPs) pollution has become a major environmental problem and poses a risk to a variety of organisms. In this study, the photoaging behavior of acrylonitrile butadiene styrene microplastics (ABS-MP) in aqueous environment was investigated under simulated solar irradiation. Results showed that the long chains of ABS-MP broke under the light irradiation, and its thermal stability was reduced. ABS-MP was oxidized during photoaging and produced a large number of oxygen-containing functional groups. Structure destruction of ABS-MP decreased the formation of environmentally persistent free radicals (EPFRs) and further photoirradiation generated secondary EPFRs. Nuclear magnetic resonance (NMR) analysis of the aged leachates confirmed that ABS-MP was oxidized and some small molecular fragments were dropped during photoaging. Meanwhile, C-Br bond broke of additive tetrabromobisphenol A (TBBPA) resulting in more bromine released into water and Sb(III) of additive Sb2O3 was oxidized to Sb(V) during photoaging. These findings illustrate the necessity of considering the aging of MPs in natural environment, expand the understanding of the potential harm and fate of MPs in aqueous environment, which is important for the management of MPs.
