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Capital is the most basic element of production organization, and green bonds are important financing sources for the green industry. Financingcosts directly affect the steady development of the green industry. In order to verify whether third-party certification can reduce the financing cost of green bonds and whether the nature of property rights has an impact on the reduction, 285 green bonds issued from 2016 to 2019 are taken as samples, and then a hierarchical multiple statistical regression analysis method is adopted to conduct a quantitative study. The results show that third-party certification, as a unique credit enhancement method for green bonds, can significantly reduce green bond spreads. In addition, it isgreater that the effect on non-state-owned enterprises’ green bonds is. Since bond spread isamain part of financing costs, the above means that third-party certification can reduce the financing costs of green bonds, especiallyfor non-state-owned enterprises. In this regard, starting from reducing the cost of green bonds, the paper proposes to improve the green bond evaluation and certification standards as soon as possible, non-state-owned enterprises particularly should actively carry out third-party certification, and the government needs to further increase the implementation of third-party certification as well as strengthen multi-party cooperation.

An approach, i.e., atmospheric-pressure nonequilibrium oxygen plasma jet technology, has been reported for the first time to in situ prepare ultrathin nonstoichiometric nickel oxide layers with mix...

A plasma assisted phosphorization method has been developed for preparation of multi-phase materials with rich crystalline interfaces and oxygen vacancies. Specifically, P-NiMoP nanorods (NRs) comprising tri-phases of MoP2, Ni3P and MoO2 have been synthesized by plasma-assisted phosphorization of NiMoO4 NRs. The specific features of the plasma technique provide P-NiMoP NRs with rich crystalline interfaces and oxygen vacancies in MoO2. P-NiMoP NRs are demonstrated to be a stable and efficient catalyst for the hydrogen evolution reaction (HER). They only need overpotentials of 3 and 162 mV to drive current densities of 10 and 150 mA cm−2, both of which are lower than those of Pt/C. The DFT calculations indicate that the tri-phase structure with rich crystalline interfaces and oxygen vacancies plays an important role in the high catalytic activity of P-NiMoP NRs. It promotes a strong electronic coupling between MoP2, Ni3P and MoO2, which facilitates H2O adsorption and dissociation and reduces the |ΔGH*| value.

Over the past decades, controversial and conflict-laden water allocation issues among competing domestic, industrial and agricultural water use as well as urban environmental flows have raised increasing concerns (Huang & Chang, 2003); Particularly, Such competition has been exacerbated by the growing population, rapidly economic growth, deteriorating quality of water resources, and shrinking water availability due to a number of natural and human-induced impacts. A sounding strategy for water resources allocation and management can help to reduce or avoid the losses which are caused by water resources scarcity. However, in the water management system, many components and their interactions are uncertain. Such uncertainties could be multiplied not only by fasting changes of socioeconomic boundary conditions but also by unpredictable extreme weather events which caused by climate change. Thus, water resources management should be able to deal with all challenges above. Therefore, an effective integrated approach is desired for urban water adaptive management. Many methods, such as stochastic, fuzzy, and interval-parameter programming techniques, have been employed to counteract uncertainties in different fields of water management and have made great progresses in managing uncertainties in model scale. Water resource is an integral part of the socio-economic-environmental (SEE) system, which is a complex system dominated by human. In order to reach a sounding decision, it is necessary for decision-makers to obtain a better understanding of the significant factors that shape the urban and the way the water resources system reacting to certain policy. Therefore, study of sustainable water resource management should be based on general system theory that addresses dynamic interactions amongst the related social-economic, environmental, and institutional factors as well as non-linearity and multi-loop feedbacks. System dynamics (SD) aims at solving of complex systems problems by simulating development trends of the system and identifying the interrelations of each factor in the system. This will help to explore the hidden mechanism and thus improve the performance of the whole system. Hence, after proposed by W. Forrester (Forrester, 1968), SD model has been widely used in global, national, and regional scales for sustainability assessment and system development programme (Meadows 1973; Mashayekhi, 1990; Saeed, 1994). Due to

In this work, an in situ plasma method is developed for the synthesis of Fe•Li-antisite-free LiFePO4 nanoparticles with full nitrogen-doped carbon coating (NC) and graphitic nanosheet support (LFP@NC/GNS). Briefly, plasma discharge promotes in-situ formation of graphitic nanosheets on LFP@NC via Fe0-catalyzed glucose pyrolysis and simultaneously removes Fe•Li antisites in LFP. The resultant LFP@NC/GNS shows enhanced reversible capacities as a cathode material for Li-ion batteries. Even at the super-high current rate of 150 C, LFP@NC/GNS still delivers a stable reversible capacity of 100.7 mAh g−1. Furthermore, LFP@NC/GNS exhibits a super-long cycling stability with no apparent capacity drop when charged/discharged at 10 C and 100 C for 1200 cycles. The absence of the Fe•Li antisites is expected to play a significant role in the high performance of LFP@NC/GNS because it provides further expedited Li+ diffusion paths in the LFP lattices, thereby allowing for ready migration of Li+ ions and better utilization of LFP. The full NC coating and GNS support also aid the performance of LFP@NC/GNS significantly because both can improve the Li storage kinetics and utilization of the active material.

Bifunctional catalyst, which presents excellent electrocatalytic activity for oxygen reduction/evolution reaction (ORR/OER), is the key factor to explore the rechargeable Zn-air batteries (ZABs) with excellent performance and high stability. Herein, the polyaniline is coated on the outer surface of N-doped graphene hollow spheres (NGHS), which can further form the N-doped carbide (CNx) through the high temperature calcination process. The prepared CNx decorated N-doped graphene hollow spheres (CNx-NGHS) could enhance the conductivity of substrate material caused by the doping of N atoms, and the stretchy 3D hollow structure can prevent the deformation and collapse of fabricated electrode catalyst during the charge/discharge processes. Furthermore, the doped N atoms, acting as active sites, could facilitate the nucleation and growth of the core/shell metals/oxides nanoparticles on the surface of the CNx-NGHS, and at the same time enhance the interaction between metals/oxides nanoparticles and CNx-NGHS, both of which are helpful for promoting the electrocatalytic activity. Based on the investigation results, it can be concluded that CNx-NGHS loaded Co/CoO core/shell nanoparticles (Co/CoO/CNx-NGHS) show obvious better bifunctional electrocatalytic performance than that of CNx-NGHS supported Ni/NiO core/shell nanoparticles (Ni/NiO/CNx-NGHS). The synthesized Co/CoO/CNx-NGHS presents the lowest ΔE (~0.728) (ΔE= Ej10-E1/2), which indicates the better bifunctional electrocatalytic activity. The ZAB equipped with the Co/CoO/CNx-NGHS catalyst can deliver a maximum power density of 117 mW cm−2, which is larger than that of commercial 10% Pt/C (95 mW cm−2). The specific capacity of the Co/CoO/CNx-NGHS at both 100 and 200 mA cm−2 can reach ~99% of the theoretical capacity (~824 mAh g−1). Moreover, a minimum visible voltage loss can be seen for the synthesized Co/CoO/CNx-NGHS after cycled for 100 h, indicating the good durability and stability. All investigated results demonstrated that the outperformance of the fabricated Co/CoO/CNx-NGHS catalyst makes it a potential choice to replace the noble metal/carbon-based materials.

This work reports a novel approach for the synthesis of FeCo alloy nanoparticles (NPs) embedded in the N,P‐codoped carbon coated nitrogen‐doped carbon nanotubes (NPC/FeCo@NCNTs). Specifically, the synthesis of NCNT is achieved by the calcination of graphene oxide‐coated polystyrene spheres with Fe3+, Co2+ and melamine adsorbed, during which graphene oxide is transformed into carbon nanotubes and simultaneously nitrogen is doped into the graphitic structure. The NPC/FeCo@NCNT is demonstrated to be an efficient and durable bifunctional catalyst for oxygen evolution (OER) and oxygen reduction reaction (ORR). It only needs an overpotential of 339.5 mV to deliver 10 mA cm−2 for OER and an onset potential of 0.92 V to drive ORR. Its bifunctional catalytic activities outperform those of the composite catalyst Pt/C + RuO2 and most bifunctional catalysts reported. The experimental results and density functional theory calculations have demonstrated that the interplay between FeCo NPs and NCNT and the presence of N,P‐codoped carbon structure play important roles in increasing the catalytic activities of the NPC/FeCo@NCNT. More impressively, the NPC/FeCo@NCNT can be used as the air‐electrode catalyst, improving the performance of rechargeable liquid and flexible all‐solid‐state zinc–air batteries., The FeCo alloy nanoparticles embedded in the N,P‐codoped carbon coated nitrogen‐doped carbon nanotubes (NPC/FeCo@NCNT) have been synthesized and demonstrated to be efficient and durable catalysts for oxygen evolution and reduction reactions. It is usable as the air‐electrode catalysts to improve the performance of rechargeable liquid and flexible all‐solid‐state zinc–air batteries.

With the increasing of network attacks, the traditional machine learning method can not solve the classification problem of massive intrusion data effectively. This paper proposes a Xgboost based on stacked sparse autoencoder network(SSAE-XGB) method to learn latent representation of original data. Due to inconsistent category distribution of training and test dataset, we use the sparsity constraint to enhance the generalization ability of the model. Stacked sparse autoencoder network is employed to reduce the dimension of high-dimensional and unlabeled original data, so as to obtain the deep feature representation of the original data. Due to the class imbalance of intrusion data, this paper proposes a novel hybrid classifier, which is constructed by using binary tree and ensemble method. Our experiments with all NSL-KDD dataset demonstrate that our proposed SSAE-XGB binary tree and ensemble method can achieve incredibly high performance in terms of F1 and it outperforms the previous work.