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A large number of PAH molecules is collected from recent literature. The HOMO-LUMO gap value of PAHs was computed at the level of B3LYP/6-311+G (d,p). The gap values lie in the range of 0.64–6.59 eV. It is found that the gap values of all PAH molecules exhibit a size dependency to some extent. However, the gap values may show a big variation even at the same size due to the complexity in the molecular structure. All collected PAHs are further classified into seven groups according to features in the structures, including the types of functional groups and the molecular planarity. The impact of functional groups, including –OH, –CHO, –COOH, =O, –O– and –CnHm on the bandgap is discussed in detail. The substitution of ketone group has the greatest reduction on the HOMO-LUMO gap of PAH molecules. Besides functional groups, we found that both local structure and the position of five-member rings make critical impacts on the bandgap via a detailed analysis of featured PAHs with unexpected low and high gap values. Among all these factors, the five-member rings forming nonplanar PAHs impact the gap most. Furthermore, we developed a machine learning model to predict the HOMO-LUMO gaps of PAHs, and the average absolute error is only 0.19 eV compared with the DFT calculations. The excellent performance of the machine learning model provides us an accurate and efficient way to explore the band information of PAHs in soot formation.

Carbon flux is the main basis for judging the carbon source/sink of forest ecosystems. Bamboo forests have gained much attention because of their high carbon sequestration capacity. In this study, we used a boreal ecosystem productivity simulator (BEPS) model to simulate the gross primary productivity (GPP) and net primary productivity (NPP) of bamboo forests in China during 2001–2018, and then explored the spatiotemporal evolution of the carbon fluxes and their response to climatic factors. The results showed that: (1) The simulated and observed GPP values exhibited a good correlation with the determination coefficient (R2), root mean square error (RMSE), and absolute bias (aBIAS) of 0.58, 1.43 g C m−2 day−1, and 1.21 g C m−2 day−1, respectively. (2) During 2001–2018, GPP and NPP showed fluctuating increasing trends with growth rates of 5.20 g C m−2 yr−1 and 3.88 g C m−2 yr−1, respectively. The spatial distribution characteristics of GPP and NPP were stronger in the south and east than in the north and west. Additionally, the trend slope results showed that GPP and NPP mainly increased, and approximately 30% of the area showed a significant increasing trend. (3) Our study showed that more than half of the area exhibited the fact that the influence of the average annual precipitation had positive effects on GPP and NPP, while the average annual minimum and maximum temperatures had negative effects on GPP and NPP. On a monthly scale, our study also demonstrated that the influence of precipitation on GPP and NPP was higher than that of the influence of temperature on them.

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A microcalorimeter (C600) was used to conduct dynamic heating experiments on dihydroxylammonium 5,5′-bistetrazole-1,1′-diolate (TKX-50), and the results were compared with those of differential scanning calorimetry (DSC). The effect of mass scale on the thermal decomposition characteristics of TKX-50 was also analyzed. The thermal decomposition curves were decoupled by mathematical method, and the kinetic parameters of each step were obtained. The thermal decomposition characteristics of TKX-50 were further analyzed by thermal history and isothermal experiment, and the thermal safety parameters were calculated by thermal analysis software (AKTS). The decomposition temperature and decomposition enthalpy of TKX-50 in the microcalorimetric experiment were higher than the corresponding parameters in the DSC experiment, and the apparent activation energy was lower than the one in the DSC experiment. When the times to maximum rate under adiabatic conditions were 2.0, 4.0, 8.0, and 24.0 h, the corresponding temperatures were 198.5, 189.6, 181.0, and 168.0 °C, respectively. After decoupling, the range of exothermic peak temperature and decomposition enthalpy of the first and second stages of TKX-50 were 226.9−245.3 and 276.3−295.7 °C and 1300.7 and 727.7 J g −1 , respectively, and the apparent activation energy of the second stage was higher than that of the first stage. The thermal history reduced the decomposition temperature and the apparent activation energy of TKX-50, the safety was reduced, and this had a great influence on the thermal decomposition kinetics of TKX-50. Thermal history and isothermal experiment showed that the first stage decomposition reaction of TKX-50 has autocatalytic properties. Therefore, it should be prevented from being placed in an adiabatic environment, and it is necessary to avoid the storage of a large mass of TKX-50 and keep the heat source off the storage location in the process of industrial production and storage, so as to prevent the formation of adiabatic environments and thermal history in the interior and further reduce the risk of explosion in TKX-50.

Urbanization inevitably poses a threat to urban ecology by altering its external structure and internal attributes. Nighttime light (NTL) has become increasingly extensive and practical, offering a special perspective on the world in revealing urbanization. In this study, we applied the Normalized Impervious Surface Index (NISI) constructed by NTL and MODIS NDVI to examine the urbanization process in the Yangtze River Delta (YRD). Geographical detectors combined with factors involving human and natural influences were utilized to investigate the drive mechanism. Urban ecology stress was evaluated based on changes in urban morphological patterns and fractional vegetation cover (FVC). The results showed that the NISI can largely overcome the obstacle of directly coupling NTL data in performing urbanization and has efficient applicability in the long-term pixel scale. Built-up areas in the YRD increased by 2.83 times during the past two decades, from 2053.5 to 7872.5 km2. Urbanization intensity has saturated the city center and is spilling over into the suburbs, which show a “cold to hot” spatial clustering distribution. Economic factors are the primary forces driving urbanization, and road network density is becoming essential as factor that reflects urban infrastructure. Urban geometry pattern changes in fractal dimension (FD) and compactness revealed the ecological stress from changing urban external structure, and internal ecological stress was clear from the negative effect on 63.4% FVC. This impact gradually increased in urban expanded area and synchronously decreased when urbanization saturated the core area. An analysis of ecological stress caused by urbanization from changing physical structure and social attributes can provide evidence for urban management and coordinated development.

In this study, few-layered tungsten disulfide (WS2) was prepared using a liquid phase exfoliation (LPE) method, and its thermal catalytic effects on an important kind of energetic salts, dihydroxylammonium-5,5′-bistetrazole-1,1′-diolate (TKX-50), were investigated. Few-layered WS2 nanosheets were obtained successfully from LPE process. And the effects of the catalytic activity of the bulk and few-layered WS2 on the thermal decomposition behavior of TKX-50 were studied by using synchronous thermal analysis (STA). Moreover, the thermal analysis data was analyzed furtherly by using the thermokinetic software AKTS. The results showed the WS2 materials had an intrinsic thermal catalysis performance for TKX-50 thermal decomposition. With the few-layered WS2 added, the initial decomposition temperature and activation energy (Ea) of TKX-50 had been decreased more efficiently. A possible thermal catalysis decomposition mechanism was proposed based on WS2. Two dimensional-layered semiconductor WS2 materials under thermal excitation can promote the primary decomposition of TKX-50 by enhancing the H-transfer progress.

The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.