Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.

[Spatio-temporal Evolution and Prediction of Carbon Storage in Huaibei City Based on InVEST-PLUS Model].

This study aimed to investigate the impact of spatiotemporal changes in land use on ecosystem carbon storage. The study analyzed the spatiotemporal changes in carbon storage in the study area based on land use data from five periods (1985, 1995, 2005, 2015, and 2020) using the InVEST model. The PLUS model was used to predict land use changes in the study area under four different scenarios (natural development, farmland protection, ecological protection, and double protection of farmland and ecology) in 2035, and the ecosystem carbon storage under different scenarios was estimated. The results of the study indicated that the farmland in the area under investigation had been decreasing consistently from 1985 to 2020, with a more rapid rate of change observed between 2015 and 2020. During this period, the overall dynamic attitude towards land use reached 34.62 %. Additionally, the carbon storage in the area showed a decreasing trend over the years, with a decrease of 1.55×105 t from 1985 to 2020. Between 2005 and 2015, the carbon storage showed a decrease of 1.22×105 t, with an average annual decrease of 1.22×104 t. The areas with higher carbon storage were located in the eastern part of the study area, whereas areas with lower carbon storage were found in the central and northwestern parts. Although the proportion of carbon storage in farmland decreased from 66.89 % to 57.73 %, farmland remained the most important carbon pool in the study area. The conversion of other land use types to grassland and forestland was advantageous for increasing ecosystem carbon storage. Finally, the study projected that by 2035, the carbon storage in the natural development scenario, the farmland protection scenario, the ecological protection scenario, and the dual protection scenario would be 81.77×105, 82.45×105, 82.82×105, and 82.51×105 t, respectively.

YOLO-SK: A lightweight multiscale object detection algorithm.

YOLOv5 is an excellent object-detection model. However, it fails to fully use multiscale information when detecting objects with significant scale variations. It might use irrelevant contextual information, leading to incorrect predictions, particularly for low-performance devices. In this study, we selected lightweight YOLOv5s as the baseline model and proposed an improved model called YOLO-SK to overcome this limitation. YOLO-SK introduced several key improvements, the most important being the collaborative work of the weighted dense feature fusion network and SK attention prediction head. The proposed weighted dense feature fusion network could dynamically fuse features at different scales using autonomous learning parameters and cross-layer fusion capabilities. This enabled a balanced feature fusion ability in the output feature maps of different scales, thereby enhancing the richness of the effective information in the fused feature maps. The prediction head equipped with the SK attention mechanism broadened the scope of the model's receptive field and sharpened the focus on the target characteristics. This made it possible to glean more information about the target from the feature map output by employing a weighted dense feature fusion network. In addition, in order to improve the model's performance in terms of both accuracy and volume, we implemented the SIoU loss function and the Ghost Conv. The use of the model allowed for a more precise and in-depth comprehension of the event, which was made possible by all of these various methods of improvement. Extensive testing done on the PASCAL VOC 2007 and 2012 datasets showed that YOLO-SK was able to achieve considerable gains in prediction accuracy when compared with the baseline model (YOLOv5s), all while keeping the same level of model complexity. To be more specific, mAP@.5 increased by 2.6 %, and mAP@.5:.95 increased by 4.8 %. The advancements that were made and detailed in this paper could serve as a springboard for additional research that aims to improve the precision of multiscale object identification models for low-performance devices.

[Spatial Prediction Modeling for Soil pH Based on Multiscale Geographical Weighted Regression(MGWR) and Its Influencing Factors].

Anhui, Henan, Jiangsu, and Shandong provinces were selected as the study area. A total of 599 soil samples and nine environmental factors of soil pH were collected. The spatial distribution of soil pH was modeled based on multi-scale geographically weighted regression(MGWR), mixed geographically weighted regression(Mixed GWR), geographically weighted regression(GWR), and multiple linear regression(MLR) models. Then, the spatial difference in the effect of environmental factors on soil pH was revealed using MGWR and quantile regression models. The results showed that:① soil pH showed significant global and local spatial autocorrelation at different spatial distances, and the clustering characteristics were obvious. ② The MGWR model was the best among the four models, and the Radj2 of MGWR, Mixed GWR, GWR, and MLR were 0.64, 0.62, 0.59, and 0.48, respectively. The residual of MGWR had the strongest independent distribution and the weakest spatial autocorrelation with a global Moran's I of 0.07. ③ Three types of GWR predictions showed that the spatial distribution of soil pH decreased gradually from north to south in the study area, with the highest in northern Henan and the lowest in southern Anhui. ④ MGWR modeling results showed that there was strong spatial heterogeneity of mean annual precipitation(MAP), multi-resolution valley bottom flatness(MRVBF), and elevation affecting soil pH. MAP had a stronger effect on soil pH in northern Jiangsu and most parts of Shandong. The positive effect of MRVBF on soil pH was stronger in northern Jiangsu and western Shandong. The negative effect of elevation on soil pH was stronger in northern and central Jiangsu. ⑤ The quantile regression analysis showed that the mean annual precipitation had a significant negative effect on soil pH at different quantile levels of soil pH, and influence intensity decreased with the increase in pH quantile level. MRVBF had a significant negative effect on soil pH at a low quantile level(θ=0.1 to 0.4) but had no significant effect on soil pH at a high quantile level(θ=0.5 to 0.9). These results can provide an important reference for mapping soil properties and analyzing its influence factors based on the MGWR model in large regions.

DeepSA: a deep-learning driven predictor of compound synthesis accessibility.

With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that using SMILES alone can also efficiently visualize and extract compound's informative features. DeepSA is available online on the below web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepsa/ ) of our group, and the code is available at https://github.com/Shihang-Wang-58/DeepSA .

Improved multivariate modeling for soil organic matter content estimation using hyperspectral indexes and characteristic bands.

Soil organic matter (SOM) is a key index of soil fertility. Calculating spectral index and screening characteristic band reduce redundancy information of hyperspectral data, and improve the accuracy of SOM prediction. This study aimed to compare the improvement of model accuracy by spectral index and characteristic band. This study collected 178 samples of topsoil (0-20 cm) in the central plain of Jiangsu, East China. Firstly, visible and near-infrared (VNIR, 350-2500 nm) reflectance spectra were measured using ASD FieldSpec 4 Std-Res spectral radiometer in the laboratory, and inverse-log reflectance (LR), continuum removal (CR), first-order derivative reflectance (FDR) were applied to transform the original reflectance (R). Secondly, optimal spectral indexes (including deviation of arch, difference index, ratio index, and normalized difference index) were calculated from each type of VNIR spectra. Characteristic bands were selected from each type of spectra by the competitive adaptive reweighted sampling (CARS) algorithm, respectively. Thirdly, SOM prediction models were established based on random forest (RF), support vector regression (SVR), deep neural networks (DNN) and partial least squares regression (PLSR) methods using optimal spectral indexes, denoted here as SI-based models. Meanwhile, SOM prediction models were established using characteristic wavelengths, denoted here as CARS-based models. Finally, this research compared and assessed accuracy of SI-based models and CARS-based models, and selected optimal model. Results showed: (1) The correlation between optimal spectral indexes and SOM was enhanced, with absolute value of correlation coefficient between 0.66 and 0.83. The SI-based models predicted SOM content accurately, with the coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.80 to 0.87, 2.40 g/kg to 2.88 g/kg in validation sets, and relative percent deviation (RPD) value between 2.14 and 2.52. (2) The accuracy of CARS-based models differed with models and spectral transformations. For all spectral transformations, PLSR and SVR combined with CARS displayed the best prediction (R2 and RMSE values ranged from 0.87 to 0.92, 1.91 g/kg to 2.56 g/kg in validation sets, and RPD value ranged from 2.41 to 3.23). For FDR and CR spectra, DNN and RF models achieved more accuracy (R2 and RMSE values ranged from 0.69 to 0.91, 1.90 g/kg to 3.57 g/kg in validation sets, and RPD value ranged from 1.73 to 3.25) than LR and R spectra (R2 and RMSE values from 0.20 to 0.35, 5.08 g/kg to 6.44 g/kg in validation sets, and RPD value ranged from 0.96 to 1.21). (3) Overall, the accuracy of SI-based models was slightly lower than that of CARS-based models. But spectral index had a good adaptability to the models, and each SI-based model displayed the similar accuracy. For different spectra, the accuracy of CARS-based model differed from modeling methods. (4) The optimal CARS-based model was model CARS-CR-SVR (R2 and RMSE: 0.92 and 1.91 g/kg in validation set, RPD: 3.23). The optimal SI-based model was model SI3-SVR (R2 and RMSE: 0.87 and 2.40 g/kg in validation set, RPD: 2.57) and model SI-SVR (R2 and RMSE: 0.84 and 2.63 g/kg in validation set, RPD: 2.35).

Coupling Effect of LDPE Molecular Chain Structure and Additives on the Rheological Behaviors of Cable Insulating Materials.

The rheological behaviors of low-density polyethylene doped with additives (PEDA) determine the dynamic extrusion molding and structure of high-voltage cable insulation. However, the coupling effect of additives and molecular chain structure of LDPE on the rheological behaviors of PEDA is still unclear. Here, for the first time, the rheological behaviors of PEDA under uncross-linked conditions are revealed by experiment and simulation analysis, as well as rheology models. The rheology experiment and molecular simulation results indicate that additives can reduce the shear viscosity of PEDA, but the effect degree of different additives on rheological behaviors is determined by both chemical composition and topological structure. Combined with experiment analysis and the Doi-Edwards model, it demonstrates that the zero-shear viscosity is only determined by LDPE molecular chain structure. Nevertheless, different molecular chain structures of LDPE have different coupling effects with additives on the shear viscosity and non-Newtonian feature. Given this, the rheological behaviors of PEDA are predominant by the molecular chain structure of LDPE and are also affected by additives. This work can provide an important theoretical basis for the optimization and regulation of rheological behaviors of PEDA materials used for high-voltage cable insulation.

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

Soil Microbial Fuel Cell Based Self-Powered Cathodic Biosensor for Sensitive Detection of Heavy Metals.

Soil microbial fuel cells (SMFCs) are an innovative device for soil-powered biosensors. However, the traditional SMFC sensors relied on anodic biosensing which might be unstable for long-term and continuous monitoring of toxic pollutants. Here, a carbon-felt-based cathodic SMFC biosensor was developed and applied for soil-powered long-term sensing of heavy metal ions. The SMFC-based biosensor generated output voltage about 400 mV with the external load of 1000 Ω. Upon the injection of metal ions, the voltage of the SMFC was increased sharply and quickly reached a stable output within 2~5 min. The metal ions of Cd2+, Zn2+, Pb2+, or Hg2+ ranging from 0.5 to 30 mg/L could be quantified by using this SMFC biosensor. As the anode was immersed in the deep soil, this SMFC-based biosensor was able to monitor efficiently for four months under repeated metal ions detection without significant decrease on the output voltage. This finding demonstrated the clear potential of the cathodic SMFC biosensor, which can be further implemented as a low-cost self-powered biosensor.

Metagenomics revealing molecular profiles of microbial community structure and metabolic capacity in Bamucuo lake, Tibet.

Microorganisms play critical ecological roles in the global biogeochemical cycles. However, extensive information on the microbial communities in Qinghai-Tibet Plateau (QTP), which is the highest plateau in the world, is still lacking, particularly in high elevation locations above 4500 m. Here, we performed a survey of th e soil and water microbial communities in Bamucuo Lake, Tibet, by using shotgun metagenomic methods. In the soil and water samples, we reconstructed 75 almost complete metagenomic assembly genomes, and 74 of the metagenomic assembly genomes from the water sample represented novel species. Proteobacteria and Actinobacteria were found to be the dominant bacterial phyla, while Euryarchaeota was the dominant archaeal phylum. The largest virus, Pandoravirus salinus, was found in the soil microbial community. We concluded that the microorganisms in Bamucuo Lake are most likely to fix carbon mainly through the 3-hydroxypropionic bi-cycle pathway. This study, for the first time, characterized the microbial community composition and metabolic capacity in QTP high-elevation locations with 4555 m, confirming that QTP is a vast and valuable resource pool, in which many microorganisms can be used to develop new bioactive substances and new antibiotics to which pathogenic microorganisms have not yet developed resistance.

Computer-Aided Prediction of the Interactions of Viral Proteases with Antiviral Drugs: Antiviral Potential of Broad-Spectrum Drugs.

Human society is facing the threat of various viruses. Proteases are promising targets for the treatment of viral infections. In this study, we collected and profiled 170 protease sequences from 125 viruses that infect humans. Approximately 73 of them are viral 3-chymotrypsin-like proteases (3CLpro), and 11 are pepsin-like aspartic proteases (PAPs). Their sequences, structures, and substrate characteristics were carefully analyzed to identify their conserved nature for proposing a pan-3CLpro or pan-PAPs inhibitor design strategy. To achieve this, we used computational prediction and modeling methods to predict the binding complex structures for those 73 3CLpro with 4 protease inhibitors of SARS-CoV-2 and 11 protease inhibitors of HCV. Similarly, the complex structures for the 11 viral PAPs with 9 protease inhibitors of HIV were also obtained. The binding affinities between these compounds and proteins were also evaluated to assess their pan-protease inhibition via MM-GBSA. Based on the drugs targeting viral 3CLpro and PAPs, repositioning of the active compounds identified several potential uses for these drug molecules. As a result, Compounds 1-2, modified based on the structures of Ray1216 and Asunaprevir, indicate potential inhibition of DENV protease according to our computational simulation results. These studies offer ideas and insights for future research in the design of broad-spectrum antiviral drugs.

Surprising Hydrophobic Polymer Surface with a High Content of Hydrophilic Polar Groups.

The wetting property of a solid surface has been a hotspot for centuries, and many studies suggest that the hydrophobicity is highly related to the polar components. However, the underlying mechanism of polar moieties on the hydrophobicity remains unclear. Here, we tailor the surface polar moieties of epoxy resin (EP) by ozone modification and assess their wetting properties. Our results show that, for the modified EP with more (60.54%) polar moieties, the polar effect on hydrophobicity cannot be empirically observed. To reveal the underlying mechanism, the absorption parameters, including equilibrium distance, adsorption radius, and effective adsorption sites for water on EP before and after ozone treatment, are calculated on the basis of molecular simulations. After ozone modification, the equilibrium distance (from 1.95 to 1.70 Å), adsorption radius (from 3.80 to 4.50 Å), and effective adsorption sites (from 1 to 2) change slightly and the EP surface remains hydrophobic, although the polar groups significantly increase. Therefore, it is concluded that the wetting properties of solid surfaces are dominated by the equilibrium distance, adsorption radius, and effective adsorption sites for water on solids, and the nonlinear relationship between polar groups and hydrophilicity is clarified.

Development of Pressure Sensor Based Wearable Pulse Detection Device for Radial Pulse Monitoring.

Wearable pulse detection devices can be used for daily human healthcare monitoring; however, the relatively poor flexibility and low sensitivity of the pulse detection devices are hindering the scrutiny of pulse information during pulse diagnosis of different pulse positions. This paper developed a novel and wearable pulse detection device based on three flexible pressure sensors using synthetic graphene and silver composites as the pressure sensing material. The structural design of the pulse detection device is firstly presented; the core component of pressure sensors is using the sawtooth protrusions to convert pressure induced by radial pulse vibrations into localized deformation of graphene composites. The fabricated pulse detection device is characterized by high pressure sensing performance, including relatively high sensitivity (8.65% kPa-1), broad sensing range (12 kPa), and good dynamic response with a response time of about 100 ms. Then, the pulse detection device is worn on a human wrist to detect the pulses from three pulse positions, namely, 'Cun', 'Guan', and 'Chi', and the results demonstrated the capability of using our device to detect pulse signals. The physical conditions of the subject, such as arterial stiffness index, can be further analyzed through the characteristics of the acquired pulse signals, demonstrating the potential application of using wearable pulse detection devices for human health monitoring.

Clarifying real receptor binding site between coronavirus HCoV-HKU1 and 9-O-Ac-Sia based on molecular docking.

HCoV-HKU1 is a [Formula: see text]-coronavirus with low pathogenicity, which usually leads to respiratory diseases. At present, a controversial issue is that whether the receptor binding site (RBS) of HCoV-HKU1 is located in the N-terminal domain (NTD) or the C-terminal domain (CTD) in the HCoV-HKU1 S protein. To address this issue, we used molecular docking technology to dock the NTD and CTD with 9-oxoacetylated sialic acid (9-O-Ac-Sia), respectively, with the results showing that the RBS of HCoV-HKU1 is located in the NTD (amino acid residues 80-95, 25-32). Our findings clarified the structural basis and molecular mechanism of the HCoV-HKU1 infection, providing important information for the development of therapeutic antibody drugs and the design of vaccines.

Molecular evolutionary characteristics of SARS-CoV-2 emerging in the United States.

SARS-CoV-2 is a newly discovered beta coronavirus at the end of 2019, which is highly pathogenic and poses a serious threat to human health. In this paper, 1875 SARS-CoV-2 whole genome sequences and the sequence coding spike protein (S gene) sampled from the United States were used for bioinformatics analysis to study the molecular evolutionary characteristics of its genome and spike protein. The MCMC method was used to calculate the evolution rate of the whole genome sequence and the nucleotide mutation rate of the S gene. The results showed that the nucleotide mutation rate of the whole genome was 6.677 × 10-4 substitution per site per year, and the nucleotide mutation rate of the S gene was 8.066 × 10-4 substitution per site per year, which was at a medium level compared with other RNA viruses. Our findings confirmed the scientific hypothesis that the rate of evolution of the virus gradually decreases over time. We also found 13 statistically significant positive selection sites in the SARS-CoV-2 genome. In addition, the results showed that there were 101 nonsynonymous mutation sites in the amino acid sequence of S protein, including seven putative harmful mutation sites. This paper has preliminarily clarified the evolutionary characteristics of SARS-CoV-2 in the United States, providing a scientific basis for future surveillance and prevention of virus variants.

Simultaneously Enhanced Thermal Conductivity and Breakdown Performance of Micro/Nano-BN Co-Doped Epoxy Composites.

Micro/nano- BN co-doped epoxy composites were prepared and their thermal conductivity, breakdown strength at power frequency and voltage endurance time under high frequency bipolar square wave voltage were investigated. The thermal conductivity and breakdown performance were enhanced simultaneously in the composite with a loading concentration of 20 wt% BN at a micro/nano proportion of 95/5. The breakdown strength of 132 kV/mm at power frequency, the thermal conductivity of 0.81 W·m-1·K-1 and voltage endurance time of 166 s were obtained in the composites, which were approximately 28%, 286% and 349% higher than that of pristine epoxy resin. It is proposed that thermal conductive pathways are mainly constructed by micro-BN, leading to improved thermal conductivity and voltage endurance time. A model was introduced to illustrate the enhancement of the breakdown strength. The epoxy composites with high thermal conductivity and excellent breakdown performance could be feasible for insulating materials in high-frequency devices.

Regional simulation of soil organic carbon dynamics for dry farmland in Northeast China using the CENTURY model.

Soil organic carbon content has a significant impact on soil fertility and grain yield, making it an important factor affecting agricultural production and food security. Dry farmland, the main type of cropland in China, has a lower soil organic carbon content than that of paddy soil, and it may have a significant carbon sequestration potential. Therefore, in this study we applied the CENTURY model to explore the temporal and spatial changes of soil organic carbon (SOC) in Jilin Province from 1985 to 2015. Dry farmland soil polygons were extracted from soil and land use layers (at the 1:1,000,000 scale). Spatial overlay analysis was also used to extract 1282 soil polygons from dry farmland. Modelled results for SOC dynamics in the dry farmland, in conjunction with those from the Yushu field-validation site, indicated a good level of performance. From 1985 to 2015, soil organic carbon density (SOCD) of dry farmland decreased from 34.36 Mg C ha-1 to 33.50 Mg C ha-1 in general, having a rate of deterioration of 0.03 Mg C ha-1 per year. Also, SOC loss was 4.89 Tg from dry farmland soils in the province, with a deterioration rate of 0.16 Tg C per year. 35.96% of the dry farmland its SOCD increased but 64.04% of the area released carbon. Moreover, SOC dynamics recorded significant differences between different soil groups. The method of coupling the CENTURY model with a detailed soil database can simulate temporal and spatial variations of SOC at a regional scale, and it can be used as a precise simulation method for dry farmland SOC dynamics.

[Assessing soil pH in Anhui Province based on different features mining methods combined with generalized boosted regression models]. / 基于不同特征挖掘方法结合广义提升回归模型估测安徽省土壤pH.

We explored the application of different feature mining methods combined with genera-lized boosted regression models in digital soil mapping. Environmental covariates were selected by two feature selection methods i.e., recursive feature elimination and selection by filtering. Using the original environmental covariates and the selected optimal variable combination as independent varia-bles, soil pH prediction model of Anhui Province was established and mapped based on the genera-lized boosted regression model and random forest model. The results showed that both kinds of feature mining methods could effectively improve the accuracy of soil pH prediction by generalized boosted regression models and random forest model, and could reduce dimensionality. Compared with the random forest model, the prediction accuracy of the validation set of the generalized boosted regression model was slightly lower. In the training set, the accuracy of the generalized boosted regression models was much higher than that of the random forest model, with higher interpretation and better overall effect. The main parameters of the random forest model, ntree and mtry, had limi-ted effect on the model. Different parameters and their combination could affect the prediction accuracy of the generalized boosted regression models, and thus should be tuned before modeling. The results of spatial mapping showed that soil pH in Anhui Province showed a pattern of "south acid and north alkali".

Effect of Air Gap on Electrical Tree in Epoxy Resin Under High Frequency Bipolar Square-Wave Voltage.

Insulation fails quickly under high-frequency AC high voltage, especially bipolar square-wave voltage with a high dV/dt. It is of great significance to study the failure mechanism of epoxy casting insulation under such kind of voltage. In this paper, pin-plane epoxy casting insulation samples with air gaps were prepared, and the relation between the electrical trees under the high frequency bipolar square-wave voltage and the air gap conditions and voltage frequencies (1~20 kHz) were studied. Results indicated that, with the presence of air gaps, the electrical trees were bush-type and had a relatively slow growth rate, which was different from the fast-growing branch-type trees in the samples without air gap. The electrical tree characteristics related with the size of air gap and voltage frequency were also studied. The electrical tree grew faster under higher voltage frequency or with a smaller air gap. Results proved that discharge introduced a lot of defects for the surface layer of the epoxy resin samples and hence induced the possibility of multi-directional expansion of electrical trees. In addition, the resulting heat accumulation and unique charge transport synergistically affected the electrical tree characteristics under the high frequency bipolar square-wave voltage.

Influence of Oxygen Diffusion on Thermal Ageing of Cross-Linked Polyethylene Cable Insulation.

Thermal ageing of cross-linked polyethylene (XLPE) cable insulation is an important issue threatening the safe operation of power cables. In this paper, thermal ageing of XLPE was carried out at 160 °C in air for 240 h. The influence of oxygen diffusion on thermal ageing of XLPE was investigated by Ultraviolet-visible spectrophotometer (UV-Vis), tensile testing, and Fourier transformed infrared spectroscopy (FTIR). It was observed that the degradation degree not only depended on ageing time but also on sample positions. The thermally aged samples were more oxidized in the surface region, presented a darker color, more carbon atoms appeared in the conjugate cluster, had smaller elongation at break and tensile strength, and a larger carbonyl index. As ageing time increased, the non-uniform oxidation of the XLPE samples became more prominent. The degree of non-uniform oxidation caused by oxygen diffusion was quantitatively studied by first order oxidation kinetic. The calculated results demonstrated that carbonyl index measured by FTIR was more sensitive to non-uniform oxidation with a shape parameter in the range of 1-2. The result shown in this paper is helpful for interpreting and predicting the non-uniform ageing behavior of high voltage XLPE cables.