Microstructural characterisation of organic matter pores in coal-measure shale

Waste cooking oil’s (WCO’s) potential as a rejuvenator of aged asphalt has received attention in recent years, with the acid value of WCO affecting its rejuvenation effect. This study explored the rejuvenation effect of WCO with a high acid value on aged asphalt by using molecular dynamics simulation. First, the representative molecules of WCO with a high acid value and asphalt were determined. The rejuvenation effect of WCO on aged asphalt was analyzed by adding different contents of WCO to an aged asphalt model. The effect of WCO on the thermodynamic properties of the aged asphalt was analyzed. The results show that WCO can restore the thermodynamic properties of aged asphalt binder to a certain extent. Regarding the microstructure of rejuvenated asphalt, WCO molecules dispersed around asphaltenes weakened the latter’s aggregation and improved the colloidal structure of the aged asphalt. In terms of interface adhesion properties, WCO can improve the adhesion properties between asphalt binder and SiO₂, but it has limited influence on water sensitivity. The results allowed us to comprehensively evaluate the rejuvenation effect of WCO with a high acid value on aged asphalt and to explore its rejuvenation mechanism.

Copper nanoparticles (CuNPs) are extensively used in electronics, cosmetics, fungicides, and various other fields due to their distinctive qualities. However, this widespread usage can contribute to environmental contamination and heightened health risks for living organisms. Despite their prevalent use, the ecological impacts and biosafety of CuNPs remain inadequately understood. The present study aims to delve into the potential toxic effects of CuNPs on zebrafish (Danio rerio) embryos, focusing on multiple indexes such as embryonic development, neurotoxicity, oxidative stress, and inflammatory response. The results revealed a notable increase in the death rate and deformity rate, alongside varying degrees of decrease in hatching rate and heart rate following CuNPs exposure. Particularly, the frequency of spontaneous tail coiling significantly declined under exposure to CuNPs at concentrations of 500 µg/L. Furthermore, CuNPs exposure induced alterations in the transcriptional expression of GABA signaling pathway-related genes (gabra1, gad, abat, and gat1), indicating potential impacts on GABA synthesis, release, catabolism, recovery, and receptor binding. Additionally, CuNPs triggered oxidative stress, evidenced by disruption in superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) activities, along with elevated malondialdehyde (MDA) levels. This oxidative stress subsequently led to a proinflammatory cascade, as demonstrated by the increased transcriptional expression of inflammatory markers (il-1β, tnf-α, il-6, and il-8). Comparative analysis with copper ion (provided as CuCl₂) exposure highlighted more significant changes in most indexes with CuCl₂, indicating greater toxicity compared to CuNPs at equivalent concentrations. In conclusion, these findings provide valuable insights into the toxic effects of CuNPs on zebrafish embryo development and neurotransmitter conduction. Furthermore, they present technical methodologies for assessing environmental and health risks associated with CuNPs, contributing to a better understanding of their biosafety and ecological impact.

Natural gas generation is the result of organic matter degradation under the effects of biodegradation and thermal degradation. Early Cambrian black shales in the Upper Yangtze Region are rich in organic matter and have shown great shale gas potentiality in recent years. Nevertheless, the enrichment mechanism and distribution of organic matter in these black shales between different sedimentary settings, such as intra-platform basin, slope, and deep basin, are still poorly understood. In this paper, based mainly on elemental geochemistry, a comprehensive study of the marine redox conditions, primary productivity, sedimentation rate, terrigenous input, hydrothermal activity, and water mass restrictions was conducted on the Early Cambrian Niutitang black shale in the Upper Yangtze Region. Our data showed that an intra-platform basin received a higher terrigenous input and that it deposited under more restricted conditions than the slope and deep basin settings. The primary productivity in the slope and deep basin settings was higher than that in the intra-platform basin setting. In the intra-platform basin, the productivity increased from its inner part to its margin. For the slope and deep basin settings, the high paleoproductivity generated large amounts of organic matter and its preservation was synergistically affected by the redox conditions. In contrast to the slope and deep basin, the preservation of organic matter in the inner part of the intra-platform basin was mainly controlled by redox conditions because the paleoproductivity in it was much lower than in the slope and deep basin settings. The intra-platform basin margin was the most favorable area for accumulating organic matter.

The tunneling face poses a significant risk for gas disaster in coal mining due to the complex interplay of geological conditions, ventilation strategies, and construction techniques, resulting in nonlinear and spatiotemporal dynamics in gas concentration distribution. Accurate prediction of gas levels is crucial for ensuring the safety of coal mining operations. This study introduces a novel approach for gas concentration forecasting at the tunneling face by integrating the Kolmogorov–Arnold Network (KAN) with an enhanced iTransformer model, leveraging multi-source sensor data for enhanced predictive capabilities. KAN improves the feature extraction ability due to flexible mapping kernel function that is capable of capturing complicated nonlinearities between gas emission volume and environmental variables. iTransformer, with concentrated attention mechanism and sparsity pattern, can further model very long-term sequence dependencies and learn to capture multi-scale features. As a whole, they address the problem of gradient vanishing and insufficient feature extraction in the temporal sequential prediction models based on deep learning methods with long sequences input, leading to significant improvements in prediction accuracy and model stability. Experiments on site monitoring datasets demonstrate that the proposed KAN + iTransformer model achieved better fitting and generalization capacity than two baseline models (iTransformer, Transformer) for gas concentration prediction.