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Abstract With the increasing depletion of shallow mineral resources, deep mining has become important; however, basic research on deep resource development remains insufficient, and basic laws are unclear. In particular, comprehensive research on the pore structure characteristics of deep coal bodies at different scales is needed, and how burial depth affects pore structure characteristics should be investigated. Herein, this study performed mercury intrusion porosimetry, liquid nitrogen method, and low-temperature carbon dioxide adsorption method to systematically determine the pore structure characteristics of deep coal and comprehensively analyze coal pore complexity based on the Menger Sponge, Frenkel–Halsey–Hill (FHH), and density function models. The influence of depth on coal pore structure was also discussed. In the study zone, the stage pore volume was mainly concentrated in the macroporous (43.6–83.4%) and mesoporous Sect. (16.3–54.7%) and sparsely distributed in the microporous Sect. (0.3–2.1%). The accumulated pore volume decreased with an increase in coal sample burial depth, with a gentle curve in the micropore section and obvious fluctuations in the mesopore and macropore sections. The main distribution of stage surface area was in the mesoporous Sect. (79.5–91.8%), followed by the microporous (7.8–19.8%) and macroporous Sect. (0.3–0.6%). The accumulated surface area curve fluctuated greatly in the mesoporous and microporous sections, and that in the macroporous section was relatively flat with limited distribution. According to the Menger Sponge model, the fractal dimensions of the macroporous (Dmac) and mesoporous (Dmes) sections ranged from 2.4 to 2.8 and 3.7–3.9, respectively, with obvious fractal characteristics; Dmes was higher than Dmac, indicating that the structure of small and medium-sized micropores is more complex and irregular. The fractal dimension in the high-pressure zone of the FHH model (DHP) ranged from 2.8 to 2.9, and that in the low-pressure zone (DLP) ranged from 2.1 to 2.4, indicating that coal pore structure under low-pressure conditions is relatively simple. The fractal dimension of the density functional model ranged from 2.5 to 3.0, closer to 3, indicating that the pores are relatively complex and rough. A single-variable analysis revealed a strong correlation between burial depth and aperture parameters. This is the first study to reveal the relationship between burial depth and aperture parameters. Moreover, characterization of critical depth based on aperture structure was performed, providing a feasible method for quantifying “critical depth.” Our results enrich our understanding of the structure of deep coal bodies and serve as a reference for deep mining engineering.

To study the pore structure and fractal characteristics of deep medium-rank coal, combined characterization using mercury intrusion and liquid nitrogen adsorption methods was conducted on coal samples from the main coal seams in typical deep mining areas, including Shenyang Hongyang Third Mine, Kailuan Linxi Mine, Huainan Xinji Second Mine, and Pingdingshan Pingmei Sixth Mine. Parameters such as pore size, pore volume, and specific surface area were obtained, and the pore fractal characteristics were studied based on the Menger sponge model and the FHH model. The results showed that: (1) Among the pore structure parameters tested with mercury intrusion method, the average pore size ranged from 31.10 to 34.70 nm, pore volume from 0.048 3 to 0.059 4 mL/g, and specific surface area from 5.590 9 to 7.652 8 m2/g. The pore development in the main coal seams of typical deep mining areas was relatively similar. The pore volume distribution was dominated by macropores, with micropores and transition pores contributing roughly equally, and mesopores having a relatively small distribution. This indicated that macropores had better connectivity and mesopores were more closed. Micropores accounted for more than 70% of the total specific surface area, while the proportions of mesopores and macropores were minimal, indicating that micropores had the strongest adsorption capacity, which was negatively affected gas management in deep coal seams. The fractal dimensions based on the Menger sponge model ranged from 2.6 to 3.0, indicating irregular pore shapes, complex pore structures, and generally rough pore surfaces. (2) The effective pore size tested using liquid nitrogen adsorption method ranged from 3 to 177 nm with significant differences in total pore volume and specific surface area among the mining areas. Pore volume distribution was dominated by transition pores and mesopores, with a lower distribution of micropores and no macropores. This indicated that liquid nitrogen adsorption was effective for characterizing mesopores and transition pores but struggled to characterize macropore structures. Moreover, the connectivity of micropores was relatively poor. The specific surface area was mainly composed of transition pores, micropores, and mesopores, with no macropores. Among them, transition pores were mostly dominant and had relatively strong adsorption capacity. The fractal dimensions based on the FHH model ranged from 2.0 to 2.7, indicating a relatively simple and regular structure. (3) The differences in pore structures of deep medium-rank coal were discussed. The pore structure parameters (specific surface area and pore volume) determined by mercury intrusion and liquid nitrogen adsorption methods showed a non-linear concave curve variation with increasing burial depth. The fractal dimensions derived from the Menger sponge model and the FHH model showed a convex curve trend with increasing burial depth.

Currently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the identification, segmentation, and reconstruction of road potholes, ultimately leading to the quantification of the damage volume. An RGB-D depth sensor is employed to collect point cloud data of road potholes. Voxel filtering and voxelization downsampling are used for denoising, filtering, and enhancing data processing efficiency. Surface segmentation is achieved through RANSAC (Random Sample Consensus) and Euclidean clustering, while the Alpha Shapes algorithm is utilized for three-dimensional volume reconstruction, facilitating the volumetric quantification of potholes. For evaluation, comparative experiments were conducted under different lighting conditions and shooting distances. The experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.4% in volumetric damage measurement of road potholes, accurately determining the damage volume of pothole.

Abstract The recent discovery of the persistence of long-range magnetic order when van der Waals magnets are thinned towards monolayers provides a tunable platform for engineering of novel magnetic structures and devices. Here, we study the evolution of the electronic structure of CrGeTe3 as a function of surface electron doping. From angle-resolved photoemission, we observe spectroscopic fingerprints that this electron doping drives a marked increase in T C, reaching values more than double that of the undoped material, in agreement with recent studies using electrostatic gating. Together with density functional theory calculations and Monte Carlo simulations, we show that, surprisingly, the increased T C is mediated by the population of spin-minority Cr t 2g states, forming a half-metallic 2D electron gas. This promotes a novel variant of double exchange, and unlocks a significant influence of Ge – which was previously thought to be electronically inert in this system – in mediating Cr-Cr exchange.

This study investigates two extraction methods—ultrasound-assisted extraction (UAE) and compound enzyme extraction (CE)—for the isolation of Lonicera macranthoides polysaccharides (LMP) and evaluates their physicochemical properties, bioactivity, and therapeutic potential. The optimization of extraction conditions was performed using single-factor experiments and response surface methodology. The yield of LMP from UAE was optimized to 8.02 % ± 0.12 %, while the yield from CE was 10.03 % ± 0.32 %. Both polysaccharide extracts were characterized in terms of chemical composition, monosaccharide composition, molecular weight, thermal stability, and functional group analysis. Notably, UAE-LMP exhibited superior antioxidant, hypolipidemic, nitrite scavenging, and anti-tumor activities compared to CE-LMP. The ultrasound-assisted method enhanced the stability and bioactivity of LMP, with improved water- and oil-holding capacities and the integrity of the active groups. Our findings demonstrate that the extraction method significantly influences the structural and functional properties of LMP, suggesting that UAE may be a more effective approach for extracting bioactive polysaccharides with potential applications in medicine and nutrition.

In recent years, a group of rapidly growing technology-based entrepreneurial enterprises have emerged in China, playing an increasingly important role in the national economy. However, small and mediumsized technology startups have low profitability and high risk, making it difficult to obtain sufficient funds from traditional financing channels. At present, research on venture capital evaluation mainly focuses on the evaluation of projects before venture capital, and empirical methods are mostly used to study the promoting effect of venture capital on performance after investment, ignoring the issue of continuous evaluation after investment. The performance evaluation of risk investment in small and medium-sized high-technology venture enterprises is MADM. In such study, the generalized weighted geometric Bonferroni mean (GWGBM) technique is illustrated for MADM under single-valued neutrosophic sets (SVNSs). We begin by constructing the single-valued neutrosophic number GWGBM (SVNNGWGBM) operator, followed by the introduction of the MADM based on this technique. Subsequently, we provide an illustrative example that evaluates the performance of risk investment in small and medium-sized high-technology venture enterprises, supplemented by some comparative analyses to demonstrate the SVNNGWGBM technique. The principal contributions of this research are highlighted: (1) The maximizing deviation method is employed to derive the weight; (2)The GSVNNWBM technique is developed, setting the stage for the introduction of MADM methods based on GSVNNWBM technique; (3) Numerical example is presented that focuses on the performance evaluation of risk investment in small and medium-sized high-technology venture enterprises, along with a comparative decision analysis. [ABSTRACT FROM AUTHOR]