Search Results (60)

This study focuses on the complex pore structure and pronounced heterogeneity of tight sandstone reservoirs in the Linxing area of the Ordos Basin and develops a multi-scale quantitative characterization approach to investigate the coupling mechanism between pore structure and reservoir properties. Six core samples were selected from the Shiqianfeng Formation (depth interval: 1326–1421 m) for detailed analysis. Cast thin sections and scanning electron microscopy (SEM) experiments were employed to characterize pore types and structural features. Nuclear magnetic resonance (NMR) experiments were conducted to obtain T₂ spectra, which were used to classify bound and movable pores, and their corresponding fractal dimensions were calculated separately. In addition, NMR logging data from the corresponding well intervals were integrated to assess the applicability and consistency of the fractal characteristics at the logging scale. The results reveal that the fractal dimension of bound pores shows a positive correlation with porosity, whereas that of movable pores is negatively correlated with permeability, indicating that different scales of pore structural complexity exert distinct influences on reservoir performance. Mineral composition affects the evolution of pore structures through mechanisms such as framework support, dissolution, and pore-filling, thereby further enhancing reservoir heterogeneity. The consistency between logging responses and experimental observations verifies the regional applicability of fractal analysis. Bound pores dominate within the studied interval, and the vertical variation of the PMF/BVI ratio aligns closely with both the NMR T₂ spectra and fractal results. This study demonstrates that fractal dimension is an effective descriptor of structural characteristics across different pore types and provides a theoretical foundation and methodological support for the evaluation of pore complexity and heterogeneity in tight sandstone reservoirs. Full article

During the past decades, agricultural soil heavy metal pollution has been becoming increasingly severe due to urbanization and industrialization. However, the impact of externally input heavy metals on deep soils remains unclear because most previous relevant research only focused on surface soils. In the present study, Concentrations of eight heavy metals (Cu, Zn, Ni, Pb, Cr, Cd, As, and Hg) were determined for 72 pairs of surface and deep soil samples collected from an agricultural region close to the Pearl River estuary. Subsequently, heavy metal pollution and potential health risks were assessed using the Geo-accumulation Index and Potential Ecological Risk Index, a dose response model and Monte Carlo simulation, respectively. Principal component analysis (PCA) and the positive matrix factorization (PMF) receptor model were combined to analyze heavy metal sources. The results indicated that average concentrations of all heavy metals exceeded their corresponding background values. Cd was identified as the main pollutant due to its extremely high values of I_geo and Er. Unacceptable potential heavy metal non-carcinogenic and carcinogenic risks indicated by respectively calculated HI and TCR, higher than thresholds 1.0 and 1.0 × 10⁻⁴, mainly arose from heavy metals As, Cd, Cr, and Ni through food ingestion and dermal absorption. Anthropogenic sources respectively contributed 19.7% and 38.9% for soil As and accounted for the main contributions to Cd, Cu, and Hg (Surface: 90.2%, 65.4%, 67.3%; Deep: 53.8%, 54.6%, 56.2%) within surface and deep layers. These results indicate that soil heavy metal contents with deep layers were also significantly influenced by anthropogenic input. Therefore, we suggest that both surface and deep soils should be investigated simultaneously to gain relatively accurate results for soil heavy metal pollution and source apportionments. Full article

Potentially toxic elements (PTEs) are the most important indicators of environmental pollution and represent a potential risk to the ecology and human health in industrial regions. Eight potentially toxic elements (Mn, Ni, Co, Cr, Pb, Zn, Cd, As) in soils formed on the territory of the industrial site of the Udachny Mining and Processing Division were considered in this study. The potential ecological risk index (RI) was calculated to determine environmental risks of soil contamination. The concentrations of PTEs decreased in the following order Mn > Ni > Zn > Co > Pb > Cr > As > Cd. In total, 19.51% of the sites in the study area exhibited a high potential ecological risk for Mn and Ni, while only 4.87% exhibited a low potential ecological risk for other PTEs. The greatest impacts on soil contamination are exerted by the areas of the Udachny and Zarnitsa pipes, tailings ponds, and the area’s highly mineralized water outlet. The results of correlation analysis (CA) and hierarchical cluster analysis (HCA) revealed that the same groups of elements were present: Co-Cr-Ni and Cd-Zn. The PMF findings demonstrate that the five main diverse sources of PTEs in this study area’s soils were natural, mining activities, transportation, and industrialization, as well as highly mineralized waters. Full article

Heavy metal pollution in agricultural soil has been tightly associated with anthropogenic emissions. Although there are many studies that focus on a regional scale, the source identification of heavy metal contamination on a field scale around industrial areas remains unclear. The average concentrations in topsoils of Hg, Cd, As, Pb, Cr, Ni, Zn, and Cu were 2.07, 0.13, 8.56, 42.3, 81.1, 37.3, 105, and 43.8 mg kg⁻¹, respectively. The enrichment of Hg was particularly presented on topsoils, with the highest single pollution index (Pi) (9.00) and ecological risk index (Eri) (922) values. An integrated methodology was employed in source identification of heavy metals contamination, especially for Hg, including Pearson’s and PCA analysis, soil profile morphology, mathematical modeling, and Hg isotope analysis. Results revealed that the concentrations of Hg decreased as a function of depth, suggesting Hg contamination was an anthropogenic source and can be supported by Hg isotope analysis. The negative Δ¹⁹⁹Hg values of the residual Hg (F4-Hg) and soil profile in 80–100 cm deviate from those of the soil profiles in 0–80 cm, indicating exogenous input of Hg occurred in the study area. According to the UNMIX model, the contribution of coal combustion, agricultural activities, parent material, and industrial/traffic emissions to Hg accumulation in soils were 66.2%, 16.9%, 9.81%, and 7.0%, respectively. However, the contribution rates calculated with the PMF model of mixed industrial source, traffic emissions, and parent material were 71.4%, 27.8%, and 0.8%, respectively. This study can accurately quantify and identify the factors contributing to heavy metal contamination in agricultural soil on a field scale. Full article

Accurate evaluation of groundwater quality and identification of key characteristics are essential for maintaining groundwater resources. The purpose of this study is to strengthen water quality evaluation through the SHAP and XGBoost algorithms, analyze the key indicators affecting water quality in depth, and quantify their impact on groundwater quality through interpretable tools. The XGBoost algorithm shows that zinc (0.183), nitrate (0.159), and chloride (0.136) are the three indicators with the highest weight. The SHAP algorithm shows that zinc (34.62%), nitrate (17.65%), and chloride (16.98%) have higher contribution values, which explains the output results of XGBoost. According to the calculation scores and classification standards of the water quality model, 49% of the groundwater samples in the study area have excellent water quality, 33% of the samples are better, and 18% of the samples are polluted. The results of positive matrix factorization (PMF) show that natural conditions, metal processing, metal smelting and mining, and agricultural activities all cause pollution to groundwater. Zinc, chloride, nitrate, and manganese were the key variables determined by the SHAP algorithm to explain the vast majority of human health risk sources. These findings indicate that interpretable machine learning not only improves the correlation of water quality assessment but also quantifies the judgment basis of each sample and helps to track key pollution indicators. Full article

Polymorphic transformation is important in chemical industries, in particular, in those involving explosive molecular crystals. However, due to simulating challenges in the rare event method and collective variables, understanding the transformation mechanism of molecular crystals with a complex structure at the molecular level is poor. In this work, with the constructed order parameters (OPs) and K-means clustering algorithm, the potential of mean force (PMF) along the minimum free-energy path connecting β-HMX and δ-HMX was calculated by the finite temperature string method in the collective variables (SMCV), the free-energy profile and nucleation kinetics were obtained by Markovian milestoning with Voronoi tessellations, and the temperature effect on nucleation was also clarified. The barriers of transformation were affected by the finite-size effects. The configuration with the lower potential barrier in the PMF corresponded to the critical nucleus. The time and free-energy barrier of the polymorphic transformation were reduced as the temperature increased, which was explained by the pre-exponential factor and nucleation rate. Thus, the polymorphic transformation of HMX could be controlled by the temperatures, as is consistent with previous experimental results. Finally, the HMX polymorph dependency of the impact sensitivity was discussed. This work provides an effective way to reveal the polymorphic transformation of the molecular crystal with a cyclic molecular structure, and further to prepare the desired explosive by controlling the transformation temperature. Full article

Purpose: This study employed a Life Cycle Assessment (LCA) methodology to evaluate the environmental impacts of a novel plant cell-based biomanufacturing process for producing plant natural product ingredients. The primary purpose was to assess the relative sustainability of the process and to provide insights into potential areas of improvement in the biomanufacturing process. Method: The LCA method used an MS Excel (Ver. 2407) -based approach with a cradle-to-gate system boundary covering raw material sourcing (A1), raw material transportation (A2), and product extract manufacturing (A3) stages. Energy use and material inventory data are presented for different unit operations, and environmental impact factors were obtained from the Ecoinvent database. The study included a Material Circularity Index (MCI) calculation to assess the circularity of the biomanufacturing process for the production of saponin emulsifiers that are normally extracted from the woody tissue of the Chilean soapbark tree (Quillaja saponaria). Comparative analyses were performed against a wild-harvest approach for plant tannin extraction from spruce (Picea abies) tree bark. Key Results: The environmental impact assessment focused on determining relative Global Warming Potential (GWP), Acidification Potential (AP), Freshwater Eutrophication (FE), Particulate Matter Formation (PMF), and Ozone Depletion Potential (ODP). Results indicated that the extract manufacturing stage (A3) contributed significantly to adverse environmental impacts, with varying levels of effects based on the energy source used. Comparative analysis with the wild harvest approach highlights the lower environmental impact of the alternative biomanufacturing process. The biomanufacturing process showed a 23% reduction in GWP, AP, and FE and a 25% reduction in PMF and ODP relative to the wild harvest approach. However, the MCI for the biomanufacturing process was estimated to be 0.186, indicating a low material circularity. Conclusions: The results revealed that the extract manufacturing stage, particularly energy consumption, significantly influences the relative environmental impacts of the alternative production processes. Different energy sources exhibit varying effects, with renewable energy sources showing lower environmental impacts. The Material Circularity Index indicated a low circularity for the biomanufacturing process, suggesting opportunities for improvement, such as incorporating recycled or reused materials. Compared with the tannin extraction process, the plant cell-based biomanufacturing process demonstrated lower environmental impacts, emphasising the importance of sustainable practices and the use of renewable energy sources in future plant natural product sourcing. Recommendations include implementing more sustainable practices, optimising raw material choices, and extending product life spans to enhance circularity and overall environmental benefits. Full article

To investigate the characteristics and sources of volatile organic compounds (VOCs) as well as their impacts on secondary organic aerosols (SOAs) formation during high-incidence periods of PM_2.5 pollution, a field measurement was conducted in December 2019 in Hefei, a typical city of the Yangtze River Delta (YRD). During the whole process, the mixing ratios of VOCs were averaged as 21.1 ± 15.9 ppb, with alkanes, alkenes, alkyne, and aromatics accounting for 59.9%, 15.3%, 15.0%, and 9.8% of the total VOCs, respectively. It is worth noting that the contributions of alkenes and alkyne increased significantly during PM_2.5 pollution periods. Based on source apportionment via the positive matrix factorization (PMF) model, vehicle emissions, liquefied petroleum gas/natural gas (LPG/NG), and biomass/coal burning were the main sources of VOCs during the research in Hefei. During pollution periods, however, the contribution of biomass/coal burning to VOCs increased significantly, reaching as much as 47.6%. The calculated SOA formation potential (SOAFP) of VOCs was 0.38 ± 1.04 µg m⁻³ (range: 0.04–7.30 µg m⁻³), and aromatics were the dominant contributors, with a percentage of 96.8%. The source contributions showed that industrial emissions (49.1%) and vehicle emissions (28.3%) contributed the most to SOAFP during non-pollution periods, whereas the contribution of biomass/coal burning to SOA formation increased significantly (32.8%) during PM_2.5 pollution periods. These findings suggest that reducing VOCs emissions from biomass/coal burning, vehicle, and industrial sources is a crucial approach for the effective control of SOA formation in Hefei, which provides a scientific basis for controlling PM_2.5 pollution and improving air quality in the YRD region. Full article

This paper presents the design of a strain-sensitive, dual ball-shaped tunable zone (DBT) taper structure for light intensity modulation. Unlike conventional tapered optical fibers, the DBT incorporates a central light field modulation zone within the taper. By precisely controlling the fusion parameters between single-mode fiber (SMF) and polarization-maintaining fiber (PMF), the ellipticity of the modulation zone can be finely adjusted, thereby optimizing spectral characteristics. Theoretical analysis based on polarization mode interference (PMI) coupling confirms that the DBT structure achieves a more uniform spectral response. In cantilever beam strain tests, the DBT exhibits high sensitivity and a highly linear intensity–strain response (R² = 0.99), with orthogonal linear polarization mode interference yielding sensitivities of 0.049 dB/με and 0.023 dB/με over the 0–244.33 με strain range. Leveraging the DBT’s light intensity sensitivity, a temperature-compensated intensity difference and ratio calculation method is proposed, effectively minimizing the influence of light source fluctuations on sensor performance and enabling high-precision strain measurements with errors as low as ±6 με under minor temperature variations. The DBT fiber device, combined with this innovative demodulation technique, is particularly suitable for precision optical sensing applications. The DBT structure, combined with the novel demodulation method, is particularly well-suited for high-precision and stable measurements in industrial monitoring, aerospace, civil engineering, and precision instruments for micro-deformation sensing. Full article

In scenarios where the global navigation satellite system is unavailable, unmanned aerial vehicles (UAVs) can employ visual algorithms to process aerial images. These images are integrated with satellite maps and digital elevation models (DEMs) to achieve global localization. To address the localization challenge in unfamiliar areas devoid of prior data, an iterative computation-based localization framework is commonly used. This framework iteratively refines its calculations using multiple observations from a downward-facing camera to determine an accurate global location. To improve the rate of convergence for localization, we introduced an innovative observation model. We derived a terrain descriptor from the images captured by a forward-facing camera and integrated it as supplementary observation into a point-mass filter (PMF) framework to enhance the confidence of the observation likelihood distribution. Furthermore, within this framework, the methods for the truncation of the convolution kernel and that of the probability distribution were developed, thereby enhancing the computational efficiency and convergence rate, respectively. The performance of the algorithm was evaluated using real UAV flight sequences, a satellite map, and a DEM in an area measuring 7.7 km × 8 km. The results demonstrate that this method significantly accelerates the localization convergence during both takeoff and ascent phases as well as during cruise flight. Additionally, it increases localization accuracy and robustness in complex environments, such as areas with uneven terrain and ambiguous scenes. The method is applicable to the localization of UAVs in large-scale unknown scenarios, thereby enhancing the flight safety and mission execution capabilities of UAVs. Full article

External interfaces, such as the air–water and solid–liquid interfaces, are ubiquitous in nature. Hydrophobic interactions are considered the fundamental driving force in many physical and chemical processes occurring in aqueous solutions. It is important to understand the effects of external interfaces on hydrophobic interactions. According to the structural studies on liquid water and the air–water interface, the external interface primarily affects the structure of the topmost water layer (interfacial water). Therefore, an external interface may affect hydrophobic interactions. The effects of interfaces on hydrophobicity are related not only to surface molecular polarity but also to the geometric characteristics of the external interface, such as shape and surface roughness. This study is devoted to understanding the effects of a smooth interface on hydrophobicity. Due to hydrophobic interactions, the solutes tend to accumulate at external interfaces to maximize the hydrogen bonding of water. Additionally, these can be demonstrated by the calculated potential mean forces (PMFs) using molecular dynamic (MD) simulations. Full article

According to our recent studies on hydrophobicity, this work is aimed at understanding the dependence of hydrophobic interactions on the shape of a solute’s surface. It has been observed that dissolved solutes primarily affect the structure of interfacial water, which refers to the top layer of water at the interface between the solute and water. As solutes aggregate in a solution, hydrophobic interactions become closely related to the transition of water molecules from the interfacial region to the bulk water. It is inferred that hydrophobic interactions may depend on the shape of the solute surface. To enhance the strength of hydrophobic interactions, the solutes tend to aggregate, thereby minimizing their surface area-to-volume ratio. This also suggests that hydrophobic interactions may exhibit directional characteristics. Moreover, this phenomenon can be supported by calculated potential mean forces (PMFs) using molecular dynamics (MD) simulations, where different surfaces, such as convex, flat, or concave, are associated with a sphere. Furthermore, this concept can be extended to comprehend the molecular packing parameter, commonly utilized in studying the self-assembly behavior of amphiphilic molecules in aqueous solutions. Full article

Thyroxine (T4) is a drug extensively utilized for the treatment of hypothyroidism. However, the oral absorption of T4 presents certain limitations. This research investigates the efficacy of CO₂ nanobubbles in water as a potential oral carrier for T4 administration to C57BL/6 hypothyroid mice. Following 18 h of fasting, the formulation was administered to the mice, demonstrating that the combination of CO₂ nanobubbles and T4 enhanced the drug’s absorption in blood serum by approximately 40%. To comprehend this observation at a molecular level, we explored the interaction mechanism through which T4 engages with the CO₂ nanobubbles, employing molecular simulations, semi-empirical quantum mechanics, and PMF calculations. Our simulations revealed a high affinity of T4 for the water–gas interface, driven by additive interactions between the hydrophobic region of T4 and the gas phase and electrostatic interactions of the polar groups of T4 with water at the water–gas interface. Concurrently, we observed that at the water–gas interface, the cluster of T4 formed in the water region disassembles, contributing to the drug’s bioavailability. Furthermore, we examined how the gas within the nanobubbles aids in facilitating the drug’s translocation through cell membranes. This research contributes to a deeper understanding of the role of CO₂ nanobubbles in drug absorption and subsequent release into the bloodstream. The findings suggest that utilizing CO₂ nanobubbles could enhance T4 bioavailability and cell permeability, leading to more efficient transport into cells. Additional research opens the possibility of employing lower concentrations of this class of drugs, thereby potentially reducing the associated side effects due to poor absorption. Full article

Bioventing is an established technique extensively employed in the remediation of soil contaminated with petroleum hydrocarbons. In this study, the objective was to develop an improved foundational bioventing model that characterizes gas flow in vadose zones where aqueous and non-aqueous phase liquid (NAPL) are present and immobile, accounting for interphase mass transfer and first order biodegradation kinetics. By incorporating a correlation for the biodegradation rate constant, which is a function of soil properties including initial population of petroleum degrader microorganisms in soil, sand content, clay content, water content, and soil organic matter content, this model offers the ability to integrate a specific biodegradation rate constant tailored to the soil properties for each site. The governing equations were solved using the finite volume method in OpenFOAM employing the “porousMultiphaseFoam v2107” (PMF) toolbox. The equation describing gas flow in unsaturated soil was solved using a mixed pressure-saturation method, where calculated values were employed to solve the component transport equations. Calibration was done against a set of experimental data for a meso-scale reactor considering contaminant volatilization rate as the pre-calibration parameter and the mass transfer coefficient between aqueous and NAPL phase as the main calibration parameter. The calibrated model then was validated by simulating a large-scale reactor. The modelling results showed an error of 2.9% for calibrated case and 4.7% error for validation case which present the fitness to the experimental data, proving that the enhanced bioventing model holds the potential to improve predictions of bioventing and facilitate the development of efficient strategies to remediate soil contaminated with petroleum hydrocarbons. Full article

Ion channels exhibit strong selectivity for specific ions over others under electrochemical potentials, such as KcsA for K⁺ over Na⁺. Based on the thermodynamic analysis, this study is focused on exploring the mechanism of ion selectivity in nanopores. It is well known that ions must lose part of their hydration layer to enter the channel. Therefore, the ion selectivity of a channel is due to the rearrangement of water molecules when entering the nanopore, which may be related to the hydrophobic interactions between ions and channels. In our recent works on hydrophobic interactions, with reference to the critical radius of solute (Rc), it was divided into initial and hydrophobic solvation processes. Additionally, the different dissolved behaviors of solutes in water are expected in various processes, such as dispersed and accumulated distributions in water. Correspondingly, as the ion approaches the nanopore, there seems to exist the “repulsive” or “attractive” forces between them. In the initial process (<Rc), the energy barrier related to “repulsive” force may be expected as ions enter the channel. Regarding the ion selectivity of nanopores, this may be due to the energy barrier between the ion and channel, which is closely related to the ion size and pore radius. Additionally, these may be demonstrated by the calculated potential mean forces (PMFs) using molecular dynamics (MD) simulations. Full article