Understanding the nanoconfined water-CO2 interactions at the molecular scale is of great importance for the fluid transport in confined porous media. Here, a series typical water film and water bridge scenarios are determined, and the associated impacts on nanoconfined water-CO2 interactions as well as the geological hydrocarbon recovery and CO2 storage are investigated in nanopores. Our results confirm either in water film or water bridge scenarios, the competitive adsorptions of nanoconfined water and CO2 reduce the adsorbed water amount and derive the new water bridge with CO2 additions. Such a phenomenon indicates the substrate surface shifts from water-wet to partially CO2-wet, with lower fluid molecule diffusions and illite-water-CO2 sandwich-structured adsorption layer. Overall, our work investigates the mechanism of CO2 effects on distributions and aggregations of nanoconfined water molecules in nanopores, which also provides molecular-scale insights into the nanoconfined water-CO2 interactions in the processes of geological CO2 storage and utilization.
The transfer of farmland is an important area of rural development research; however, the impact of rural social networks has been neglected in studies. The aim of this study is to explore the effects, mechanisms, and heterogeneity of neighbors’ behavior on the process of land renting by farmers. Based on the data of the China Family Panel Studies in 2018, this research empirically analyzes the impact of community-level, local social interactions on the land rental behavior of farmers and its mechanisms using a spatial probit model. The results of this study indicate that neighbors’ land rental behavior positively and significantly affects that of other farmers in the same village. In addition, neighbors’ land rental encourages other farmers in the same village to follow suit through an increase in the perceived importance of the Internet among the farmers. In addition, there is heterogeneity in neighborhood influence. Notably, the impact of social networks on the renting out of the land by farmers, as evidenced in this study, is a key factor in accelerating the circulation of rural land and promoting rural development, thus contributing to the process of rural revitalization and its recording in the literature.
Nitrogen (N) is the most abundant element in Earth's atmosphere, but is extremely depleted in the silicate Earth. However, it is not clear whether core sequestration or early atmospheric loss was responsible for this depletion. Here we study the effect of core formation on the inventory of nitrogen using laser-heated diamond anvil cells. We find that, due to the simultaneous dissolution of oxygen in the metal, N becomes much less siderophile (iron-loving) at pressures and temperatures up to 104 GPa and 5000 K, a thermodynamic condition relevant to the bottom of the magma ocean in the aftermath of the moon-forming giant impact. Using a core–mantle–atmosphere coevolution model, we show that the impact-induced processes (core formation and/or atmospheric loss) are unlikely to account for the observed N anomaly, which is instead best explained by the accretion of mainly N-poor impactors. The terrestrial volatile pattern requires severe N depletion on precursor bodies, prior to their accretion to the proto-Earth.
ABSTRACT Climate change requires an immediate transition from fossil fuels to clean energy sources. Although hydrogen is considered a future energy source, over 90% of hydrogen is currently produced from fossil fuels, and large-scale renewable-fed hydrogen processes are limited by current technologies and economic processes. Therefore, hydrogen production from fossil fuels is a significant topic, particularly if fossil fuel-fed hydrogen production and utilization can be absolutely carbon-free. For the first time, this review critically discusses and analyzes the current advances and fundamentals of fossil fuel dehydrogenation from the perspective of techno-economic-environmental assessments while considering all potential fossil resources and hydrogen technology. This review concludes that the preference of fossil fuels for any hydrogen production technology is as follows: fossil gas > heavy fossil liquid > light fossil liquid > fossil minerals. Thermo-catalytic hydrocarbon decomposition can outperform most other hydrocarbon reforming and pyrolysis processes owing to its energy efficiency, economic effectiveness, and environmental friendliness. Further, we explore potentially new “green hydrogen” technology and confirm that fossil fuels could be completely decarbonized throughout the full-chain stages from exploration to production and consumption. Overall, this work reveals that fossil fuels can be utilized completely carbon-free and provides technical support for future fossil fuel dehydrogenation and energy decarbonization in academic research, industrial practice, and governmental strategies.
It is significant to accurately evaluate the relative permeability of oil–water two phase for multiphase seepage in porous media in low permeability and tight oil reservoir. However, stress sensitivity is an important characteristic for low permeability and tight oil reservoir. It is an effective way for fractal theory to describe the complexity and heterogeneity of the microstructure of porous media. To describe the relative permeability of oil–water two phase in porous media with complex and irregularity pores, a new relative permeability model oil–water two phases is proposed by the fractal theory and the stress sensitivity is taken into the established model in this paper. Meanwhile, the effects of effective stress, elastic modulus, porosity, maximum and minimum flow radius on oil–water relative permeability are analyzed. The new model is verified by comparing with the laboratory data and the results demonstrate that irreducible water and residual oil saturation have a negative correlation with effective stress. The relative permeability of the oil–water two-phase will shrink to the middle as the rise of effective stress, and the region of co-infiltration will decrease. The deformation quantity of porous media, irreducible water and residual oil saturation will increase as the elastic modulus decreases. The larger the maximum flow radius is, the lower the irreducible water saturation and residual oil saturation is. Both the porosity and the minimum flow radius have slight influences on the relative permeability of oil–water two-phase. The proposed relative permeability model can effectively predict the relative permeability of oil and water and help to describe and reveal the multiphase flow in porous media.
The infamous numerical sign problem poses a fundamental obstacle to particle- based stochastic Wigner simulations in high-dimensional phase space. Although the existing particle annihilation (PA) via uniform mesh significantly alleviates the sign problem when dimensionality D <= 4, the mesh size grows dramatically when D >= 6 due to the curse of dimensionality and consequently makes the annihilation very inefficient. In this paper, we propose an adaptive PA algorithm, termed sequential-clustering particle annihilation via discrepancy estimation (SPADE), to overcome the sign problem. SPADE follows a divide-and-conquer strategy: adaptive clustering of particles via controlling their number-theoretic discrepancies and independent random matching in each cluster. The target is to alleviate the oversampling problem induced by the overpartitioning of phase space and to capture the nonclassicality of the Wigner function simultaneously. Combining SPADE with the variance reduction technique based on the stationary phase approximation, we attempt to simulate the proton-electron couplings in six- and 12-dimensional phase space. A thorough performance benchmark of SPADE is provided with the reference solutions in six-dimensional phase space produced by a characteristic-spectral-mixed scheme under a 733*803 uniform grid, which fully explores the limit of grid-based deterministic Wigner solvers.
In the era of a green economy, green innovation has become a way for enterprises to gain competitive advantage, and it is of great theoretical and practical significance to explore the driving force of enterprises' green innovation. This study explores the peer effect of an enterprise's green innovation and conducts an empirical test using data from 3338 Chinese listed companies in 2020. The results show a significant positive peer effect of enterprises' green innovation, and the green innovation of individual enterprises increases by 0.869 for each unit increase in industry-average green innovation. Further research shows that market power is the channel by which peer influence affects an enterprise's green innovation. Moreover, regional heterogeneity exists in the strength of the peer effect, which varies according to firm maturity and board size. These findings provide a reference for enterprises and governments to promote green transformation.
The plastisphere may act as reservoir of antibiotic resistome, accelerating global antimicrobial resistance dissemination. However, the environmental risks in the plastisphere of field microplastics (MPs) in farmland remain largely unknown. Here, antibiotic resistance genes (ARGs) and virulence factors (VFs) on polyethylene microplastics (PE-MPs) and polybutylene adipate terephthalate and polylactic acid microplastics (PBAT/PLA-MPs) from residues were investigated using metagenomic analysis. The results suggested that the profiles of ARG and VF in the plastisphere of PBAT/PLA-MPs had greater number of detected genes with statistically higher values of diversity and abundance than soil and PE-MP. Procrustes analysis indicated a good fitting correlation between ARG/VF profiles and bacterial community composition. Actinobacteria was the major host for tetracycline and glycopeptide resistance genes in the soil and PE-MP plastisphere, whereas the primary host for multidrug resistance genes changed to Proteobacteria in PBAT/PLA-MP plastisphere. Besides, three human pathogens, Sphingomonas paucimobilis, Lactobacillus plantarum and Pseudomonas aeruginosa were identified in the plastisphere. The PE-MP plastisphere exhibited a higher transfer potential of ARGs than PBAT/PLA-MP plastisphere. This work enhances our knowledge of potential environmental risks posed by microplastic in farmland and provides valuable insights for risk assessment and management of agricultural mulching applications.
The atmospheric aqueous-phase chemistry has received increasing attention in the last decades for its non-negligible environmental significance. Yet, the insufficient experimental data on oxidative reaction rate constants (kaq) obstructs the further analysis and modeling of this system. Predictive models based on machine learning (ML) algorithms have shown potential as an effective estimation tool, however, they are restricted to the lack of training data as well. To overcome this data limitation, we developed multi-task (MT) models that could exploit the common knowledge from reactions in gas- and aqueous-phases simultaneously. Toward kaq of organic compounds with hydroxyl radical (OH), nitrate radical (NO3), and ozone (O3), the MT models showed a notably better predictive ability compared to benchmark models, while obtaining wide applicability on compounds from different chemical classes. By interpreting the models using Shapley additive explanations (SHAP), we evidenced that the MT models utilized the common knowledge in both phases and correctly identified the reaction mechanisms. This study aims to provide new insight into the estimation of necessary kinetic parameters in atmospheric aqueous-phase chemistry, as well as a reference to ML research for other predictive tasks of atmospheric interest.
Mineral scale refers to the hard inorganic solids nucleated on substrates or deposited from the aqueous phase. The formation and deposition of barium sulfate and strontium sulfate in various industries, such as water treatment and oilfield operations, can significantly impact facility operations, posing serious threats. Machine learning (ML) approaches have been adopted recently in scale threat predictions to address the limitations of conventional scaling prediction models. However, there are few reports on collecting sulfate mineral scaling data, employing ML methods for data analysis, and evaluating the modeling results to gain deeper insights of sulfate mineral scaling process and to improve the accuracy of sulfate scaling threat prediction. Despite comprehensive experimental studies, the literature does not provide adequate guidance for identifying the influence on the solubility of barium sulfate and strontium sulfate under different aqueous environments and actual operating conditions. To this end, this study collected 1600 experimental datasets of barium/strontium sulfate from the literature to construct and evaluate the reliability and versatility of a ML-based model for sulfate solubility calculations. Single neural networks, hybrid neural networks, and optimization algorithms were employed to build solubility prediction models for barium sulfate and strontium sulfate across a wide range of temperatures, pressures, and different ions. The model's applicability in predicting sulfate scaling threats in various actual operating environments demonstrated its broad usability, consistent with its actual performance. This study marks the first stride towards constructing a reliable model for identifying the scaling trends of barium sulfate and strontium sulfate across various operating conditions, underscoring the importance of developing robust and accurate prediction models to address challenges in various industrial systems.
Isoprene hydroxy peroxy radicals (ISOPOO), derived from isoprene oxidation by hydroxy radicals (OH), are key intermediates for ozone and secondary organic aerosol (SOA) formation in the atmosphere. Although ISOPOO-water complexes are ubiquitous, their impacts on ISOPOO chemistry remain obscure. Here the previously overlooked water effect on the bimolecular reaction kinetics of ISOPOO was investigated in an oxidative flow reactor. The major first-generation products of ISOPOO, isoprene hydroxy hydroperoxides (ISOPOOH), methacrolein (MACR), and methyl vinyl ketone (MVK), were measured simultaneously at various relative humidity (RH) with the help of a cold trap to avoid potential losses in direct gas sampling. We found that ISOPOO reactions were accelerated significantly under wet conditions, with a greater enhancement on 1,2-ISOPOO than 4,3-ISOPOO. 1,2-ISOPOOH yield appeared faster growth with RH than 4,3-ISOPOOH. MVK yield showed an upward-downward trend with RH, while MACR yield plateaued from 30% RH. To explain the enhancement in the ISOPOOH yield from 3% to 80% RH, the overall rate constants of 1,2-ISOPOO + HO2 and 4,3-ISOPOO + HO2 reactions at 80% RH should be 13 times and twice those at 3% RH, respectively. The empirical formulas were proposed for the first time to parameterize the water effect on ISOPOO + HO2 reactions. The updated kinetics of ISOPOO reactions were incorporated in a box model to simulate the RH-dependent ISOPOOH and C4 carbonyl yields under typical atmospheric conditions. High RH can enhance the ISOPOOH yield in urban, rural, and forest areas, and promote SOA formation correspondingly. Our findings shed light on the critical role of humidity in the reactions of ISOPOO and benefit evaluating the fate of isoprene and its impacts on air quality more accurately in the ambient atmosphere.