Spontaneous imbibition is an important phenomenon of fluid transports in porous media, particularly in liquid environment with a certain range of interfacial tension and wettability. Meanwhile, the spontaneous imbibition could be enhanced with the additions of surfactants, through modifying the oil–water interfacial tension (IFT) and wettability. However, ultra-low IFT impairs the capillary pressure. Hence, appropriate ranges of the IFT and contact angle (CA), though lacking adequate investigations, are key to optimizing spontaneous imbibition. Here, a series of physical experiments were conducted to evaluate the spontaneous imbibition efficiency of surfactant solutions with wide-range IFTs (10−3–101 mN/m) in the porous media of permeability 11 mD, through designed interfacial property measurements with different surfactant concentrations. Besides, the inverse Bond number was employed to determine the optimized interfacial properties during the imbibition. Overall, the best imbibition-induced hydrocarbon recovery is reached at oil viscosity of 25.26 mPa·s, an IFT of 0.1–0.2 mN/m and a CA of 70–80°.
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.
Antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) pose a significant threat to both ecosystems and human health. Owing to the excellent catalytic activity, eco-safety, and convenience for defect engineering, BiOBr with oxygen vacancies (OVs) of different density thus were fabricated and employed to activate H2O2 for ARB disinfection/ARGs degradation in present study. We found that BiOBr with OVs of appropriate density induced via ethanol reduction (BOB-E) could effectively activate H2O2, achieving excellent ARB disinfection and ARGs degradation efficiency. Moreover, this disinfection system exhibited remarkable tolerance to complex water environments and actual water conditions. In-situ characterization and theoretical calculations revealed that OVs in BOB-E could effectively capture and activate aqueous H2O2 into HO· and O2·−. The generated reactive oxygen species combined with electron transfer could damage the cell membrane system and degrade genetic materials of ARB, leading to effective disinfection. The impressive reusability, high performance achieved in two immobilized reaction systems (packed column and baffled ditch reactor), excellent degradation of emerging organic pollutants supported the feasibility of BOB-E/H2O2 system towards practical water decontamination. Overall, this study not only provides insights into fabrication of bismuth-based catalysts for efficient ARB disinfection/ARGs degradation via OVs regulation, but also paves the way for their practical applications.
The chemistry of ozone (O3) on indoor surfaces leads to secondary pollution, aggravating the air quality in indoor environments. Here, we assess the heterogeneous chemistry of gaseous O3 with glass plates after being 1 month in two different kitchens where Chinese and Western styles of cooking were applied, respectively. The uptake coefficients of O3 on the authentic glass plates were measured in the dark and under UV light irradiation typical for indoor environments (320 nm < $łambda$ < 400 nm) at different relative humidities. The gas-phase product compounds formed upon reactions of O3 with the glass plates were evaluated in real time by a proton-transfer-reaction quadrupole-interface time-of-flight mass spectrometer. We observed typical aldehydes formed by the O3 reactions with the unsaturated fatty acid constituents of cooking oils. The formation of decanal, 6-methyl-5-hepten-2-one (6-MHO), and 4-oxopentanal (4-OPA) was also observed. The employed dynamic mass balance model shows that the estimated mixing ratios of hexanal, octanal, nonanal, decanal, undecanal, 6-MHO, and 4-OPA due to O3 chemistry with authentic grime-coated kitchen glass surfaces are higher in the kitchen where Chinese food was cooked compared to that where Western food was cooked. These results show that O3 chemistry on greasy glass surfaces leads to enhanced VOC levels in indoor environments.
Chengdu Plain Urban Agglomeration (CPUA) is one of the most serious areas suffering from ozone pollution in China. A comprehensive field observation focused on the ozone production rate and its sensitivity was conducted at CPUA in the summer of 2019. Six sampling sites were set and two ozone pollution episodes were recognized. The daily maximum 8-h average (MDA8) O3 concentration reached 137.9 ppbv in the urban sites during the ozone pollution episode. Peak concentration of O3 was closely related to intense solar radiation, high temperatures, and precursor emissions. The OH-HO2-RO2 radical chemistry and ozone production rate (P(O3)) were calculated using an observation-based model (OBM). The daily peak OH concentration varied in the range of 3-13 × 106 molecules cm−3, and peak HO2 and RO2 were in the range of 2–14 × 108 molecules cm−3 during ozone pollution episodes. During the ozone pollution episode, the average maximum of P(O3) in suburban sites (about 30 ppbv h-1.) was compared with urban sites, and the maximum of P(O3) was 18 ppbv h-1 in rural sites. The relative incremental reactivity (RIR) results demonstrate that it was a VOCs-limited regime in the central urban area of Chengdu, with NOx suppression effect in some regions. In the southern neighboring suburb of Chengdu, it was VOCs-limited as well. However, the northern suburban area was a transition region. In the remote rural areas of the southern CPUA, it was highly NOx-limited. Local ozone production driven by the photochemical process is crucial to the ozone pollution formation in CPUA. The geographically differentiated recognition of the ozone regime found by this study can help to tailor control strategies for local conditions and avoid the negative effects of a one-size-fits-all approach.
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.
Sulphate-reducing microorganisms, or SRMs, are crucial to organic decomposition, the sulphur cycle, and the formation of pyrite. Despite their low energy-yielding metabolism and intense competition with other microorganisms, their ability to thrive in natural habitats often lacking sufficient substrates remains an enigma. This study delves into how Desulfovibrio desulfuricans G20, a representative SRM, utilizes photoelectrons from extracellular sphalerite (ZnS), a semiconducting mineral that often coexists with SRMs, for its metabolism and energy production. Batch experiments with sphalerite reveal that the initial rate and extent of sulphate reduction by G20 increased by 3.6 and 3.2 times respectively under light conditions compared to darkness, when lactate was not added. Analyses of microbial photoelectrochemical, transcriptomic, and metabolomic data suggest that in the absence of lactate, G20 extracts photoelectrons from extracellular sphalerite through cytochromes, nanowires, and electron shuttles. Genes encoding movement and biofilm formation are upregulated, suggesting that G20 might sense redox potential gradients and migrate towards sphalerite to acquire photoelectrons. This process enhances the intracellular electron transfer activity, sulphur metabolism, and ATP production of G20, which becomes dominant under conditions of carbon starvation and extends cell viability in such environments. This mechanism could be a vital strategy for SRMs to survive in energy-limited environments and contribute to sulphur cycling.
Polyhydroxyalkanoates (PHAs) are a class of microbially synthesized polyesters with diverse structures with renewability, biodegradability, good biocompatibility, and broad application prospects. However, the level of commercialization of PHAs remains low. The high recovery cost is one of the main reasons preventing the widespread use of these "green polymers". For decades, efforts have been made to explore lower-cost, greener, and more economical PHAs recovery strategies, and significant progress has been made. This review presents cell lysis and yeast surface display (YSD)-based bio-recovery strategies for PHAs, and then proposes a model hypothesis for protein-mediated secretion of PHAs drawing on the lipid secretion model to provide essential information for further cost reduction and efficiency in the recovery of PHAs. In addition, this review also highlights the bio-recovery strategy of extracellular PHAs based on synthetic biology and exploring specific PHAs secretion mechanism is a promising strategy for reducing the cost of PHAs recovery in the future.
Carbonyl compounds are important precursors of aqueous aerosols in the atmosphere, while their gas-particle partitioning behaviors and roles in particulate sulfur formation are poorly understood. In this study, we investigate the partitioning of five carbonyl compounds (formaldehyde, acetaldehyde, acetone, glyoxal, and methylglyoxal) during haze episodes in Beijing, China. On haze days, the values of field-derived effective Henry’s law coefficients (KHf) on aerosols for these carbonyl compounds are 106–108 M atm–1, which are significantly higher (102–104 times) than those in pure water. Sulfate is observed to have a pronounced "salting-in" effect on these carbonyl compounds, resulting in at least 1-order-of-magnitude increase in their particle-phase concentrations. Parameterization schemes for their partitioning in the ambient aerosols were provided and applied to the multiphase chemical box model (RACM2-CAPRAM). When incorporated into the field-derived parametrization, the model significantly increased hydroxymethanesulfonate (HMS) production by 50-fold compared to using the parameters obtained in pure water, increasing from 2.6 × 10–2 to 1.23 μg m–3 h–1. The formed HMS can facilitate sulfate formation in turn through further oxidation by OH radicals and enhance aerosol hygroscopicity. These findings indicate a positive feedback loop between the partitioning of carbonyl compounds and particulate sulfur formation during haze episodes, providing new insights for controlling particulate pollution and reducing SO2 levels in urban areas.
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.
