科研成果

As has been the case in North America and western Europe, the SO2 emissions have substantially reduced in the North China Plain (NCP) in recent years. Differential rates of reduction in SO2 and NOx concentrations result in the frequent occurrence of particulate matter pollution dominated by nitrate (pNO(3)(-)) over the NCR. In this study, we observed a polluted episode with the particulate nitrate mass fraction in nonrefractory PM1 (NR-PM1) being up to 44 % during wintertime in Beijing. Based on this typical pNO(3)(-)-dominated haze event, the linkage between aerosol water uptake and pNO(3)(-) enhancement, further impacting on visibility degradation, has been investigated based on field observations and theoretical calculations. During haze development, as ambient relative humidity (RH) increased from similar to 10 % to 70 %, the aerosol particle liquid water increased from similar to 1 mu g m(-3) at the beginning to similar to 75 mu g m(-3) in the fully developed haze period. The aerosol liquid water further increased the aerosol surface area and volume, enhancing the condensational loss of N2O5 over particles. From the beginning to the fully developed haze, the condensational loss of N2O5 increased by a factor of 20 when only considering aerosol surface area and volume of dry particles, while increasing by a factor of 25 when considering extra surface area and volume due to water uptake. Furthermore, aerosol liquid water favored the thermodynamic equilibrium of HNO3 in the particle phase under the supersaturated HNO3 and NH3 in the atmosphere. All the above results demonstrated that pNO(3)(-) is enhanced by aerosol water uptake with elevated ambient RH during haze development, in turn facilitating the aerosol take-up of water due to the hygroscopicity of particulate nitrate salt. Such mutual promotion between aerosol particle liquid water and particulate nitrate enhancement can rapidly degrade air quality and halve visibility within 1 d. Reduction of nitrogen-containing gaseous precursors, e.g., by control of traffic emissions, is essential in mitigating severe haze events in the NCP.

Abstract Understanding nanoconfined water effect on CO2 utilization and storage has tremendous implications in academic research and practical applications, especially for extremely low-permeability shale reservoirs. Here, a new nanoscale-extended cubic-plus association equation of state is developed by including the confinement effects and intermolecular interactions, based on which the phase behavior and interfacial tension of the pure water and water-CO2 system are accurately calculated. Moreover, three important parameters, caprock-sealing pressure, maximum storage height, and storage capacity, are quantitatively determined for assessing the potential for the CO2 storage. On the basis of the results from this study, the negative effect of nanoconfiend water can be substantially reduced or even converted to be positive for the CO2 utilization and storage in the shale reservoirs due to the extremely small pore scale as well as the associated strong confinements and intermolecular interactions. Overall, this study supports the foundation of general practical applications pertaining to CO2 utilization and geological storage in unconventional low-permeability shale formations with existence of nanoconfined water.

The detection of ultra-low concentration of biomacromolecules remains the focus of research in micro-nanofluidic systems. Sample enrichment is primarily targeted at very low concentration of sample detection tasks. The use of ion concentration polarization principle is the most efficient means to solve the problem of electrokinetic ion enrichment. In this paper, numerical simulation of nano-electrokinetic ion enrichment in a micro-nanofluidic preconcentrator with nanochannel's Cantor fractal wall structure was performed based on Poisson-Nernst-Planck equation combined with the Navier-Stokes equation. The results show that reducing the initial length L-0, increasing the initial height h(0), increasing the fractal step n and using the unstaggered structure in the Cantor fractal principle can increase the ion enrichment concentration and peak voltage. The initial ion concentration is 0.1 mol/m(3). When the applied voltage is 30 V and the initial height h(0) increases from 35 to 45 nm, the ion enrichment concentration drastically increases from 1.007 to 1.410 mol/m(3) by 40%. This study provides a theoretical basis and a novel design method for improving the sensitivity of micro-nanofluidic chips and the design of ultra-low concentration sample testing equipment.

Anthropogenic emissions alter secondary organic aerosol (SOA) formation chemistry from naturally emitted isoprene. We use correlations of tracers and tracer ratios to provide new perspectives on sulfate, NOx, and particle acidity influencing isoprene-derived SOA in two isoprene-rich forested environments representing clean to polluted conditions—wet and dry seasons in central Amazonia and Southeastern U.S. summer. We used a semivolatile thermal desorption aerosol gas chromatograph (SV-TAG) and filter samplers to measure SOA tracers indicative of isoprene/HO2 (2-methyltetrols, C5-alkene triols, 2-methyltetrol organosulfates) and isoprene/NOx (2-methylglyceric acid, 2-methylglyceric acid organosulfate) pathways. Summed concentrations of these tracers correlated with particulate sulfate spanning three orders of magnitude, suggesting that 1 $μ$g m–3 reduction in sulfate corresponds with at least ∼0.5 $μ$g m–3 reduction in isoprene-derived SOA. We also find that isoprene/NOx pathway SOA mass primarily comprises organosulfates, ∼97% in the Amazon and ∼55% in Southeastern United States. We infer under natural conditions in high isoprene emission regions that preindustrial aerosol sulfate was almost exclusively isoprene-derived organosulfates, which are traditionally thought of as representative of an anthropogenic influence. We further report the first field observations showing that particle acidity correlates positively with 2-methylglyceric acid partitioning to the gas phase and negatively with the ratio of 2-methyltetrols to C5-alkene triols.

Ferrihydrite nanoparticles (Fh NPs) are ubiquitous in natural environments. However, their colloidal stability, and fate and transport behavior are difficult to predict in the presence of heterogeneous natural organic matter (NOM) mixtures. Here, we investigated the adsorption and aggregation behavior of Fh NPs exposed to NOM fractions with different molecular weights (MW). The NOM fraction with MW < 3 kDa destabilized the NPs, resulting in accelerated aggregation even at high C/Fe mass ratios, whereas higher MW NOM fractions imparted better colloidal stability with increasing MW and C/Fe ratio. Despite differences in the functional group composition of the bulk (dissolved) NOM fractions, all NOM fractions produced similar adsorbed layer compositions on the NPs, suggesting minimal contribution of chemical properties to the distinctive aggregation behavior. Rather, the higher adsorbed mass and larger size of the higher MW fractions were key factors in stabilizing the NPs through steric repulsion, whereas the lowest MW fraction had low adsorbed mass and was unable to counter electrostatic patch-charge attraction when the NPs are positively charged. This mechanistic understanding helps us predict the transport and fate of Fh NPs and the associated contaminants in natural environments with varying NOM compositions.

Complex networks have been an effective paradigm to represent a variety of complex systems, such as social networks, collaborative networks, and biomolecular networks, where network topology is unkown in advance and has to be inferred with limited observed measurements. Compressive sensing (CS) theory is an efficient technique to achieve accurate network reconstruction in complex networks by formulating the problem as a series of convex optimization models and utilizing the sparsity of networks. However, previous CS-based works have to solve a large number of convex optimization models, which is time-consuming especially when the network scale becomes large. Further, since partial link information shared among multiple convex models, data conflict problem may incur when the derived common variables are inconsistent, which may badly degrade infer precision. To address the issues above, we propose a new model for network reconstruction based on compressive sensing. To be specific, a single convex optimization model is formulated for inferring global network structure by combing the series of convex optimization models, which can effectively improve computation efficiency. Further, we devise a vector to represent the connection states of all the nodes without redundant link information, which is used for representing the unkown topology variables in the proposed optimization model based a devised transformation method. In this way, the proposed model can eliminate data conflict problem and improve infer precision. The comprehensive simulation results shows the superiority of the proposed model compared with the competitive algorithms under a wide variety of scenarios.

Electron emission (EE) from electron sources based on island metal films (IMFs) was mainly attributed to field emission or thermionic emission from IMFs. Here, we propose a new mechanism of EE from IMF-based sources, namely, EE from horizontal tunneling junctions formed in the substrate. The devices with and without IMFs fabricated on silicon oxide substrates are found to exhibit similar EE properties, while the island-metal-film-based devices fabricated on Si3N4/Si substrate show no EE. The comparative results indicate that EE originates from the underlying silicon oxide substrate that was ignored in previous mechanisms, but not from IMFs. EE from the devices is thought to be generated from horizontal tunneling junctions in electroformed silicon oxide substrate due to the rupture of conducting filaments. Even though metal island films are not the origin of EE, they can greatly decrease the forming voltage of the devices. The insights into the emission mechanism are helpful for optimizing the electron source performances.

This paper proposes a new error upper bound formula for the Gaussian integration of the near-singular integral using the Boundary Element Method. First, this study found through numerical tests that the maximum relative error of the Gaussian integration has a downward concave shape but an approximately linear relationship with the relative distance, which is defined as the ratio of the distance from the source point to the element over the element length in a semi-logarithmic plot. Thus, the error upper bound can be defined as a line that closely approaches the computed error data points from the upper side. This line can be obtained by connecting two specified data points that are located outside, but very close to, the considered error range. Further research indicates that one parameter of the fitted line has a linear relationship with the number of Gaussian integration points and singularity orders and the other parameter can be treated as a constant, which together make the proposed Gaussian integration error upper bound formula widely applicable. Compared to the Lachat and Watson criterion, the proposed formula requires fewer integration points when the source point is very close to the element and thus serves to improve computational efficiency. The proposed formula also avoids calculation failure that can occur when using the Davies and Bu criterion. The numerical example results show that the proposed error upper bound formula can evaluate the integration accuracy well and improve computational efficiency when using an adaptive Gaussian integration method.

Perceptual learning, which improves stimulus discrimination, typically results from training with a single stimulus condition. Two major learning mechanisms, early cortical neural plasticity and response reweighting, have been proposed. Here we report a new format of perceptual learning that by design may have bypassed these mechanisms. Instead it is more likely based on abstracted stimulus evidence from multiple stimulus conditions. Specifically, we had observers practice orientation discrimination with Gabors or symmetric dot patterns at up to 47 random or rotating location´orientation conditions. Although each condition received sparse trials (16 trials/session), the practice produced significant orientation learning. Learning also transferred to a Gabor at a single untrained condition with 2-3 time lower orientation thresholds. Moreover, practicing a single stimulus condition with matched trial frequency (16 trials/session) failed to produce significant learning. These results suggested that learning with multiple stimulus conditions may not come from early cortical plasticity or response reweighting with each particular condition. Rather, it may materialize through a new format of perceptual learning, in which orientation evidence invariant to particular orientations and locations is first abstracted from multiple stimulus conditions, and then reweighted by later learning mechanisms. The coarse-to-fine transfer of orientation learning from multiple Gabors or symmetric-dot-patterns to a single Gabor also suggested the involvement of orientation concept learning by the learning mechanisms.

Estimating permeability of carbonate rocks using mercury injection capillary pressure (MICP) data has been carried out by many researchers in the past few decades. However, a major issue with almost all of the existing models is that they focus on a single aperture value from the capillary pressure curve. This study builds a new model to extract permeability from the entire pore throat sizes. Fermic-Dirac function was applied to fit the MICP curve to obtain some critical parameters such as R1 (the large curvature value) and R2 (the small curvature value). Afterwards, the partial least squares regression method was employed to develop a new permeability model. To verify the new model and check other models, we studied ten carbonate rock samples from an Iranian oil reservoir. The results showed that the R1 values vary from 1.00 to 2.73 while R2 values are found between 0.23 and 1.00. The new model performed better than the published models. The idea of building the model for the carbonates can be used in developing the permeability estimating model for shale samples, which could be a new model for the shale permeability estimation.

Patients with central vision loss depend on peripheral vision for everyday functions. A preferred retinal locus (PRL) on the intact retina is commonly trained as a new “fovea” to help. However, reprogramming the fovea-centered oculomotor control is difficult, so saccades often bring the defunct fovea to block the target. Aligning PRL with distant targets also requires multiple saccades and sometimes head movements. To overcome these problems, we attempted to train normal-sighted observers to form a preferred retinal annulus (PRA) around a simulated scotoma, so that they could rely on the same fovea-centered oculomotor system and make short saccades to align PRA with the target. Observers with an invisible simulated central scotoma (5° radius) practiced making saccades to see a tumbling-E target at 10° eccentricity. The otherwise blurred E target became clear when saccades brought a scotoma-abutting clear window (2° radius) to it. The location of the clear window was either fixed for PRL training, or changing among 12 locations for PRA training. Various cues aided the saccades through training. Practice quickly established a PRL or PRA. Comparing to PRL-trained observers whose first saccades persistently blocked the target with scotoma, PRA-trained observers produced more accurate first saccades. The benefits of more accurate PRA-based saccades also outweighed the costs of slower latency. PRA training may provide a very efficient strategy to cope with central vision loss, especially for aging patients who have major difficulties adapting to a PRL.