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Mercury transformation processes in nature: Critical knowledge gaps and perspectives for moving forward
We report the transformation of mercury (Hg) in the environment plays a vital role in the cycling of Hg and its risk to the ecosystem and human health. Of particular importance are Hg oxidation/reduction and methylation/demethylation processes driven or mediated by the dynamics of light, microorganisms, and organic carbon, among others. Advances in understanding those Hg transformation processes determine our capacity of projecting and mitigating Hg risk. Here, we provide a critical analysis of major knowledge gaps in our understanding of Hg transformation in nature, with perspectives on approaches moving forward. Our analysis focuses on Hg transformation processes in the environment, as well as emerging methodology in exploring these processes. Future avenues for improving the understanding of Hg transformation processes to protect ecosystem and human health are also explored.
Competitive exchange between divalent metal ions [Cu(II), Zn(II), Ca(II)] and Hg(II) bound to thiols and natural organic matter
Mercuric Hg(II) ion forms exceptionally strong complexes with various organic ligands, particularly thiols and dissolved organic matter (DOM) in natural water. Few studies, however, have experimentally determined whether or not the presence of base cations and transition metal ions, such as Ca(II), Cu(II), and Zn(II), would compete with Hg(II) bound to these ligands, as concentrations of these metal ions are usually orders of magnitude higher than Hg(II) in aquatic systems. Different from previous model predictions, a significant fraction of Hg(II) bound to cysteine (CYS), glutathione (GSH), or DOM was found to be competitively exchanged by Cu(II), but not by Zn(II) or Ca(II). About 20–75% of CYS-bound-Hg(II) [at 2:1 CYS:Hg(II)] and 14–40% of GSH-bound-Hg(II) [at 1:1 GSH:Hg(II)] were exchanged by Cu(II) at concentrations 1–3 orders of magnitude greater than Hg(II). Competitive exchange was also observed between Cu(II) and Hg(II) bound to DOM, albeit to a lower extent, depending on relative abundances of thiol and carboxylate functional groups on DOM and their equilibrium time with Hg(II). When complexed with ethylenediaminetetraacetate (EDTA), most Hg(II) could be exchanged by Cu(II) and Zn(II), as well as Ca(II) at increasing concentrations. Furthermore, these results shed additional light on competitive exchange reactions between Hg(II) and coexisting metal ions and have important implications in Hg(II) chemical speciation and biogeochemical transformation, particularly in contaminated environments containing relatively high concentrations of Hg(II) and metal ions.
Contrary effects of phytoplankton Chlorella vulgaris and its exudates on mercury methylation by iron- and sulfate-reducing bacteria
Mercury (Hg) is a pervasive environmental pollutant and poses serious health concerns as inorganic Hg(II) can be converted to the neurotoxin methylmercury (MeHg), which bioaccumulates and biomagnifies in food webs. Phytoplankton, representing the base of aquatic food webs, can take up Hg(II) and influence MeHg production, but currently little is known about how and to what extent phytoplankton may impact Hg(II) methylation by itself or by methylating bacteria it harbors. This study investigated whether some species of phytoplankton could produce MeHg and how the live or dead phytoplankton cells and excreted algal organic matter (AOM) impact Hg(II) methylation by several known methylators, including iron-reducing bacteria (FeRB), Geobacter anodireducens SD-1 and Geobacter sulfurreducens PCA, and the sulfate-reducing bacterium (SRB) Desulfovibrio desulfuricans ND132 (or Pseudodesulfovibrio mercurii). Here our results indicate that, among the 4 phytoplankton species studied, none were capable of methylating Hg(II). However, the presence of phytoplankton cells (either live or dead) from Chlorella vulgaris (CV) generally inhibited Hg(II) methylation by FeRB but substantially enhanced methylation by SRB D. desulfuricans ND132. Enhanced methylation was attributed in part to CV-excreted AOM, which increased Hg(II) complexation and methylation by ND132 cells. In contrast, inhibition of methylation by FeRB was attributed to these bacteria incapable of competing with phytoplankton for Hg(II) binding and uptake. These observations suggest that phytoplankton could play different roles in affecting Hg(II) methylation by the two groups of anaerobic bacteria, FeRB and SRB, and thus shed additional light on how phytoplankton blooms may modulate MeHg production and bioaccumulation in the aquatic environment.
Sonochemical oxidation and stabilization of liquid elemental mercury in water and soil
Over 3000 mercury (Hg)-contaminated sites worldwide contain liquid metallic Hg [Hg(0) 1 ] representing a continuous source of elemental Hg(0) in the environment through volatilization and solubilization in water. Currently, there are few effective treatment technologies available to remove or sequester Hg(0) 1 in situ. We investigated sonochemical treatments coupled with complexing agents, polysulfide and sulfide, in oxidizing Hg(0) 1 and stabilizing Hg in water, soil and quartz sand. Results indicate that sonication is highly effective in breaking up and oxidizing liquid Hg(0) 1 beads via acoustic cavitation, particularly in the presence of polysulfide. Without complexing agents, sonication caused only minor oxidation of Hg(0) 1 but increased headspace gaseous Hg(0) g and dissolved Hg(0) aq in water. However, the presence of polysulfide essentially stopped Hg(0) volatilization and solubilization. As a charged polymer, polysulfide was more effective than sulfide in oxidizing Hg(0) 1 and subsequently stabilizing the precipitated metacinnabar (β-HgS) nanocrystals. Sonochemical treatments with sulfide yielded incomplete oxidation of Hg(0) 1 , likely resulting from the formation of HgS coatings on the dispersed µm-size Hg(0) 1 bead surfaces. Sonication with polysulfide also resulted in rapid oxidation of Hg(0) 1 and precipitation of HgS in quartz sand and in the Hg(0) 1 -contaminated soil. This research indicates that sonochemical treatment with polysulfide could be an effective means in rapidly converting Hg(0) 1 to insoluble HgS precipitates in water and sediments, thereby preventing its further emission and release to the environment. We suggest that future studies are performed to confirm its technical feasibility and treatment efficacy for remediation applications.
Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach
Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Overall, results show that an estimated around 950 million m3 of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.
Lattice strains and diffraction elastic constants of cubic polycrystals
In situ synchrotron X-ray and neutron diffraction experiments provide a powerful approach to measure lattice strains in bulk polycrystalline materials. They are being increasingly used for quantitative characterization of microscale deformation within and between grains and phases. Here we use a self-consistent micromechanics model to obtain a general analytic solution of the grain-level lattice strains and diffraction elastic constants for a broad class of elastically isotropic polycrystals with cubic crystal symmetry. This analytic solution reveals a direct linear relationship between the reciprocal of the elastic diffraction constant and the orientation index parameter along the direction of any diffraction vector, including tensile loading and transverse directions. The straightforward numerical implementation of this solution provides diffraction elastic constants for 26 representative cubic polycrystals. Finally, analytic solutions of this kind can serve to benchmark in situ diffraction measurements of lattice strains and also facilitate high-throughput studies of microscale stresses and diffraction elastic constants in polycrystalline materials.
Atomistic determination of Peierls barriers of dislocation glide in nickel
The Peierls barrier measures the lattice resistance to dislocation glide in crystalline solids. We use the nudged elastic band (NEB) method to calculate the Peierls barriers for screw and edge dislocation glide in a face-centered cubic (FCC) metal of Ni. The minimum energy paths (MEPs) across single or sequential Peierls barriers are determined under shear loading. The NEB results show the decreasing Peierls barrier with increasing shear stress, giving the Peierls stress at which the Peierls barrier vanishes. The effects of boundary condition and system size on Peierls barriers are studied by comparing strain- and stress-controlled NEB results. Furthermore, the free-end NEB methods are applied to determine MEPs with improved computational efficiency. The NEB results are also used to evaluate the energetic driving force of dislocation glide, which is consistent with that determined from the Peach-Koehler force. The accuracy of the present NEB results based on an empirical interatomic potential is assessed by comparison with a machine-learning potential. This work demonstrates the robust and efficient quantification of Peierls barriers to dislocation glide in an FCC metal, and it lays a solid foundation for the atomistic determination of Peierls barriers in compositionally complex alloys with the FCC structure in future studies.
Application of the core shell model for strengthening polymer filament interfaces
While surface segregation has been intensively studied in thin films, relatively little has been investigated in filament geometries. In contrast to thin films, the filament surface forms a continuum, with interactions both at the free air and internal filament interface, which makes it difficult to achieve an equilibrium configuration. This asymmetry has become particularly apparent with the increasing use of additive manufacturing where entire structures are constructed via layered deposition of filamentous layers from a moving thermal nozzle. In addition, this process is highly non-equilibrated with poor thermal retention and is responsible for insufficient filament–filament interfacial fusion and internal pore formation along the printing direction. These features all contribute to poor mechanical and structural properties, which are exacerbated in semi-crystalline polymers, such as Polylactic Acid (PLA), where the melting point, T m , is much higher than the glass transition, T g . Even though system is liquid the molecules become ordered within the crystal structure and this ordering interferes with interdiffusion, which in turn prevents proper interfacial fusion. Capitalizing on the differential in surface tension, we show that addition of less than 1%, or trace amounts, of longer chain polymers, an oriented core shell structure, will be formed with a self-limiting thickness of three time the radius of gyration, R g , of the tracer chains. The lower T g of the tracers and their orientation makes them poised for interdiffusion when subsequent layers are deposited producing structures with significant improvement of the mechanical properties and internal structural integrity.
Shear band velocity and activation volume during shear deformation by acoustic emission in a Zr-based bulk metallic glass
Here, an analysis is presented for the shear band velocity of plastic deformation and activation volume of the shear transformation zone (STZ) in a Zr 60 Cu 30 Al 10 bulk metallic glass (BMG). Compared to the values calculated by the stick-slip model, the values of shear band velocity calculated after optimization by the acoustic emission technique are much larger than those reported previously. Plastic flow in BMGs is found to be described by a power-law correlation between the maximum velocity of the shear band and the strain rates. Combining the free volume theory and the STZ theory, the relationship between the volume change in the STZ and the shear band velocity is derived as $Ω=A$(27–ln$v/l$). The value of A, which is a material dependent constant, is 5.43 × 10 –3 for the current BMG at room temperature.
The discriminative capacity of CSF β-amyloid 42 and Tau in neurodegenerative diseases in the Chinese population
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A linked-scale coupled model of mass erosion and redistribution in plasma-exposed micro-foam surfaces
Surface evolution due to exposure to harsh environments is of importance in many scientific and technological applications. In plasma-exposed materials, the surface receives charged particles from the plasma, leading to a series of processes that drive the system far from equilibrium and may lead to the severe deterioration of the surface properties. Although surface morphological changes are driven by atomic collisions taking place over picoseconds and nanometers, these processes result in mass loss and redistribution of matter over much larger length and time scales. This necessitates a multi-scale approach capable of capturing the range of processes linking primary atomic collision events with engineering-level surface geometry changes. In this paper, we develop a computational model to simulate the morphological evolution and effective erosion rate of micro-architected tungsten foams during low-energy plasma ion bombardment. Furthermore, the model acts on several length scales, with the energy and angular dependence of the sputtering yield of flat tungsten surfaces determined using the SRIM code based on the binary collision approximation. This information is introduced into a low-fluence, short-term Monte Carlo raytracing model, and further into a high-fluence, long-term particle transport model. In the latter, material particles representing billions of atoms are described in a digitized 3-D representation of the foam structure from X-ray tomography data, and are sputtered off and redeposited using the atomistic information. We show that the redeposition of sputtered atoms leads to partial self-healing in the bottom layers with a sharp reduction in the sputtering coefficient of low density foams, roughly 25% of the solid W value. This is in qualitative agreement with recent experiments on low porosity W structures. At high fluence, the foam structure degrades considerably as there are fewer ligaments available to recapture sputtered atoms and, consequently, the sputtering rate increases again.
Uniform corrosion of FeCrAl cladding tubing for accident tolerant fuels in light water reactors
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Understanding effects of chemical complexity on helium bubble formation in Ni-based concentrated solid solution alloys based on elemental segregation measurements
Here, helium bubble formation and swelling were systematically studied in Ni-based concentrated solid solution alloys containing different numbers and types of elements. Our microscopy analysis showed that although increasing the alloy chemical complexity helps suppress bubble formation in general, there is no monotonic relationship between the bubble growth rate and the number of alloying elements. Certain elements (e.g., Fe and Pd) are more effective in suppressing bubble growth than others (e.g., Cr and Mn). Atom probe tomography was applied to accurately measure elemental segregation around bubbles, revealing unique effects of certain alloying elements on vacancy migration towards bubbles. More specifically, the high vacancy mobility via Cr sites leads to a large vacancy flux and an increased bubble size, while the high degree of atomic size mismatch introduced by Pd helps deflect vacancy flow away from bubbles and decrease the amount of swelling. The effects identified in this study provide new strategies to design concentrated solid solutions with superior resistance to swelling.
Effect of the redox potential on the general corrosion behavior of industrial nuclear alloys
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Using dynamic mode decomposition to predict the dynamics of a two-time non-equilibrium Green’s function
Computing the numerical solution of the Kadanoff–Baym equations, a set of nonlinear integral differential equations satisfied by the two-time Green's functions derived from many-body perturbation theory for a quantum many-body system away from equilibrium, is a challenging task. Recently, we have successfully applied dynamic mode decomposition (DMD) to construct a data driven reduced order model that can be used to extrapolate the time-diagonal of a two-time Green's function from numerical solutions of the KBE within a small time window. In this paper, we extend the previous work and use DMD to predict off-diagonal elements of the two-time Green's function. We partition the two-time Green's function into a number of one-time functions along the diagonal and subdiagonals of the two-time window as well as in horizontal and vertical directions. We use DMD to construct separate reduced order models to predict the dynamics of these one-time functions in a two-step procedure. We extrapolate along diagonal and several subdiagonals within a subdiagonal band of a two-time window in the first step. In the second step, we use DMD to extrapolate the Green's function outside of the sub-diagonal band. In conclusion, we demonstrate the efficiency and accuracy of this approach by applying it to a two-band Hubbard model problem.