Ion exchange (IX) is the most common approach to treating nitrate-contaminated drinking water sources, but the cost of salt to make regeneration brine, as well as the cost and environmental burden of waste brine disposal, are major disadvantages. A hybrid ion exchange-catalyst treatment system, in which waste brine is catalytically treated for reuse, shows promise for reducing costs and environmental burdens of the conventional IX system. An IX model with separate treatment and regeneration cycles was developed, and ion selectivity coefficients for each cycle were separately calibrated by fitting experimental data. Of note, selectivity coefficients for the regeneration cycle required fitting the second treatment cycle after incomplete resin regeneration. The calibrated and validated model was used to simulate many cycles of treatment and regeneration using the hybrid system. Simulated waste brines and a real brine obtained from a California utility were also evaluated for catalytic nitrate treatment in a packed-bed, flow-through column with 0.5 wt%Pd–0.05 wt%In/activated carbon support (PdIn/AC). Consistent nitrate removal and no apparent catalyst deactivation were observed over 23 d (synthetic brine) and 45 d (real waste brine) of continuous-flow treatment. Ion exchange and catalyst results were used to evaluate treatment of 1 billion gallons of nitrate-contaminated source water at a 0.5 MGD water treatment plant. Switching from a conventional IX system with a two bed volume regeneration to a hybrid system with the same regeneration length and sequencing batch catalytic reactor treatment would save 76% in salt cost. The results suggest the hybrid system has the potential to address the disadvantages of a conventional IX treatment systems.
Rapid reduction of aqueous ClO4– to Cl– by H2 has been realized by a heterogeneous Re(hoz)2–Pd/C catalyst integrating Re(O)(hoz)2Cl complex (hoz = oxazolinyl-phenolato bidentate ligand) and Pd nanoparticles on carbon support, but ClOx– intermediates formed during reactions with concentrated ClO4– promote irreversible Re complex decomposition and catalyst deactivation. The original catalyst design mimics the microbial ClO4– reductase, which integrates Mo(MGD)2 complex (MGD = molybdopterin guanine dinucleotide) for oxygen atom transfer (OAT). Perchlorate-reducing microorganisms employ a separate enzyme, chlorite dismutase, to prevent accumulation of the destructive ClO2– intermediate. The structural intricacy of MGD ligand and the two-enzyme mechanism for microbial ClO4– reduction inspired us to improve catalyst stability by rationally tuning Re ligand structure and adding a ClOx– scavenger. Two new Re complexes, Re(O)(htz)2Cl and Re(O)(hoz)(htz)Cl (htz = thiazolinyl-phenolato bidentate ligand), significantly mitigate Re complex decomposition by slightly lowering the OAT activity when immobilized in Pd/C. Further stability enhancement is then obtained by switching the nanoparticles from Pd to Rh, which exhibits high reactivity with ClOx– intermediates and thus prevents their deactivating reaction with the Re complex. Compared to Re(hoz)2–Pd/C, the new Re(hoz)(htz)–Rh/C catalyst exhibits similar ClO4– reduction activity but superior stability, evidenced by a decrease of Re leaching from 37% to 0.25% and stability of surface Re speciation following the treatment of a concentrated “challenge” solution containing 1000 ppm of ClO4–. This work demonstrates the pivotal roles of coordination chemistry control and tuning of individual catalyst components for achieving both high activity and stability in environmental catalyst applications.
This study develops synthetic strategies for N,N-trans and N,N-cis Re(O)(LO–N)2Cl complexes and investigates the effects of the coordination spheres and ligand structures on ancillary ligand exchange dynamics and catalytic perchlorate reduction activities of the corresponding [Re(O)(LO–N)2]+ cations. The 2-(2′-hydroxyphenyl)-2-oxazoline (Hhoz) and 2-(2′-hydroxyphenyl)-2-thiazoline (Hhtz) ligands are used to prepare homoleptic N,N-trans and N,N-cis isomers of both Re(O)(hoz)2Cl and Re(O)(htz)2Cl and one heteroleptic N,N-trans Re(O)(hoz)(htz)Cl. Selection of hoz/htzligands determines the preferred isomeric coordination sphere, and the use of substituted pyridine bases with varying degrees of steric hindrance during complex synthesis controls the rate of isomer interconversion. The five corresponding [Re(O)(LO–N)2]+ cations exhibit a wide range of solvent exchange rates (1.4 to 24,000 s–1 at 25 °C) and different LO–N movement patterns, as influenced by the coordination sphere of Re (trans/cis), the noncoordinating heteroatom on LO–N ligands (O/S), and the combination of the two LO–N ligands (homoleptic/heteroleptic). Ligand exchange dynamics also correlate with the activity of catalytic reduction of aqueous ClO4– by H2 when the Re(O)(LO–N)2Cl complexes are immobilized onto Pd/C. Findings from this study provide novel synthetic strategies and mechanistic insights for innovations in catalytic, environmental, and biomedical research.
Four sets of nonreactive solute transport experiments were conducted with micromodels. Each set consisted of three experiments with one variable, i.e., flow velocity, grain diameter, pore-aspect ratio, and flow-focusing heterogeneity. The data sets were offered to pore-scale modeling groups to test their numerical simulators. Each set consisted of two learning experiments, for which all results were made available, and one challenge experiment, for which only the experimental description and base input parameters were provided. The experimental results showed a nonlinear dependence of the transverse dispersion coefficient on the Peclet number, a negligible effect of the pore-aspect ratio on transverse mixing, and considerably enhanced mixing due to flow focusing. Five pore-scale models and one continuum-scale model were used to simulate the experiments. Of the pore-scale models, two used a pore-network (PN) method, two others are based on a lattice Boltzmann (LB) approach, and one used a computational fluid dynamics (CFD) technique. The learning experiments were used by the PN models to modify the standard perfect mixing approach in pore bodies into approaches to simulate the observed incomplete mixing. The LB and CFD models used the learning experiments to appropriately discretize the spatial grid representations. For the continuum modeling, the required dispersivity input values were estimated based on published nonlinear relations between transverse dispersion coefficients and Peclet number. Comparisons between experimental and numerical results for the four challenge experiments show that all pore-scale models were all able to satisfactorily simulate the experiments. The continuum model underestimated the required dispersivity values, resulting in reduced dispersion. The PN models were able to complete the simulations in a few minutes, whereas the direct models, which account for the micromodel geometry and underlying flow and transport physics, needed up to several days on supercomputers to resolve the more complex problems.
Characterizing subsurface properties is crucial for reliable and cost-effective groundwater supply management and contaminant remediation. With recent advances in sensor technology, large volumes of hydrogeophysical and geochemical data can be obtained to achieve high-resolution images of subsurface properties. However, characterization with such a large amount of information requires prohibitive computational costs associated with “big data” processing and numerous large-scale numerical simulations. To tackle such difficulties, the principal component geostatistical approach (PCGA) has been proposed as a “Jacobian-free” inversion method that requires much smaller forward simulation runs for each iteration than the number of unknown parameters and measurements needed in the traditional inversion methods. PCGA can be conveniently linked to any multiphysics simulation software with independent parallel executions. In this paper, we extend PCGA to handle a large number of measurements (e.g., 106 or more) by constructing a fast preconditioner whose computational cost scales linearly with the data size. For illustration, we characterize the heterogeneous hydraulic conductivity (K) distribution in a laboratory-scale 3-D sand box using about 6 million transient tracer concentration measurements obtained using magnetic resonance imaging. Since each individual observation has little information on the K distribution, the data were compressed by the zeroth temporal moment of breakthrough curves, which is equivalent to the mean travel time under the experimental setting. Only about 2000 forward simulations in total were required to obtain the best estimate with corresponding estimation uncertainty, and the estimated K field captured key patterns of the original packing design, showing the efficiency and effectiveness of the proposed method.