Coupling of the Biogeochemical Cycles of Uranium and Manganese: Implications for the Fate and Transport of Uranium in Subsurface Environments

Organic electron donor stimulated microbial reduction of U(VI) to U(IV) has been proposed as a strategy for the in situ immobilization of uranium contamination in the subsurface. The success of the bioremediation of uranium relies on the stability of the reduced U(IV) species: e.g., UO2) with respect to reoxidation and/or remobilization under groundwater conditions. Manganese is present at appreciable concentrations at several uranium-contaminated sites, and the redox cycling of manganese may significantly impact uranium\u27s fate and transport. The biogeochemical coupling of uranium and manganese involves multiple interaction pathways that occur in the aqueous phase as well as at solid-water interfaces. A mechanistic and quantitative understanding of these processes is needed to establish input parameters for reactive transport models and to enable decision-making for remediation strategies. Coupling of the biogeochemical cycles of uranium and manganese involves various interfacial reactions that occur between UO2 and Mn species of various oxidation states: +IV, +III and +II). This study investigated the physical and chemical factors controlling the interactions between uraninite: UO2) and manganese oxide: MnO2), which are both poorly soluble minerals. A multi-chamber reactor with a permeable membrane was used to simulate a barrier for direct contact of the two solids. The results suggested that an effective redox reaction between UO2 and MnO2 requires physical contact. Continuously-stirred tank reactors: CSTRs) were used to evaluate the dissolution rates of UO2. MnO2 dramatically promoted UO2 dissolution, but the degree of promotion leveled off once the MnO2:UO2 ratio exceeded a critical value. The fate of uranium and manganese after the reaction was investigated by chemical extraction and X-ray absorption spectroscopy: XAS). Substantial amounts of U(VI) and Mn(II) were retained on MnO2 surfaces, and the fate of Mn products may involve Mn(III) phases. A conceptual model was proposed to describe the oxidation of UO2 by MnO2, which is potentially applicable to other environmental redox processes involving two poorly soluble minerals. Although MnO2 can oxidize UO2, the U(VI) produced may not be readily released into the aqueous phase due to its strong adsorption to MnO2. This study integrated batch experiments of U(VI) adsorption to synthetic and biogenic MnO2, surface complexation modeling: SCM), and molecular-scale characterization of adsorbed U(VI) with extended X-ray absorption fine structure: EXAFS) spectroscopy. The surface complexation model incorporated the surface complexes that are consistent with EXAFS analysis, and it could successfully simulate adsorption results over a broad range of pH and dissolved inorganic carbon concentrations. The description of bidentate surface complexes, which are widely observed for contaminant adsorption to metal oxides including the U(VI)-MnO2 system, is a subject with considerable confusion in the literature. Consequently, a critical review was prepared that discussed the theoretical and practical aspects of mass action expressions for bidentate surface complexation reactions. Suggestions were provided for handling bidentate reactions and publishing results without ambiguity or confusion. The effects of soluble Mn species: +III and +II oxidation states) on UO2 dissolution were also investigated. Soluble Mn(III) species were recently identified as important intermediates in Mn biogeochemical cycling. This study evaluated the kinetics of oxidative UO2 dissolution by soluble Mn(III) stabilized by pyrophosphate: PP) and desferrioxamine B: DFOB). The Mn(III)-PP complex was a potent oxidant that induced rapid UO2 dissolution at a rate higher than by a comparable concentration of dissolved O2. However, the Mn(III)-DFOB complex was not able to induce oxidative dissolution of UO2. The potency of Mn(III) with respect to oxidizing UO2 was governed by the identity of the ligand and water chemistry parameters that affect the speciation of the Mn(III). The effect of soluble Mn(II) was more complicated than that of non-redox-active divalent cations: e.g., Ca and Zn). Under anoxic conditions, Mn(II) inhibited UO2 dissolution, which may be attributed to both Mn(II) adsorption to the UO2 surface and precipitation of MnCO3, both of which could decrease the exposure of U(IV) surface sites. In contrast to the anoxic conditions, Mn(II) promoted UO2 dissolution under oxidizing condition. The promotional effect was likely due to Mn redox cycling in which oxidized forms of Mn species were: re)generated as oxidants of UO2 that were more potent than O2. The observed effects of soluble Mn(II, III) species on UO2 dissolution highlighted the need to consider Mn redox intermediates in facilitating electron transfer processes in subsurface biogeochemical cycles

Synthesis and Characterization of Nanocomposite Microparticles (nCmP) for the Treatment of Cystic Fibrosis-Related Infections

Purpose: Pulmonary antibiotic delivery is recommended as maintenance therapy for cystic fibrosis (CF) patients who experience chronic infections. However, abnormally thick and sticky mucus present in the respiratory tract of CF patients impairs mucus penetration and limits the efficacy of inhaled antibiotics. To overcome the obstacles of pulmonary antibiotic delivery, we have developed nanocomposite microparticles (nCmP) for the inhalation application of antibiotics in the form of dry powder aerosols. Methods: Azithromycin-loaded and rapamycin-loaded polymeric nanoparticles (NP) were prepared via nanoprecipitation and nCmP were prepared by spray drying and the physicochemical characteristics were evaluated. Results: The nanoparticles were 200 nm in diameter both before loading into and after redispersion from nCmP. The NP exhibited smooth, spherical morphology and the nCmP were corrugated spheres about 1 μm in diameter. Both drugs were successfully encapsulated into the NP and were released in a sustained manner. The NP were successfully loaded into nCmP with favorable encapsulation efficacy. All materials were stable at manufacturing and storage conditions and nCmP were in an amorphous state after spray drying. nCmP demonstrated desirable aerosol dispersion characteristics, allowing them to deposit into the deep lung regions for effective drug delivery. Conclusions: The described nCmP have the potential to overcome mucus-limited pulmonary delivery of antibiotics

Stochastic optimal controls with delay

This thesis investigates stochastic optimal control problems with discrete delay and those with both discrete and exponential moving average delays, using the stochastic maximum principle, together with the methods of conjugate duality and dynamic programming. To obtain the stochastic maximum principle, we first extend the conjugate duality method presented in [2, 44] to study a stochastic convex (primal) problem with discrete delay. An expression for the corresponding dual problem, as well as the necessary and sufficient conditions for optimality of both problems, are derived. The novelty of our work is that, after reformulating a stochastic optimal control problem with delay as a particular convex problem, the conditions for optimality of convex problems lead to the stochastic maximum principle for the control problem. In particular, if the control problem involves both the types of delay and is jump-free, the stochastic maximum principle obtained in this thesis improves those obtained in [29, 30]. Adapting the technique used in [19, Chapter 3] to the stochastic context, we consider a class of stochastic optimal control problems with delay where the value functions are separable, i.e. can be expressed in terms of so-called auxiliary functions. The technique enables us to obtain second-order partial differential equations, satisfied by the auxiliary functions, which we shall call auxiliary HJB equations. Also, the corresponding verification theorem is obtained. If both the types of delay are involved, our auxiliary HJB equations generalize the HJB equations obtained in [22, 23] and our verification theorem improves the stochastic verification theorem there

Probing Surface Spin Interaction Dynamics using Nitrogen-Vacancy Center Quantum Sensors with High-Fidelity State-Selective Transition Control

As a demand from developing nanotechnology and quantum information technology based on mesoscopic material, quantum sensors with better spatial resolution and sensitivity are required. However, few material meets the requirements of nanoscale sensors including stability and small size. Single spin of Nitrogen-Vacancy(NV) centers in diamond crystal is one kind of the ideal quantum sensors for magnetometry that has been investigated in recent years. It provides a potential way to study the dynamics in a mesoscopic system with high sensitivity at room temperature. This thesis proposes a theoretical method to realize spin interaction detection based on NV centers on an atomic force-microscopy(AFM) tip. To realize this method, a robust control on NV centers and target electron spins at zero magnetic field is necessary. A pulse control technique for NV centers is proposed to realize transitions between two degenerate states at zero magnetic field, which is an important part of the sensing method. The key to realizing this transition is a circularly polarized microwave pulse generated by two parallel wires. Combined with optimal control techniques, this pulse can achieve a gate fidelity over 99.95% theoretically

Stochastic optimal controls with delay

This thesis investigates stochastic optimal control problems with discrete delay and those with both discrete and exponential moving average delays, using the stochastic maximum principle, together with the methods of conjugate duality and dynamic programming. To obtain the stochastic maximum principle, we first extend the conjugate duality method presented in [2, 44] to study a stochastic convex (primal) problem with discrete delay. An expression for the corresponding dual problem, as well as the necessary and sufficient conditions for optimality of both problems, are derived. The novelty of our work is that, after reformulating a stochastic optimal control problem with delay as a particular convex problem, the conditions for optimality of convex problems lead to the stochastic maximum principle for the control problem. In particular, if the control problem involves both the types of delay and is jump-free, the stochastic maximum principle obtained in this thesis improves those obtained in [29, 30]. Adapting the technique used in [19, Chapter 3] to the stochastic context, we consider a class of stochastic optimal control problems with delay where the value functions are separable, i.e. can be expressed in terms of so-called auxiliary functions. The technique enables us to obtain second-order partial differential equations, satisfied by the auxiliary functions, which we shall call auxiliary HJB equations. Also, the corresponding verification theorem is obtained. If both the types of delay are involved, our auxiliary HJB equations generalize the HJB equations obtained in [22, 23] and our verification theorem improves the stochastic verification theorem there

Conjugate duality in stochastic controls with delay

This paper uses the method of conjugate duality to investigate a class of stochastic optimal control problems where state systems are described by stochastic differential equations with delay. For this, we first analyse a stochastic convex problem with delay and derive the expression for the corresponding dual problem. This enables us to obtain the relationship between the optimalities for the two problems. Then, by linking stochastic optimal control problems with delay with a particular type of stochastic convex problem, the result for the latter leads to sufficient maximum principles for the former

Nonrigid Object Contact Estimation With Regional Unwrapping Transformer

Acquiring contact patterns between hands and nonrigid objects is a common concern in the vision and robotics community. However, existing learning-based methods focus more on contact with rigid ones from monocular images. When adopting them for nonrigid contact, a major problem is that the existing contact representation is restricted by the geometry of the object. Consequently, contact neighborhoods are stored in an unordered manner and contact features are difficult to align with image cues. At the core of our approach lies a novel hand-object contact representation called RUPs (Region Unwrapping Profiles), which unwrap the roughly estimated hand-object surfaces as multiple high-resolution 2D regional profiles. The region grouping strategy is consistent with the hand kinematic bone division because they are the primitive initiators for a composite contact pattern. Based on this representation, our Regional Unwrapping Transformer (RUFormer) learns the correlation priors across regions from monocular inputs and predicts corresponding contact and deformed transformations. Our experiments demonstrate that the proposed framework can robustly estimate the deformed degrees and deformed transformations, which makes it suitable for both nonrigid and rigid contact.Comment: Accepted by ICCV202

Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning

In contrastive self-supervised learning, the common way to learn discriminative representation is to pull different augmented "views" of the same image closer while pushing all other images further apart, which has been proven to be effective. However, it is unavoidable to construct undesirable views containing different semantic concepts during the augmentation procedure. It would damage the semantic consistency of representation to pull these augmentations closer in the feature space indiscriminately. In this study, we introduce feature-level augmentation and propose a novel semantics-consistent feature search (SCFS) method to mitigate this negative effect. The main idea of SCFS is to adaptively search semantics-consistent features to enhance the contrast between semantics-consistent regions in different augmentations. Thus, the trained model can learn to focus on meaningful object regions, improving the semantic representation ability. Extensive experiments conducted on different datasets and tasks demonstrate that SCFS effectively improves the performance of self-supervised learning and achieves state-of-the-art performance on different downstream tasks