Project description:As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosion weight gain prediction model. Eleven classic machine learning algorithms for regression were compared and a ten-fold cross validation method was used to choose the highest accuracy algorithm, which was the extra trees algorithm. Feature selection methods, including the extra trees and Pearson correlation analysis methods, were used to select the most important four factors in corrosion weight gain. As a result, the prediction accuracy of the corrosion weight gain prediction model was 96.8%, which could determine a good prediction of corrosion for uranium and uranium alloys.
Project description:Perovskite oxides are extensively utilized in energy storage and conversion. However, they are conventionally screened via time-consuming and cost-intensive experimental approaches and density functional theory. Herein, interpretable machine learning is applied to identify perovskite oxides from virtual perovskite-type combinations by constructing classification and regression models to predict their thermodynamic stability and energy above the convex hull (Eh), respectively, and interpreting the models using SHapley Additive exPlanations. The highest occupied molecular orbital energy and the elastic modulus of the B-site elements of perovskite oxides are the top two features for stability prediction, whereas the Stability Label and features involving the elastic modulus and ionic radius are crucial for Eh regression. A classification model, which displays an accuracy of 0.919, precision of 0.937, F1-score of 0.932, and recall of 0.935, screens 682 143 stable perovskite oxides from 1 126 668 virtual perovskite-type combinations. The Eh values of the predicted stable perovskites are forecasted by a regression model with a coefficient of determination of 0.916, and root mean square error of 24.2 meV atom-1. Good agreement is observed between the regression model predicted and density functional theory-calculated Eh values.
Project description:Uranium oxide microparticles ingestion is one of the potential sources of internal radiation doses to the humans at accidental or undesirable releases of radioactive materials. It is important to predict the obtained dose and possible biological effect of these microparticles by studying uranium oxides transformations in case of their ingestion or inhalation. Using a combination of methods, a complex examination of structural changes of uranium oxides in the range from UO2 to U4O9, U3O8 and UO3 as well as before and after exposure of uranium oxides in simulated biological fluids: gastro-intestinal and lung-was carried out. Oxides were thoroughly characterized by Raman and XAFS spectroscopy. It was determined that the duration of expose has more influence on all oxides transformations. The greatest changes occurred in U4O9, that transformed into U4O9-y. UO2.05 and U3O8 structures became more ordered and UO3 did not undergo significant transformation.
Project description:Spent nuclear fuel contains both uranium (U) and high yield fission products, including strontium-90 (90Sr), a key radioactive contaminant at nuclear facilities. Both U and 90Sr will be present where spent nuclear fuel has been processed, including in storage ponds and tanks. However, the interactions between Sr and U phases under ambient conditions are not well understood. Over a pH range of 4-14, we investigate Sr sorption behavior in contact with two nuclear fuel cycle relevant U(IV) phases: nano-uraninite (UO2) and U(IV)-silicate nanoparticles. Nano-UO2 is a product of the anaerobic corrosion of metallic uranium fuel, and UO2 is also the predominant form of U in ceramic fuels. U(IV)-silicates form stable colloids under the neutral to alkaline pH conditions highly relevant to nuclear fuel storage ponds and geodisposal scenarios. In sorption experiments, Sr had the highest affinity for UO2, although significant Sr sorption also occurred to U(IV)-silicate phases at pH ≥ 6. Extended X-ray absorption fine structure (EXAFS) spectroscopy, transmission electron microscopy, and desorption data for the UO2 system suggested that Sr interacted with UO2 via a near surface, highly coordinated complex at pH ≥ 10. EXAFS measurements for the U(IV)-silicate samples showed outer-sphere Sr sorption dominated at acidic and near-neutral pH with intrinsic Sr-silicates forming at pH ≥ 12. These complex interactions of Sr with important U(IV) phases highlight a largely unrecognized control on 90Sr mobility in environments of relevance to spent nuclear fuel management and storage.
Project description:Discovering new stable materials with large dielectric permittivity is important for future energy storage and electronics applications. Theoretical and computational approaches help design new materials by elucidating microscopic mechanisms and establishing structure-property relations. Ab initio methods can be used to reliably predict the dielectric response, but for fast materials screening, machine learning (ML) approaches, which can directly infer properties from the structural information, are needed. Here, random forest and graph convolutional neural network models are trained and tested to predict the dielectric constant from the structural information. We create a database of the dielectric properties of oxides and design, train, and test the two ML models. Both approaches show similar performance and can successfully predict response based on the structure. The analysis of the feature importance allows identification of local geometric features leading to the high dielectric permittivity of the crystal. Dimensionality reduction and clustering further confirms the relevance of descriptors and compositional features for obtaining high dielectric permittivity.
Project description:The molecule of glutaroimidedioxime, a cyclic imidedioxime moiety that can form during the synthesis of the poly(amidoxime)sorbent and is reputedly responsible for the extraction of uranium from seawater. Complexation of manganese (II) with glutarimidedioxime in aqueous solutions was investigated with potentiometry, calorimetry, ESI-mass spectrometry, electrochemical measurements and quantum chemical calculations. Results show that complexation reactions of manganese with glutarimidedioxime are both enthalpy and entropy driven processes, implying that the sorption of manganese on the glutarimidedioxime-functionalized sorbent would be enhanced at higher temperatures. Complex formation of manganese with glutarimidedioxime can assist redox of Mn(II/III). There are about ~15% of equilibrium manganese complex with the ligand in seawater pH(8.3), indicating that manganese could compete to some degree with uranium for sorption sites.
Project description:We use a recently developed plasma-flow reactor to experimentally investigate the formation of oxide nanoparticles from gas phase metal atoms during oxidation, homogeneous nucleation, condensation, and agglomeration processes. Gas phase uranium, aluminum, and iron atoms were cooled from 5000 K to 1000 K over short-time scales (∆t < 30 ms) at atmospheric pressures in the presence of excess oxygen. In-situ emission spectroscopy is used to measure the variation in monoxide/atomic emission intensity ratios as a function of temperature and oxygen fugacity. Condensed oxide nanoparticles are collected inside the reactor for ex-situ analyses using scanning and transmission electron microscopy (SEM, TEM) to determine their structural compositions and sizes. A chemical kinetics model is also developed to describe the gas phase reactions of iron and aluminum metals. The resulting sizes and forms of the crystalline nanoparticles (FeO-wustite, eta-Al2O3, UO2, and alpha-UO3) depend on the thermodynamic properties, kinetically-limited gas phase chemical reactions, and local redox conditions. This work shows the nucleation and growth of metal oxide particles in rapidly-cooling gas is closely coupled to the kinetically-controlled chemical pathways for vapor-phase oxide formation.
Project description:Designing and fabricating nanocomposite magnetic sorbents (with more accessible active sites for achieving high sorption capacities, selectivity and rapid kinetics) has become an impending challenge in the removal of radionuclides. Two core-shell multifunctional magnetic-nanocomposites have been prepared suitably to be used as sorbents using facile two-step processes. In the first step, after synthesis of parent PGMA microparticles (by a dispersion polymerization method), the grafting of aminoalkylcarboxylate and aminoalkylphosphonic ligands (via an intermediary amination step of PGMA) allows increasing sorption capacities due to the specific reactivity of carboxylate and phosphonate groups, giving iminodiacetate (IDA-PGMA) and iminodiphosphonate (IDP-PGMA), respectively. In the second step, functionalized-PGMA was ball-milled with pre-formed magnetic nanoparticles using high-energy planetary milling, resulting in a magnetic nanocomposite structure (M-IDA-PGMA and M-IDP-PGMA). These sorbents were characterized by elemental analysis, FTIR, XRD, pHZPC, TEM, and VSM. The magnetic nanocomposite sizes were around 10.0 nm. The super paramagnetic properties of the hybrid materials make their solid/liquid separation quite easy using an external magnetic field. These materials were investigated for uranium sorption. Optimum pH was found to be close to 4.0; the maximum monolayer chemisorption capacities reach 122.9 and 147.0 mg g-1 for M-IDA- and M-IDP-PGMA, respectively. The adsorption activation energies were calculated from the Arrhenius equation. The sorption is spontaneous, endothermic and controlled by entropic change. Sorbents were tested for U(vi) removal from a real acidic leachate of ores collected in the El-Sella mining area. Finally, sodium bicarbonate revealed efficiency for uranium desorption and the re-use of sorbents was successfully tested for five cycles.
Project description:Waste (packaging plastic and industrial water) accumulation is one of the great global challenges over the world. Combining waste recycling science and water treatment knowledge are fascinating as applied sciences add value to the safe disposal of waste plastic packaging materials and wastewater. Active carbons (ACs) are prepared from polyethylene terephthalate (PET) at two pyrolysis temperatures (i.e. 450 and 500 °C) and compressed in well-defined designed molds to form cylinder shapes as applied in industry. Particle size (817 and 1074 nm), zeta potential (- 7.17 and - 25.6 mV), surface area (544 and 632 m2/g), and topography of prepared ACs were investigated and discussed. Zeta potential exhibited nice dispersion in accordance to charge value and surficial SEM images prove space hole filling with adsorbed materials after treatment. The prepared activated carbon sorbents have been applied for the removal of radioactive elements from wastewater. The displayed data declare that both sorbents have the same sorption performance, whereas the uranium sorption process using both sorbents is obeyed to pseudo-second-order kinetic model and Langmuir isotherm model. Nevertheless, it is worth noting that the prepared AC at a pyrolysis temperature of 500 °C exhibits higher sorption capacity (38.9 mg g-1) than that prepared at lower temperature, i.e., 450 °C (36.2 mg g-1) which indicates that the increase in pyrolysis temperature improves the sorption characteristics of the yield-activated carbon.
Project description:The effect of competing ions on the sorption behaviour of uranium onto carboxyl-functionalised graphene oxide (COOH-GO) were studied in batch experiments in comparison to graphene oxide (GO) and graphite. The effect of increasing the abundance of select chemical functional groups, such as carboxyl groups, on the selectivity of U sorption was investigated. In the course of the study, COOH-GO demonstrated superior performance as a sorbent material for the selective removal of uranyl ions from aqueous solution with a distribution coefficient of 3.72 ± 0.19 × 103 mL g-1 in comparison to 3.97 ± 0.5 × 102 and 2.68 ± 0.2 × 102 mL g-1 for GO and graphite, respectively.