Iterative construction of Gaussian process surrogate models for Bayesian inference
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For this work, we use density functional theory to investigate the interactions of cerium, americium, and curium cations with crown ethers. Our calculations reveal that the modeled structure of cerium integrated within the crown ether is in good agreement with experimental data, with the negative binding energy indicating that capturing the cerium nitrates is thermodynamically favorable. Our results demonstrate that crown ethers can also bind americium and curium, providing insights into the potential applications of crown ether in radionuclide sequestration. Finally, we explore the impact of the skeleton modification of different crown ethers through by substitution of nitrogen atoms in the core of the crown ether for oxygen atoms and find that this structural modification significantly increases the radionuclide binding energies. These findings provide insights on the potential for the use of organic linkers such as crown ethers to address the urgent needs in radionuclide sequestration, separation and sensing.
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The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student t process regression. We apply Student t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.
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We present an accelerated nudged elastic band (NEB) study of phenol direct deoxygenation (DDO) on Fe-based bimetallic surfaces using a recently developed Gaussian process regression (GPR) calculator. Our test calculations demonstrate that the GPR calculator achieves up to 3 times speedup compared to conventional density functional theory calculations while maintaining high accuracy, with energy barrier errors below 0.015 eV. Using GPR-NEB, we systematically examine the DDO mechanism on pure Fe(110) and surfaces modified with Co and Ni in both top and subsurface layers. Our results show that subsurface Co and Ni substitutions preserve favorable thermodynamics and kinetics for both C–O bond cleavage and C–H bond formation, comparable to those on the pure Fe(110) surface. In contrast, top-layer substitutions generally increase the C–O bond cleavage barrier, render the step endothermic, and result in significantly higher reverse reaction rates, making DDO unfavorable on these surfaces. This work demonstrates the effectiveness of GRR-accelerated transition state searches for complex surface reactions and provides insights into rational design of bimetallic catalysts for selective deoxygenation.
Electrocoagulation has attracted significant attention as an alternative to conventional chemical coagulation because it is capable of removing a wide range of contaminants and has several potential advantages. In contrast to most electrocoagulation research that has been performed with nonporous electrodes, in this study, we demonstrate energy-efficient iron electrocoagulation using porous electrodes. In batch operation, investigation of the external pore structures through optical microscopy suggested that a low porosity electrode with sparse connection between pores may lead to mechanical failure of the pore network during electrolysis, whereas a high porosity electrode is vulnerable to pore clogging. Electrodes with intermediate porosity, instead, only suffered a moderate surface deposition, leading to electrical energy savings of 21% and 36% in terms of electrocoagulant delivery and unit log virus reduction, respectively. Neutron computed tomography revealed the critical role of electrode porosity in utilizing the electrode’s internal surface for electrodissolution and effective delivery of electrocoagulant to the bulk. Energy savings of up to 88% in short-term operation were obtained with porous electrodes in a continuous flow-through system. Further investigation on the impact of current density and porosity in long-term operation is desired as well as the capital cost of porous electrodes.
The precise preparation of monodisperse nanomaterials is among the most fundamental tasks in inorganic synthesis and materials science. Achieving this goal by galvanic exchange is hardly predictable, and often results in major structural changes and polydisperse mixtures. Here, taking advantage of the enhanced stability imparted by ambiphilic carbenes, we report and rationalize the absolute templating — the complete exchange of metals in a template — of group 11 clusters across the entire coinage metal family by means of galvanic exchange. We further delineate that these species provide a molecular model for better understanding the reduction of CO 2 at M(111) coinage metal surfaces.
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Ground heat flux (G 0 ) is a key component of the land-surface energy balance of high-latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G 0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G 0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G 0 data (measured or modeled) for snow-free period as a reference. When observed G 0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state-of-the-art uncertainty quantification methods, the developed G 0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.
Performance assessment in deep geological nuclear waste repository systems necessitates an extended knowledge of the pore water chemical conditions prevailing in host-rock formations. In the last two decades, important progress has been made in the experimental characterization and thermodynamic modeling of pore water speciation, but the influence of experimental artifacts and uncertainties of thermodynamic input parameters are seldom evaluated. In this respect, we conducted an uncertainty propagation study in a reference geochemical model describing the pore water chemistry of the Callovian-Oxfordian clay formation. Nineteen model input parameters were perturbed, including those associated to experimental characterization (leached anions, exchanged cations, cation exchange selectivity coefficients) and those associated to generic thermodynamic databases (solubilities). A set of 13 quantities of interest were studied by the use of polynomial chaos expansions built non-intrusively with a least-squares forward stepwise regression approach. Training and validation sets of simulations were carried out using the geochemical speciation code PHREEQC. The statistical results explored the marginal distribution of each quantity of interest, their bivariate correlations as well as their global sensitivity indices. The influence of the assumed distributions for input parameters uncertainties was evaluated by considering two parametric domain sizes.
Theranostics, a combined approach of diagnostics and therapeutics, often employs F-block therapeutic radionuclides including 225 Ac, 177 Lu, and 161 Tb. While there is a lack of F-block PET imaging radionuclides, the in vivo PET generator pair 140 Nd/ 140 Pr can act as a theranostic imaging counterpart to the F-block therapeutic radionuclides. In this study, we explored the production and separation of high purity 140 Nd via the 141 Pr(p,2n) 140 Nd reaction route. Monoisotopic 141 Pr targets irradiated with 20 MeV protons for 10 min with 10 µA beam current yielded 21.45 ± 0.82 MBq (580 ± 22 µCi) of 140 Nd. A two-step separation method was developed for the purification of 140 Nd from the 141 Pr target material. Recoveries of 27.4 ± 2.1% 140Nd were obtained upon separation with < 20 ppb of 141 Pr target material in the final product. Radiolabeling of Macropa and DOTA chelators with 140 Nd resulted in [ 140 Nd]Nd-Macropa with a molar activity of 74.0 MBq/µmol (2.0 mCi/µmol) and [ 140 Nd]Nd-DOTA with a molar activity of 70.3 MBq/µmol (1.9 mCi/µmol). An imaging study with a phantom indicated the PET spatial resolution of 140 Nd/ 140 Pr was distinguishable down to 2.4 mm. This study sets the stage for the 140 Nd/ 140 Pr in vivo PET generator to be explored in radiopharmaceutical applications.