Performance Characteristics of Mass Spectrometry-Based Analytical Procedures for Quantitation of Nitrosamines in Pharmaceuticals: Insights from an Inter-laboratory Study
Not Available
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
Not Available
Subglacial drainage models, often motivated by the relationship between hydrology and ice flow, sensitively depend on numerous unconstrained parameters. We explore using borehole water-pressure time series to calibrate the uncertain parameters of a popular subglacial drainage model, taking a Bayesian perspective to quantify the uncertainty in parameter estimates and in the calibrated model predictions. To reduce the computation time associated with Markov Chain Monte Carlo sampling, we construct a fast Gaussian process emulator to stand in for the subglacial drainage model. We first carry out a calibration experiment using synthetic observations consisting of model simulations with hidden parameter values as a demonstration of the method. Using real borehole water pressures measured in western Greenland, we find meaningful constraints on four of the eight model parameters and a factor-of-three reduction in uncertainty of the calibrated model predictions. These experiments illustrate Gaussian process-based Bayesian inference as a useful tool for calibration and uncertainty quantification of complex glaciological models using field data. However, significant differences between the calibrated model and the borehole data suggest that structural limitations of the model, rather than poorly constrained parameters or computational cost, remain the most important constraint on subglacial drainage modelling.
Subglacial hydrology models struggle to reproduce seasonal drainage patterns that are consistent with observed subglacial water pressures and surface velocities. We modify the standard sheet-flow parameterization within a coupled sheet–channel subglacial drainage model to smoothly transition between laminar and turbulent flow based on the locally computed Reynolds number in a physically consistent way (the ‘transition’ model). We compare the transition model to standard laminar and turbulent models to assess the role of the sheet-flow parameterization in reconciling observed and modelled water pressures under idealized and realistic forcing. Relative to the turbulent model, the laminar and transition models improve seasonal simulations by increasing winter water pressure and producing a more prominent late-summer water pressure minimum. In contrast to the laminar model, the transition model remains consistent with its own internal assumptions across all flow regimes. Based on the internal consistency of the transition model and its improved performance relative to the standard turbulent model, we recommend its use for transient simulations of subglacial drainage.
Abstract We present a set of peculiar radio sources detected using an unsupervised machine learning method. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope to train a self-organizing map (SOM). The radio maps from three ASKAP surveys, Evolutionary Map of Universe pilot survey (EMU-PS), Deep Investigation of Neutral Gas Origins pilot survey (DINGO), and Survey With ASKAP of GAMA-09 + X-ray (SWAG-X), are used to search for the rarest or unknown radio morphologies. We use an extension of the SOM algorithm that implements rotation and flipping invariance on astronomical sources. The SOM is trained using the images of all ‘complex’ radio sources in the EMU-PS which we define as all sources catalogued as ‘multi-component’. The trained SOM is then used to estimate a similarity score for complex sources in all surveys. We select 0.5% of the sources that are most complex according to the similarity metric and visually examine them to find the rarest radio morphologies. Among these, we find two new odd radio circle (ORC) candidates and five other peculiar morphologies. We discuss multiwavelength properties and the optical/infrared counterparts of selected peculiar sources. In addition, we present examples of conventional radio morphologies including: diffuse emission from galaxy clusters, and resolved, bent-tailed, and FR-I and FR-II type radio galaxies. We discuss the overdense environment that may be the reason behind the circular shape of ORC candidates.
Nonribosomal peptide synthetases (NRPS) biosynthesize numerous natural products with therapeutic, agricultural, and industrial significance. Reliably altering substrate selection in these enzymes has been a longstanding goal, as this would enable the production of tailor-made peptides with desired activities. In this study, the NRPS EntF and the associated biosynthesis of the siderophore enterobactin (ENT) were used as a model system to interrogate substrate selection by an adenylation (A) domain. We employed a directed evolution pipeline that harnesses an in vivo genetic selection for siderophore production to alter A domain substrate selection. Surprisingly, this led to the formation of a new, physiologically active catechol siderophore in Escherichia coli. We characterized the enzyme variants in vitro and demonstrated transferability of our findings to the well-studied TycC and GrsB NRPSs. Furthermore, this work identifies critical binding pocket residues that allow for altered substrate selection in our model system and expands upon our understanding of iron acquisition in E. coli.
Not Available
The search for new wide-band-gap materials is intensifying to satisfy the need for more advanced and energy-efficient power electronic devices. Ga 2 O 3 has emerged as an alternative to SiC and GaN, sparking a renewed interest in its fundamental properties beyond the main β-phase. Here, three polymorphs of Ga 2 O 3 , α, β, and ε, are investigated using X-ray diffraction, X-ray photoelectron and absorption spectroscopy, and ab initio theoretical approaches to gain insights into their structure–electronic structure relationships. Valence and conduction electronic structure as well as semicore and core states are probed, providing a complete picture of the influence of local coordination environments on the electronic structure. State-of-the-art electronic structure theory, including all-electron density functional theory and many-body perturbation theory, provides detailed understanding of the spectroscopic results. The calculated spectra provide very accurate descriptions of all experimental spectra and additionally illuminate the origin of observed spectral features. This paper provides a strong basis for the exploration of the Ga 2 O 3 polymorphs as materials at the heart of future electronic device generations.
Multivalent-ion batteries have attracted growing attention due to their high theoretical energy density that potentially outperforms Li-ion batteries. One of the critical challenges of realizing a multivalent-ion battery is the strong polarization that results in the sluggish intercalation of ions in the host lattice, which motivates a fundamental understanding of multivalent-ion dynamics in solid-state materials. In this contribution, we investigate the diffusion mechanisms of divalent ions in a novel Mg anode coating, BiOCl, using first-principles informed learning-on-the-fly molecular dynamics. Based on nanosecond-scale dynamics observations, we gained insights into the concerted diffusion mechanism of Mg cation site-to-site hopping facilitated by synchronous anion rotational motion. Furthermore, we compute the Mg-ion diffusion in additional candidate host structures screened from available layered materials space. Here, the results suggest the co-operative divalent cation-anion motion is likely a common phenomenon in layered oxyhalide structures. Our findings provide a new perspective on how to enhance multivalent-ion diffusion in layered materials.
Triple cation, mixed halide perovskite compositions have been reported to be more thermally stable, exhibit fewer phase impurities, and show higher power conversion efficiency and better reproducibility than single cation perovskites. In this work, we explain the formation of Cs0.05FA0.81MA0.14Pb(I0.85Br0.15)3 via a multimodal in situ study combining structural information from synchrotron grazing-incidence wide-angle X-ray scattering (GIWAXS) and optical properties from photoluminescence (PL) spectroscopy with density functional theory calculations (DFT). The focus here is on the effects of the solvent and antisolvent during crystallization. The predominantly used solvents N,N-dimethylformamide (DMF), dimethyl sulfoxide (DMSO), and the antisolvent chlorobenzene (CB) as well as the solvent-antisolvent-precursor interactions are investigated. Given the high elemental complexity and mutual interdependencies between solvent, antisolvent, and perovskite precursors, we found significant differences in the crystallization pathways. DMF-pure precursors show the formation of the DMF-containing intermediate phase and the nucleation of compositionally distinct perovskite phases, while when DMSO is added, only crystalline α- and δ-phases were found. In addition, the presence of DMSO helps the formation of α-perovskite. Coordination energy and bond order (BO) calculations support our experimental findings. Dripping of CB induces nucleation at room temperature, slows the α-phase formation rate, and appears to reduce the nucleation radius. These findings provide novel insights into solvent, antisolvent, and perovskite precursor interactions and their formation pathways. The complexity of interactions between solvents and reagents highlights the importance of understanding these effects to further improve the reproducibility and optimize processing conditions.
Quantum materials are driving a technology revolution in sensing, communication, and computing, while simultaneously testing many core theories of the past century. Materials such as topological insulators, complex oxides, superconductors, quantum dots, color center-hosting semiconductors, and other types of strongly correlated materials can exhibit exotic properties such as edge conductivity, multiferroicity, magnetoresistance, superconductivity, single photon emission, and optical-spin locking. These emergent properties arise and depend strongly on the material’s detailed atomic-scale structure, including atomic defects, dopants, and lattice stacking. In this review, we describe how progress in the field of electron microscopy (EM), including in situ and in operando EM, can accelerate advances in quantum materials and quantum excitations. We begin by describing fundamental EM principles and operation modes. We then discuss various EM methods such as (i) EM spectroscopies, including electron energy loss spectroscopy (EELS), cathodoluminescence (CL), and electron energy gain spectroscopy (EEGS); (ii) four-dimensional scanning transmission electron microscopy (4D-STEM); (iii) dynamic and ultrafast EM (UEM); (iv) complementary ultrafast spectroscopies (UED, XFEL); and (v) atomic electron tomography (AET). We describe how these methods could inform structure–function relations in quantum materials down to the picometer scale and femtosecond time resolution, and how they enable precision positioning of atomic defects and high-resolution manipulation of quantum materials. For each method, we also describe existing limitations to solve open quantum mechanical questions, and how they might be addressed to accelerate progress. Among numerous notable results, our review highlights how EM is enabling identification of the 3D structure of quantum defects; measuring reversible and metastable dynamics of quantum excitations; mapping exciton states and single photon emission; measuring nanoscale thermal transport and coupled excitation dynamics; and measuring the internal electric field and charge density distribution of quantum heterointerfaces- all at the quantum materials’ intrinsic atomic and near atomic-length scale. Finally, we conclude by describing open challenges for the future, including achieving stable sample holders for ultralow temperature (below 10K) atomic-scale spatial resolution, stable spectrometers that enable meV energy resolution, and high-resolution, dynamic mapping of magnetic and spin fields. With atomic manipulation and ultrafast characterization enabled by EM, quantum materials will be poised to integrate into many of the sustainable and energy-efficient technologies needed for the 21st century.
The chemistry of technetium (t 1/2 ( 99 Tc) = 2.11 × 10 5 years) is of particular importance in the context of nuclear waste disposal and historic contaminated sites. Polycarboxylate ligands may be present in some sites and are potentially capable of strong complexing interactions, thus increasing the solubility and mobility of 99 Tc under environmentally relevant conditions. This work aimed to determine the impact of five organic complexing ligands [L = oxalate, phthalate, citrate, nitrilotriacetate (NTA), and ethylenediaminetetraacetate (EDTA)] under anoxic, alkaline conditions (pH ≈ 9–13) on the solubility of technetium. X-ray absorption spectroscopy confirmed that TcO 2 (am,hyd) remained the solubility-controlling solid phase in undersaturation solubility experiments. Ligands with maximum coordination numbers (CN) ≥ 3 (EDTA, NTA, and citrate) exhibited an increase in solubility from pH 9 to 11, while ligands with CN ≤ 2 (oxalate and phthalate) at all investigated pH and CN ≥ 3 at pH ≈ 13 were outcompeted by hydrolysis reactions. Though most available thermodynamic values were determined under acidic conditions, these models satisfactorily explained high-pH undersaturation solubility of technetium for citrate and NTA, whereas experimental data for Tc(IV)–EDTA were highly overestimated. Finally, this work illustrates the predominance of hydrolysis under hyperalkaline conditions and provides experimental support for existing thermodynamic models of Tc–L except Tc–EDTA, which requires further research regarding aqueous speciation and solubility.
Marine dissolved organic matter (DOM) contains a complex mixture of small molecules that eludes rapid biological degradation. Spatial and temporal variations in the abundance of DOM reflect the existence of fractions that are removed from the ocean over different time scales, ranging from seconds to millennia. However, it remains unknown whether the intrinsic chemical properties of these organic components relate to their persistence. Here, we elucidate and compare the molecular compositions of distinct DOM fractions with different lability along a water column in the North Atlantic Gyre. Our analysis utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry at 21 T coupled to liquid chromatography and a novel data pipeline developed in CoreMS that generates molecular formula assignments and metrics of isomeric complexity. Clustering analysis binned 14 857 distinct molecular components into groups that correspond to the depth distribution of semilabile, semirefractory, and refractory fractions of DOM. The more labile fractions were concentrated near the ocean surface and contained more aliphatic, hydrophobic, and reduced molecules than the refractory fraction, which occurred uniformly throughout the water column. These findings suggest that processes that selectively remove hydrophobic compounds, such as aggregation and particle sorption, contribute to variable removal rates of marine DOM.
Geologic hydrogen production and underground storage are increasingly important for meeting rising energy demands while providing clean-combustion advantages. However, hydrogen’s high diffusivity and propensity for leakage through porous media necessitate direct evaluation of its transport behavior in subsurface materials. Whereas X-ray microcomputed tomography (μCT) studies often employ contrast agents or surrogate gases, this study leverages neutron transmission radiography/CT to observe hydrogen migration in situ. This work represents the first demonstration of real-time neutron radiography of hydrogen migration in reservoir and caprock lithologies. Cylindrical cores of Indiana limestone, Amherst Gray sandstone, and Tumey shale were subjected to constant-pressure hydrogen charging and scanned in real time using high-resolution neutron radiography. Results indicate immediate hydrogen infiltration in sandstone and limestone, with homogeneous distribution detected throughout their pore structure. In contrast, hydrogen remained largely absent from fine-grained shale under the same pressure, except in an apparently localized fracture zone, where neutron signatures confirmed the presence of hydrogen. Subsequent neutron CT of the sandstone sample, using image subtraction against an uncharged reference, corroborated hydrogen distribution patterns. Even under lowpressure, single-phase conditions, distinct neutron imaging signatures of hydrogen were achieved. These preliminary findings underscore the potential of neutron imaging for advancing subsurface hydrogen migration research.
Nonstoichiometric lead oxides play a key role in the formation and cycling of the positive electrodes in a lead acid battery. These phases have been linked to the underutilization of the positive active material but also play a key role in the battery’s cycle life, providing inter-particle adhesion and the connection to the underlying lead grid. Similar phases have previously been identified by mass loss or color change during thermal annealing of PbO2 to PbO, suggesting that at least two intermediate PbOx phases exist. Using multiple in situ analysis techniques (powder diffraction, x-ray absorption, x-ray photoelectron spectroscopy) and ex situ nuclear magnetic resonance measurements, the structural conversion and changes in the lead oxidation states were identified during this process. Isolation of the PbOx phases enabled confirmation of Pb3O5 and Pb2O3 by diffraction and the first 207Pb NMR measurement of these intermediates.
In actinide chemistry, the formal number of open-shell 5f electrons, n open , is a well-defined quantity with an integer value. The effective 5f occupation, n f , additionally takes donation and back-donation into account, generally has a non-integer value, and has varying numerical definitions. The present study explores the important distinction between n f and n open in actinide chemistry with the example of PuO2, by using electronic structure methods with a relativistic Hamiltonian in combination with experimental Pu M 5 -edge high-energy-resolution X-ray absorption and emission spectroscopic data. The total donation to the metal in PuO 2 is between 3.1 and 2.4 electrons, depending on the type of calculation, most of which is to the Pu 6d and 5f shells. The donation into 5f is sensitive to the approximations in the electronic structure model but likely amounts to 1.6/0.8 electrons when the diffuse regions of the 5f shell are included/excluded. Valence band resonant inelastic X-ray scattering experiments demonstrate that Pu 5f electron density is present in the valence band; thus, there is a clear experimental signature of covalent bonding in PuO 2 . Pu M 5 -edge and M 3 -edge high-energy-resolution X-ray absorption near-edge structures for Pu 3+ and Pu 4+ in an aqueous solution are compared to PuO 2 , showing that Pu in PuO 2 has an nf closer to Pu 4+ (aq).
Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.