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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Experimental and computational investigation of the bond energy of thorium dicarbonyl cation and theoretical elucidation of its isomerization mechanism to the thermodynamically most stable isomer, thorium oxide ketenylidene cation, OTh + CCO

Collision-induced dissociation (CID) of [Th,2C,2O] + with Xe is performed using a guided ion beam tandem mass spectrometer (GIBMS). The only products observed are ThCO + and Th + by sequential loss of CO ligands. The experimental findings and theoretical calculations support that the structure of [Th,2C,2O] + is the bent homoleptic thorium dicarbonyl cation, Th + (CO) 2 , having quartet spin, which is both thermodynamically and kinetically stable enough in the gas phase to be observed in our GIBMS instrument. Analysis of the kinetic energy-dependent cross sections for this CID reaction yields the first experimental determination of the bond dissociation energy (BDE) of (CO)Th + –CO at 0 K as 1.05 ± 0.09 eV. A theoretical BDE calculated at the CCSD(T) level with cc-pVXZ (X = T and Q) basis sets and a complete basis set (CBS) extrapolation is in very good agreement with the experimental result. Although the doublet spin bent thorium oxide ketenylidene cation, OTh + CCO, is calculated to be the most thermodynamically stable structure, it is not observed in our experiment where [Th,2C,2O] + is formed by association of Th + and CO in a direct current discharge flow tube (DC/FT) ion source. Potential energy profiles of both quartet and doublet spin are constructed to elucidate the isomerization mechanism of Th + (CO) 2 to OTh + CCO. The failure to observe OTh + CCO is attributed to a barrier associated with C–C bond formation, which makes OTh + CCO kinetically inaccessible under our experimental conditions. Furthermore, chemical bonding patterns in low-lying states of linear and bent Th + (CO) 2 and OTh + CCO isomers are also investigated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗

DOE OSTI2023

Validation of the Cossee–Arlman mechanism for propylene oligomerization on Ni/UiO-66

Steady state rate expressions can be derived to distinguish the Cossee–Arlman and metallacycle mechanisms postulated for propylene oligomerization on nickel-based catalysts based on product selectivities, where product selectivities for the former are a function of olefin pressure because sequential coordination and insertion steps lead to independent mechanistic pathways for different hexene isomers. In contrast, the metallacycle mechanism presents pressure-independent product selectivities due to successive coordination prior to the kinetically relevant steps in each mechanism. In this work, steady state propylene oligomerization rates and selectivities were measured in the absence of an activator on nickel functionalized UiO-66 metal organic framework (MOF), Ni/UiO-66, to validate the Cossee–Arlman mechanism for light olefin oligomerization. In situ NO titrations reveal that ~5% of nickel sites were active during the reaction, and thus, not all nickel sites are relevant for catalysis. Propylene dimerization was first order in propylene pressure from 5 to 500 kPa with an apparent activation energy of ~20 kJ mol -1 from 453 to 493 K. Calculated apparent activation energies with density functional theory (DFT) calculations on cluster models of Ni/UiO-66 are in agreement with experiment to corroborate the Cossee–Arlman mechanism. Selectivities of hexene products and the ratio of hexene product selectivities on Ni/UiO-66 are in accordance with selectivity expressions derived from the Cossee–Arlman mechanism. In conclusion, analysis of product selectivities can be used more extensively to demarcate the Cossee–Arlman and metallacycle mechanisms for olefin oligomerization on metal-based catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Burns, Kory (ORCID:0000000158019909)↗

DOE OSTI2024

Microgrid Service Restoration Incorporating Unmonitored Automatic Voltage Controllers and Net Metered Loads

Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2020

Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems with Application of an Ironmaking Blast Furnace

In a world where the increasing complexity of modern industrial processes brings difficulties for accurate mathematical modeling, taking advantage of data has become an efficient solution to complex dynamic process modeling issue. In this paper, we develop a novel robust online sequential version of random vector functional-link networks (RVFLNs) for data-driven modeling of dynamic time-varying system and applied it in a blast furnace (BF) ironmaking process. First, to overcome the time-varying dynamics of process and to enable the RVFLNs to learn online with avoiding data saturation, an improved online sequential version of RVFLNs (OS-RFVLNs) is first presented by online sequential learning with forgetting factor. This improved OS-RVFLNs algorithm is not only suitable for the real-time and large data transfer situation, but also can adjust the sensitivity of the algorithm to different samples with the help of the introduced forgetting factor. Second, since the output weights of the improved OS-RVFLNs as well as other RVFLNs algorithms are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with various outliers. To solve this problem, a Cauchy distribution weighted M-estimator is introduced to improve the robustness of the improved OS- RVFLNs. For this proposed robust OS-RVFLNs (R-OS- RVFLNs), since the weights of different outlier data are properly determined by the Cauchy distribution function, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Experiments using actual industrial data of BF ironmaking process and comparative studies have demonstrated that the proposed method produces a better estimation accuracy and stronger robustness than other methods.

Blast furnace (BF), Modelling, Dynamic systems↗

NASA NTRS2009

High-Grading Lunar Samples

Astronauts on long-duration lunar missions will need the capability to high-grade their samples to select the highest value samples for transport to Earth and to leave others on the Moon. We are supporting studies to define the necessary and sufficient measurements and techniques for high-grading samples at a lunar outpost. A glovebox, dedicated to testing instruments and techniques for high-grading samples, is in operation at the JSC Lunar Experiment Laboratory. A reference suite of lunar rocks and soils, spanning the full compositional range found in the Apollo collection, is available for testing in this laboratory. Thin sections of these samples are available for direct comparison. The Lunar Sample Compendium, on-line at http://www-curator.jsc.nasa.gov/lunar/compendium.cfm, summarizes previous analyses of these samples. The laboratory, sample suite, and Compendium are available to the lunar research and exploration community. In the first test of possible instruments for lunar sample high-grading, we imaged 18 lunar rocks and four soils from the reference suite using the Multispectral Microscopic Imager (MMI) developed by Arizona State University and JPL (see Farmer et. al. abstract). The MMI is a fixed-focus digital imaging system with a resolution of 62.5 microns/pixel, a field size of 40 x 32 mm, and a depth-of-field of approximately 5 mm. Samples are illuminated sequentially by 21 light emitting diodes in discrete wavelengths spanning the visible to shortwave infrared. Measurements of reflectance standards and background allow calibration to absolute reflectance. ENVI-based software is used to produce spectra for specific minerals as well as multi-spectral images of rock textures.

Allen, Carlton↗

NASA NTRS2002

Development of Biocompatibility Procedures for Assessment of Plant Growth in Ground Test Hardware for the EMCS

The European Modular Cultivation System (EMCS) is an European Space Agency-developed facility designed to support plant research in microgravity on the IS NASA is responsible for providing US specific hardware to use within the EMCS. In preparation for flight, research will be developed and tested at Ames Research Center in the EMCS ground test hardware, the Experiment Reference Module (ERM) In order to determine the acceptability of the ERM for such purposes, biocompatibility tests will be performed to determine that the hardware functions as intended and successfully supports the' growth of plants. In this report, we describe the development of procedures and the collection of baseline data against which to compare ERM function, e.g. biocompatibility testing. A simple and robust system was developed to grow whole Arabidopsis thaliana plants within the confined volumes characteristic of spaceflight hardware. Our system for growing plants eliminated the necessity of a water/nutrient delivery system and allowed for quantifiable assessment of individual plants, as well as entire population dynamics. To insure uniform germination, seeds were started in small straw segments and transplanted into modified scintillation vials. Seedlings were selected prior to transplantation to decrease genetic variability. Plants were grown for a total of 24 days in standard laboratory plant growth chambers under controlled conditions. Sequential digital still images were taken on a daily basis. Analysis of these images allowed for the quantification of even minute environmental effect, on growth dynamics whole plants. The data collected provide reliable growth curves against which to compare plants grown in the ERM.

Bowman, R. N.↗

DOE OSTI2024

Understanding Process–Structure Relationships during Lamination of Halide Perovskite Interfaces

Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process–structure–property relationships. The lamination temperature is found to be the key parameter controlling these properties. Furthermore, a temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.

14 SOLAR ENERGY↗

NASA NTRS2012

Development of Two Color Fluorescent Imager and Integrated Fluidic System for Nanosatellite Biology Applications

Nanosatellites offer frequent, low-cost space access as secondary payloads on launches of larger conventional satellites. We summarize the payload science and technology of the Microsatellite in-situ Space Technologies (MisST) nanosatellite for conducting automated biological experiments. The payload (two fused 10-cm cubes) includes 1) an integrated fluidics system that maintains organism viability and supports growth and 2) a fixed-focus imager with fluorescence and scattered-light imaging capabilities. The payload monitors temperature, pressure and relative humidity, and actively controls temperature. C. elegans (nematode, 50 m diameter x 1 mm long) was selected as a model organism due to previous space science experience, its completely sequenced genome, size, hardiness, and the variety of strains available. Three strains were chosen: two green GFP-tagged strains and one red tdTomato-tagged strain that label intestinal, nerve, and pharyngeal cells, respectively. The integrated fluidics system includes bioanalytical and reservoir modules. The former consists of four 150 L culture wells and a 4x5 mm imaging zone the latter includes two 8 mL fluid reservoirs for reagent and waste storage. The fluidic system is fabricated using multilayer polymer rapid prototyping: laser cutting, precision machining, die cutting, and pressure-sensitive adhesives it also includes eight solenoid-operated valves and one mini peristaltic pump. Young larval-state (L2) nematodes are loaded in C. elegans Maintenance Media (CeMM) in the bioanalytical module during pre-launch assembly. By the time orbit is established, the worms have grown to sufficient density to be imaged and are fed fresh CeMM. The strains are pumped sequentially into the imaging area, imaged, then pumped into waste. Reagent storage utilizes polymer bags under slight pressure to prevent bubble formation in wells or channels. The optical system images green and red fluorescence bands by excitation with blue (473 nm peak) and amber (587 nm peak) LEDs it achieves 8 m lateral resolution using a CMOS imaging chip (as configured for serial data speeds) or 4 m resolution using USB imaging chips. The imager consists of a modified commercial off-the-shelf CMOS chip camera, amber, blue and white LEDs, as well as a relay lens and dual-band filters to obviate moving parts while supporting both fluorescence wavelengths.

Wu, Diana Terri↗

DOE OSTI2021

Geomechanical and flow implications with continued bioconversion of coal to methane: Experiments and modeling

Microbial conversion of coal to methane is a promising technology during transition of coal-based energy from conventional coal mining to natural gas recovery from coal. Significant research advances have been made towards engineering ideal microbial communities and nutrients for bio-stimulation of coal. However, actual field applications require geomechanical and flow behavior characterization of coal during the bioconversion process as well as gas production over the life of the created “biogenic gas reservoirs”. This work presents the results of an experimental investigation to estimate gas/methane production using bioconversion of coal and analyses of variations in bulk modulus, strain and permeability. Bulk modulus of coal demonstrated time-dependent behavior with continued bioconversion of solid coal. The phenomenon was modeled using mass balancing in a closed environment and the logistic equation based biogenic gas production with time. The modeled gas production and changes in modulus of coal showed excellent agreement with the corresponding experimental results. Next, numerical simulation of biogenic conversion of coal in a constant stress in-situ condition, replicating the Huff ‘n Puff method under field conditions, was carried out. The results showed that bioconversion can lead to decrease in effective stress, increase in permeability and decrease in the modulus of coal with time. Repeated recharge of coal with nutrients and recovering the produced gas, that is, sequential cycles of Huff n’ Puff, showed further increase in permeability and decrease in coal strength, potentially leading to coal failure in-situ, further increasing the permeability, thus enhancing the prospect of field application of the technology.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Computational and Experimental Mechanistic Insights into the Ethanol-to-Butanol Upgrading Reaction over MgO

The mechanism of ethanol upgrading to higher products is still under debate, especially regarding intermediate species and hydrogenation and dehydrogenation steps. In this work, we conducted a combined theoretical and experimental approach to contribute to this discussion. For such, detailed electronic structure density functional theory calculations (aiming at probing density of states, infrared spectra, geometric parameters, charge densities, and reaction energetics) and diffuse reflectance infrared Fourier transform spectroscopy experiments were carried out revealing the relevance of an appropriate combination of reactive surface sites to support the formation of several intermediates that are formed in the C-C coupling over MgO. The roles of Mg and O sites were also studied under an electronic perspective and different geometrical arrangements. We found that a kink configuration was the most adequate for ethanol to 1-butanol upgrading. Our calculations also gave us arguments to propose distinct reaction routes, whose mutual predominance would depend upon reaction temperature. At temperatures up to 573 K, the so-called β-route, which goes through scission of a Cβ-H bond and formation of an oxametallacycle-like intermediate, would dominate the coupling, whereas at higher temperatures, up to 673 K, a more usual Guerbet mechanism, via an aldol coupling step and then consecutive hydrogenations, would be expected. Here, the theoretical conclusions were followed by a careful experimental strategy using sequential experimental planning techniques in order to estimate accurate parameters with the lowest possible experimental load. Information from these different sources were coupled to develop a mathematical model for the rate of the ethanol upgrading reaction, using a Langmuir-Hinshelwood-Hougen-Watson approach. The developed and statistically validated model adequately described the experimental data at 673 K and 1.1 bar total pressure for ethanol partial pressures in the range from 0 to 20 kPa.

09 BIOMASS FUELS↗

DOE OSTI2021

Combined Technologies for In Situ Remediation of Tc-99 and U in Subsurface Sediments

In this study, combinations of chemical remedies were tested in bench-scale batch experiments to evaluate a two-step reduction-sequestration approach to effectively stabilize high concentrations of inorganic contaminant mixtures. Bench tests simulated contaminant and geochemical conditions of a perched aquifer located within the Central Plateau at the Hanford Site, located in southeastern Washington State (USA). Pairwise combinations of a reductant [e.g., zero valent iron, sulfur modified iron (SMI), or calcium polysulfide] and a sequestering agent [e.g., calcite, apatite, or dilute alkaline solution (e.g., NaOH)] were evaluated for immobilization and stabilization of technetium (Tc) (50,000 pCi/L), uranium (U) (150 mg/L), and nitrate (NO 3 ) (200 mg/L) in high ionic strength groundwater. The results of these batch studies demonstrated that reduction by SMI and sequestration in apatite or calcite are the most effective combination for these contaminant mixtures and conditions. Aqueous concentrations of Tc and U decreased by 95.6% ± 2.5% and 101.1% ± 5.2%, respectively, with SMI-apatite and 98.3% ± 0.0% and 101.2% ± 5.2%, respectively, with SMI-calcite. Sequential extractions showed that sequestered contaminants had limited capacity for re-oxidation; in fact, less than 10% of immobilized Tc and U was recovered by selective extraction of mineral phases most susceptible to oxidation. In addition, X-ray absorption near edge structure analysis of the sediment samples treated with SMI-calcite showed the presence of only U(IV), while both U(IV) and U(VI) were present in the SMI apatite combination [ratio of 0.43 U(IV):0.59 U(VI)].This study describes preliminary results that a two-step approach for stabilizing contaminant mixtures of long-lived radionuclides can be effective at reducing contaminant fluxes to groundwater from vadose and perched water zones.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Cool flame chemistry of diesel surrogate compounds: n -Decane, 2-methylnonane, 2,7-dimethyloctane, and n -butylcyclohexane

Elucidating the formation of combustion intermediates is crucial to validate reaction pathways, develop reaction mechanisms and examine kinetic modeling predictions. While high-temperature pyrolysis and oxidation intermediates of alkanes have been thoroughly studied, comprehensive analysis of cool flame intermediates from alkane autoxidation is lacking and challenging due to the complexity of intermediate species produced. Here, jet-stirred reactor autoxidation of four C 10 alkanes: n -decane, 2-methylnonane, 2,7-dimethyloctane, and n -butylcyclohexane, as model compounds of diesel fuel, was investigated from 500 to 630 K using synchrotron vacuum ultraviolet photoionization molecular beam mass spectrometry (SVUV-PIMS). Around 100 intermediates were detected for each fuel. The classes of molecular structures present during the autoxidation of the representative paraffinic functional groups in transport fuels, i.e., n -alkanes, branched alkanes, and cycloalkanes were established and were found to be similar from the oxidation of various alkanes. A theoretical approach was applied to estimate the photoionization cross sections of the intermediates with the same carbon skeleton as the reactants, e.g., alkene, alkenyl keto, cyclic ether, dione, keto-hydroperoxide, diketo-hydroperoxide, and keto-dihydroperoxide. These species are indicators of the first, second, and third O 2 addition reactions for the four C 10 hydrocarbons, as well as bimolecular reactions involving keto-hydroperoxides. Chemical kinetic models for the oxidation of these four fuels were examined by comparison against mole fraction of the reactants and final products obtained in additional experiments using gas chromatography analysis, as well as the detailed species pool and mole fractions of aforementioned seven types of intermediates measured by SVUV-PIMS. This works reveals that the models in the literature need to be improved, not only the prediction of the fuel reactivity and final products, but also the reaction network to predict the formation of many previous undetected intermediates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Windowed least-squares model reduction for dynamical systems

Here we propose a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Electron–electrophile coupled dinitrogen reduction in a cerium– meta -tetraphenolate system: a computational study

The use of lanthanide complexes for catalytic dinitrogen reduction is a new development in homogeneous catalysis. Density functional theory calculations on our recently reported cerium phenolate catalyst [K 2 Ce 2 (sol) 4 (mTP) 2 ] (mTP = {(OC 6 H 2 - 2- t Bu-4-Me) 2 CH} 2 - 1,3-C 6 H 4 ; sol = OMe 2 here; THF in the experiment) have been undertaken to elucidate the reduction, activation and silylation steps at the bound dinitrogen molecule, in the presence of the reductant, potassium metal (K 0 ) and the electrophile Me 3 SiCl (TMSCl). Out of the total of six electron reductions required to cleave the N 2 , the first two-electron reduction step was found to be highly disfavoured unless potassium cations (K + ) are included, upon which the step is rendered strongly exergonic; N–Si bond formation at the two-electron stage is predicted to be unfavourable. The three-electron-reduced N 2 -adduct is found to be at the reductive activation limit in the absence of added electrophiles, which can form N-element bonds and lower the overall charge. Added electron density beyond three-electron reduction no longer localises on N 2 , preventing formal N 2 4− formation. A pathway in which both K 0 and Me 3 SiCl work in concert was modelled, and six sequential reduction–silylation steps were calculated, showing how the N–N bond is cleaved after the third reduction, eventually releasing two equivalents of N(SiMe 3 ) 3 , and regenerating the starting complex with the highest barrier of any step being 22 kcal mol −1 . We establish alkali metal coordination and coupled electron–electrophile transfer as key factors in the design of rare-earth-mediated dinitrogen functionalisation.

Ahmad, Shahbaz [Univ. of Manchester (United Kingdo↗

DOE OSTI2020

Radiation damage and dose limits in serial synchrotron crystallography at cryo- and room temperatures

Radiation damage limits the accuracy of macromolecular structures in X-ray crystallography. Cryogenic (cryo-) cooling reduces the global radiation damage rate and, therefore, became the method of choice over the past decades. The recent advent of serial crystallography, which spreads the absorbed energy over many crystals, thereby reducing damage, has rendered room temperature (RT) data collection more practical and also extendable to microcrystals, both enabling and requiring the study of specific and global radiation damage at RT. Here, we performed sequential serial raster-scanning crystallography using a microfocused synchrotron beam that allowed for the collection of two series of 40 and 90 full datasets at 2- and 1.9-Å resolution at a dose rate of 40.3 MGy/s on hen egg white lysozyme (HEWL) crystals at RT and cryotemperature, respectively. The diffraction intensity halved its initial value at average doses ( D 1/2 ) of 0.57 and 15.3 MGy at RT and 100 K, respectively. Specific radiation damage at RT was observed at disulfide bonds but not at acidic residues, increasing and then apparently reversing, a peculiar behavior that can be modeled by accounting for differential diffraction intensity decay due to the nonuniform illumination by the X-ray beam. Specific damage to disulfide bonds is evident early on at RT and proceeds at a fivefold higher rate than global damage. The decay modeling suggests it is advisable not to exceed a dose of 0.38 MGy per dataset in static and time-resolved synchrotron crystallography experiments at RT. This rough yardstick might change for proteins other than HEWL and at resolutions other than 2 Å.

36 MATERIALS SCIENCE↗