Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “model compounds”

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.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗
DOE OSTI2024

Comparing plant litter molecular diversity assessed from proximate analysis and 13 C NMR spectroscopy

Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using 13 C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Carbon isotope evidence for the substrates and mechanisms of prebiotic synthesis in the early solar system

Meteorites contain prebiotic, bio-relevant organic compounds including amino acids. Their syntheses could result from diverse sources and mechanisms and provide a window on the conditions and materials present in the early solar system. Here we constrain alanine’s synthetic history in the Murchison meteorite using site-specific 13 C/ 12 C measurements, reported relative to the VPDB standard. The δ 13 C VPDB values of -29 ± 10‰, 142 ± 20‰, and -36 ± 20‰ for the carboxyl, amine-bound, and methyl carbons, respectively, are consistent with Strecker synthesis of interstellar-medium-derived aldehydes, ammonia, and low-δ 13 C nebular or interstellar-medium-derived CN. We report experimentally measured isotope effects associated with Strecker synthesis, and use them to constrain the δ 13 C values of the alanine precursors, which we then use to construct a model that predicts the molecular-average δ 13 C values of 19 other organic compounds of prebiotic significance found in Murchison if they were made by our proposed synthetic network. Finally, most of these predictions agree with previous measurements, suggesting that interstellar-medium-derived aldehydes and nebular and/or pre-solar CN could have served as substrates for synthesis of a wide range of prebiotic compounds in the early solar system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Multi-scale modeling of wastage layer formation in metallic fuel cladding

Fuel-cladding chemical interaction (FCCI) is a major concern for U-Zr metallic fuels' performance, primarily due to the formation of a brittle layer (wastage) in the cladding. This brittle layer, resulting from intermetallic compounds between cladding constituents Fe, Cr, and lanthanide fission products, significantly impacts the cladding's mechanical integrity. Recent efforts focus on developing a mechanistic modeling framework to understand lanthanide production, transport to the fuel-cladding interface, and phase transformation to intermetallic phases. A multi-scale computational approach has been used to calculate lanthanide transport rates, with atomistic calculations determining Nd diffusivities through the solid fuel matrix and along pore surfaces. These diffusivities inform a mesoscale model to determine an effective diffusion coefficient, accounting for porosity and infiltration with bond sodium. This effective diffusivity is used in engineering-scale simulations via the BISON fuel performance code, which has been validated against EBR-II and FFTF reactor experiments.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Experimental and modeling studies of PR and ND oxalate solubility to high ionic strengths: Insight into actinide(III) oxalates

Actinide oxalates are chemical compounds important to nuclear industry, ranging from actinide separation in waste reprocessing, to production of specialty actinides, and to disposal of high level nuclear waste (HLW) and spent nuclear fuel (SNF). In this study, the solubility constants for Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O by performing solubility experiments in HNO 3 and mixtures of HNO 3 and H 2 C 2 O 4 at 23.0 ± 0.2 °C have been determined. The targeted starting materials, Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O, were successfully synthesized at room temperature using PrCl 3 , NdCl 3 and oxalic acid as the source metrials. Then, we utilized the targeted solubility-controlling phases to conduct solubility measurements. There was no phase change over the entire periods of experiments, demonstrating that Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O were the solubility-controlling phases in our respective experiments. Based on our experimental data, we have developed a thermodynamic model for Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O in the mixtures of HNO 3 and H 2 C 2 O 4 to high ionic strengths. The model for Pr 2 (C 2 O 4 ) 3 •10H 2 O reproduces well the reported experimental data for Pu 2 (C 2 O 4 ) 3 •10H 2 O, which are not utilized for the model development, demonstrating that Pr(III) is an excellent analog for Pu(III). Similarly, the model for Nd 2 (C 2 O 4 ) 3 •10H 2 O reproduces the solubility of Am 2 (C 2 O 4 ) 3 •10H 2 O and Cm 2 (C 2 O 4 ) 3 •10H 2 O. The Pitzer model was used for the calculation of activity coefficients. Based on the published, well established model for dissociation constants for oxalic acid and stability constants for actinide-oxalate complexes [i.e., AmC 2 O 4 + , and Am(C 2 O 4 ) 2 - ] to high ionic strengths, we have obtained the solubility constants (log 10 K 0 ) for the following reactions at 25 °C, Pr 2 (C 2 O 4 ) 3 •10H 2 O ⇌ 2Pr 3+ + 3C 2 O 4 2- + 10H 2 O(l). Nd 2 (C 2 O 4 ) 3 •10H 2 O ⇌ 2Nd 3+ + 3C 2 O 4 2- + 10H 2 O(l). to be -30.82 ± 0.30 (2σ), and - 31.14 ± 0.35 (2σ), respectively. These values for can be directly applied to Pu 2 (C 2 O 4 ) 3 •10H 2 O, Am 2 (C 2 O 4 ) 3 •10H 2 O and Cm 2 (C 2 O 4 ) 3 •10H 2 O. The model established for actinide oxalates by this study provides the needed knowledge with regard to solubilities of actinide/REE oxalates at various ionic strengths, and is expected to find applications in many fields, including the geological disposal of nuclear waste and the mobility of REE under the surface conditions, as Pr 2 (C 2 O 4 ) 3 •10H 2 O and Nd 2 (C 2 O 4 ) 3 •10H 2 O can be regarded as the pure Pr and Nd end-members of deveroite, a recently discovered natural REE oxalate with the following stoichiometry, (Ce 1.01 Nd 0.33 La 0.32 Pr 0.11 Y 0.11 Sm 0.01 Pb 0.04 U 0.03 Th 0.01 Ca 0.04 ) 2.01 (C 2 O 4 ) 2.99 •9.99H 2 O. Regarding its importance in the geological disposal of nuclear waste, Am 2 (C 2 O 4 ) 3 •10H 2 O/Pu 2 (C 2 O 4 ) 3 •10H 2 O/Cm 2 (C 2 O 4 ) 3 •10H 2 O can be the source-term phase for actinides, as demonstrated by the instance in the disposal in clay/shale formations. This is exemplified by the stability of Am 2 (C 2 O 4 ) 3 •10H 2 O in comparison with Am(OH) 3 (am), Am(OH) 3 (s) and AmCO 3 (OH)(s) under the relevant geological repository conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Effective point-charge analysis of crystal fields: Application to rare-earth pyrochlores and tripod kagome magnets 𝑅 ⁢3⁢ Mg⁢ 2 ⁢Sb⁢ 3⁢ O⁢ 14

An indispensable step to understand collective magnetic phenomena in rare-earth compounds is the determination of spatially anisotropic single-ion properties resulting from spin-orbit coupling and crystal field (CF). The CF Hamiltonian has a discrete energy spectrum—accessible to spectroscopic probes such as neutron scattering—controlled by a number of independent parameters reflecting the point symmetry of the magnetic sites. Determining these parameters in low-symmetry systems is often challenging. Here, we describe a general method to analyze CF excitation spectra using adjustable effective point-charges. We benchmark our method to existing neutron-scattering measurements on pyrochlore rare-earth oxides and obtain a universal point-charge model that describes a large family of related materials. We adapt this model to the newly discovered tripod kagome magnets (𝑅 3 ⁢Mg 2 ⁢Sb 3 ⁢O 14 , 𝑅 = Tb, Ho, Er, Yb) for which we report broadband inelastic neutron-scattering spectra. Analysis of these data using adjustable point-charges yields the CF wave functions for each compound. From this, we calculate thermomagnetic properties that accurately reflect our measurements on powder samples and predict the effective gyromagnetic tensor for pseudospin degrees of freedom—a crucial step to understand the exotic collective properties of these kagome magnets at low temperature. We present further applications of our method to other tripod kagome materials and triangular rare-earth compounds 𝑅⁢MgGaO 4 (𝑅 =Yb, Tm). Overall, this study establishes a widely applicable methodology to predict CF and single-ion properties of rare-earth compounds based on interpretable and adjustable models of effective point charges.

Crystal-field theory↗
DOE OSTI2022

Differing behavioral changes in crayfish and bluegill under short- and long-chain PFAS exposures: Field study in Northern Michigan, USA

The emergent contaminant family, per- and poly-fluorinated alkyl substances (PFAS) has gained research attention due to their widespread detection and stability within the environment. Despite the growing amount of research on perfluorooctanesulfonic acid (PFOS) and perfluoro-n-octanoic acid (PFOA) in aquatic organisms, investigations detailing behavioral and physiological effects of aquatic organisms exposed to a mixture of PFAS analytes in the wild have been limited. The objective of this study was to evaluate the potential behavioral and histological effects of environmental exposure to PFAS compounds within multiple trophic levels of aquatic ecosystems. The current study investigates effects of environmentally relevant PFAS concentration exposures in crayfish (Faxonius immunis, F. rusticus, F. virilis) and bluegill (Lepomis macrochirus) sourced from four water bodies in Northern Michigan. Antipredator response and foraging behavioral assays were used to investigate potential effects on crayfish; a swimming speed behavioral assay and liver and gill histology analysis were used to investigate potential effects on fish. Linear mixed model and multiple regression analyses resulted in significant relationships between tissue accumulation levels of long chain PFAS compounds and crayfish foraging and fish critical swimming speed responses. Crayfish foraging decreased and fish critical swim speeds increased with PFAS exposure which may lead to energetic and population concerns. Antipredator response in crayfish and liver and gill histology in fish were not significantly related to PFAS tissue or water concentrations. The sensitivity of crayfish and bluegill behavior contributes to the growing body of research regarding the differential toxicity of short-chain and long-chain PFAS compounds. The sensitivity of some aquatic organism behaviors to PFAS accumulated in tissue may have implications for PFAS transfer and alterations to ecosystem functioning; based on the results of this field study, further laboratory research is recommended to further evaluate these relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine Learning for Joint Quality Control

The use of lightweight material combinations has been highly demanded in manufacturing automotive structures. However, making robust dissimilar material joints of such lightweight materials is still challenging. A significant barrier to achieving high-quality and repeatable joint performance is a deficient understanding of the relationship between the welding process, joint attributes, and joint performance. In this context, welding factors refer to material, equipment, environment, and process parameters, while joint features comprise specific microstructural attributes of the weld such as nugget size, heat affected zone (HAZ) topology, intermetallic layer thickness, and sheet thickness reduction. Joint performance is quantified in terms of strength (e.g., tensile shear, coach peel, cross-tension), weld size, and hardness, among other factors. While there have been many attempts to establish this process-structure-property relationship by developing a model derived from the associated physics and first principles, the complexity of the joining processes compounded by the complex interactions with different materials in an automotive assembly line environment, has hindered the usefulness of such attempts. The complexity is further exacerbated using different stacking materials, especially comprising dissimilar material combinations. In practice, the common approach has been the laborious process of creating welds, characterizing them, and then physically testing them through experimentation. With the emergence of artificial intelligence (AI) methods, an alternative pathway to eliciting the desired process-structure-property relationship at an accelerated pace is to use a data-driven approach by employing machine-learning (ML) techniques. This approach is benefitted by the availability of large streams of data, generated through years of research and testing by original equipment manufacturers, in the form of material, process, environmental, equipment, microstructural, and bulk-scale performance information from multimodal, multiscale sensors making measurements from laboratory-scale to production-scale processes. During Phase I efforts, which ended in fiscal year (FY) 2021, the Oak Ridge National Laboratory and Pacific Northwest National Laboratory (ORNL/PNNL) team demonstrated the effectiveness of different ML/AI frameworks in modeling complex relationships between resistance spot welding (RSW) process parameters, weld attributes, and joint properties using a subset of data from General Motors (GM). In FY 2022, the project team further refined and expanded their respective ML models to analyze additional welds with new weld stack-ups and materials to enhance the ML model predictive capability. ORNL extended its unified deep neural networks (DNN) ML training and prediction framework with new data streams of process parameters, and PNNL extended its model describing RSW process parameters’ associations with weld attributes. In FY 2023, the project team completed the development of the AI/ML architecture for analyzing aluminum/steel joints manufactured by GM via RSW and transitioned into the inline welding quality monitoring task for steel/steel RSW joints provided by GM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2022

Climate change increases risk of extreme rainfall following wildfire in the western United States

Post-wildfire extreme rainfall events can have destructive impacts in the western United States. Using two climate model large ensembles, we assess the future risk of extreme fire weather events being followed by extreme rainfall in this region. By mid-21st century, in a high warming scenario (RCP8.5), we report large increases in the number of extreme fire weather events followed within 1 year by at least one extreme rainfall event. By 2100, the frequency of these compound events increases by 100% in California and 700% in the Pacific Northwest in the Community Earth System Model v1 Large Ensemble. We further project that more than 90% of extreme fire weather events in California, Colorado, and the Pacific Northwest will be followed by at least three spatially colocated extreme rainfall events within five years. Our results point to a future with substantially increased post-fire hydrologic risks across much of the western United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Magnetic excitation linking quasi-one-dimensional Chevrel-type selenide and arsenide superconductors

The quasi-one-dimensional Chevrel phases, A 2 M o6 Se 6 (A=Tl, In, K, Rb, Cs), are of interest due to their atypical electronic properties. The Tl and In analogs undergo a superconducting transition whereas the alkali metal analogs show charge gapping of another, not well understood type. Here we report the results of inelastic neutron scattering on polycrystalline In 2 Mo 6 Se 6 (T c =2.85 K) and Rb 2 Mo 6 Se 6 (nonsuperconducting) samples, which reveal a column of intensity with linear dispersion from [0 0 1/2] to [0 0 1] in both compounds. The observed temperature and |Q| independence together suggest the presence of unconventional carriers with a spin contribution to the excitation. This is contrary to the prevailing model for these materials, which is that they are nonmagnetic. The excitation has similar dispersion and S(Q,E,T) behavior as one observed in the structurally related superconducting compounds A 2 Cr 3 As 3 and A 2 Mo 3 As 3 (A=K, Rb, Cs), which has been interpreted as magnetic in origin and related to Fermi surface nesting. The connection is unexpected because the calculated Fermi surface of the arsenides differs substantially from the A 2 Mo 6 Se 6 compounds, and many consider them distinct classes of materials. This observation suggests a hidden link in the physics between both classes of superconductors, perhaps originating from their quasi-low-dimensional character.

1-dimensional systems↗
DOE OSTI2021

Prospects for Surrogate Neutron Capture Measurements with Radioactive Ion Beams and GODDESS

Neutron capture reactions are responsible for the synthesis of almost all of the elements heavier than iron through the slow s-process, that proceeds close to the line of stability, and the rapid r-process, with very neutron-rich waiting points. Uncertainties in (n,γ) rates in neutron rich nuclei, especially near closed neu- tron shells, can have significant impact [1] on the predictions of final abundances for different astrophysical scenarios for the r process. Understanding (n,γ) rates on neutron-rich fission fragments is also important for nuclear forensics and stockpile stewardship science. Ratkiewicz et al. [2 and references therein] has recently demonstrated that the (d,pγ) reaction is a valid surrogate for (n,γ), where the formation of the compound nucleus from the breakup of the deuteron has been calculated in a reaction model and the subsequent measured gamma-decay probabilities are reproduced with standard level density and strength functions in a Bayesian approach. In parallel to the surrogate validation efforts, we have demonstrated that the (d,pγ) reaction can be measured in inverse kinematics with Gammasphere ORRUBA: Dual Detectors for Experimental Structure Studies (GODDESS) [3] where the Gammasphere array of Compton-suppressed HPGe detectors is coupled to the Oak Ridge Rutgers University Barrel Array of position-sensitive silicon strip detectors. During the commissioning campaign we measured the (d,pγ) reaction with 134 Xe and 95 Mo beams, the latter to demonstrate the surrogate method in inverse kinematics. The present talk will present preliminary results from this campaign including γ-decay probabilities and prospects for surrogate (n,γ) measurements with 143 Ba fission-fragment beams.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

13 C-Isotope-Assisted Assessment of Metabolic Quenching During Sample Collection from Suspension Cell Cultures

Metabolomics and fluxomics are core approaches to directly profile and interrogate cellular metabolism in response to various genetic or environmental perturbations. In order to accurately measure the abundance and isotope enrichment of intracellular metabolites, cell culture samples must be rapidly harvested and cold-quenched to preserve the in vivo metabolic state of the cells at the time of sample collection. When dealing with suspension cultures, this process is complicated by the need to separate the liquid culture media from cellular biomass prior to metabolite extraction. Here, we examine the efficacy of several commonly used metabolic quenching methods, using the model cyanobacterium Synechocystis sp. PCC 6803 as an example. Multiple 13 C-labeled compounds, including 13 C-bicarbonate, 13 C-glucose and 13 C-glutamine, were used as tracers during the sample collection and cold-quenching process to assess the extent of metabolic turnover after cells were harvested from culture flasks. We show that the combination of rapid filtration followed by 100% cold (–80°C) methanol quenching exhibits the highest quenching efficiency, while mixing cell samples with a partially frozen 30% methanol slurry (–24°C) followed by centrifugation is slightly less effective at quenching metabolism but enables less laborious sample processing. By contrast, rapidly mixing the cells with a saline ice slurry (~0°C) is less effective as indicated by high isotope-labeling rates after sample harvest, while mixing the cells with 60% cold methanol (–65°C) prior to centrifugation causes significant metabolite loss. Furthermore, this study demonstrates a rigorous, quantitative, and broadly applicable method for assessing the metabolic quenching efficacy of protocols used for sample collection in metabolomics and fluxomics studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Prospects of Nanoscience with Nanocrystals: 2025 Edition

Abstract Nanocrystals (NCs) of various compositions have made important contributions to science and technology, with their impact recognized by the 2023 Nobel Prize in Chemistry for the discovery and synthesis of semiconductor quantum dots (QDs). Over four decades of research into NCs has led to numerous advancements in diverse fields, such as optoelectronics, catalysis, energy, medicine, and recently, quantum information and computing. The last 10 years since the predecessor perspective “Prospect of Nanoscience with Nanocrystals” was published in ACS Nano have seen NC research continuously evolve, yielding critical advances in fundamental understanding and practical applications. Mechanistic insights into NC formation have translated into precision control over NC size, shape, and composition. Emerging synthesis techniques have broadened the landscape of compounds obtainable in colloidal NC form. Sophistication in surface chemistry, jointly bolstered by theoretical models and experimental findings, has facilitated refined control over NC properties and represents a trusted gateway to enhanced NC stability and processability. The assembly of NCs into superlattices, along with two-dimensional (2D) photolithography and three-dimensional (3D) printing, has expanded their utility in creating materials with tailored properties. Applications of NCs are also flourishing, consolidating progress in fields targeted early on, such as optoelectronics and catalysis, and extending into areas ranging from quantum technology to phase-change memories. In this perspective, we review the extensive progress in research on NCs over the past decade and highlight key areas where future research may bring further breakthroughs.

Chemistry↗
DOE OSTI2026

High-pressure neutron powder diffraction study of an arsenolite deuterium inclusion compound: structure and formation kinetics

High-pressure variable-temperature neutron powder diffraction was employed to investigate the synthesis and structural properties of arsenolite (As 4 O 6 ), a cubic arsenic( III ) oxide polymorph, and its inclusion compound with deuterium. The compound forms with the stoichiometry As 4 O 6 ·2D 2 , consistent with earlier findings. At 295 K and 1.96(2) GPa, D 2 molecules occupy the 16c site (0, 0, 0) and are aligned along the <111> direction. The diffraction data suggest that the D 2 molecules at this site become rotationally disordered upon decompression to 0.78(2) GPa at 120 K, while no occupancy is observed at the 8a position (⅛, ⅛, ⅛) under any of the studied conditions. The kinetic data of the high-pressure inclusion compound synthesis at 1.56(2) and 1.96(2) GPa were analyzed using the Avrami model. The Avrami exponent, 0.135(16), remains invariant with pressure, demonstrating that the reaction topochemistry is unaffected by compression. Similarly, the reaction rates, 0.29(3) and 0.23(3) min −0.135 at 1.56(2) and 1.96(2) GPa, respectively, exhibit negligible pressure dependence and suggest a surprisingly low activation volume of 1.6 cm 3 mol −1 for the reaction. These results provide detailed insight into the structural dynamics and kinetics of deuterium inclusion in arsenolite under high pressure.

Guńka, Piotr A. [Warsaw University of Technology (↗