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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Raman Spectroscopy Coupled with Chemometric Analysis for Speciation and Quantitative Analysis of Aqueous Phosphoric Acid Systems

Complex chemical systems that exhibit varied and matrix-dependent speciation are notoriously difficult to monitor and characterize on-line and in real-time. Optical spectroscopy is an ideal tool for in situ characterization of chemical species that can enable quantification as well as species identification. Chemometric modeling, a multivariate method, has been successfully paired with optical spectroscopy to enable measurement of analyte concentrations even in complex solutions where univariate methods such as Beer’s law analysis fail. Here, Raman spectroscopy is used to quantify the concentration of phosphoric acid and its three deprotonated forms during a titration. In this system, univariate approaches would be difficult to apply due to multiple species being present simultaneously within the solution as pH is varied. Locally Weighted Regression (LWR) modeling was used to determine phosphate concentration from spectral signature. LWR results, in tandem with Multivariate Curve Resolution modeling, provide direct measurement of the concentration of each phosphate species using only the Raman signal. Furthermore, results are presented within the context of fundamental solution chemistry, including Pitzer equations to compensate for activity coefficients and non-idealities associated with high ionic strength systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Stereoisomer-dependent unimolecular kinetics of 2,4-dimethyloxetanyl peroxy radicals

2,4,dimethyloxetane is an important cyclic ether intermediate that is produced from hydroperoxyalkyl (QOOH) radicals in the low-temperature combustion of n-pentane. However, the reaction mechanisms and rates of consumption pathways remain unclear. In the present work, the pressure- and temperature-dependent kinetics of seven cyclic ether peroxy radicals, which stem from 2,4,dimethyloxetane via H-abstraction and O 2 addition, were determined. The automated kinetic workflow code, KinBot, was used to model the complexity of the chemistry in a stereochemically resolved manner and solve the resulting master equations from 300–1000 K and from 0.01–100 atm. The main conclusions from the calculations include (i) diastereomeric cyclic ether peroxy radicals show significantly different reactivities, (ii) the stereochemistry of the peroxy radical determines which QOOH isomerization steps are possible, (iii) conventional QOOH decomposition pathways, such as cyclic ether formation and HO 2 elimination, compete with ring-opening reactions, which primarily produce OH radicals, the outcome of which is sensitive to stereochemistry. Ring-opening reactions lead to unique products, such as unsaturated, acyclic peroxy radicals, that form direct connections with species present in other chemical kinetics mechanisms through "cross-over" reactions that may complicate the interpretation of experimental results from combustion of n-pentane and, by extension, other alkanes. For example, one cross-over reaction involving 1-hydroperoxy-4-pentanone-2-yl produces 2-(hydroperoxymethyl)-3-butanone-1-yl, which is an iso-pentane-derived ketohydroperoxide (KHP). At atmospheric pressure, the rate of chemical reactions of all seven peroxy radicals compete with that of collisional stabilization, resulting in well-skipping reactions. However, at 100 atm, only one out of seven peroxy radicals undergoes significant well-skipping reactions. Here, the rates produced from the master equation calculations provide the first foundation for the development of detailed sub-mechanisms for cyclic ether intermediates. In addition, analysis of the complex reaction mechanisms of 2,4-dimethyloxetane-derived peroxy radicals provides insights into the effects of stereoisomers on reaction pathways and product yields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Atomically Precise Hexanuclear Ce(IV) Clusters as Functional Fluorescent Nanosensors for Rapid One-Step Detection of PFAS

Here, the presence of poly- and perfluoroalkyl substances (PFAS) in the environment is associated with adverse health effects but measuring PFAS is challenging due to the associated high cost and technical complexities of the analysis. Here, the reactivity of atomically precise metal-oxo clusters is reported and the foundation for their use is provided as fluorescent nanosensors for PFAS detection. The material comprises crystalline, water soluble, hexanuclear cerium-oxo clusters [Ce 6 (µ 3 -O) 4 (µ 3 -OH) 4 ] 12+ decorated with glycine molecules (Ce-Gly) characterized by fluorescence emission at 353 nm. The Ce-Gly fluorescence is found sensitive to long chain carboxylated PFAS of CF 3 –(CF 2 ) n –, where n ≥ 6, such as perfluorooctanoic, perfluorononanoic and perfluorodecanoic acids. This unique reactivity leads to a change in the emission spectra in a concentration dependent manner, enabling PFAS detection through ligand exchange and aggregation-induced emission (AIE) enhancement. No significant cross-reactivity from potentially co-existing species, including sulfonated PFAS, octanoic and dodecanoic acids, humic acid, and inorganic ions is observed. With an optimal concentration of 3.3 µg mL -1 Ce-Gly, the method demonstrated detection limits of 0.24 ppb for PFOA and 0.4 ppb for PFNA. These findings highlight the potential of fluorescence-based detection strategies utilizing nanoscale probes such as Ce-Gly as fluorescent probes and nanosensors for PFAS.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Lewis Acid Supported Nickel Nitrenoids

Abstract Metalation of the polynucleating ligand F,tbs LH 6 (1,3,5‐C 6 H 9 (NC 6 H 3 −4‐F−2‐NSiMe 2 t Bu) 3 ) with two equivalents of Zn(N(SiMe 3 ) 2 ) 2 affords the dinuclear product ( F,tbs LH 2 )Zn 2 ( 1 ), which can be further deprotonated to yield ( F,tbs L)Zn 2 Li 2 (OEt 2 ) 4 ( 2 ). Transmetalation of 2 with NiCl 2 (py) 2 yields the heterometallic, trinuclear cluster ( F,tbs L)Zn 2 Ni(py) ( 3 ). Reduction of 3 with KC 8 affords [KC 222 ][( F,tbs L)Zn 2 Ni] ( 4 ) which features a monovalent Ni centre. Addition of 1‐adamantyl azide to 4 generates the bridging μ 3 ‐nitrenoid adduct [K(THF) 3 ][( F,tbs L)Zn 2 Ni(μ 3 ‐NAd)] ( 5 ). EPR spectroscopy reveals that the anionic cluster possesses a doublet ground state ( S = ). Cyclic voltammetry of 5 reveals two fully reversible redox events. The dianionic nitrenoid [K 2 (THF) 9 ][( F,tbs L)Zn 2 Ni(μ 3 ‐NAd)] ( 6 ) was isolated and characterized while the neutral redox isomer was observed to undergo both intra‐ and intermolecular H‐atom abstraction processes. Ni K‐edge XAS studies suggest a divalent oxidation state for the Ni centres in both the monoanionic and dianionic [Zn 2 Ni] nitrenoid complexes. However, DFT analysis suggests Ni‐borne oxidation for 5 .

Juda, Cristin E.↗
DOE OSTI2023

Lewis Acid Supported Nickel Nitrenoids

Metalation of the polynucleating ligand F,tbs LH 6 (1,3,5-C 6 H 9 (NC 6 H 3 −4-F−2-NSiMe 2 t Bu) 3 ) with two equivalents of Zn(N(SiMe 3 ) 2 ) 2 affords the dinuclear product ( F,tbs LH 2 )Zn 2 (1), which can be further deprotonated to yield ( F,tbs L)Zn 2 Li 2 (OEt 2 ) 4 (2). Transmetalation of 2 with NiCl 2 (py) 2 yields the heterometallic, trinuclear cluster ( F,tbs L)Zn 2 Ni(py) (3). Reduction of 3 with KC 8 affords [KC 222 ][( F,tbs L)Zn 2 Ni] (4) which features a monovalent Ni centre. Addition of 1-adamantyl azide to 4 generates the bridging μ 3 -nitrenoid adduct [K(THF) 3 ][( F,tbs L)Zn 2 Ni(μ 3 -NAd)] (5). EPR spectroscopy reveals that the anionic cluster possesses a doublet ground state (S = 1/2). Cyclic voltammetry of 5 reveals two fully reversible redox events. The dianionic nitrenoid [K 2 (THF) 9 ][( F,tbs L)Zn 2 Ni(μ 3 -NAd)] (6) was isolated and characterized while the neutral redox isomer was observed to undergo both intra- and intermolecular H-atom abstraction processes. Here, Ni K-edge XAS studies suggest a divalent oxidation state for the Ni centres in both the monoanionic and dianionic [Zn 2 Ni] nitrenoid complexes. However, DFT analysis suggests Ni-borne oxidation for 5.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Omics-driven onboarding of the carotenoid producing red yeast Xanthophyllomyces dendrorhous CBS 6938

Transcriptomics is a powerful approach for functional genomics and systems biology, yet it can also be used for genetic part discovery. Here, we derive constitutive and light-regulated promoters directly from transcriptomics data of the basidiomycete red yeast Xanthophyllomyces dendrorhous CBS 6938 (anamorph Phaffia rhodozyma) and use these promoters with other genetic elements to create a modular synthetic biology parts collection for this organism. X. dendrorhous is currently the sole biotechnologically relevant yeast in the Tremellomycete class-it produces large amounts of astaxanthin, especially under oxidative stress and exposure to light. Thus, we performed transcriptomics on X. dendrorhous under different wavelengths of light (red, green, blue, and ultraviolet) and oxidative stress. Differential gene expression analysis (DGE) revealed that terpenoid biosynthesis was primarily upregulated by light through crtI, while oxidative stress upregulated several genes in the pathway. Further gene ontology (GO) analysis revealed a complex survival response to ultraviolet (UV) where X. dendrorhous upregulates aromatic amino acid and tetraterpenoid biosynthesis and downregulates central carbon metabolism and respiration. The DGE data was also used to identify 26 constitutive and regulated genes, and then, putative promoters for each of the 26 genes were derived from the genome. Simultaneously, a modular cloning system for X. dendrorhous was developed, including integration sites, terminators, selection markers, and reporters. Each of the 26 putative promoters were integrated into the genome and characterized by luciferase assay in the dark and under UV light. The putative constitutive promoters were constitutive in the synthetic genetic context, but so were many of the putative regulated promoters. Notably, one putative promoter, derived from a hypothetical gene, showed ninefold activation upon UV exposure. Thus, this study reveals metabolic pathway regulation and develops a genetic parts collection for X. dendrorhous from transcriptomic data. Therefore, this study demonstrates that combining systems biology and synthetic biology into an omics-to-parts workflow can simultaneously provide useful biological insight and genetic tools for nonconventional microbes, particularly those without a related model organism. This approach can enhance current efforts to engineer diverse microbes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Solid Cylinder Torsion for Large Shear Deformation and Failure of Engineering Materials

Using a thin-walled tube torsion test to characterize a material’s shear response is a well-known technique; however, the thin walled specimen tends to buckle before reaching large shear deformation and failure. An alternative technique is the surface stress method (Nadai 1950; Wu et al. J Test Eval 20:396–402, 1992), which derives a shear stress-strain curve from the torque-angular displacement relationship of a solid cylindrical bar. The solid bar torsion test uniquely stabilizes the deformation which allows us to control and explore very large shear deformation up to failure. However, this method has rarely been considered in the literature, possibly due to the complexity of the analysis and experimental issues such as twist measurement and specimen uniformity.

42 ENGINEERING↗
DOE OSTI2025

Optimization of direct air capture processes using reactive transport models of adsorption-desorption cycles

In this study, we develop and implement a reactive transport model in COMSOL Multiphysics® to address the challenges of direct air carbon capture. The model is validated against experimental data and used to simulate the cyclic steady state of the adsorption-desorption process. The optimization of this model is achieved through advanced trust-region methods integrated with Gaussian Processes. Key decision variables, including adsorption and desorption times, desorption temperature and pressure, input velocity, bed porosity, column length, and radius were optimized to minimize the capture cost. After optimization, a sensitivity analysis revealed the complex interplay between the decision variables and their effect on the specific energy and cost of removing the CO 2 . We optimized the capture cost while taking into account the trade-off between energy consumption and productivity. The resulting minimum capture cost was determined to be 265.2 $/t-CO 2 , which aligns with expected values reported in the literature. Numerical results suggest the effectiveness of the optimization strategies applied, and underscore the importance of simultaneous decision variable selection in improving the performance in direct air capture processes. We also extend the modeling approach to a 2D axisymmetric model to better visualize CO₂ uptake and temperature profiles, revealing significant radial gradients during the regeneration step. As a main drawback, this enhanced model comes with a computational cost approximately 40 times higher than that of the 1D model.

Adsorption-desorption process↗
DOE OSTI2022

Impact of cyber attacks on distributed compressive sensing based state estimation in power distribution grids

Modern power distribution grids suffer from multiple vulnerabilities due to the tight integration between the physical system and the cyber infrastructure. Sophisticated and malicious cyber attacks continue to adversely impact the grid operation leading to performance degradation, service interruption, and grid failure. State estimation plays an essential role in grid monitoring and advancing cyber-attack situational awareness. In this regard, this paper first proposes a distributed compressive sensing (CS) state estimation approach for an unobservable distribution grid. Further, the proposed distributed CS approach divides the distribution grid into sub-areas to perform local state estimation. Then an alternating direction method of multipliers (ADMM) based iterative information exchange among neighboring areas is employed to complete the estimation process. In this estimation process, the impact of loss of measurement data, false data injection (FDI), replay, and neighborhood cyber-attacks is analyzed. Extensive simulations are performed on the IEEE 37-bus and IEEE 123-bus standard networks to demonstrate the algorithm’s robustness to the aforementioned cyber-attacks. A quantitative analysis of computational complexity and simulation time of the distributed CS based approach is also presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Effect of atomic size mismatch and chemical complexity on the local lattice distortion of BCC solid solution alloys

Here, the effects of atomic size mismatch and chemical complexity on the local lattice distortion of solid-solution alloys VNbTa, TiVNbMo, TiVNbMoTa and TiVNbMoTaWRe with body-centered cubic (BCC) structure are quantitatively studied with time-of-flight neutron total scattering, extended X-ray absorption fine structure (EXAFS) measurements and first principles calculations. Neutron atomic pair distribution function (PDF) measurements found that the local lattice distortion in the ternary solid-solution alloy VNbTa is 1.1 %, obviously larger than that of the most complicated solid-solution alloy (0.27% for TiVNbMoTaWRe). Our results suggested that atomic size mismatch in high entropy alloys is more critical to the local lattice distortion than chemical complexity. Both EXAFS analysis and theoretical calculations indicate that there is a maximum of ~4 % difference between the bonding length of different atomic pairs in VNbTa.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. Here, we demonstrate improved processing and laser control performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Response of a high-pressure 4 He scintillation detector to nuclear recoils up to 9 MeV

Helium-4-based scintillation detector technology is emerging as a strong alternative to pulse-shape discrimination-capable organic scintillators for fast neutron detection and spectroscopy, particularly in extreme gamma-ray environments. The 4 He detector is intrinsically insensitive to gamma radiation, as it has a relatively low cross-section for gamma-ray interactions, and the stopping power of electrons in the 4 He medium is low compared to that of 4 He recoil nuclei. Consequently, gamma rays can be discriminated by simple energy deposition thresholding instead of the more complex pulse shape analysis. The energy resolution of 4 He scintillation detectors has not yet been well-characterized over a broad range of energy depositions, which limits the ability to deconvolve the source spectra. In this work, an experiment was performed to characterize the response of an Arktis S670 4 He detector to nuclear recoils up to 9 MeV. The 4 He detector was positioned in the center of a semicircular array of organic scintillation detectors operated in coincidence. Deuterium–deuterium and deuterium–tritium neutron generators provided monoenergetic neutrons, yielding geometrically constrained nuclear recoils ranging from 0.0925 to 8.87 MeV. The detector response provides evidence for scintillation linearity beyond the previously reported energy range. Finally, the measured response was used to develop an energy resolution function applicable to this energy range for use in high-fidelity detector simulations needed by future applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Dual particle imaging using time-of-flight neutron classification

Fast-neutron imaging technology is well-suited for passive nuclear material monitoring, secondary inspection of flagged cargo, and wide-area search for lost neutron sources. However, imaging systems that use pulse shape discrimination for event classification require complex pulse waveform analysis. In this work, we evaluate time-of-flight (TOF) based particle classification as an alternative solution for fast-neutron imaging by classifying all events with a TOF above a maximum threshold as neutrons. We measured a Cf-252 source next to Cs-137 using a 12-bar organic-glass scintillator array. By varying the TOF thresholds for neutron identification, we demonstrate a clear trade-off between event yield and backprojection image fidelity, with stricter thresholds improving precision at the cost of statistics, TOF thresholded data generated an image that predicted the neutron source direction with 20% reduced mean central angle prediction error compared to a traditional pulse shape discrimination (PSD) method with comparable event count. Time-of-flight particle classification shows promise as an alternative to pulse shape discrimination systems for fast neutron imaging systems looking to minimize costs and size of electronics with comparable imaging quality. The sources used demonstrate that the method is effective in classifying measured neutrons in a measurement environment with 150 μCi Cs-137 and 1.6 × 10 6 n/s Cf-252 sources positioned at distances of 66 cm and 81 cm from the detector. Additionally, the method classifies low-energy neutron events that pulse shape discrimination removes, so a combination of both methods would result in a higher overall neutron event efficiency.

Heriot, William [Univ. of Michigan, Ann Arbor, MI ↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
DOE OSTI2024

Two-Dimensional Projected-Momentum Covariance Mapping for Coulomb Explosion Imaging

We introduce projected-momentum covariance mapping, an extension of recoil-frame covariance mapping for 2D ion imaging studies. By considering the two-dimensional projection of the ion momenta as recorded by the detector, one opens the door to a complex suite of analysis tools adapted from three-dimensional momentum imaging studies. This includes the use of different frames of reference to unravel the dynamics of fragmentation and the application of fragment momentum constraints to isolate specific fragmentation channels. The technique is demonstrated on data from a two-dimensional ion imaging study of the Coulomb explosion of the cis and trans isomers of 1,2-dichloroethene, following strong-field ionization by an intense near-infrared femtosecond laser pulse. Classical simulations are used to guide the interpretation of projected-momentum covariance maps. The results offer a detailed insight into the distinct Coulomb explosion dynamics for this pair of isomers and lay the groundwork for future time-resolved studies of photoisomerization dynamics in this molecular system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nonlinear van der Waals Metasurfaces with Resonantly Enhanced Light Generation

Efficient nonlinear wave mixing is of paramount importance for a wide range of applications. However, weak optical nonlinearities pose significant challenges for accessing nonlinear light–matter interaction in compact systems. Here, we experimentally study second harmonic generation in deeply subwavelength 3R-MoS 2 metasurfaces (<λ/13 thick). Our measurements, supported by theoretical analysis, reveal a complex interplay and coupling between geometric resonances, optical extinction, and exciton-driven strong nonlinear susceptibility dispersion. We further demonstrate >150-fold enhancement in second harmonic signal at 740 nm mediated by the A exciton resonance. Additionally, our theoretical studies predict an enhancement of more than 10 6 in second harmonic generation in <100 nm thick structures exhibiting bound states in the continuum resonance. These findings provide insight into accessing and harnessing the unprecedented 3R-MoS 2 nonlinearities at a subwavelength scale, paving the way to ultracompact nonlinear photonic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Picoseconds-Limited Exciton Recombination in Metal–Organic Chalcogenides Hybrid Quantum Wells

Metal-organic species can be designed to self-assemble in large-scale, atomically defined, supramolecular architectures. Hybrid quantum wells, where inorganic two-dimensional (2D) planes are separated by organic ligands, are a particular example. The ligands effectively provide an intralayer confinement for charge carriers resulting in a 2D electronic structure, even in multilayered assemblies. Air-stable metal organic chalcogenides hybrid quantum wells have recently been found to host tightly bound 2D excitons with strong optical anisotropy in a bulk matrix. Here, we investigate the excited carrier dynamics in the prototypical metal organic chalcogenide [AgSePh] ∞ , disentangling three excitonic resonances by low temperature transient absorption spectroscopy. Our analysis suggests a complex relaxation cascade comprising ultrafast screening and renormalization, inter-exciton relaxation, and self-trapping of excitons within few picoseconds. Finally, the ps-decay provided by the self-trapping mechanism may be leveraged to unlock the material's potential for ultrafast optoelectronic applications.

2D excitons↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗