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At least 487 records · Page 27

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DOE OSTI2020

The effect of switchgrass plant cell wall properties on its deconstruction by thermochemical pretreatments coupled with fungal enzymatic hydrolysis or Clostridium thermocellum consolidated bioprocessing

A combination of thermochemical pretreatment and biological digestion technologies is usually required to overcome lignocellulosic recalcitrance and accomplish effective biomass deconstruction. In this study, we aimed to understand switchgrass breakdown by hydrothermal, dilute acid, dilute alkali, and co-solvent enhanced lignocellulosic fractionation (CELF) pretreatments followed by application of traditional fungal enzymatic hydrolysis (EH) and Clostridium thermocellum consolidated bioprocessing (CBP) to the resulting solids. Unpretreated and pretreated switchgrass and their EH and CBP residues were characterized by a suite of analytical techniques to understand structural changes that occurred during deconstruction. CELF pretreated solids showed the highest accessibility and digestibility by both EH and CBP followed by dilute alkali and then dilute acid/hydrothermal pretreated solids. Lignin removal from biomass had a more positive impact on substrate accessibility and digestibility than did xylan removal, while xyloglucan removal by pretreatment appeared essential for cellulose digestion by fungal enzymes. The extent of CBP digestion of cellulose and non-cellulosic glycans was larger than that by EH. Unlike dilute alkali pretreatment, cellulose crystallinity increased for acid-based pretreatments in the following order: hydrothermal, dilute acid, and CELF. Acid-based pretreatments also substantially reduced cellulose degree of polymerization. All thermochemical and biological digestion approaches increased syringyl to guaiacyl lignin (S/G) ratio and reduced β-O-4 lignin interunit linkage and hydroxycinnamates content from levels in unpretreated switchgrass. The substantial increase in S/G ratio after hydrothermal and dilute alkali preatreatments suggested that high temperatures or alkali removed a large portion of G lignin from switchgrass.

09 BIOMASS FUELS↗

DOE OSTI2023

The diversity of aluminum-based drinking water treatment residuals for use in environmental remediation

Drinking water treatment residuals (DWTRs) are complex mixtures of organic and inorganic phases generally disposed of as waste materials. However, their strong sorptive properties could be further exploited to immobilize contaminants. To characterize these materials, we applied a range of analytical techniques to a set of aluminum-based DWTRs. Here we determined surface areas, elemental compositions using CHNS analysis and X-ray fluorescence (XRF), performed thermogravimetric analyses paired with mass spectrometry (TGA-MS), and used synchrotron-based methods: X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS). Elemental analyses and specific surface area measurements – that vary between 7.23 ± 0.03 and 197.6 ± 0.9 m 2 g –1 – indicate that non-coagulant additives must play an important role in controlling sorption. High resolution powder XRD reveals the presence of only a few crystalline minerals mostly derived from source waters or additives. Fe was detected by XRF in all samples, whereas Mn was present in significant levels in samples with potassium permanganate as additive. The chemical speciation of these elements was characterized by performing spectral decompositions of XAS spectra. The spectral features of Fe are consistent with iron(III) oxides/hydroxides and structural iron in clays. On the other hand, Mn is predominantly present under its reduced form, Mn(II).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Site-specific photo-crosslinking in a double crossover DNA tile facilitated by squaraine dye aggregates: advancing thermally stable and uniform DNA nanostructures

We investigated the role of dichloro-squaraine (SQ) dye aggregates in facilitating thymine–thymine interstrand photo-crosslinking within double crossover (DX) tiles, to develop thermally stable and structurally uniform two-dimensional (2D) DNA-based nanostructures. By strategically incorporating SQ modified thymine pairs, we enabled site-selective [2 + 2] photocycloaddition under 310 nm UV light. Strong dye–dye interactions, particularly through the formation of aggregates, facilitated covalent bond formation between proximal thymines. To evaluate the impact of dye aggregation on crosslinking efficiency, ten DX tile variants with varying SQ-modified thymine positions were tested. Our results demonstrated that SQ dye aggregates significantly enhanced crosslinking, driven by precise SQ-modified thymine dimer placement within the DNA tiles. Analytical techniques, including denaturing PAGE and UV-visible spectroscopy, validated successful crosslinking in DNA tiles with multiple SQ-modified thymine pairs. This non-phototoxic method offers a potential route for creating thermally stable, homogeneous higher-order DNA–dye assemblies with potential applications in photoactive and exciton-based fields such as optoelectronics, nanoscale computing, and quantum computing. Furthermore, the insights from this study establish a foundation for further exploration of advanced DNA–dye systems, enabling the design of next-generation DNA nanostructures with enhanced functional properties.

2D DNA template↗

DOE OSTI2026

Modern insights into the mechanisms of neptunium oxalate decomposition

Neptunium oxalate (Np(C 2 O 4 ) 2 ·6H 2 O) is a historically relevant solid phase used in nuclear processing as a precursor for neptunium dioxide (NpO 2 ). Although Np oxalate has been synthesized and used for NpO 2 production for decades, the thermal decomposition mechanism of this phase remains poorly understood and has not been evaluated in over 30 years. Conflicting reports in historical literature suggest either a direct conversion from anhydrous oxalate to NpO 2 or a decomposition that includes the formation of Np carbonate or oxidized Np intermediate phases. In this work, we reexamine the decomposition pathway of Np(C 2 O 4 ) 2 ·6H 2 O using thermal analysis coupled with evolved gas analysis and temperature-dependent Raman spectroscopy to elucidate decomposition mechanisms and intermediate phases using modern analytical techniques. Thermal analysis revealed a three-stage decomposition process, including dehydration below 200 °C, oxalate breakdown between 170 and 370 °C, and NpO 2 formation by 500 °C. However, an unidentified plateau in the thermal data was observed during measurements. Raman spectroscopy confirmed the stages of decomposition, and in the analysis of potential intermediate phases, no carbonate phases or Np 2 O 5 were identified. Raman data suggest that residual oxalate or nonstoichiometric oxide are present during decomposition before pure NpO 2 is formed. These findings clarify aspects of the Np oxalate decomposition mechanism and address longstanding discrepancies in the literature, with a specific focus on Np-specific materials chemistry.

Lawson, Kathryn M. [Oak Ridge National Laboratory ↗

DOE OSTI2023

Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Solving a furan fatty acid biosynthesis puzzle

Furan fatty acids (FuFAs), characterized by a central furan moiety, are widely dispersed in nature, but their biosynthetic origins are not clear. A new study from Lemke et al. employs a full court press of genetics, genomics, biochemical, and advanced analytical techniques to dissect the biosynthetic pathway of mono- and dimethyl FuFAs and their intermediates in two related bacteria. Overall these findings lay the foundation both for detailed study of these novel enzymes and for gaining further insights into FuFA functions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Multi-Analytical Investigation of Arsenical Transfer and Remediation on Nineteenth-Century Green Books

Books containing heavy metals, specifically nineteenth-century green arsenical books, have been identified at Northwestern University Libraries, raising health and safety concerns related to handling. Copper acetoarsenite pigments, such as emerald green, were detected on book covers, decorative page edges, labels, and other components using noninvasive analytical techniques including X-ray fluorescence (XRF), Raman spectroscopy, and Fourier-transform infrared (FTIR) spectroscopy. Further examination of selected volumes using synchrotron radiation (SR) techniques revealed pigment migration, degradation, and arsenic transfer to adjacent books. This paper expands on initial findings through two related experiments. The first explored the transfer of arsenic using mechanical friction; Staedtler Mars® white vinyl erasers rubbed on arsenical books generated crumbs which were analyzed via scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM-EDX). Results confirmed the transfer of arsenic, copper, and lead, with decorative page edges being particularly prone to shedding arsenic onto other materials. The second experiment tested remediation methods on a book contaminated by prolonged exposure to an arsenical neighbor. Surface cleaning using erasers and a vacuum removed flecks of pigment but did not eliminate non-chromophoric arsenic as confirmed by SR analyses, which highlights its presence either as a degradation product embedded within the paper or present in the paper as part of its production process. Findings demonstrate the acute toxicity risk posed by arsenical books and support the need for safe handling protocols. However, materials with only trace levels of arsenic embedded during production may pose a lower risk of transfer. Cross contamination beyond prolonged direct contact appears limited. These results highlight critical considerations for library preservation practices and future research on arsenic in historical materials.

arsenic↗

DOE OSTI2021

PT symmetry, pattern formation, and finite-density QCD

A longstanding issue in the study of quantum chromodynamics (QCD) is its behavior at nonzero baryon density, which has implications for many areas of physics. The path integral has a complex integrand when the quark chemical potential is nonzero and therefore has a sign problem, but it also has a generalized ${\mathscr{PT}}$ symmetry. We review some new approaches to ${\mathscr{PT}}$-symmetric field theories, including both analytical techniques and methods for lattice simulation. We show that ${\mathscr{PT}}$-symmetric field theories with more than one field generally have a much richer phase structure than their Hermitian counterparts, including stable phases with patterning behavior. The case of a ${\mathscr{PT}}$-symmetric extension of a φ 4 model is explained in detail. The relevance of these results to finite density QCD is explained, and we show that a simple model of finite density QCD exhibits a patterned phase in its critical region.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

First considerations on the supporting structures of FCC-ee booster and collider in the arc regions

In 2022, the FCC Feasibility Study management mandated a working group to analyse the best configuration of the FCC-ee tunnel in the arc regions, in view of the construction of a mock-up of the arc half-cell. One of the main and most challenging goals of the study, named FCC-ee Arc Half-Cell Mock-up Project, was to perform a preliminary investigation on the principles of supporting the Short-Straight Sections and dipoles of the half-cells, both for the booster and for the collider machines. This is an important input needed for the choice of the best configuration of the relative placement of the booster with respect to the collider. The structural stiffness, mass and stability of the supporting structures must be optimized to minimize the vibrations transmitted/transferred to the magnetic system of the accelerators by elements such as pumps, water cooling system, beam thermomechanical stresses, powering elements, etc. To perform the study, tools such as CAD software, FEM and analytical techniques were employed. This paper summarizes the preliminary design concepts and the results of the simulations performed.

Accelerator Subsystems and Technologies↗

DOE OSTI2022

Geometric conditions for saturating the data processing inequality

Abstract The data processing inequality (DPI) is a scalar inequality satisfied by distinguishability measures on density matrices. For some distinguishability measures, saturation of the scalar DPI implies an operator equation relating the arguments of the measure. These results are typically derived using functional analytic techniques. In a complementary approach, we use geometric techniques to derive a formula that gives an operator equation from DPI saturation for any distinguishability measure; moreover, for a broad class of distinguishability measures, the derived operator equation is sufficient to imply saturation as well. Our operator equation coincides with known results for the sandwiched Rényi relative entropies, and gives new results for α - z Rényi relative entropies and a family of of quantum f -divergences, which we compute explicitly.

Physics↗

DOE OSTI2023

Imaging plant metabolism in situ

Abstract Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Mixed Element Microparticle Characterization by Electron Probe Microanalysis

Abstract Microparticle compositional characterization for nuclear forensics has traditionally been achieved by “gold-standard” destructive analytical techniques such as large geometry-secondary ion mass spectrometry and fission track-thermal ionization mass spectrometry, but long processing times and total sample consumption eliminate the option of subsequent analysis by other methods. Electron probe microanalysis has long been widely employed for rapid, nondestructive, micrometer-scale compositional measurements and imaging in fields such as material science and geology and may therefore need to be reevaluated as an alternative tool for microparticle analysis for the nuclear forensics community. This study presents the use of electron probe microanalysis for imaging and quantitative characterization of homogeneous microparticles utilizing a calibration curve based on high-precision quadrupole-inductively coupled plasma-mass spectrometry analyses. Samples of opportunity synthesized at Savannah River National Laboratory, nickel-doped cerium oxide microparticles, were selected as analogs for plutonium-doped uranium oxide microparticles. Positive detection and accurate quantification of variable amounts of nickel dopant down to trace levels (10s of parts per million) suggest applicability of this technique to other mixed element systems (e.g., actinides). Quantitative single-particle characterization via electron probe microanalyzer may thus provide a high fidelity, nondestructive complement to techniques currently in use.

Riche, Alexis T. (ORCID:0009000121978126)↗

DOE OSTI2020

Mixed-strategy approach to band-edge analysis and modeling in semiconductors

In semiconductor physics, the Urbach rule describes an exponential dependence of the UV-Vis absorption coefficient on photon energy at the band edge. The rule is so ubiquitously applied that a single common mechanism, variability in the local potential, is generally considered to be the cause of exponential band edges. Variability in the local potential may be caused by alloy inhomogeneity, structural defects, impurities, thermal excitation, or any other mechanism that interrupts ideal crystallinity and creates disorder. A crucial point of the Urbach rule is its development for band edges which approach linearity when plotted on a logarithmic scale. The rule was not developed in the context of nonlinear band edges with complex fine structure. Even so, this model has been applied extensively to band edges with such features. Using differential analysis and deconvolution, we develop an analytical technique capable of separating the components responsible for the fine structure observed. This approach reduces challenging signal-artifact convolution problems to a simple linear combination. Furthermore, we develop a model that can be applied to the nonlinear band-edge signals extracted. In this work, we show that in the limiting case of linearity, our model is consistent with and produces the same results as the linear Urbach model. Our model is capable of bypassing “tailing” as an indirect measure of the spread in the local potential and gives a direct measure of this quantity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Unraveling the discontinuous plastic flow of a Co-Cr-Fe-Ni-Mo multiprincipal-element alloy at deep cryogenic temperatures

We report an analysis of the discontinuous plastic flow of a multiprincipal-element alloy, Co 17.5 Cr 12.5 Fe 55 Ni 10 Mo 5 (atomic percent, at. %), in the temperature range of 0.5–4.2 K showing serrated deformation curves. Using the analytical techniques, we studied the statistics of the stress drops associated with the unstable plastic flow. The analysis showed that the complexity and heterogeneity of a discontinuous plastic flow were reduced when the temperature was lowered. This behavior was associated with the effects of dynamic recovery and adiabatic heating on the dislocation-density evolution.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Quantifying the impact of precision errors on quantum approximate optimization algorithms

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance from the perspective of both algorithmic training and performance guarantees. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that fixed precision implementations of QAOA circuits are subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters at the cost of increasing circuit depth.

quantum algorithms↗

DOE OSTI2025

Solving disorder in (3D) real space: a comparative study of the three-dimensional difference pair distribution function and atomic resolution holography reconstructions

The quantitative analysis of local ordering principles in disordered crystalline systems has gained much attention over the past few years, as it is often considered crucial for optimizing material functionality. This development has been driven by significant advancements in computational and experimental methods, which have led to the establishment and widespread use of various analytical techniques. In this study, we perform model calculations to compare the effectiveness of atomic resolution holography and three-dimensional difference pair distribution function analysis (3D-ΔPDF). Using Cu 3 Au as a model system, we demonstrate an approach to derive local order parameters quantitatively and show that both techniques are well suited to quantifying chemical short-range order correlations and local bond-distance variations. By evaluating the strengths and limitations of both techniques, we advocate for their combined use to solve complex short-range order problems accurately.

3D-ΔPDF↗

DOE OSTI2022

Worldwide Physics-Based Lifetime Prediction of c-Si Modules Due to Solder-Bond Failure

Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as the performance ratio method, Statistical clear sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the nonlinear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this article, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the variations of the temperature. Integrating the worldwide weather information from NASA/NSRDB databases, the model predicts the location-specific output-power degradation and the lifetime of a module due to solder bond failure. The model parameters are calibrated against qualification tests involving thermal cycling of specific batches of modules from a specific technology/manufacturer. The results may be summarized as: 1) Modules installed at higher latitudes show a longer lifetime due to reduced damage accumulation. 2) The reduction of temperature fluctuation close to large bodies of water, such as seashores, increases solder bond lifetime significantly. 3) Relatively speaking, modules installed close to the Tropic of Cancer/Capricorn (23.5 degrees North/South) suffer from a higher solder bond damage and have a shorter lifetime, suggesting a conservative design. This model should serve as a building block of a comprehensive reliability framework that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradation.

14 SOLAR ENERGY↗

DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗