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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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To prospectively compare survival between human immunodeficiency virus (HIV)-infected versus HIV-uninfected cervical cancer patients who initiated curative chemoradiation therapy (CRT) in a limited-resource setting.
We derive functional level-set derivatives for the Hamiltonian arising in self-consistent field theory, which are required to solve free boundary problems in the self-assembly of polymeric systems such as block copolymer melts. In particular, we consider Dirichlet, Neumann and Robin boundary conditions. We provide numerical examples that illustrate how these shape derivatives can be used to find equilibrium and metastable structures of block copolymer melts with a free surface in both two and three spatial dimensions.
We present a new method for stochastic shape optimisation of engineering structures. The method generalises an existing deterministic scheme, in which the structure is represented and evolved by a level-set method coupled with mathematical programming. The stochastic element of the algorithm is built on the methods of statistical mechanics and is designed so that the system explores a Boltzmann–Gibbs distribution of structures. In non-convex optimisation problems, the deterministic algorithm can get trapped in local optima: the stochastic generalisation enables sampling of multiple local optima, which aids the search for the globally-optimal structure. The method is demonstrated for several simple geometrical problems, and a proof-of-principle calculation is shown for a simple engineering structure.
This review paper provides an in-depth analysis of the role of Direct Current (DC) power systems in residential settings, focusing on their safety, efficiency, and environmental advantages. It recognizes the significant contribution of the residential sector to global carbon dioxide emissions and explores how DC power systems can help mitigate these effects. The paper traces the historical evolution of DC power, its resurgence with renewable energy technologies, and the advancements in power electronics that facilitate its integration. Emphasizing the efficiency and safety benefits, especially in low-voltage applications, the paper highlights the seamless integration of DC systems with inherently DC-generating renewable sources like solar PV and wind. The review discusses the critical role of converter technologies in the transition to DC power and examines the compatibility of DC systems with energy storage solutions, underscoring their potential for enhanced energy management. It also addresses the environmental impacts of adopting DC power, aligning with global carbon reduction efforts. The paper analyzes regional case studies, exploring practical applications and outcomes, and addresses challenges such as the need for standardization. It concludes with recommendations for stakeholders and future research directions, emphasizing the economic, technological, and environmental aspects of DC power systems. Overall, the paper presents DC power as a viable and sustainable option for residential energy conservation, contingent upon ongoing technological progress, supportive policies, and standardization.
Reaction of MCl{sub 2}{center_dot}4H{sub 2}O (M=Zn, Cd, Mn, Co, Ni) with 2-(5-(pyrazin-2-yl)-2H-tetrazol-2-yl) acetic acid (Hpztza) yielded a set of new M(II)/pztza complexes, [Cd(pztza){sub 2}(H{sub 2}O){sub 6}]{center_dot}3H{sub 2}O{center_dot}(Hpztza) (1), [M(pztza){sub 2}(H{sub 2}O){sub 2}; M=Cd(2), Zn(7), Mn(9)], [Cd(pztza){sub 2}]{center_dot}2(CH{sub 3}OH) (3), [Co(pztza){sub 2}(H{sub 2}O){sub 2}]{center_dot}6H{sub 2}O (4), [Co(pztza)(H{sub 2}O)Cl] (6) and [M(pztza){sub 2}(H{sub 2}O){sub 2}]{center_dot}2H{sub 2}O [M=Co(5), Zn(8), Ni(10)]. These compounds were structurally characterized by elemental analysis, IR spectroscopy and X-ray single-crystal diffraction. Complex 1 featured a mononuclear structure, complexes 4, 5, 7, 8, 10 showed 1D chains and complexes 2, 3, 6, 9 displayed 2D layer structures. Furthermore, the luminescence properties of 1-10 were investigated at room temperature in the solid state. - Graphical abstract: Ten new coordination polymers with 2-(5-(pyrazin-2-yl)-2H-tetrazol-2-yl) acetic acid (Hpztza) ligand have been synthesized and their structures have been characterized. All of the complexes show photoluminescence at room temperature. Highlights: Black-Right-Pointing-Pointer Ten novel transition metal-based coordination complexes with 2-(5-(pyrazin-2-yl)-2H-tetrazol-2-yl) acetic acid (Hpztza) are reported. Black-Right-Pointing-Pointer Complexes 1-10 are described as mononuclear structure, 1D and 2D frameworks with diverse architecture. Black-Right-Pointing-Pointer Six coordination complexes show emission at room temperature in the solid state.
Four new metal coordination complexes, namely, [Na(BTA)]{sub n} (1), [K{sub 2}(BTA){sub 2}(μ{sub 2}-H{sub 2}O)]{sub n} (2), and [M(BTA){sub 2}(H{sub 2}O){sub 2}]{sub n} (M=Ca(II) and Sr(II) for 3 and 4, respectively) [BTA=2-(1H-benzotriazol-1-yl) acetic anion], have been obtained under hydrothermal condition, by reacting the different alkali and alkaline-earth metal hydroxides with HBTA. Complexes 1–4 were structurally characterized by X-ray single-crystal diffraction, EA, IR, PXRD, and thermogravimetry analysis (TGA). These complexes display low-dimensional features displaying various two-dimensional (2D) and one-dimensional (1D) coordination motifs. Complex 1 displays a 2D layer with the thickness of 1.5 nm and possesses a topologic structure of a 11 nodal net with Schläfli symbol of (3{sup 18}). Complex 2 also shows a thick 2D sheet and its topologic structure is a 9 nodes with Schläfli symbol of (3{sup 11}×4{sup 2}). Complexes 3 and 4 possess a 1D linear chain and further stack via hydrogen bonding interactions to generate a three-dimensional supramolecular architecture. These results suggest that both the coordination preferences of the metal ions and the versatile nature of this flexible ligand play a critical role in the final structures. The luminescent spectra show strong emission intensities in complexes 1–4, which display violet photoluminescence. Additionally, ferroelectric, dielectric and nonlinear optic (NLO) second-harmonic generation (SHG) properties of 2 are discussed in detail. - Graphical abstract: A set of alkali and alkaline-earth metal coordination polymers were hydrothermally synthesized by 2-(1H-benzotriazol-1-yl)acetic acid, displaying interesting topologic motifs from two-dimension to one-dimension and specific physical properties. - Highlights: • Alkali and alkaline-earth metal coordination polymers have been obtained. • The ligand 2-(1H-benzotriazol-1-yl)acetic acid has been adopted. • The two-dimensional and one-dimensional structures have been observed. • The properties of second harmonic generation and ferroelectricity for complex 2.
Here this paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a family of plasticity models where the yield surface is implicitly represented by a scalar-valued function. In particular, our focus is to introduce a practical framework that can be deployed for engineering analysis that employs a user-defined material subroutine (UMAT/VUMAT) for ABAQUS, which is written in FORTRAN. To accomplish this task while leveraging the back-propagation learning algorithm to speed up the neural-network training, we introduce an interface code where the weights and biases of the trained neural networks obtained via the PyTorch library can be automatically converted into a generic FORTRAN code that can be a part of the UMAT/VUMAT algorithm. To enable third-party validation, we purposely make all the data sets, source code used to train the neural-network-based constitutive models, and the trained models available in a public repository. Furthermore, the practicality of the workflow is then further tested on a dataset for anisotropic yield function to showcase the extensibility of the proposed framework. A number of representative numerical experiments are used to examine the accuracy, robustness and reproducibility of the results generated by the neural network models.
Studies suggest that anterior beams with in vivo range verification would improve rectal dosimetry in proton therapy for prostate cancer. We investigated whether prostate-rectum spacers would enhance or diminish the benefits of anterior proton beams in these treatments. Twenty milliliters of hydrogel was injected between the prostate and rectum of a cadaver using a transperineal approach. Computed tomography (CT) and magnetic resonance (MR) images were used to generate 7 uniform scanning (US) and 7 single-field uniform dose pencil-beam scanning (PBS) plans with different beam arrangements. Pearson correlations were calculated between rectal, bladder, and femoral head dosimetric outcomes and beam arrangement anterior scores, which characterize the degree to which dose is delivered anteriorly. The overall quality of each plan was compared using a virtual dose-escalation study. For US plans, rectal mean dose was inversely correlated with anterior score, but for PBS plans there was no association between rectal mean dose and anterior score. For both US and PBS plans, full bladder and empty bladder mean doses were correlated with anterior scores. For both US and PBS plans, femoral head mean doses were inversely correlated with anterior score. For US plans and a full bladder, 4 beam arrangements that included an anterior beam tied for the highest maximum prescription dose (MPD). For US plans and an empty bladder, the arrangement with 1 anterior and 2 anterior oblique beams achieved the highest MPD in the virtual dose-escalation study. The dose-escalation study did not differentiate beam arrangements for PBS. All arrangements in the dose-escalation study were limited by bladder constraints except for the arrangement with 2 posterior oblique beams. The benefits of anterior proton beams in the setting of prostate-rectum spacers appear to be proton modality dependent and may not extend to PBS.
Cryogenic macro-calorimeters instrumented with NTD thermistors have been developed for several decades. The choice of the optimal bias current is crucial for a proper operation of these detectors, both in terms of energy resolution and stability. In this paper we present a set of automatic measurements and analysis procedures for the characterization and optimization of the working configuration of the NTD thermistors. The presented procedures were developed for CUORE, an array of 988 cryogenic macro-calorimeters instrumented with NTD thermistors that has been taking data since 2017. These procedures made it possible to characterize a large number of detectors in a reliable way. They are suitable enough to be used also in other large arrays of cryogenic detectors, such as CUPID.
In this note we discuss subtleties associated with the efficiency corrections for measurements of off-diagonal cumulants and factorial moments for a situation when one deals with overlapping sets of particles, such as correlations between numbers of protons and positively charged particles. In particular, we discuss the situation commonly encountered in heavy-ion experiments, where first all charges are reconstructed and then protons are selected from these charges by an additional particle identification procedure. We present the efficiency correction formulas for the case when the detection efficiencies follow a binomial distribution.
ABSTRACT: For the development of a human West Nile (WN) infectious DNA (iDNA) vaccine, we created highly attenuated chimeric virus W1806 with the serological identity of highly virulent WN-NY99. Earlier, we attempted to utilize mutations found in the E protein of the SA14-14-2 vaccine to bring safety of W1806 to the level acceptable for human use (). Here, we analyzed effects of the SA14-14-2 changes on growth properties and neurovirulence of W1806. A set including the E138K, K279M, K439R and G447D changes was identified as the perspective subset for satisfying the target safety profile without compromising immunogenicity of the vaccine candidate. The genetic stability of the attenuated phenotype was found to be unsatisfactory being dependent on a subset of attenuating changes incorporated in W1806. Elucidation of underlying mechanisms influencing selection of pathways for restoration of the envelope protein functionality will facilitate resolution of the emerged genetic stability issue. - Highlights: •Effect of mutations in E on properties of WN1806 is determined. •A subset of attenuating mutations suitable for a human vaccine is defined. •Mechanism of attenuation is proposed and illustrated. •Underlying mechanisms of neurovirulence reversion are suggested.
Systems subjected to dynamic loads often require monitoring of their vibrational response, but limitations on the total number and placement of the measurement sensors can hinder the data-collection process. Here, we present an indirect approach to estimate a system’s full-field dynamic response, including all uninstrumented locations, using response measurements from sensors sparsely located on the system. This approach relies on Bayesian inference that utilizes a system model to estimate the full-field response and quantify the uncertainty in these estimates. By casting the estimation problem in the frequency domain, this approach utilizes the modal frequency response functions as a natural, frequency-dependent weighting scheme for the system mode shapes to perform the expansion. This frequency-dependent weighting scheme enables an accurate expansion, even with highly correlated mode shapes that may arise from spatial aliasing due to the limited number of sensors, provided these correlated modes do not have natural frequencies that are closely spaced. Furthermore, the inherent regularization mechanism that arises in this Bayesian-based procedure enables the utilization of the full set of system mode shapes for the expansion, rather than any reduced subset. This approach can produce estimates when considering a single realization of the measured responses, and with some modification, it can also produce estimates for power spectral density matrices measured from many realizations of the responses from statistically stationary random processes. A simply supported beam provides an initial numerical validation, and a cylindrical test article excited by acoustic loads in a reverberation chamber provides experimental validation.
Quantitative assessment of gas-particle partitioning of individual components within complex atmospheric organic aerosols (OA) mixtures is critical for predicting and comprehending the formation and evolution of OA particles in the atmosphere. This investigation leverages previously documented data obtained through a temperature programmed desorption - direct analysis in real time – high resolution mass spectrometry (TPD-DART-HRMS) platform. This methodology facilitates the bottom-up construction of volatility basis set (VBS) distributions for constituents found in three biogenic secondary organic aerosol (SOA) mixtures produced through the ozonolysis of a-pinene, limonene, and ocimene. The apparent enthalpies (ΔH*, kJ mol -1 ) and saturated vapor mass concentrations (C T *, µg∙m -3 ) of individual SOA components, determined as a function of temperature (T, K), facilitated an assessment of changes in VBS distributions and gas-particle partitioning with respect to T and atmospheric total organic mass loadings (tOM, µg∙m -3 ). Further, the VBS distributions reveal distinct differences in volatilities among monomers, dimers, and trimers, enabling their categorization into separate volatility bins. At the ambient temperature of T = 298 K, only monomers efficiently partition between gas and particle phases across a broad range of atmospherically relevant total organic mass loadings (tOM) values of 1–100 µg∙m -3 . Partitioning of dimers and trimers becomes notable only at T > 360 K and T > 420 K, respectively. The viscosity of SOA mixtures is assessed using a bottom-up calculation approach, incorporating the input of elemental formulas, ΔH*, C T *, and particle-phase mass fractions of the SOA components. Through this approach, we are able to accurately estimate the variations in SOA viscosity that result from the evaporation of its components. These variations are, in turn, influenced by atmospherically relevant changes in tOM and T. Comparison of the calculated SOA viscosity and diffusivity values with literature reported experimental results shows close agreement, thereby validating the employed calculation approach. These findings underscore the significant potential for TPD-DART-HRMS measurements in enabling the untargeted analysis of organic molecules within OA mixtures. This approach facilitates quantitative assessment of their gas-particle partitioning and allows for the estimation of their viscosity and condensed-phase diffusion, thereby contributing valuable insights to atmospheric models.
Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.
Bioenergy with carbon capture and storage (BECCS) sits at the nexus of the climate and energy security. We evaluated trade-offs between scenarios that support climate stabilization (negative emissions and net climate benefit) or energy security (ethanol production). Our spatially explicit model indicates that the foregone climate benefit from abandoned cropland (opportunity cost) increased carbon emissions per unit of energy produced by 14–36%, making geologic carbon capture and storage necessary to achieve negative emissions from any given energy crop. The toll of opportunity costs on the climate benefit of BECCS from set-aside land was offset through the spatial allocation of crops based on their individual biophysical constraints. Dedicated energy crops consistently outperformed mixed grasslands. We estimate that BECCS allocation to land enrolled in the Conservation Reserve Program (CRP) could capture up to 9 Tg C year –1 from the atmosphere, deliver up to 16 Tg CE year –1 in emissions savings, and meet up to 10% of the US energy statutory targets, but contributions varied substantially as the priority shifted from climate stabilization to energy provision. Furthermore, our results indicate a significant potential to integrate energy security targets into sustainable pathways to climate stabilization but underpin the trade-offs of divergent policy-driven agendas.
Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.
We develop a formalism for calculating forces on the nuclei within the linear-scaling stochastic density functional theory (sDFT) in a nonorthogonal atom-centered basis set representation (Fabian et al. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2019, 9, e1412, 10.1002/wcms.1412) and apply it to the Tryptophan Zipper 2 (Trp-zip2) peptide solvated in water. We use an embedded-fragment approach to reduce the statistical errors (fluctuation and systematic bias), where the entire peptide is the main fragment and the remaining 425 water molecules are grouped into small fragments. We analyze the magnitude of the statistical errors in the forces and find that the systematic bias is of the order of 0.065 eV/Å (~1.2 × 10 -3 E h / a 0 ) when 120 stochastic orbitals are used, independently of system size. This magnitude of bias is sufficiently small to ensure that the bond lengths estimated by stochastic DFT (within a Langevin molecular dynamics simulation) will deviate by less than 1% from those predicted by a deterministic calculation.
Here, geometric energy derivatives which rely on core-corrected focal-point energies extrapolated to the complete basis set (CBS) limit of coupled cluster theory with iterative and noniterative quadruple excitations, CCSDTQ and CCSDT(Q), are used as elements of molecular gradients and, in the case of CCSDT(Q), expansion coefficients of an anharmonic force field. These gradients are used to determine the CCSDTQ/CBS and CCSDT(Q)/CBS equilibrium structure of the S 0 ground state of H 2 CO where excellent agreement is observed with previous work and experimentally derived results. A fourth-order expansion about this CCSDT(Q)/CBS reference geometry using the same level of theory produces an exceptional level of agreement to spectroscopically observed vibrational band origins with a MAE of 0.57 cm –1 . Second-order vibrational perturbation theory (VPT2) and variational discrete variable representation (DVR) results are contrasted and discussed. Vibration–rotation, anharmonicity, and centrifugal distortion constants from the VPT2 analysis are reported and compared to previous work. Additionally, an initial application of a sum-over-states fourth-order vibrational perturbation theory (VPT4) formalism is employed herein, utilizing quintic and sextic derivatives obtained with a recursive algorithmic approach for response theory.