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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Depletion-Driven Assembly of Polymer-Coated Nanocrystals

Depletion-driven assembly has been widely studied for micron-sized colloids, but questions remain at the nanoscale where the governing physics are impacted by stabilizing surface ligands or wrapping polymers, whose dimensions are similar to those of the colloidal core and the depletant. Here, we probe how wrapping colloidal tin-doped indium oxide nanocrystals with polymers affects their depletion-induced interactions and assembly in solutions of polyethylene glycol. Copolymers of polyacrylic acid grafted with polyethylene oxide provide nanocrystal wrappings with different effective polymer graft densities and molecular weights. (Ultra) small angle X-ray scattering, coarse-grained molecular dynamics simulation, and molecular thermodynamic theory were combined to analyze how depletant size and polymer wrapping characteristics affect depletion interactions, structure, and phase behavior. The results show how depletant molecular weight, as well as surface density and molecular weight of polymer grafts, set thresholds for assembly. These signatures are unique to depletion-driven assembly of nanoscale colloids and mirror phase behaviors of grafted nanoparticle–polymer composites. Optical and rheological responses of depletion-driven assemblies of nanocrystals with different polymer shell architectures were probed to learn how their structural differences impact properties. Finally, we discuss how these wrinkles for depletion-driven assembly at the nanoscale may provide fresh opportunities for designing responsive depletion interactions and dynamically reconfigurable materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Topological structure of complex predictions

Abstract Current complex prediction models are the result of fitting deep neural networks, graph convolutional networks or transducers to a set of training data. A key challenge with these models is that they are highly parameterized, which makes describing and interpreting the prediction strategies difficult. We use topological data analysis to transform these complex prediction models into a simplified topological view of the prediction landscape. The result is a map of the predictions that enables inspection of the model results with more specificity than dimensionality-reduction methods such as tSNE and UMAP. The methods scale up to large datasets across different domains. We present a case study of a transformer-based model previously designed to predict expression levels of a piece of DNA in thousands of genomic tracks. When the model is used to study mutations in the BRCA1 gene, our topological analysis shows that it is sensitive to the location of a mutation and the exon structure of BRCA1 in ways that cannot be found with tools based on dimensionality reduction. Moreover, the topological framework offers multiple ways to inspect results, including an error estimate that is more accurate than model uncertainty. Further studies show how these ideas produce useful results in graph-based learning and image classification.

Computer Science↗
DOE OSTI2024

A core of cell wall proteins functions in wall integrity responses in Arabidopsis thaliana

Abstract Cell walls surround all plant cells, and their composition and structure are tightly regulated to maintain cellular and organismal homeostasis. In response to wall damage, the cell wall integrity (CWI) system is engaged to ameliorate effects on plant growth. Despite the central role CWI plays in plant development, our current understanding of how this system functions at the molecular level is limited. Here, we investigated the transcriptomes of etiolated seedlings of mutants of Arabidopsis thaliana with defects in three major wall polysaccharides, pectin ( quasimodo2 ), cellulose ( cellulose synthase3 je5 ), and xyloglucan ( xyloglucan xylosyltransferase1 and 2 ), to probe whether changes in the expression of cell wall‐related genes occur and are similar or different when specific wall components are reduced or missing. Many changes occurred in the transcriptomes of pectin‐ and cellulose‐deficient plants, but fewer changes occurred in the transcriptomes of xyloglucan‐deficient plants. We hypothesize that this might be because pectins interact with other wall components and/or integrity sensors, whereas cellulose forms a major load‐bearing component of the wall; defects in either appear to trigger the expression of structural proteins to maintain wall cohesion in the absence of a major polysaccharide. This core set of genes functioning in CWI in plants represents an attractive target for future genetic engineering of robust and resilient cell walls.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Anomalies in global SMEFT analyses. A case study of first-row CKM unitarity

Recent developments in the Standard Model analysis of semileptonic charged-current processes involving light quarks have revealed ~ 3σ tensions in Cabibbo universality tests involving meson, neutron, and nuclear beta decays. In this paper, we explore beyond the Standard Model explanations of this so-called Cabibbo Angle Anomaly in the framework of the Standard Model Effective Field Theory (SMEFT), including not only low-energy charged current processes (‘L’), but also electroweak precision observables (‘EW’) and Drell-Yan collider processes (‘C’) that probe the same underlying physics across a broad range of energy scales. The resulting ‘CLEW’ framework not only allows one to test explanations of the Cabibbo Angle Anomaly, but is set up to provide near model-independent analyses with minimal assumptions on the flavor structure of the SMEFT operators. Besides the global analysis, we consider a large number of simpler scenarios, each with a subset of SMEFT operators, and investigate how much they improve upon the Standard Model fit. We find that the most favored scenarios, as judged by the Akaike Information Criterion, are those that involve right-handed charged currents. Additional interactions, namely oblique operators, terms modifying the Fermi constant, and operators involving right-handed neutral currents, play a role if the CDF determination of the W mass is included in the analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

On the relation between the subadditivity cone and the quantum entropy cone

Given a multipartite quantum system, what are the possible ways to impose mutual independence among some subsystems, and the presence of correlations among others, such that there exists a quantum state which satisfies these demands? This question and the related notion of a pattern of marginal independence (PMI) were introduced in [1], and then argued in [2] to be central in the derivation of the holographic entropy cone. Here we continue the general information theoretic analysis of the PMIs allowed by strong subadditivity (SSA) initiated in [1]. We show how the computation of these PMIs simplifies when SSA is replaced by a weaker constraint, dubbed Klein’s condition (KC), which follows from the necessary condition for the saturation of subadditivity (SA). Formulating KC in the language of partially ordered sets, we show that the set of PMIs compatible with KC forms a lattice, and we investigate several of its structural properties. One of our main results is the identification of a specific lower dimensional face of the SA cone that contains on its boundary all the extreme rays (beyond Bell pairs) that can possibly be realized by quantum states. We verify that for four or more parties, KC is strictly weaker than SSA, but nonetheless the PMIs compatible with SSA can easily be derived from the KC-compatible ones. For the special case of 1-dimensional PMIs, we conjecture that KC and SSA are in fact equivalent. To make the presentation self-contained, we review the key ingredients from lattice theory as needed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Harnessing Machine Learning to Predict MoS 2 Solid Lubricant Performance

Physical vapor deposited (PVD) molybdenum disulfide (MoS 2 ) solid lubricant coatings are an exemplar material system for machine learning methods due to small changes in process variables often causing large variations in microstructure and mechanical/tribological properties. Here, in this work, a gradient boosted regression tree machine learning method is applied to an existing experimental data set containing process, microstructure, and property information to create deeper insights into the process-structure–property relationships for molybdenum disulfide (MoS 2 ) solid lubricant coatings. The optimized and cross-validated models show good predictive capabilities for density, reduced modulus, hardness, wear rate, and initial coefficients of friction. The contribution of individual deposition variables (i.e., argon pressure, deposition power, target conditioning) on coating properties is highlighted through feature importance. The process-property relationships established herein show linear and non-linear relationships and highlight the influence of uncontrolled deposition variables (i.e., target conditioning) on the tribological performance.

MoS2↗
DOE OSTI2023

A semi-agnostic ansatz with variable structure for variational quantum algorithms

Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Low-Energy Isomers of the Magic Number H + (H 2 O) 21 Cluster

Electronic structure calculations are used to characterize low-energy isomers of H + (H 2 O) 21 . Eleven different classes of isomers, based on the (H 2 O) 20 pentagonal dodecahedron with the excess proton localized on the surface (as a hydrated hydronium ion) and the “extra” water molecule located in the interior of the cluster, are characterized. In 10 of these classes, the internal water molecule is engaged in six 5-membered rings, but in the remaining class, which is predicted to start at only 0.6 kcal/mol above the global minimum, the internal water is engaged in a 4- membered ring, an additional 6-membered ring, and four 5- membered rings. In addition, isomers with two 4-membered rings and two 6-membered rings on the cluster surface are predicted to start at only ∼1.3 kcal/mol above the lowest-energy dodecahedralbased structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Transversely pumped laser driven particle accelerator

We present a novel acceleration scheme capable of accelerating electrons and ions in an underdense plasma. Transversely Pumped Acceleration (TPA) uses multiple arrays of counter-propagating laser beamlets that focus onto a central acceleration axis. Tuning the injection timing and the spacing between the adjacent beamlets allows for precise control over the position and velocity of the intersection point of the counter-propagating beam arrays. This results in an accelerating structure that propagates orthogonal to the direction of laser propagation. We present the theory that sets the injection timing of the incoming pulses to accelerate electrons and ions with a tunable phase velocity plasma wave. Simulation results are also presented which demonstrate 1.12 GeV proton beams accelerated in 3.6 mm of plasma and electron acceleration gradients on the order of 1 TeV/m in a scheme that circumvents dephasing. This work has potential applications as a compact accelerator for medical physics and high energy physics colliders.

Laser-produced plasmas↗
DOE OSTI2023

Growth or Decay – I: universality of the turbulent dynamo saturation

The turbulent small-scale dynamo (SSD) is likely to be responsible for the magnetisation of the interstellar medium (ISM) that we observe in the Universe today. The SSD efficiently converts kinetic energy E kin into magnetic energy E mag , and is often used to explain how an initially weak magnetic field with E mag $\ll$ E kin is amplified, and then maintained at a level E mag ≲ E kin . Usually, this process is studied by initialising a weak seed magnetic field and letting the turbulence grow it to saturation. However, in this Part I of the Growth or Decay series, using three-dimensional, visco-resistive magnetohydrodynamical turbulence simulations up to magnetic Reynolds numbers of 2000, here we show that the same final state in the integral quantities, energy spectra, and characteristic scales of the magnetic field can also be achieved if initially E mag ~ E kin or even if initially E mag $\gg$ E kin . This suggests that the final saturated state of the turbulent dynamo is set by the turbulence and the material properties of the plasma, independent of the initial structure or amplitude of the magnetic field. We discuss the implications this has for the maintenance of magnetic fields in turbulent plasmas and future studies exploring the dynamo saturation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Attosecond X-Ray Core-Level Chronoscopy of Aromatic Molecules

Attosecond photoemission or photoionization delays are a unique probe of the structure and the electronic dynamics of matter. However, the spectral congestion of valence photoelectron spectra sets fundamental limits to the complexity of systems that can be studied, and the delocalization of valence electron wave functions blurs the spatial origin of the photoelectron wave packet. Using attosecond x-ray pulses from LCLS, we demonstrate the key advantages of measuring core-level delays: The photoelectron spectra remain atomlike, the measurements become element specific, and the observed scattering dynamics originate from a pointlike source when multicenter interference effects are negligible. We exploit these unique features to reveal the effects of changing functional groups (C-H vs N) and symmetry on attosecond scattering dynamics by measuring and calculating the photoionization delays between N−1⁢𝑠 and C−1⁢𝑠 core shells of a series of aromatic azabenzene molecules. Remarkably, the delays increase with the number of nitrogen atoms in the molecule and reveal multiple resonances. We identify two previously unknown mechanisms regulating the associated attosecond dynamics, namely the enhanced confinement of the trapped wave function with the replacement of C-H groups by N atoms and the decrease of the coupling strength among the photoemitted partial waves with increasing symmetry. This study demonstrates the unique opportunities opened by measurements of core-level photoionization delays for unraveling attosecond electron dynamics in complex matter.

Ji, Jia-Bao [Eidgenoessische Technische Hochschule↗
DOE OSTI2022

Towards a more general understanding of the algorithmic utility of recurrent connections

Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Studies on the Fundamental Mechanisms of Niobium Electropolishing

To improve the superconducting performance of niobium SRF cavities, electropolishing (EP) with a sulfuric and hydroflouric acid mixture is used. The chemistry of this reaction is complex due to the interactions between diffusion mechanisms, surface oxide structure, and multiple chemical species. Past studies on the EP process have produced a certain set of optimum parameters that have been used successfully for a long time. However, two recent developments have called the efficacy of the existing EP process into question. Since the introduction of nitrogen doping the surface quality of some cavities has been very poor. Also, EP performed at colder than standard temperatures leads to an increase in the cavity performance. To understand these questions, we perform a multivariate study on the EP process using niobium test samples electropolished at different temperatures and potentials. We find that electropolishing at lower potentials leads to rough surface features such as pitting and grain etching. Some of the surface features show similarities to features seen in niobium cavities. The effect of electropolishing temperature is not clear based on the results of this study.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Quantum-inspired tempering for ground state approximation using artificial neural networks

A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local minima, especially for frustrated systems and even if the representation is sufficiently expressive. We propose a parallel tempering method that facilitates escape from such local minima. This methods involves training multiple ANNs independently, with each simulation governed by a Hamiltonian with a different "driver" strength, in analogy to quantum parallel tempering, and it incorporates an update step into the training that allows for the exchange of neighboring ANN configurations. We study instances from two classes of Hamiltonians to demonstrate the utility of our approach using Restricted Boltzmann Machines as our parameterized ANN. The first instance is based on a permutation-invariant Hamiltonian whose landscape stymies the standard training algorithm by drawing it increasingly to a false local minimum. The second instance is four hydrogen atoms arranged in a rectangle, which is an instance of the second quantized electronic structure Hamiltonian discretized using Gaussian basis functions. We study this problem in a minimal basis set, which exhibits false minima that can trap the standard variational algorithm despite the problem’s small size. We show that augmenting the training with quantum parallel tempering becomes useful to finding good approximations to the ground states of these problem instances.

Albash, Tameem↗
DOE OSTI2024

Cell-free bioelectrocatalytic platform for carbon dioxide reduction (Final Technical Report)

The University of Minnesota (UMN) EcoSynBio Team aimed to develop a cell-free, enzyme-based platform for the electro- biocatalytic conversion of CO2 into formate as a platform chemical for further upgrading. This type of bio electrocatalytic process delivers a clean product stream without the need for extensive separation from the electrolyte as in electrochemical synthesis and microbial processes. The reduction reaction is catalyzed by metal-dependent formate dehydrogenases (mFDHs) that are capable of efficient electrocatalytic CO2 reduction without the need of costly co-factors. The development of an efficient, scalable electrobiocatalytic process with high total turnover numbers and viable space time yields, however, was not without its challenges. The UM team has developed a protein-based scaffolding system that facilitates enzyme stabilization and attachment to electrodes along with electron transfer. Yet, although FDHs are highly promising enzymes for cell-free, electrobiochemical CO2 reduction, they are also greatly understudied and especially for applications in electrocatalysis. The UM team used the best described mFDH from Clostridium as its benchmark system and spent significant time and effort in attempting to replicate published data and finally, redesigned a recombinant production system for proper metal co-factor incorporation. The UM team has also identified a small set of new enzyme homologs from extreme microorganisms with superior stabilities that have yielded initial structural data for further engineering. In addition, a new bioelectrocatalytic reactor system has been developed that can be 3D printed and used for enzyme attachment to electrodes. In summary the project has generated critical basic information for the further development of this class of enzymes for the electricity driven reduction of CO2 into formate as platform chemical for upgrading into various other chemicals, including fuels.

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

Dynamic Analysis of a Six-Cable Parallel Robot for Automated Panelized Building Retrofits

Cable-Driven Parallel Robots (CDPRs) are highly suitable for automated panelized building retrofits, thanks to their compact footprint and high payload-to-weight ratio. A common CDPR configuration featuring eight cables, where the anchors form a rectangular prism in front of the building facade, offers a large wrench feasible workspace and good control versatility. However, installing upper anchor points requires additional support structures, such as towers or beams, increasing setup complexity and posing logistical challenges in construction settings. To mitigate these challenges, we propose a six-cable CDPR model specifically designed for automated panelized building retrofits. Although the feasible workspace is limited, our analysis shows that the proposed CDPR adequately covers the critical areas required for panel installation. To validate that the six-cable system can effectively transport the end effector to the desired installation pose, we calculated optimal trajectories based on a constrained dynamic model. The simulation results of the six-cable CDPR demonstrate promising potential for automated panelized building retrofits, effectively balancing simplicity, cost-effectiveness, and functionality.

Liu, Yifang [ORNL] (ORCID:0000000190817417)↗
DOE OSTI2021

In situ EXAFS study of Sr adsorption on TiO 2 (110) under high ionic strength wastewater conditions

In order to provide important details concerning the adsorption reactions of Sr, batch reactions and a set of both ex situ and in situ Grazing Incidence X-ray Absorption Fine Structure (GIXAFS) adsorption experiments were completed on powdered TiO 2 and on rutile(110), both reacted with either SrCl 2 or SrCO 3 solutions. TiO 2 sorption capacity for strontium (Sr) ranges from 550 ppm (SrCl 2 solutions, second order kinetics) to 1400 ppm (SrCO 3 solutions, first order kinetics), respectively, and is rapid. Sr adsorption decreased as a function of chloride concentration but significantly increased as carbonate concentrations increased. In the presence of carbonate, the ability of TiO 2 to remove Sr from the solution increases by a factor of ~4 due to rapid epitaxial surface precipitation of an SrCO 3 thin film, which registers itself on the rutile(110) surface as a strontianite-like phase (d-spacing 2.8 Å). Extended X-ray Absorption Fine Structure (EXAFS) results suggest the initial attachment is via tetradental inner-sphere Sr adsorption. Moreover, adsorbates from concentrated SrCl 2 solutions contain carbonate and hydroxyl species, which results in both inner- and outer-sphere adsorbates and explains the reduced Sr adsorption in these systems. These results not only provide new insights into Sr kinetics and adsorption on TiO 2 but also provide valuable information concerning potential improvements in effluent water treatment models and are pertinent in developing treatment methods for rutile-coated structural materials within nuclear power plants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗