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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Synergistic Enzyme Mixtures to Realize Near-Complete Depolymerization in Biodegradable Polymer/Additive Blends

Embedding catalysts inside of plastics affords accelerated chemical modification with programmable latency and pathways. Nanoscopically embedded enzymes can lead to near-complete degradation of polyesters via chain-end mediated processive depolymerization. The overall degradation rate and pathways have a strong dependence on the morphology of semicrystalline polyesters. Yet, most studies to date focus on pristine polymers instead of mixtures that contain additives and other components despite their nearly universal use in plastic production. Here, for this study, additives are introduced to purposely change the morphology of polycaprolactone (PCL) by increasing the bending and twisting of crystalline lamellae. These morphological changes immobilize chain ends preferentially at the crystalline/amorphous interfaces and limit chain-end accessibility by the embedded processive enzyme. This chain-end redistribution reduces the polymer-to-monomer conversion from >95% to less than 50%, causing formation of highly crystalline plastic pieces, including microplastics. By synergizing both random chain scission and processive depolymerization, it is feasible to navigate morphological changes in polymer/additive blends and to achieve near-complete depolymerization. The random scission enzymes in the amorphous domains create new chain ends that are subsequently bound and depolymerized by processive enzymes. Present studies further highlight the importance to consider how the host polymer's morphologies affect the reactions catalyzed by embedded catalytic species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Make safety awareness a priority: Use a login software in your research facility

We report on a facility login software, whose objective is to improve safety in multi-user research facilities. Its most important safety features are: 1) blocks users from entering the lab after being absent for more than a predetermined number of days; 2) gives users a random safety quiz question, which they need to answer satisfactorily in order to use the facility; 3) blocks unauthorized users from using the facility afterhours; and 4) displays the current users in the facility. Besides restricting access to unauthorized users, the software keeps users mindful of key safety concepts. In addition, integration of the software with a door controller system can convert it into an effective physical safety mechanism. Depending on DOE approval, the code may be available as open source.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

Characteristics of level-spacing statistics in chaotic graphene billiards

A fundamental result in nonrelativistic quantum nonlinear dynamics is that the spectral statistics of quantum systems that possess no geometric symmetry, but whose classical dynamics are chaotic, are described by those of the Gaussian orthogonal ensemble (GOE) or the Gaussian unitary ensemble (GUE), in the presence or absence of time-reversal symmetry, respectively. For massless spin-half particles such as neutrinos in relativistic quantum mechanics in a chaotic billiard, the seminal work of Berry and Mondragon established the GUE nature of the level-spacing statistics, due to the combination of the chirality of Dirac particles and the confinement, which breaks the time-reversal symmetry. A question is whether the GOE or the GUE statistics can be observed in experimentally accessible, relativistic quantum systems. We demonstrate, using graphene confinements in which the quasiparticle motions are governed by the Dirac equation in the low-energy regime, that the level-spacing statistics are persistently those of GOE random matrices. We present extensive numerical evidence obtained from the tight-binding approach and a physical explanation for the GOE statistics. We also find that the presence of a weak magnetic field switches the statistics to those of GUE. For a strong magnetic field, Landau levels become influential, causing the level-spacing distribution to deviate markedly from the random-matrix predictions. Issues addressed also include the effects of a number of realistic factors on level-spacing statistics such as next nearest-neighbor interactions, different lattice orientations, enhanced hopping energy for atoms on the boundary, and staggered potential due to graphene-substrate interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Optical quantum memory for noble-gas spins based on spin-exchange collisions

Optical quantum memories, which store and preserve the quantum state of photons, rely on a coherent mapping of the photonic state onto matter states that are optically accessible. Here we outline and characterize schemes to map the state of photons onto long-lived but optically inaccessible collective states of noble-gas spins. The mapping employs coherent spin-exchange interaction arising from random collisions with alkali vapor. We propose efficient storage strategies in two operating regimes and analyze their performance for several proposed experimental configurations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Computational Thermodynamics of Materials Zi-Kui Liu and Yi Wang

This authoritative volume introduces the reader to computational thermodynamics and the use of this approach to the design of material properties by tailoring the chemical composition. The text covers applications of this approach, introduces the relevant computational codes, and offers exercises at the end of each chapter. The book has nine chapters and two appendices that provide background material on computer codes. Chapter 1 covers the first and second laws of thermodynamics, introduces the spinodal as the limit of stability, and presents the Gibbs-Duhem equation. Chapter 2 focuses on the Gibbs energy function. Starting with a homogeneous system with a single phase, the authors proceed to phases with variable compositions, and polymer blends. The discussion includes the contributions of external electric and magnetic fields to the Gibbs energy. Chapter 3 deals with phase equilibria in heterogeneous systems, the Gibbs phase rule, and phase diagrams. Chapter 4 briefly covers experimental measurements of thermodynamic properties used as input for thermodynamic modeling by Calculation of Phase Diagrams (CALPHAD). Chapter 5 discusses the use of density functional theory to obtain thermochemical data and fill gaps where experimental data is missing. The reader is introduced to the Vienna Ab Initio Simulation Package (VASP) for density functional theory and the YPHON code for phonon calculations. Chapter 6 introduces the modeling of Gibbs energy of phases with the CALPHAD method. Chapter 7 deals with chemical reactions and the Ellingham diagram for metal-oxide systems and presents the calculation of the maximum reaction rate from equilibrium thermodynamics. Chapter 8 is devoted to electrochemical reactions and Pourbaix diagrams with application examples. Chapter 9 concludes this volume with the application of a model of multiple microstates to Ce and Fe3Pt. CALPHAD modeling is briefly discussed in the context of genomics of materials. The book introduces basic thermodynamic concepts clearly and directs readers to appropriate references for advanced concepts and details of software implementation. The list of references is quite comprehensive. The authors make liberal use of diagrams to illustrate key concepts. The two Appendices at the end discuss software requirements and the file structure, and present templates for special quasi-random structures. There is also a link to download pre-compiled binary files of the YPHON code for Linux or Microsoft Windows systems. The exercises at the end of the chapters assume that the reader has access to VASP, which is not freeware. Readers without access to this code can work on a limited number of exercises. However, results from other first principles codes can be organized in the YPHON format as explained in the Appendix. This book will serve as an excellent reference on computational thermodynamics and the exercises provided at the end of each chapter make it valuable as a graduate level textbook. Reviewer: Ram Devanathan is Acting Director of Earth Systems Science Division, Pacific Northwest National Laboratory, USA.

36 MATERIALS SCIENCE↗
NASA NTRS1996

Chance and necessity in the selection of nucleic acid catalysts

In Tom Stoppard's famous play [Rosencrantz and Guildenstern are Dead], the ill-fated heroes toss a coin 101 times. The first 100 times they do so the coin lands heads up. The chance of this happening is approximately 1 in 10(30), a sequence of events so rare that one might argue that it could only happen in such a delightful fiction. Similarly rare events, however, may underlie the origins of biological catalysis. What is the probability that an RNA, DNA, or protein molecule of a given random sequence will display a particular catalytic activity? The answer to this question determines whether a collection of such sequences, such as might result from prebiotic chemistry on the early earth, is extremely likely or unlikely to contain catalytically active molecules, and hence whether the origin of life itself is a virtually inevitable consequence of chemical laws or merely a bizarre fluke. The fact that a priori estimates of this probability, given by otherwise informed chemists and biologists, ranged from 10(-5) to 10(-50), inspired us to begin to address the question experimentally. As it turns out, the chance that a given random sequence RNA molecule will be able to catalyze an RNA polymerase-like phosphoryl transfer reaction is close to 1 in 10(13), rare enough, to be sure, but nevertheless in a range that is comfortably accessible by experiment. It is the purpose of this Account to describe the recent advances in combinatorial biochemistry that have made it possible for us to explore the abundance and diversity of catalysts existing in nucleic acid sequence space.

Review, Tutorial↗
DOE OSTI2024

Slow thermalization and subdiffusion in $U$(1) conserving Floquet random circuits

Random quantum circuits are paradigmatic models of minimally structured and analytically tractable chaotic dynamics. We study a family of Floquet unitary circuits with Haar random U(1) charge conserving dynamics; the minimal such model has nearest-neighbor gates acting on spin-1/2 qubits, and a single layer of even/odd gates repeated periodically in time. We find that this minimal model is not robustly thermalizing at numerically accessible system sizes, and displays slow subdiffusive dynamics for long times. We map out the thermalization dynamics in a broader parameter space of charge conserving circuits, and understand the origin of the slow dynamics in terms of proximate localized and integrable regimes in parameter space. In contrast, we find that small extensions to the minimal model are sufficient to achieve robust thermalization; these include (i) increasing the interaction range to three-site gates, (ii) increasing the local Hilbert space dimension by appending an additional unconstrained qubit to the conserved charge on each site, or (iii) using a larger Floquet period comprised of two independent layers of gates. Furthermore, our results should inform future studies of charge conserving circuits, which are relevant for a wide range of topical theoretical questions.

1-dimensional spin chains↗
DOE OSTI2021

Protein-Mimetic Self-Assembly with Synthetic Macromolecules

Protein-mimetic amphiphiles have significant promise as a platform to access the complex functions of natural biological materials and incorporate the tunability and environmental resilience of synthetic materials. The fields of polymer chemistry and chemical biology have concurrently approached the development of biomimetic amphiphiles with materials ranging from random amphiphilic copolymers to peptide–lipid conjugates. In this Perspective, we incorporate strategies from diverse chemical arenas for controlling assembled morphologies and dynamics of protein-mimetic synthetic macromolecules. An overview of significant advances in peptide amphiphiles and single-chain polymer nanoparticles provides the foundation for comparing recent advances in the implementation of multiple intermolecular interactions and computational strategies to fine-tune the assembled structures. We aim to bridge these fields, combining insights from multiple disciplines to inspire new approaches for the development of protein-mimetic materials, as these assemblies have far-reaching applications including in the development of new sensors, catalysts, and therapeutics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Three ages of Venus

A central question for any planet is the age of its surface. Based on comparative planetological arguments, Venus should be as young and active as the Earth (Wood and Francis). The detection of probable impact craters in the Venera radar images provides a tool for estimating the age of the surface of Venus. Assuming somewhat different crater production rates, Bazilevskiy et al. derived an age of 1 + or - 0.5 billion years, and Schaber et al. and Wood and Francis estimated an age of 200 to 400 million years. The known impact craters are not randomly distributed, however, thus some area must be older and others younger than this average age. Ages were derived for major geologic units on Venus using the Soviet catalog of impact craters (Bazilevskiy et al.), and the most accessible geologic unit map (Bazilevskiy). The crater counts are presented for (diameters greater than 20 km), areas, and crater densities for the 7 terrain units and coronae. The procedure for examining the distribution of craters is superior to the purely statistical approaches of Bazilevskiy et al. and Plaut and Arvidson because the bins are larger (average size 16 x 10(6) sq km) and geologically significant. Crater densities define three distinct groups: relatively heavily cratered (Lakshmi, mountain belts), moderately cratered (smooth and rolling plains, ridge belts, and tesserae), and essentially uncratered (coronae and domed uplands). Following Schaber et al., Grieve's terrestrial cratering rate of 5.4 + or - 2.7 craters greater than 20 km/10(9) yrs/10(6) sq km was used to calculate ages for the geologic units on Venus. To improve statistics, the data was aggregated into the three crater density groups, deriving the ages. For convenience, the three similar age groups are given informal time stratigraphic unit names, from youngest to oldest: Ulfrunian, Sednaian, Lakshmian.

Wood, Charles A.↗
DOE OSTI2021

EcoPLOT: dynamic analysis of biogeochemical data

Motivation: We have created EcoPLOT (parameterized linkage of omics-driven technologies), a web-app for the dynamic, interactive analysis of biogeochemical datasets that combines state-of-the-art analysis tools to statistically and graphically explore environmental, geochemical and microbiome datasets. Using the iterative random forest, a machine learning algorithm, EcoPLOT allows for the de novo discovery of drivers which exhibit significant impact on plant, microbial or soil dynamics. Availability and implementation: EcoPLOT is built entirely within the R language. It can be accessed through any system where R is installed, including Windows, Mac and most Linux systems. EcoPLOT is free to use and can be accessed at https://github.com/cdsanchez18/EcoPLOT.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Influence of hydrophilic groups and metal-ion adsorption on polymer-chain conformation of amidoxime-based uranium adsorbents

In this study, we focus on the influence of hydrophilic groups and metal-ion loading on adsorbent polymer conformation, which controls access to adsorption sites and may limit adsorption capacity. Gaining a better understanding of the factors that influence conformation may yield higher-capacity adsorbents. Polyamidoxime (PAO), deuterated-PAO polyacrylic acid diblock copolymers (d-PAO-b-PAA), and randomly configured copolymers (PAO-co-PAA) were synthesized and characterized by neutron reflectometry in air and D 2 O. For d-PAO-b-PAA, characterization was also performed after alkali conditioning and in simulated seawater. PAO and PAO-co-PAA, with similar molecular weight and grafting density, extended from 95-Å thickness in air to 180 and 280-Å in D 2 O, respectively. This result suggests that polymer swelling may cause the additional adsorption capacity observed when polymer hydrophilicity increases. Two d-PAO-b-PAA samples, A and B, with a d-PAO thickness of 55-Å swelled to 110-Å and 140-Å, respectively, with an overall thickness increase of ~160% in D 2 O. After alkali conditioning, molecular interactions increased the density of PAA near the PAO-PAA interface, while the d-PAO thickness only decreased by ~10 Å. The d-PAO thickness of both samples declined to ~90-Å after adsorption in simulated seawater due to polymer-chain crosslinking. In conclusion, these results are expected to aid in improving adsorbent synthesis to increase uranium capacity.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS1991

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 17: The relationship between seven variables and the use of US government technical reports by US aerospace engineers and scientists

A study was undertaken to investigate the relationship between the use of U.S. government technical reports by U.S. aerospace engineers and scientists and seven selected sociometric variables. Data were collected by means of a self-administered mail survey which was distributed to a randomly drawn sample of American Institute of Aeronautics and Astronautics (AIAA) members. Two research questions concerning the use of conference meeting papers, journal articles, in-house technical reports, and U.S. government technical reports were investigated. Relevance, technical quality, and accessibility were found to be more important determinants of the overall extent to which U.S. government technical reports and three other information products were used by U.S. aerospace engineers and scientists.

Pinelli, Thomas E.↗
DOE OSTI2011

Modeling the determinants of the social impacts of agricultural development projects

In an attempt to help policy-makers improve the social sustainability of development projects, this study identifies the key determinants of farmers' attitudes relating to the social impacts of the floodwater spreading project (FWSP) on the Gareh-Bygone plain in Iran. In order to analyze the links between the various factors that affect the experience of social impact, a theoretical framework was developed. Stratified random sampling was used to survey 138 farm households from the four villages in the region. One male and one female from each house were interviewed face-to-face using a questionnaire, resulting in a total of 276 interviews. Structural factors were found to be the largest contribution to stakeholders' attitudes relating to the social impacts of the project. Results from a cluster analysis suggested that the level of floodwater information, level of participation, water access, ownership change, and environmental worldview were the most important factors explaining attitude towards social impact of the FWSP.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Energy metric prediction for double insertion mutants via the RoseNet deep learning framework

Studying the structural and functional implications of protein mutations is an important task in computational biology and bioinformatics. We leverage our previously proposed RoseNet neural network architecture to predict energy metrics of proteins with double amino acid insertions or deletions (InDels). We train models on previously generated benchmark datasets containing the exhaustive double InDel mutations for three proteins, as well as an additional three proteins for which ∼145k random mutants, each with two InDels, have been generated. We expand on our previous work by evaluating three additional proteins and analyzing domain features that impact the prediction capabilities of RoseNet. These features include InDels into secondary structures and the solvent accessible surface area (SASA) scores of the residues. We uncover further evidence to support that RoseNet has a higher proficiency of generalizing to unseen residue combinations than unseen insertion positions. We also observe that RoseNet produces higher-quality predictions when inserting into a β-sheet over an α-helix. Additionally, when the insertions fall in an area of high SASA, RoseNet often displays better performance than inserting into areas of low SASA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Configurational subdiffusion of peptides: A network study

Molecular dynamics (MD) simulation of linear peptides reveals configurational subdiffusion at equilibrium extending from 10-12 to 10-8 s. Rouse chain and continuous-time random walk models of the subdiffusion are critically discussed. Network approaches to analyzing MD simulations are shown to reproduce the time dependence of the subdiffusive mean squared displacement, which is found to arise from the fractal-like geometry of the accessible volume in the configuration space. Convergence properties of the simulation pertaining to the subdiffusive dynamics are characterized and the effect on the subdiffusive properties of representing the solvent explicitly or implicitly is compared. Non-Markovianity and other factors limiting the range of applicability of the network models are examined.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

PCM-Based Durable Write Cache for Fast Disk I/O

Flash based solid-state devices (FSSDs) have been adopted within the memory hierarchy to improve the performance of hard disk drive (HDD) based storage system. However, with the fast development of storage-class memories, new storage technologies with better performance and higher write endurance than FSSDs are emerging, e.g., phase-change memory (PCM). Understanding how to leverage these state-of-the-art storage technologies for modern computing systems is important to solve challenging data intensive computing problems. In this paper, we propose to leverage PCM for a hybrid PCM-HDD storage architecture. We identify the limitations of traditional LRU caching algorithms for PCM-based caches, and develop a novel hash-based write caching scheme called HALO to improve random write performance of hard disks. To address the limited durability of PCM devices and solve the degraded spatial locality in traditional wear-leveling techniques, we further propose novel PCM management algorithms that provide effective wear-leveling while maximizing access parallelism. We have evaluated this PCM-based hybrid storage architecture using applications with a diverse set of I/O access patterns. Our experimental results demonstrate that the HALO caching scheme leads to an average reduction of 36.8% in execution time compared to the LRU caching scheme, and that the SFC wear leveling extends the lifetime of PCM by a factor of 21.6.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗