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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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This presentation covers the methodology and key findings from an uncertainty quantification study of monoethanolamine (MEA) solvent-based systems operating under high CO₂ capture conditions. The goal is to identify critical operational parameters, assess their impact on system performance, and provide insights to support risk-informed design and optimization of carbon capture processes.
Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and -related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.
Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.
Solid-state nuclear magnetic resonance (ssNMR) is an indispensable tool for elucidating the structure and dynamics of insoluble and non-crystalline biomolecules. The recent advances in the sensitivity-enhancing technique magic-angle spinning dynamic nuclear polarization (MAS-DNP) have substantially expanded the territory of ssNMR investigations and enabled the detection of polymer interfaces in a cellular environment. This article highlights the emerging MAS-DNP approaches and their applications to the analysis of biomolecular composites and intact cells to determine the folding pathway and ligand binding of proteins, the structural polymorphism of low-populated biopolymers, as well as the physical interactions between carbohydrates, proteins, and lignin. Furthermore, these structural features provide an atomic-level understanding of many cellular processes, promoting the development of better biomaterials and inhibitors. It is anticipated that the capabilities of MAS-DNP in biomolecular and biomaterial research will be further enlarged by the rapid development of instrumentation and methodology.
A new small-diameter Monitored Drift Tube (sMDT) chamber has been developed for the muon spectrometer of the ATLAS experiment to handle the higher collision rates expected at the CERN High Luminosity Large Hadron Collider (HL-LHC). This paper presents measurements of the tracking resolution and hit efficiency of two prototype sMDT chambers constructed at the University of Michigan. Using cosmic-ray muons the sMDT tracking resolution of 103.7 ± 8.1 μm was measured for one chamber and 101.8 ± 7.8 μm for the other, compared with a design resolution of 106 μm. A further tracking resolution improvement to 83.4 ± 7.8 μm was obtained by using new high-gain readout electronics which will be added for HL-LHC. An average tracking efficiency of (98.5 ± 0.2)% was found for both chambers. The methodology used to determine the detector tracking resolution and efficiency, including reconstruction of sMDT data and a Geant4 simulation of the test chamber, is presented in detail.
We explore the microscopic mechanisms of ultralow lattice thermal conductivity ($\kappa_{l}$) in Tl$_{3}$VSe$_{4}$~by combining a first-principles density-functional theory (DFT) based framework of anharmonic lattice dynamics with the Peierls-Boltzmann transport equation (PBTE) for phonons. We include contributions of the three- and four-phonon scattering processes to the phonon lifetimes as well as the temperature-dependent anharmonic renormalization of phonon energies arising from an unusually strong quartic anharmonicity in Tl$_{3}$VSe$_{4}$. In contrast to a recent report by Mukhopadhyay~\etal [\textcolor{blue}{Science 360, 1455 (2018)}] which suggested that a significant contribution to $\kappa_{l}$ arises from random walks among uncorrelated oscillators, we show that particle-like propagation of phonon excitations can successfully explain the experimentally observed ultralow $\kappa_{l}$. Our results are also supported by explicit calculations of the off-diagonal terms of the heat-current operator, which are found to be small and indicate that wave-like tunneling of heat-carrying vibrations is of minor importance. Our results (i) resolve the discrepancy between the theoretical and experimental $\kappa_{l}$, (ii) offer new insights into the minimum $\kappa_{l}$ achievable in \TlVSe, and (iii) highlight the importance of high-order anharmonicity in low-$\kappa_{l}$ systems. The methodology demonstrated here may be used to resolve the discrepancies between the experimentally measured and the theoretically calculated $\kappa_{l}$ in skutterides and perovskites, as well as to understand the glasslike $\kappa_{l}$ in complex crystals with strong anharmonicity, leading towards the goal of rational design of new materials.
The Center for Disease Control has recommended the public to wear cloth face coverings in public settings that reduce potential exposure to COVID-19 where other social distancing measures are difficult to maintain (e.g., grocery stores and pharmacies) especially in areas of significant community based transmission. These face coverings and other Emulated-Personal Protective Equipment (EPPE) can be made by using Commonly Available Materials (CAMs). As part of the Sandia COVID-19 LDRD effort (funded under the Materials Science Investment Area), the Sandia E-PiPEline task evaluated E-PPE design options for face coverings and face shields considering their effectiveness, durability, build difficulty, build cost, and comfort. Observations from this investigation are presented here to provide guidelines for home construction of E-PPE. This executive summary includes a brief roadmap of the analysis methodology, two one-page handouts geared to be distributed to the public at large (one for E-PPE face coverings and one for E-PPE face shields), and additional observations regarding the potential solutions for E-PPE face coverings and face shields included to further support the one-page handouts.
This document is designed to help agency fleet managers understand and implement optimal petroleum reduction strategies for each fleet location by evaluating the most appropriate combination of the four core principles of sustainable fleet management: Right-sizing the fleet to agency mission by implementing a vehicle allocation methodology study; Minimizing vehicle miles traveled; Increasing fleet fuel efficiency by replacing inefficient vehicles with more fuel-efficient vehicles, maintaining vehicles, driving more efficiently, and avoiding excessive idling; Optimizing cost-effective alternative fuel use, including maximizing utilization of existing alternative fuel infrastructure, installing alternative fuel infrastructure where practical (including electric vehicle charging stations), and aligning deployment of alternative fuel vehicles with fueling infrastructure.
TinyTPC is a small scale liquid-argon time projection chamber (LArTPC) featuring a pixelated readout system (LArPix) designed to improve the detection of charged particles. To enhance energy measurements at MeV scales, TinyTPC will study the impact of isobutylene, a photosensitive dopant that converts light to charge, and xenon, a wavelength shifter. This presentation will detail TinyTPC's commissioning, operation, data collection methodologies, noise levels, and upcoming tests involving isobutylene, xenon, and radioactive sources.
In order to design neutron irradiation experiments at a given nuclear reactor, one requires a representative neutron spectrum for the configuration of the reactor that will exist during the experiment. This usually implies modeling the operation of the reactor using a neutron transport code. Since the modeling of pulsed neutron reactors is quite complex, requiring coupled modeling of neutronics and thermo-structural response, the typical methodology employs analysis of the neutron spectrum which is obtained from a static keff calculation. While this time-independent Eigenfunction is not truly representative of that during a reactor pulse, we postulate that it is a reasonable representation for our purposes. The Nuclear Survivability (NS) Program at LLNL is considering fielding such experiments at the Molly-G (‘Molybdenum Godiva-II’) Fast Burst Reactor (FBR) at the White Sands Missile Range (WSMR). The goal of these experiments is to (a) test / proof / calibrate diagnostics, and (b) test the efficacy of neutron shield configurations, before fielding at other nuclear reactors, such as the Annular Core Research Reactor (ACRR) at SNL/NM. After attempting to secure either drawings or a copy of an existing MCNP model of the Molly-G FBR from various institutions, it was decided to develop a new model for use with the Mercury Monte Carlo transport code. The model described herein will be used to design these experiments.
Summary This paper will present a robust workflow to address multiobjective optimization (MOO) of carbon dioxide (CO2)-enhanced oil recovery (EOR)-sequestration projects with a large number of operational control parameters. Farnsworth unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) EOR, will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU’s numerical model is used to demonstrate the proposed optimization workflow. Because using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian-SVR) is used to construct proxies. An iterative self-adjusting process prepares the training knowledge base to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian optimization to achieve better generalization performance. Trained proxies will be coupled with multiobjective particle swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology using a multilayer neural network (MLNN) to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proved that the workflow coupling Gaussian-SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledge base while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian-SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale MOO problems in various projects with similar desired outcomes.
The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).
A simple repeating step-filtered structure has been designed and deployed for the detection of MeV-energy proton beams produced from high-intensity short-pulse laser-driven experiments. Due to the diagnostic's ability to recover energy-resolved images of proton beams, it is called the proton beam imager and energy spectrometer (or PROBIES). The flexible design has two useful applications. First, it can be fielded in a configuration with a scintillator and imaging setup in order to collect proton beam information in high-repetition rate experiments. Here we show a configuration that collects the equivalent of a nine-film radiochromic film (RCF) stack, and a technique for using a neural network (NN) to rapidly analyze the images on millisecond time-scales. Second, when using RCF stacks as the detector, the energy resolution capability is greatly increased compared to traditional RCF stacks, while simultaneously reducing the total number of films required to make each measurement. We demonstrate a configuration that provides up to 90 spatially- and energy-resolved datapoints using just ten films. Both designs presented here can be easily customized for anticipated experimental outputs and manufactured at low cost. Such designs allow for rapid prototyping using modern high-accuracy 3D printers or on-demand computer numerical control machining to reduce the reliance on time-consuming film media, analysis, or bulky electro-magnetically dispersing spectrometers. The analysis of data is discussed and a methodology for constructing a NN that is capable of rapidly analyzing data on-the-fly with greater than 95% accuracy is presented.
The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.
Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists have been aware of the precursors of non-replicable research for many decades and have many safeguards to ensure that the published results are as reliable as possible. The experiments require large investments of time and effort to design, construct, and operate. Large collaborations produce and check the results, and many papers are signed by more than three thousand authors. This paper gives an introduction to what experimental particle physics is and to some of the tools that are used to analyze the data. It describes the procedures used to ensure that results can be computationally reproduced, both by collaborators and by non-collaborators. It describes the status of publicly available data sets and analysis tools that aid in reproduction and recasting of experimental results. It also describes methods particle physicists use to maximize the reliability of the results, which increases the probability that they can be replicated by other collaborations or even the same collaborations with more data and new personnel. Examples of results that were later found to be false are given, both with failed replication attempts and one with alarmingly successful replications. While some of the characteristics of particle physics experiments are unique, many of the procedures and techniques can be and are used in other fields.
Currently, over one-third of the global population is infected with tuberculosis (TB), one of the world’s most deadly infectious diseases. The current gold standard for TB diagnosis is microbiological culture, which can take up to 6-8 weeks, which is not conducive to timely treatment. There are no reliable diagnostics for TB. Disease manifestation and outcome of diagnostic tests for TB is influenced by co-morbidities such as HIV co-infection, further complicating diagnostics development. Thus, there is a need for reliable diagnostics for TB. This is especially true in pediatric populations where the differential disease presentation further complicates outcomes. This dissertation describes the development and evaluation of new tailored methods-lipoprotein capture and membrane insertion- for the discriminative detection of an amphiphilic mycobacterial biomarker, lipoarabinomannan (LAM), for diagnosis of TB in adult and pediatric populations. LAM is released by Mycobacterium tuberculosis during active infection and associates with serum lipoproteins such as high and low density lipoproteins (HDL/LDL) in the human host. The guiding hypothesis of this work is that the vi sequestration of LAM in immune complexes and by lipoprotein carriers in the host contributes to difficulty in detection of the amphiphilic antigen in blood. To this end, we have evaluated the use of tailored amphiphile detection assays to process and measure sequestered LAM in host blood, and examined the application of this methodology in adults and children with TB disease. In addition, we have developed and studied an in vitro cell model to further extrapolate the impact of association of LAM with host carriers on its ability to induce innate immune processes, in order to better refine and design strategies for its direct detection. Overall, our conclusions indicate that the amphiphilic biochemistry of LAM and its interaction with lipoproteins is a feature which should be taken into account for the effective development of diagnostics.
This presentation is on the comparative analysis of standard and advanced USL methodologies. Each method has a different way to address/account for experimental biases and uncertainties. This study showed why bias and uncertainty predictions by these popular methods differ significantly in some cases. In order to establish confidence to our validation efforts, especially with new reactor concepts, in designing new critical experiments and claiming experimental coverage for our existing applications, we need to understand capabilities of our methods further.
The vision of IMASC is to advance the fundamental science necessary to alter fundamental aspects of the chemical industries sector and its carbon footprint. The IMASC mission is to develop design principles for increasing the efficiency of key chemical syntheses using dilute alloy catalysts by quantitatively scaling from model studies to catalytic operation using advanced experiment and theory. One of the major challenges in catalysis is predicting catalytic behavior—especially selectivity. The IMASC team developed a methodology for obtaining critical information for selected catalytic processes using dilute alloy catalysts, that is widely applicable to modeling catalytic processes.