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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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PEG- b -PPS bicontinuous nanospheres resist degradation and stably retain payloads within cells. When loaded with a photosensitizing agent, these nanospheres demonstrate off–on inducible photo-responsive release of cytotoxic agents into the cytosol of cells.
In this study, spherical P-doped activated carbon (CSP) was obtained from poplar bark by microwave carbonization with phosphoric acid as the dopant and activating agent. Phosphorus bonded with four oxygen atoms to form a phosphate-like structure rather than directly bonding with C atoms in CSP; such groups can provide Lewis acid active sites with moderately strong acidity. The Lewis acid and total acid content increased with increasing phosphoric acid doping ratio. The use of phosphoric acid as an activating agent promotes the formation of a developed porous structure. CSP 1 (mass ratio of raw material to acid = 1 : 1) had a specific surface area of 1115 m 2 g –1 with developed micro–mesoporous structures, mainly micropores. Investigation of the activity of CSP in catalytic cellulose hydrolysis during hydrothermal degradation showed that Brønsted acid sites promote hydrolysis and dehydration of cellulose, Lewis acid sites promote subsequent reactions such as isomerization, rearrangement, and oligomerization, and the developed pores can provide reaction sites and facilitate reactant transportation. The optimal reaction conditions, i.e. , 1 h at 230 °C and 1 MPa with CSP 1 (0.4 g) as the catalyst, gave a cellulose conversion of >99.9%, with 42.30% selectivity for 1,2,4-benzenetriol. This study provides an effective method for preparing 1,2,4-benzenetriol from cellulose with biomass-based activated carbon as the catalyst, which enables green synthesis of 1,2,4-benzenetriol and promotes the use of biomass waste resources.
Polyurethanes are among the top six polymers produced in the world and are widely used in the automotive, furniture, construction and appliance industry for their light weight, impact resistance, and insulating properties. However, the use of hazardous diisocyanates used in polyurethane formulations has led many to search for more sustainable alternatives. The lignin component of biomass has been targeted to replace the often toxic and petroleum-derived precursors to polymer synthesis in support of the biorefinery concept where natural materials are used as the feedstock for commodity plastics. The use of lignin is often hampered due to its low reactivity, heterogeneity and the necessity of employing extensive purification and/or functionalization measures to ensure materials are of comparable quality. In this report, a unique method is presented for the synthesis and processing of a non-toxic, non-isocyanate polyurethane foam utilizing unmodified Kraft lignin and a biobased curing agent. Here, raw Kraft lignin is functionalized with green organic carbonates and a non-toxic approach is taken to solubilize the precursors with a curing agent from renewable fatty acids enabling rapid gel-times. For the first time, a NIPU foam from lignin is synthesized while the structure–property relationship of different reaction mixtures is studied demonstrating shape memory capacity and 100% biobased carbon content.
Thin-film solid-state interfacial dealloying (thin-film SSID) is an emerging technique to design nanoarchitecture thin films. The resulting controllable 3D bicontinuous nanostructure is promising for a range of applications including catalysis, sensing, and energy storage. Using a multiscale microscopy approach, we combine X-ray and electron nano-tomography to demonstrate that besides dense bicontinuous nanocomposites, thin-film SSID can create a very fine (5–15 nm) nanoporous structure. Not only is such a fine feature among one of the finest fabrications by metal-agent dealloying, but a multilayer thin-film design enables creating nanoporous films on a wider range of substrates for functional applications. Through multimodal synchrotron diffraction and spectroscopy analysis with which the materials’ chemical and structural evolution in this novel approach is characterized in details, we further deduce that the contribution of change in entropy should be considered to explain the phase evolution in metal-agent dealloying, in addition to the commonly used enthalpy term in prior studies. Here, the discussion is an important step leading towards better explaining the underlying design principles for controllable 3D nanoarchitecture, as well as exploring a wider range of elemental and substrate selections for new applications.
Large and faceted nanoparticles, such as gold bipyramids, presently require synthesis using alkyl ammonium halide ligands in aqueous conditions to stabilize the structure, which impedes subsequent transfer and suspension of such nanoparticles in low polarity solvents despite success with few nanometer gold nanoparticles of shapes such as spheres. Phase transfer methodologies present a feasible avenue to maintain colloidal stability of suspensions and move high surface energy particles into organic solvent environments. Here, we present a method to yield stable suspensions of gold bipyramids in low-polarity solvents, including methanol, dimethylformamide, chloroform, and toluene, through the requisite combination of two capping agents and the presence of a co-solvent. By utilizing PEG-SH functionalization for stability, dodecanethiol (DDT) as the organic-soluble capping agent, and methanol to aid in the phase transfer, gold bipyramids with a wide-range of aspect ratios and sizes can be transferred between water and chloroform readily and maintain colloidal stability. Subsequent transfer to various organic and low-polarity solvents is then demonstrated for the first time.
Actinide oxalate precipitation has been ubiquitous in nuclear processing since the 1950s due to its simplicity at laboratory and production scales as both a means of actinide purification and as a key intermediate solid phase towards oxide production. Historical processing information has emphasized the need to reduce actinides, including neptunium (Np), to the tetravalent or trivalent state with chemical reducing agents prior to oxalate precipitation. This work revisits the established method and reveals that oxalic acid has an unexpected role as a reducing agent for Np(v/vi). Also, the study uncovers insights into the direct precipitation of Np(iv) oxalate from neptunyl solutions, as identified by spectroscopic and X-ray diffraction analyses. These findings challenge the long-standing notion that redox adjustment of actinides is necessary for oxalate processing.
Epoxies are a broad class of polymer materials often used as adhesive, structural or binding materials. Epon 828 is an epoxy resin that can be polymerized with a variety of curing agents with the choice of curing agent potentially having an effect on the resulting epoxy polymer’s material properties. In this study, the dynamic behavior of Epon 828 epoxy resin cured with diethanolamine (DEA) is investigated through a series of tamped Richtmyer-Meshkov instability (RMI) experiments measured with x-ray phase-contrast imaging. The measured shock and particle velocities are combined with data in the literature to calibrate Mie-Grüneisen equations-of-state (EOS) for portions and combinations of the collective dataset. The calibrated Mie-Grüneisen EOS are validated against particle velocity profiles extracted from published literature using the Eulerian hydrocode CTH. Here, the Mie-Grüneisen EOS fit to only the tamped RMI experimental data presented here most closely follows the particle velocity profile in the published literature.
In this work, we show how to bound and calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous-time Monte Carlo trajectory and receives a reward conditioned upon the values of certain path-extensive quantities. Evolution produces progressively fitter agents, potentially allowing the calculation of a piece of a large-deviation rate function for a particular model and path-extensive quantity. For models with small state spaces, the evolutionary process acts directly on rates, and for models with large state spaces, the process acts on the weights of a neural network that parameterizes the model's rates. This approach shows how path-extensive physics problems can be considered within a framework widely used in machine learning.
The Linac Coherent Light Source (LCLS) is the world’s first x-ray free electron laser. It is a scientific user facility operated by the SLAC National Accelerator Laboratory, at Stanford, for the U.S. Department of Energy. As beam time at LCLS is extremely valuable and limited, experimental efficiency—getting the most high quality data in the least time—is critical. Our overall project employs cognitive engineering methodologies with the goal of improving experimental efficiency and increasing scientific productivity at LCLS by refining experimental interfaces and workflows, simplifying tasks, reducing errors, and improving operator safety and stress. Here, in this study, we describe a multi-agent, multi-scale computational cognitive interaction model of instrument operations at LCLS. Our model simulates the aspects of human cognition at multiple cognitive and temporal scales, ranging from seconds to hours, and among agents playing multiple roles, including instrument operator, real time data analyst, and experiment manager. The model can roughly predict impacts stemming from proposed changes to operational interfaces and workflows. Example results demonstrate the model’s potential in guiding modifications to improve operational efficiency. We discuss the implications of our effort for cognitive engineering in complex experimental settings and outline future directions for research. The model is open source, and the videos of the supplementary material provide extensive detail.
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Francisella tularensis is the etiological agent of tularemia, or rabbit fever. Although F. tularensis is a recognized biothreat agent with broad and expanding geographical range, its mechanism of infection and environmental persistence remain poorly understood. Previously, we identified seven F. tularensis proteins that induce a rapid encystment phenotype (REP) in the free-living amoeba, Acanthamoeba castellanii. Encystment is essential to the pathogen's long term intracellular survival in the amoeba. Here, we characterize the cellular and molecular function of REP34, a REP protein with a mass of 34 kDa. A REP34 knock-out strain of F. tularensis has a reduced ability to both induce encystment in A. castellanii and invade human macrophages. We determined the crystal structure of REP34 to 2.05-Å resolution and demonstrate robust carboxypeptidase B-like activity for the enzyme. REP34 is a zinc-containing monomeric protein with close structural homology to the metallocarboxypeptidase family of peptidases. REP34 possesses a novel topology and substrate binding pocket that deviates from the canonical funnelin structure of carboxypeptidases, putatively resulting in a catalytic role for a conserved tyrosine and distinct S1' recognition site. Taken together, these results identify REP34 as an active carboxypeptidase, implicate the enzyme as a potential key F. tularensis effector protein, and may help elucidate a mechanistic understanding of F. tularensis infection of phagocytic cells.
Chapare virus (CHAPV) is an emerging New World arenavirus that is the causative agent of Chapare hemorrhagic fever (CHHF) responsible for recent outbreaks with alarmingly high case fatality rates in Bolivia near the Brazilian border. Here, we describe a nonhuman primate (NHP) model of CHHF infection which represents an essential tool to understand this emerging biological threat agent. Cynomolgus macaques challenged intravenously with CHAPV develop clinical disease, which recapitulates several key features of human CHHF. All subjects lost weight and had clinical scores following CHAPV challenge. Notably, one of four NHPs developed lethal disease with viral hepatitis and hemorrhagic features. Clinical chemistry and hematology revealed leukopenia, anemia, thrombocytopenia, and increased transaminase levels. In all four subjects, viremia was detectable for the first week following challenge and viral RNA was detectable in serum and many tissues persisting 35 days-post challenge. Several medical countermeasures (MCM) have efficacy against CHAPV infection in vitro, but the current model for MCM testing and approval of new drugs is reliant on the availability of animal models. This work lays the foundation for future CHHF MCM development.
Quenching of light emission from an LAB based scintillator by the addition of organic amines and carboxylic acids is examined. Chemical functional groups of the quenching agents play an important role in this reduction. It is shown that "salt" formation at a 1:1 mole ratio in a mixed amine-acid system, reduces quenching by a factor of ~ 2. Supporting NMR spectra are presented. Finally, this "quenching neutralization" has the potential to reduce the light loss incurred when metals complexed with quenching agents are loaded into organic scintillators.
Abstract The complexity and sheer volume of information — encompassing documents, papers, data, and other resources — from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.To tackle this issue, a Retrieval Augmented Generation (RAG)-based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain [1], which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail. The application can be accessed athttps://rags4eic-ai4eic.streamlit.app.[A tagged version of the source code can be found inhttps://github.com/ai4eic/EIC-RAG-Project/releases/tag/AI4EIC2023_PROCEEDING.]
Abstract Objective. Proton Radiography can be used in conjunction with proton therapy for patient positioning, real-time estimates of stopping power, and adaptive therapy in regions with motion. The modeling capability shown here can be used to evaluate lens-based radiography as an instantaneous proton-based radiographic technique. The utilization of user-friendly Monte Carlo program TOPAS enables collaborators and other users to easily conduct medical- and therapy- based simulations of the Los Alamos Neutron Science Center (LANSCE). The resulting transport model is an open-source Monte Carlo package for simulations of proton and heavy ion therapy treatments and concurrent particle imaging. Approach. The four-quadrupole, magnetic lens system of the 800-MeV proton beamline at LANSCE is modeled in TOPAS. Several imaging and contrast objects were modelled to assess transmission at energies from 230–930 MeV and different levels of particle collimation. At different proton energies, the strength of the magnetic field was scaled according to βγ, the inverse product of particle relativistic velocity and particle momentum. Main results. Materials with high atomic number, Z, (gold, gallium, bone-equivalent) generated more contrast than materials with low-Z (water, lung-equivalent, adipose-equivalent). A 5-mrad collimator was beneficial for tissue-to-contrast agent contrast, while a 10-mrad collimator was best to distinguish between different high-Z materials. Assessment with a step-wedge phantom showed water-equivalent path length did not scale directly according to predicted values but could be mapped more accurately with calibration. Poor image quality was observed at low energies (230 MeV), but improved as proton energy increased, with sub-mm resolution at 630 MeV. Significance. Proton radiography becomes viable for shallow bone structures at 330 MeV, and for deeper structures at 630 MeV. Visibility improves with use of high-Z contrast agents. This modality may be particularly viable at carbon therapy centers with accelerators capable of delivering high energy protons and could be performed with carbon therapy.
Abstract The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.
The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.
Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.