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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Vinylsilanes as Chain-Transfer Agents in Ethylene Polymerization: Direct Synthesis of Heterotelechelic Polyolefins

A single-step route to heterotelechelic polyethylene is enabled by palladium-diimine-catalyzed polymerization of ethylene using vinylsilanes as chain-transfer agents. The reaction affords α-alkenyl-ω-silyl end-capped heterotelechelic polymers whose molecular weights are controllable over a wide range by adjusting the [ethylene]/[vinylsilane] molar ratios. Notably, highly efficient end-capping with silanes carrying a variety of functionalities can be achieved under the optimized conditions. Furthermore, the alkenyl- and silyl-terminated telechelics serve as polymer precursors for further reactions and can be converted into additional telechelic functionalized polyolefins in good yields.

Chen, Zhi-Hao [University of Houston, TX (United S↗
DOE OSTI2021

The Response of Precipitation Extremes to the Twentieth- and Twenty-First-Century Global Temperature Change in a Comprehensive Suite of CESM1 Large Ensemble Simulation: Revisiting the Role of Forcing Agents Vs. the Role of Forcing Magnitudes

The response of precipitation extremes (PEs) to global warming is found to be nonlinear in Community Earth System Model version 1 (CESM1) and other global climate models (Pendergrass et al., 2019), which led to the concern that it is not accurate to approximate the response of PE to a single forcing as the difference between simulations with all forcing agents and those that exclude one specific forcing. This calls into question previous model-based results that the sensitivity of PE with warming due to aerosol forcing is significantly larger than that due to greenhouse gases (GHGs). We reevaluate the PE sensitivity to GHGs and aerosols using available CESM1 ensemble simulations. We show that although the PE response to warming is nonlinear in CESM1, especially for the high warming projected in the twenty-first-century, PE sensitivity to aerosols is still significantly stronger than that due to GHGs when the comparison is made within similar warming regimes to avoid the bias induced by the nonlinear behavior. But the difference is smaller than previously estimated. We also conclude that the additivity assumption is largely valid to isolate the PE response due to aerosol forcing from the paired simulations including the “all forcing” experiment when the warming regime is small (e.g., 1°C–2°C in the next few decades when aerosol forcing is projected to decline and becomes a major source of uncertainty for model projection).

58 GEOSCIENCES↗
DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-temperature high-pressure microfluidic system for rapid screening of supercritical CO 2 foaming agents

CO 2 foam helps to increase the viscosity of CO 2 flood fluid and thus improve the process efficiency of the anthropogenic greenhouse gas’s subsurface utilization and sequestration. Successful CO 2 foam formation mandates the development of high-performance chemicals at close to reservoir conditions, which in turn requires extensive laboratory tests and evaluations. This work demonstrates the utilization of a microfluidic reservoir analogue for rapid evaluation and screening of commercial surfactants (i.e., Cocamidopropyl Hydroxysultaine, Lauramidopropyl Betaine, Tallow Amine Ethoxylate, N,N,N' Trimethyl-N'-Tallow-1,3-diaminopropane, and Sodium Alpha Olefin Sulfonate) based on their performance to produce supercritical CO 2 foam at high salinity, temperature, and pressure conditions. The microfluidic analogue was designed to represent the pore sizes of the geologic reservoir rock and to operate at 100 °C and 13.8 MPa. Values of the pressure drop across the microfluidic analogue during flow of the CO 2 foam through its pore network was used to evaluate the strength of the generated foam and utilized only milliliters of liquid. The transparent microfluidic pore network allows in-situ quantitative visualization of CO 2 foam to calculate its half-life under static conditions while observing if there is any damage to the pore network due to precipitation and blockage. The microfluidic mobility reduction results agree with those of foam loop rheometer measurements, however, the microfluidic approach provided more accurate foam stability data to differentiate the foaming agent as compared with conventional balk testing. The results obtained here supports the utility of microfluidic systems for rapid screening of chemicals for carbon sequestration or enhanced oil recovery operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Development of a CNS-permeable reactivator for nerve agent exposure: an iterative, multi-disciplinary approach

Nerve agents have experienced a resurgence in recent times with their use against civilian targets during the attacks in Syria (2012), the poisoning of Sergei and Yulia Skripal in the United Kingdom (2018) and Alexei Navalny in Russia (2020), strongly renewing the importance of antidote development against these lethal substances. The current standard treatment against their effects relies on the use of small molecule-based oximes that can efficiently restore acetylcholinesterase (AChE) activity. Despite their efficacy in reactivating AChE, the action of drugs like 2-pralidoxime (2-PAM) is primarily limited to the peripheral nervous system (PNS) and, thus, provides no significant protection to the central nervous system (CNS). This lack of action in the CNS stems from their ionic nature that, on one end makes them very powerful reactivators and on the other renders them ineffective at crossing the Blood Brain Barrier (BBB) to reach the CNS. In this report, we describe the use of an iterative approach composed of parallel chemical and in silico syntheses, computational modeling, and a battery of detailed in vitro and in vivo assays that resulted in the identification of a promising, novel CNS-permeable oxime reactivator. Additional experiments to determine acute and chronic toxicity are ongoing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Surface softening in palladium nanoparticles: effects of a capping agent on vibrational properties

The presence of a capping agent (CTAB) on Pd nanoparticles produces a strong static disorder in the surface region. This results in a surface softening, which contributes to an overall increase in the Debye–Waller coefficient measured by X-ray powder diffraction. Here, molecular dynamics and density functional theory simulations show that the adsorption-induced surface disorder is strong enough to overcome the effects of nanoparticle size and shape.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

In this work, we investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si–O–Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

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

Molecularly engineered Li compensation agent-integrated separator enabling regeneration of degraded LiFePO 4

Lithium replenishment separators (LRSs) integrating pre-lithiation agents can regenerate degraded lithium cathodes via facile reassembly with a fresh anode and the LRS. A persistent challenge is the formation of gas or solid residues during pre-lithiation. To address this, for the first time, we develop an LRS based on a molecularly engineered dilithium salt of tetrafluorohydroquinone, which compensates for lithium loss while generating decomposition products that dissolve in the electrolyte as a favorable additive, without forming gas or solid residues, thus offering a green route for lithium compensation. A pristine LiFePO 4 ‖graphite full cell with the LRS exhibits 9.3% higher overall capacity than a polypropylene separator (PPS) cell after 50 cycles at 0.5C, and the degraded LiFePO 4 ‖graphite full cell incorporating this LRS achieves a 44.9% higher capacity than the PPS-based cell after 200 cycles at 0.5C. Our LRS demonstrates strong potential for high-performance lithium-ion batteries and spent battery regeneration.

Tao, Fujun [Worcester Polytechnic Institute, MA (U↗
DOE OSTI2022

Quantum model learning agent: characterisation of quantum systems through machine learning

Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ≥ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Efficacy and Safety of an Intra-articular Dual-Acting Antibacterial Agent (TNP-2092) for Implant Infection–Associated Methicillin-Resistant Staphylococcus aureus

Abstract Owing to the presence of microbial biofilm on the implant, the eradication of biofilm-associated infections poses a challenge for antibiotic therapies. The study aimed to investigate the efficacy and safety of the novel antibiotic agent TNP-2092 in the context of implant infections. In vivo, rats with periprosthetic joint infection (PJI) treated with antibiotics showed an increase in body weight and decrease in swelling, temperature, and width of knee, compared with the control group. Meanwhile, inflammatory markers in synovium and serum were decreased in the TNP-2092 group, consistent with the pathological results. Moreover, TNP-2092 was effective in eliminating bacteria and disruption biofilm formation, and further alleviated the abnormal bone absorption and reactive bone changes around the prosthesis. In conclusion, intra-articular injection of TNP-2092 is safe and effective in treating knee PJI in a rat model. The study provides a foundation for the future utilization of TNP-2092 in the management of implant-related infections.

Dai, Tianyu↗
DOE OSTI2025

Viroid-like “obelisk” agents are widespread in the ocean and exceed the abundance of RNA viruses in the prokaryotic fraction

Abstract “Obelisks” are recently discovered ribonucleic acid (RNA) viroid-like elements present in diverse environments with no phylogenetic similarity to any known biological agent. obelisks were first identified in the human gut and in a commensal bacterium acting as a replicative host. They have a circular ∼1 kb RNA genome, rod-like secondary structures, and the encoding of a protein superfamily called “Oblins”. We performed a large-scale search of obelisks in the ocean using the Pebblescout program and the transcriptomic Sequence Archive Read databases, revealing the biogeography and abundance of these viroid-like RNA elements. We detected 55 obelisk genomes resulting in 35 marine clusters at the species level. These obelisks were detected in the prokaryotic fraction and to a lesser extent in the eukaryotic fraction, and distributed across all the oceans from surface to mesopelagic including the Arctic, and even in the coldest seawater of Earth beneath the Antarctic Ross Ice Shelf. The obelisk hallmark protein Oblin-1 confirmed by 3D models was found in various marine samples. Some of the detected marine obelisks harbor hammerhead self-cleaving ribozymes in both polarities. In the prokaryotic, but not the eukaryotic, fraction of the Tara Ocean dataset, relative abundance of obelisks calculated by transcriptomic fragment recruitment indicated that they are abundant in marine samples, reaching or even exceeding the relative abundance of the previously discovered uncultured RNA viruses. In conclusion, obelisks are abundant and widespread viroid-like elements that should be included in ocean biogeochemical models.

Environmental Sciences & Ecology↗