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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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Data Science Infrastructure (DSI) is developing searchable databases and workflows for simulation and experimental data derived from ASC clients. Short term goal: Make data easily accessible through metadata indexing and querying while respecting data permissions. Longer term goal: Use this data for data science activities.
MontePy is an open-source python software library for reading, editing, and writing MCNP input files. This presentation was given to a university research group interested in it. MontePy provides an object-oriented interface for working with these MCNP input files. It uses a lexer and parser system in order to create a concrete syntax tree representing the input from the input file.
Animals must learn to ignore stimuli that are irrelevant to survival and attend to ones that enhance survival. When a stimulus regularly fails to be associated with an important consequence, subsequent excitatory learning about that stimulus can be delayed, which is a form of nonassociative conditioning called ‘latent inhibition’. Honey bees show latent inhibition toward an odor they have experienced without association with food reinforcement. Moreover, individual honey bees from the same colony differ in the degree to which they show latent inhibition, and these individual differences have a genetic basis. To investigate the mechanisms that underly individual differences in latent inhibition, we selected two honey bee lines for high and low latent inhibition, respectively. We crossed those lines and mapped a Quantitative Trait Locus for latent inhibition to a region of the genome that contains the tyramine receptor gene Amtyr1 [We use Amtyr1 to denote the gene and AmTYR1 the receptor throughout the text.]. We then show that disruption of Amtyr1 signaling either pharmacologically or through RNAi qualitatively changes the expression of latent inhibition but has little or slight effects on appetitive conditioning, and these results suggest that AmTYR1 modulates inhibitory processing in the CNS. Electrophysiological recordings from the brain during pharmacological blockade are consistent with a model that AmTYR1 indirectly regulates at inhibitory synapses in the CNS. Our results therefore identify a distinct Amtyr1 -based modulatory pathway for this type of nonassociative learning, and we propose a model for how Amtyr1 acts as a gain control to modulate hebbian plasticity at defined synapses in the CNS. We have shown elsewhere how this modulation also underlies potentially adaptive intracolonial learning differences among individuals that benefit colony survival. Finally, our neural model suggests a mechanism for the broad pleiotropy this gene has on several different behaviors.
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For presentation at, and publication in proceedings of, AMPP Annual Conference + Expo 2024, New Orleans, LA, March 3-7, 2024.
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The formation of eutectic mixtures from active pharmaceutical ingredients (APIs) has emerged as an effective technique to develop liquid pharmaceutical formulations. API-based deep eutectic solvents (DESs) enhance the water solubility of APIs and avoid their recrystallization. The physico-chemical properties of API-DESs can be easily modulated by judicious selection of a hydrogen bond acceptor (HBA) or hydrogen bond donor (HBD) excipient that is mixed with the API. Solvation properties of API-DESs can help in understanding the role of ionic and non-ionic excipients towards the overall solvation interactions of these DESs. In this study, the solvation characteristics of three subclasses of API-DESs containing API-HBAs, API-HBDs, or API-HBAs and API-HBDs are investigated. Inverse gas chromatography is employed to study solute-solvent interactions of a variety of probe molecules for twenty different API-DESs. Among the API-DESs comprised of API-HBAs and carboxylic acid or diol-based HBDs, DESs containing diol-based HBDs exhibited stronger dipolar, hydrogen bond basicity, and hydrogen bond acidity interactions compared to carboxylic acid-based HBDs. No significant differences were observed in the solvation properties of DESs comprised of API HBAs and API HBDs. API-DESs comprised of the choline chloride HBA and API-HBDs exhibited stronger hydrogen bond acidity and dipolarity and weaker dispersive-type interactions among the DESs evaluated in this study. An in-depth evaluation of solvation properties for these DESs is important to completely exploit their potential in separation science as well as the fields of biomaterial science, pharmaceutical chemistry, and biotechnology.
Tensor Cores have been an important unit to accelerate Fused Matrix Multiplication Accumulation (MMA) in all NVIDIA GPUs since Volta Architecture. To program Tensor Cores, users have to use either legacy wmma APIs or current mma APIs. Legacy wmma APIs are more easy-to-use but can only exploit limited features and power of Tensor Cores. Specifically, wmma APIs support fewer operand shapes and can not leverage the new sparse matrix multiplication feature of the newest Ampere Tensor Cores. However, the performance of current programming interface has not been well explored. Furthermore, the computation numeric behaviors of low-precision floating points (TF32, BF16, and FP16) supported by the newest Ampere Tensor Cores are also mysterious. In this paper, we explore the throughput and latency of current programming APIs. Further, we intuitively study the numeric behaviors of Tensor Cores MMA and profile the intermediate operations including multiplication, addition of inner product, and accumulation. All codes used in this work can be found in https://github.com/sunlex0717/DissectingTensorCores.
Summary Foam flooding can minimize bypassing in gasfloods in fractured reservoirs. Finding a foam formulation effective in high-salinity brine is challenging, especially with divalent cations, e.g., American Petroleum Institute (API) brine (8% NaCl with 2% CaCl2). When formulating with nanoparticles, the colloidal dispersion stability is difficult due to the dramatic reduction in zeta potential and the Debye length at high salinity. The aim of this work was to develop a strong foam in API brine at the ambient temperature, using a nonionic surfactant and ethyl cellulose nanoparticles (ECNP), for gasflooding in fractured carbonate reservoirs. ECNPs was synthesized and dispersed in API brine using a nonionic surfactant (also denoted as SF). SF and SF/ECNP foams were generated, and their stability was studied at atmospheric pressure and 950 psi. Foam mobility was measured in a sandpack at high pressure. Foam flood experiments were conducted in oil-saturated fractured carbonate cores. The nonionic surfactant proved to be a good dispersion agent for ECNP in API brine. The SF/ECNP mixture stabilized foam in API brine, even in the presence of oil. Injecting a partially miscible gas (below its minimum miscibility pressure) as an SF foam into a fractured core more than doubles the oil recovery over injection of the gas alone. The injection of the strong foam (SF/ECNP) further improves the oil recovery over that of the SF foam, indicating the synergy between ECNP and surfactant. ECNP accumulates in the foam lamella and induces larger pressure gradients in the fracture to divert more gas into the matrix for oil displacement.
Gas-phase ion chemistry is a useful approach for the investigation of physical organic chemistry and the study of reactivity, structure, and thermochemical properties of ionic and neutral organic substrates. Herein, one-step dehydration of gas-phase benzyl amine alcohols to give benzazetidines was discovered without the use of catalysts. Mechanistic investigations of the gas-phase dehydration reactions were explored to study the possible influence of charged microdroplet acceleration during electrospray ionization (ESI) and collisions occurring during ion transfer through the atmospheric pressure interface (API) of the mass spectrometer. The product ion distribution was observed to be less sensitive to droplet effects (spray distance and voltage), and temperature of transfer capillary in the API. However, the product ion distribution exhibit high sensitivity to different S-lens radio frequency (RF) voltage of the API, providing evidence that the dehydration reaction is driven by collisions during ion transfer in the API. With this insight, we developed atmospheric pressure thermal dissociation platform that allowed thermal-induced collisions outside of the mass spectrometer when the benzyl amine alcohols are carried through a heated coiled tube. Up to 98% dissociation efficiency was achieved with the coiled tube heated to a temperature of 200 °C, yielding only the desired benzazetidine product. Finally, by performing the atmospheric pressure thermal dissociation experiment outside of the mass spectrometer, we believe it will be straightforward to collect the dehydration product.
Abstract Apidaecin 1b (Api), the first characterized Type II Proline-rich antimicrobial peptide (PrAMP), is encoded in the honey bee genome. It inhibits bacterial growth by binding in the nascent peptide exit tunnel of the ribosome after the release of the completed protein and trapping the release factors. By genome mining, we have identified 71 PrAMPs encoded in insect genomes as pre-pro-polyproteins. Having chemically synthesized and tested the activity of 26 peptides, we demonstrate that despite significant sequence variation in the N-terminal sequence, the majority of the PrAMPs that retain the conserved C-terminal sequence of Api are able to trap the ribosome at the stop codons and induce stop codon readthrough—all hallmarks of Type II PrAMP mode of action. Some of the characterized PrAMPs exhibit superior antibacterial activity in comparison with Api. The newly solved crystallographic structures of the ribosome complexed with Api and with the more active peptide Fva1 from the stingless bee demonstrate the universal placement of the PrAMPs’ C-terminal pharmacophore in the post-release ribosome despite variations in their N-terminal sequence.
With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.
When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.
Today, consumer battery installations are isolated, physical devices. Virtual power plants (VPPs) allow consumer devices to aggregate for grid services, but they are are vertically integrated, vendor controlled systems (e.g., Tesla’s VPP). Consumer batteries are therefore unable to participate in energy markets or other grid services outside what their vendor provides. We describe a software system that provides software control of multiple, networked battery energy storage systems in the electric grid. The system introduces two new ideas that enable flexible and dependable management of energy storage. The first is a virtual battery, which can either partition a battery or aggregate multiple batteries. The second is a reservation-based API which allows asynchronous control of batteries to meet contractual guarantees in a safe and dependable manner. Virtual batteries and a reservation-based API address the unique challenges of achieving high and efficient utilization of energy storage systems, including heterogeneity of battery systems such as varying C-rates, participation in energy markets, utility bill management systems, community resource sharing, and reliability. Using a testbed comprised of sonnen Inc. storage units installed in several homes and a lab, we demonstrate that virtualized batteries can seamlessly replace physical batteries, flexibly manage energy storage resources, isolate multiple clients using a shared battery, and create new energy storage applications.
LANL science workflows generate complex data sets and ensembles of data requiring significant compute and storage resources. The Data Science Infrastructure (DSI) project focuses on data-driven approaches to make data more readily available to LANL projects. DSI workflows leverage metadata stored in data-agnostic databases, supported by an abstraction layer API to simplify searching and accessing data across simulation runs, experimental runs, filesystems and environments. The abstraction layer API allows the user to query a range of data types: raw output, processed data, configuration data, machine learning models, performance data, etc. In addition to the abstraction backend API, the DSI project is developing client-driven query APIs and UIs to support specific user workflows.