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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 397 records · Page 22

Abundant Ammonia and Nitrogen-Rich Soluble Organic Matter in Samples From Asteroid (101955) Bennu

Organic matter in meteorites reveals clues about early Solar System chemistry and the origin of molecules important to life, but terrestrial exposure complicates interpretation. Samples returned from the B-type asteroid Bennu by the Origins, Spectral Interpretation, Resource Identifcation, and Security–Regolith Explorer mission enabled us to study pristine carbonaceous astromaterial without uncontrolled exposure to Earth’s biosphere. Here we show that Bennu samples are volatile rich, with more carbon, nitrogen and ammonia than samples from asteroid Ryugu and most meteorites. Nitrogen-15 isotopic enrichments indicate that ammonia and other N-containing soluble molecules formed in a cold molecular cloud or the outer protoplanetary disk. We detected amino acids (including 14 of the 20 used in terrestrial biology), amines, formaldehyde, carboxylic acids, polycyclic aromatic hydrocarbons and N-heterocycles (including all fve nucleobases found in DNA and RNA), along with ~10,000 N-bearing chemical species. All chiral non-protein amino acids were racemic or nearly so, implying that terrestrial life’s left-handed chirality may not be due to bias in prebiotic molecules delivered by impacts. The relative abundances of amino acids and other soluble organics suggest formation and alteration by low-temperature reactions, possibly in NH3-rich fuids. Bennu’s parent asteroid developed in or accreted ices from a reservoir in the outer Solar System where ammonia ice was stable.

Daniel P Glavin↗

The NASA OSIRIS-REx Sample Catalog: Samples Returned from Investigators

OSIRIS-REx is a sample return mission that collected carbon-rich material from B-type asteroid Bennu. After landing on September 24th, 2023, the science canister was transported to NASA Johnson Space Center (JSC) in Houston, Texas where it was opened and samples were processed by the curation team in pristine sample nitrogen gloveboxes. As the sample analysis phase of the OSIRIS-REx mission concludes, the Sample Analysis Team (SAT) has returned the majority of mission samples to NASA JSC curation and will return the remaining samples on a defined timetable. The curation team processed and documented as many of these samples as possible to include in the spring 2026 catalog update and are steadily working to process hundreds of more samples to include in the fall 2026 catalog update.

OSIRIS-REx↗

Determination of Elastic Coefficients for a Composite with Crossing Steel - 20086

For a composite material with an embedding of crossing reinforcement bars, the elastic coefficients are calculated by solving a boundary-value problem (elastostatic problem). The background material is concrete whereas the bars are made of steel. The bars cross each other horizontally at a right angle. The boundary-value problem is solved numerically by using 3-D quadratic basis functions in ABAQUS{sup R}. Under external shear loading conditions, the effective elastic coefficients on the macroscale are quite larger for a cross-section in which the bars are immediately below the ones in the cross-section normal to it. The shear modulus in the off-plane direction is about one and half times larger than that in the in-plane direction. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗

Constraining Systematics for Future Sterile Neutrino Analysis at NOvA Experiment

In this article, we report an approach used to constrain the impact of cross-section systematic parameters in the future sterile neutrino analysis at the NOvA experiment. NOvA is a long-baseline neutrino experiment built to investigate the intricate properties of neutrinos, with the principal emphasis on active three-flavor neutrino mixing phenomena. Besides that, NOvA also explores exotic oscillations, including sterile neutrino search. Uncertainties on the neutrino flux, cross-section, and detector systematic parameters significantly contribute, complicating the disentanglement of genuine physics events from background noise. We present the impact of systematic reduction via near detector neutral current sample splitting and its implications on oscillation parameters, leveraging results primarily from Monte Carlo simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of 12 state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of 469K question8 answer pairs involving 30K images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images

Maruf, M [Virginia Tech, Blacksburg]↗