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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Primordial black holes and their mass spectra: The effects of mergers and accretion within stasis cosmologies

A variety of processes in the very early Universe can give rise to a population of primordial black holes (PBHs) with an extended mass spectrum. For certain mass spectra of this sort, it has been shown that the evaporation of these PBHs into radiation can drive the universe toward an epoch of cosmological stasis which can persist for a significant number of 𝑒-folds of cosmological expansion. However, in general, the initial mass spectrum which characterizes a population of PBHs at the time of production can subsequently be distorted by processes such as mergers and accretion. Here, in this paper, we examine the effects that these processes have on the spectra that lead to a PBH-induced stasis. Within such stasis models, we find that mergers have only a negligible effect on these spectra within the regime of interest for stasis. We likewise find that the effect of accretion is negligible in many cases of interest. However, we find that the effect of accretion on the PBH mass spectrum is non-negligible in situations in which this spectrum is particularly broad. In such situations, the stasis epoch is abridged or, in extreme cases, does not occur at all. Thus accretion plays a nontrivial role in constraining the emergence of stasis within scenarios which lead to extended PBH mass spectra.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1972

Compound classification by computer treatment of low resolution mass spectra - Application to geochemical and environmental problems.

A description is given of a development of computer analysis of low-resolution chromatographic-mass spectrometric data, which provides a preliminary classification of an unknown spectrum as a listing of candidate classes of compounds. This procedure, referred to as COMSOC (Classification of Mass Spectra on Computers), operates by converting an incoming unknown mass spectrum into a simplified key word which is then compared with each of the key words held in its reference file. The advantages of COMSOC in characterizing complex mixtures are emphasized.

Smith, D. H.↗
DOE OSTI2025

Data Set Analysis to Reduce Uncertainty in Formula Assignments of Ultrahigh Resolution Mass Spectra

Environmental samples contain a vast array of organic compounds with diverse elemental compositions and heteroatom content. Molecular formula assignments of ultrahigh resolution mass spectra (HRMS) hold promise for elucidating the molecular composition of these compounds. However, the need to account for an assortment of heteroatoms increases the uncertainty associated with individual assignments – and ultimately the ecological, biological, and biogeochemical insights gleaned from the assignments. To address this challenge, we introduce a formula assignment strategy that leverages HRMS data sets to improve assignment confidence, filter false assignments, and mitigate bias in assignment routines. The strategy, implemented using CoreMS, first identifies the highest confidence assignment for a recurring ion in a data set by assessing the mass accuracy and isotopologue similarity of all assignments to the ion across the data set. The second component of the strategy examines the consistency of mass errors for an assigned ion throughout a data set and flags formulas with statistically unlikely deviations in mass error. Here, we illustrate the application and utility of the strategy by comparing its results against documented misassignment patterns within a set of oceanographic samples that were measured with 21 T Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Because the efficacy of our strategy improves with data set size, it is particularly useful for enhancing assignment confidence in large HRMS data sets common in studies of environmental systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1977

Computer programs for the interpretation of low resolution mass spectra: Program for calculation of molecular isotopic distribution and program for assignment of molecular formulas

Two FORTRAN computer programs for the interpretation of low resolution mass spectra were prepared and tested. One is for the calculation of the molecular isotopic distribution of any species from stored elemental distributions. The program requires only the input of the molecular formula and was designed for compatability with any computer system. The other program is for the determination of all possible combinations of atoms (and radicals) which may form an ion having a particular integer mass. It also uses a simplified input scheme and was designed for compatability with any system.

Miller, R. A.↗
DOE OSTI2026

Search for higgsinos in compressed mass spectra using low-momentum tracks in pp collisions at s=13 TeV with the ATLAS detector

This paper presents two searches for the electroweak production of higgsinos with compressed mass spectra using 140 fb−1 of s=13$$ \sqrt{s}=13 $$ TeV proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider. Events are required to feature an energetic jet, large missing transverse momentum, and at least one low-momentum charged particle that serves as a candidate higgsino decay product. In the first search, targeting higgsino mass splittings in the range of 0.3–1 GeV, the higgsinos are expected to predominantly decay into pions that are identified as low-momentum charged particles with large transverse impact parameters due to the long higgsino lifetime (cτ ≈ ?(0.1–10 mm)), and neural networks are used to discriminate between signal and background processes. The second search targets larger mass splittings in the range of 1–3 GeV, where the higgsinos are expected to decay promptly into low-momentum leptons, one of which is identified by dedicated low-momentum electron or muon taggers based on neural networks utilising tracking and calorimeter information. No significant excess above the Standard Model prediction is observed in either search and the results are interpreted within simplified models, to set lower limits on the masses of the higgsino-like charginos and neutralinos. Together, these searches exclude chargino masses below 126 GeV at 95% confidence level for mass splittings between the chargino and lightest neutralino in the range of 0.3–2 GeV. This represents the first ATLAS constraints in a portion of this parameter space and surpasses the limits previously set by other experiments.

Aad, G↗
NASA NTRS1965

The distinction of rock types on the basis of their mass spectra, with special reference to lunar-surface applications

It is shown that the rock types most commonly expected to be encountered on the lunar surface can for the most part be readily distinguished, chemically, by plotting their relative concentrations of Fe, Mg, and Al on a ternary variation diagram. The necessary data for characterizing as unknown as to rock type can be quite easily extracted from complete or partial mass spectra such as may be obtained by means of a robot mass spectrometer on the lunar surface. For most compositions, determination of only two nuclide or element rations will characterize the sample. Fo others, the determination of one additional ratio or comparison with a few standard spectra previously obtained in the laboratory may be necessary to clarify the unknown in terms of the chemistry of the terrestrial or meteoritic equivalents. No quantitative assay of element concentrations is necessary for such a first classification.

LUNAR SURFACE↗
DOE OSTI2026

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Jacobson, Philip [Sandia National Laboratories (SN↗
DOE OSTI2022

SIMILE enables alignment of tandem mass spectra with statistical significance

Abstract Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Mass spectra of the particle-antiparticle system with a Dirac oscillator interaction

The present view about the structure of mesons is that they are a quark-antiquark system. The mass spectrum corresponding to this system should, in principle, be given by chromodynamics, but this turns out to be a complex affair. Thus it is of some interest to consider relativistic systems of particle-antiparticle, with a simple type of interaction, which could give some insight on the spectra we can expect for mesons. This analysis is carried out when the interaction is of the Dirac oscillator type. It is shown that the Dirac equation of the antiparticle can be obtained from that of the particle by just changing the frequency omega into -omega. Following a procedure suggested by Barut, the equation for the particle-antiparticle system is derived and it is solved by a perturbation procedure. Thus, explicit expressions for the square of the mass spectra are obtained and its implications in the meson case is discussed.

Moshinsky, M.↗
DOE OSTI2025

SpectraCodec: A Hilbert curve-based method for encoding metadata in mass spectra for machine learning applications (SpectraCodec) v1

Machine learning approaches to mass spectrometry (MS) data analysis require structured metadata for optimal performance. However, current MS file formats necessitate external metadata sources, creating integration challenges that impede analytical workflows. Here, we present a novel approach for encoding metadata directly within mzML files using one-hot encoding of ASCII characters mapped via Hilbert space-filling curves. This strategy embeds metadata in the first spectrum's m/z-intensity space, ensuring persistence with the primary data, eliminating the need for external metadata files, and maintaining compatibility with existing MS software. We demonstrate that the Hilbert curve mapping efficiently utilizes the two-dimensional spectral space while maintaining robust data recovery. This method offers a practical solution for machine learning applications in mass spectrometry by ensuring metadata and spectral data remain unified through all stages of analysis.

Bowen, Benjamin [Lawrence Berkeley National Labora↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectra_for_Standard_Biomass_Samples_and_Natural_Variants_of_Switchgrass_Grown_Under_Different_Conditions

This data consists of total ion chromatogram (TIC) normalized Pyrolysis-Molecular Beam Mass Spectrometry (py-MBMS) spectra collected to predict lignin content and lignin monomeric ratios for “Variation in Biomass Yield and Cell Wall Composition in Switchgrass Natural Variants Under Two Nitrogen Regimes” (https://doi.org/10.1007/s12155-025-10838-8).

09 BIOMASS FUELS↗
NASA NTRS1977

Analysis of steranes and triterpanes in geolipid extracts by automatic classification of mass spectra

A computer method is described for the automatic classification of triterpanes and steranes into gross structural type from their mass spectral characteristics. The method has been applied to the spectra obtained by gas-chromatographic/mass-spectroscopic analysis of two mixtures of standards and of hydrocarbon fractions isolated from Green River and Messel oil shales. Almost all of the steranes and triterpanes identified previously in both shales were classified, in addition to a number of new components. The results indicate that classification of such alkanes is possible with a laboratory computer system. The method has application to diagenesis and maturation studies as well as to oil/oil and oil/source rock correlations in which rapid screening of large numbers of samples is required.

Wardroper, A. M. K.↗
NASA NTRS2021

Venus' Mass Spectra Show Signs of Disequilibria in the Middle Clouds

We present a re‐examination of mass spectral data obtained from the Pioneer Venus Large Probe Neutral Mass Spectrometer. Our interpretations of differing trace chemical species are suggestive of redox disequilibria in Venus' middle clouds. Assignments to the data (at 51.3 km) include phosphine, hydrogen sulfide, nitrous acid, nitric acid, carbon monoxide, hydrochloric acid, hydrogen cyanide, ethane, and potentially ammonia, chlorous acid, and several tentative PxOy species. All parent ions were predicated upon assignment of corresponding fragmentation products, isotopologues, and atomic species. The data reveal parent ions at varying oxidation states, implying the presence of reducing power in the clouds, and illuminating the potential for chemistries yet to be discovered. When considering the hypothetical habitability of Venus' clouds, the assignments reveal a potential signature of anaerobic phosphorus metabolism (phosphine), an electron donor for anoxygenic photosynthesis (nitrite), and major constituents of the nitrogen cycle (nitrate, nitrite, ammonia, and N2).

disequilibria↗