Search NASA⌕ Search

SEARCH · Search NASA

Results for “Mass spectra”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Interpretation of the ion mass spectra in the mass per charge range 25-35 amu/e obtained in the inner coma of Halley's comet by the HIS-sensor of the Giotto IMS experiment

The IMS-HIS double-focusing mass spectrometer that flew on the Giotto spacecraft covered the mass per charge range from 12 to 56 (amu/e). By comparing flight data, calibration data and results of model calculations of the ion population in the inner coma, the absolute mass scale is established, and ions in the mass range 25 to 35 are identified. Ions resulting from protonation of molecules with high proton affinity are relatively abundant, enabling us to estimate relative source strengths for H2CO, CH3OH, HCN, and H2S, providing for the first time a positive in situ measurement of methanol. Also, upper limits for NO and some hydrocarbons are derived.

Geiss, J.↗
NASA NTRS1991

Interpretation of the ion mass spectra in the mass range 25-35 obtained in the inner coma of Halley's comet by the HIS-sensor of the Giotto IMS Experiment

The IMS-HIS double-focussing mass spectrometer that flew on the Giotto spacecraft covered the mass per charge range from 12 to 56 (AMU/e). By comparing flight data, calibration data, and results of model calculations of the ion population in the inner coma, the absolute mass scale is established, and ions in the mass range 25 to 35 are identified. Ions resulting from protonation of molecules with high proton affinity are relatively abundant, enabling us to estimate relative source strengths for H2CO, CH3OH, HCN, and H2S, providing for the first time a positive in situ measurement of methanol. Also, upper limits for NO and some hydrocarbons are derived.

Geiss, J.↗
DOE OSTI2024

QC-GN 2 oMS 2 : a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. Throughout this work, we evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2019

Decomposition of Electron Ionization Mass Spectra for Space Application Using a Monte-Carlo approach

Rationale: Quadrupole mass spectrometers equipped with an electron ionization (EI) sources have been widely used in space exploration to investigate the composition of planetary surfaces and atmospheres. However, the complexity of the samples and the minimal calibration for the fragmentation of molecules in the ionization chambers have prevented the deconvolution of the majority of the mass spectra obtained at different targets, thus limiting the determination of the exact composition of the samples analyzed. We propose a Monte‐Carlo approach to solve this issue mathematically. Methods: We decomposed simulated mass spectra of mixtures acquired with unit resolving power mass spectrometers and EI sources into the sum of the single components fragmentation patterns weighted by their relative concentration using interior‐point least‐square fitting. To fit compounds with poorly known fragmentation patterns, we used a Monte‐Carlo method to vary the intensity of individual fragment ions. We then decomposed the spectrum thousands of times to obtain a statistical distribution. Results: By performing the deconvolution on a mixture of seven different molecules with interfering fragmentation patterns (H2O, O2, CH4, Ar, N2, C2H4, and C2H6) we show that this approach retrieves the mixing ratio of the individual components more accurately than regular mass spectra decomposition methods that rely on fragmentation patterns from general databases. It also provides the probability density function for each species's mixing ratio. Conclusions: By removing the solution degeneracy in the decomposition of mass spectra, the method described herein could significantly increase the scientific retrieval from archived space flight mass spectrometry data, where calibration of the ionization source is no longer an option.

Thomas Gautier↗
NASA NTRS1991

Preliminary results of investigations into the use of artificial neural networks for discriminating gas chromatograph mass spectra of remote samples

Neural networks trained using mass spectra data from the National Institute of Standards and Technology (NIST) are studied. The investigations also included sample data from the gas chromatograph mass spectrometer (GCMS) instrument aboard the Viking Lander, obtained from the National Space Science Data Center. The work performed to data and the preliminary results from the training and testing of neural networks are described. These preliminary results are presented for the purpose of determining the viability of applying artificial neural networks in discriminating mass spectra samples from remote instrumentation such as the Mars Rover Sample Return Mission and the Cassini Probe.

Geller, Harold A.↗
DOE OSTI2025

General search for supersymmetric particles in scenarios with compressed mass spectra using proton-proton collisions at $\sqrt{s}$ = 13 TeV

A general search is presented for supersymmetric particles (sparticles) in scenarios featuring compressed mass spectra using proton-proton collisions at a center-of-mass energy of 13 TeV, recorded with the CMS detector at the LHC. The analyzed data sample corresponds to an integrated luminosity of 138 fb −1 . A wide range of potential sparticle signatures are targeted, including pair production of electroweakinos, sleptons, and top squarks. The search focuses on events with a high transverse momentum system from initial-state-radiation jets recoiling against a potential sparticle system with significant missing transverse momentum. Events are categorized based on their lepton multiplicity, jet multiplicity, number of 𝑏-tagged jets, and kinematic variables sensitive to the sparticle masses and mass splittings. The sensitivity extends to higher parent sparticle masses than previously probed at the LHC for production of pairs of electroweakinos, sleptons, and top squarks with mass spectra featuring small mass splittings (compressed mass spectra). The observed results demonstrate agreement with the predictions of the background-only model. Lower mass limits are set at 95% confidence level on production of pairs of electroweakinos, sleptons, and top squarks that extend to 325, 275, and 780 GeV, respectively, for the most favorable compressed mass regime cases.

Hadron colliders↗
DOE OSTI2024

On the instrument-dependent appearance of ion dissociation events in atom probe tomography mass spectra

The successful application of atom probe tomography (APT) relies on the accurate interpretation of the mass spectrum (i.e. m/z histogram) from a sample. Some materials yield mass spectra that are amenable to a straightforward peak assignment/ranging, however, there are many materials that produce mass spectra with features that defy simple interpretation. One such example is Ga 2 O 3 which yields mass spectra containing several broad and difficult to interpret features. Herein, we study the GaO 2+ → O 1+ + Ga 1+ dissociation and we explain how this dissociation process gives rise to broad and previously unassigned features in the mass spectrum. Trajectory simulations are performed for the dissociation reaction utilizing realistic electrostatic models and compared to experiments using commercially available straight flight and reflectron based local electrode (LE) APT instruments. It is shown that the appearance of these features is strongly dependent on the specific design of the time-of-flight (ToF) mass analyzer. Additionally, we explore how various experimental parameters can affect the appearance of the dissociation process in the one-dimensional (1D) mass spectrum and in the two-dimensional (2D) correlation histogram. While the focus of this work is on a particular dissociation process related to Ga 2 O 3 , the understanding gained in the course of these simulations and experiments should be applicable to the interpretation of dissociation processes in other materials.

47 OTHER INSTRUMENTATION↗
NASA NTRS1988

PIA update: Correlation analyses of mass spectra

The PIA instrument aboard the Giotto spacecraft (a time of flight spectrometer) has been presented elsewhere. The mass spectra used in this analysis were decoded and mass numbers assigned according to the presence of carbon and silver, using the global values for these elements in their spectral absence. The results presented here were obtained using a frequency of occurrence based on analysis which correlated how often mass numbers appear in the mass spectra and which mass numbers tend to occur together in the same spectra; no amplitude information is utilized. The data are presented as plots of mass vs coincident mass for different subsets of the PIA data set, with both axes having units of atomic mass. Frequency contours are plotted at approximately five percent contour intervals, relative to the maximum AMU occurrence in that plot. The plots presented are symmetrical about the matrix diagonal, i.e., every mass is coincident with itself in a given spectra.

Mason, L. W.↗
NASA NTRS2017

Mass Spectra Deconvolution of Gaseous Mixtures Containing Volatile Organic Compounds

The Spacecraft Atmosphere Monitor (S.A.M.) analyzes all gaseous pollutants in the 1-150Th mass-to-charge range based on their positive-ion mass spectra. Of particular interest is the Trace Gas Analysis (TGA) mode of operation in which S.A.M detects minute amounts of volatile organic compounds (VOC). Pollutant component of the ambient air is sampled on demand in twenty full mass spectra per second. All spectra is accumulated in two-second intervals by an on-board Field Programmable Gate Arrays (FPGA) unit. Mass spectra represents the number of detected fragment ions in a given mass-to-charge ratio channel. Electron impact ionization of neutral molecules results in creation of numerous fragment ions and most of organic compounds will contribute several identical fragments. Despite the presence of these molecular isobars, we developed the deconvolution algorithm capable of identifying target species based on their characteristic fragmentation patterns. We investigate the efficiency of deconvolution algorithm as a function of mass resolution with which mass spectrum is acquired. Higher the mass resolution, better the chances are to distinguish between stereoisomers using their fragmentation propensities, but also results in higher data volumes to be processed by a set of small footprint software stacks hosted by an on-board computer. Finding the balance between deconvolution accuracy and generated data volume under time constrains and limited computing resources is the main topic of this study.

Darrach, M.↗
DOE OSTI2022

Anatomy of Protein Electrospray Ionization Mass Spectra by Superconducting Tunnel Junction Mass and Energy Spectrometry

Cryogenic superconducting tunnel junction (STJ) detectors have the advantage of single-particle sensitivity, high quantum efficiency, low noise, and the ability to detect the time and relative impact energy of deposited ions. This makes them attractive for use in mass spectrometry (MS) and as a form of energy spectrometry. STJ cryodetectors have been coupled to time-offlight (TOF) mass spectrometers equipped with a matrix-assisted laser desorption ionization (MALDI) source and to an electrospray ionization (ESI) TOF mass spectrometer. Here, a lab-made linear ion trap (LIT) mass spectrometer system was coupled to an ESI source and a 16-channel Nb-STJ array with improved readout electronics. The goal was to investigate fundamentals of ESI-generated protein ions by further exploiting the advantage of resolving these ions in a third dimension of the relative energy deposited into the STJs. The proteins equine cytochrome c, bovine carbonic anhydrase, bovine serum albumin, and murine immunoglobulin G were studied using this ESI-LIT-STJ-MS instrument. Multiply charged monomers, multimers, and fragments from metastable ions were resolved from monomer peaks by differences in ion deposition energy even when these ions have the same mass-to-charge ratio as the corresponding monomer. The determination of fragment mass from metastable decomposition is accomplished without knowing the charge state of the fragment. The average charge state of the multimers is reduced with each addition of a protein which is a direct reflection of the surface area available for charging. Multiply charged in-source fragments have also been observed and distinguished in the mass spectrum of carbonic anhydrase by using the differences in the energy deposited in the STJs.

42 ENGINEERING↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
NASA NTRS1990

Mass spectra of sputtered polyoxymethylene - Implications for comets

Laboratory mass spectra of sputtered polyoxymethylene, POM, reveal a mass pattern similar to that detected by the PICCA experiment on board the Giotto spacecraft. Both commercially available POM and radiation-synthesized POM have been used in the studies. Synthesized POM was identified by its infrared absorption spectra after proton irradiation of H2CO condensed on silicate grains at 20 K. Laboratory results suggest that a similar type of sputtering is a possible mechanism for removing species from cometary grains.

Moore, Marla H.↗
DOE OSTI2023

Search for periodic signals in the dielectron and diphoton invariant mass spectra using 139 fb -1 of $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for physics beyond the Standard Model inducing periodic signals in the dielectron and diphoton invariant mass spectra is presented using 139 fb -1 of $\sqrt{s}$ = 13 TeV pp collision data collected by the ATLAS experiment at the LHC. Novel search techniques based on continuous wavelet transforms are used to infer the frequency of periodic signals from the invariant mass spectra and neural network classifiers are used to enhance the sensitivity to periodic resonances. In the absence of a signal, exclusion limits are placed at the 95% confidence level in the two-dimensional parameter space of the clock work gravity model. Model-independent searches for deviations from the background-only hypothesis are also performed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗