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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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11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Heracles: Predictive Tools for Opioid Crisis Intervention - m/q Initiative Project Report

The opioid crisis in the United States is being fueled primarily by fentanyl and its molecular analogs, which can be anywhere from 50 to 1,000 times more potent than morphine. Fentanyl itself is straightforward to synthesize; furthermore, the structure is such that fentanyl’s flexible, rotatable side chains are easy to modify to create new analogs. Reference-free computational techniques to predict and identify new fentanyls have the potential to provide a desperately needed preemptive advantage to regulatory stakeholders and toxicologists. The computational pipeline Heracles was developed with this preemptive advantage in mind. Heracles has two primary components: 1) the creation of an in silico library of putative fentanyl analogs, and 2) a downselection pipeline to prioritize generated fentanyl analogs predicted to be potent and easy to synthesize. Experimental observables were also predicted for prioritized analogs, with validation of the observables begun. Heracles has demonstrated potential to aid in the advancement of reference-free paradigms while providing new tools to first responders and other stakeholders attempting to mitigate the opioid crisis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Development of a Platform for High-Resolution Ion Mobility Separations Coupled with Messenger Tagging Infrared Spectroscopy for High-Precision Structural Characterizations

The ability to uniquely identify a compound requires highly precise and orthogonal measurements. Here we describe a newly developed analytical platform that integrates high resolution ion mobility and cryogenic vibrational ion spectroscopy for high-precision structural characterizations. This platform allows for the temporal separation of isomeric/isobaric ions and provides a highly sensitive description of the ion’s adopted geometry in the gas phase. The combination of these orthogonal structural measurements yields precise descriptors that can be used to resolve between and confidently identify highly similar ions. The unique benefits of our instrument, which integrates a structures for lossless ion manipulations ion mobility (SLIM IM) device with messenger tagging infrared spectroscopy, include the ability to perform high-resolution ion mobility separations and to record the IR spectra of all ions simultaneously. The SLIM IM device, with its 13 m separation path length, allows for multipass experiments to be performed for increased resolution as needed. It is integrated with an Agilent qTOF MS where the collision cell was replaced with a cryogenically held (30 K) TW-SLIM module. The cryo-SLIM is operated in a novel manner that allows ions to be streamed through the device and collisionally cooled to a temperature where they can form noncovalently bound N 2 complexes that are maintained as they exit the device and are detected by the TOF mass analyzer. The instrument can be operated in two modes: IMS+IR where the IR spectra for mobility-selected ions can be recorded and IR-only mode where the IR spectra for all mass-resolved ions can be recorded. In IR-only mode, IR spectra (400 cm –1 spectral range) can be recorded in as short as 2 s for high throughput measurements. Further, this work details the construction of the instrument and modes of operation. It provides initial benchmarking of CCS and IR measurements to demonstrate the utility of this instrument for targeted and untargeted approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Assessing Gas-Phase Ion Reactivity of 50 Elements with NO and the Direct Application for 239 Pu in Complex Matrices Using ICP-MS/MS

Understanding the reactivity of metal cations with various reaction gases in ICP-MS/MS is important to determine the best gas to use for a given analyte/interference pair. In this study, nitric oxide (NO) was investigated as the reaction gas following previous experimental designs. The reactions with 50 elements were investigated to examine periodic trends in reactivity, validate theoretical modeling of reaction enthalpies as a method to screen reactant gases, and provide a baseline for potential in-line gas separation methods. ICP-MS/MS studies involving actinides are typically limited to Th, U and Pu, with analyses of Np and Am rarely reported in the literature. To date, only two previous methods have investigated the use of NO in ICP-MS/MS analyses. To showcase the utility of NO, a method was developed to measure 239 Pu in the presence of environmental matrix constituent and other actinides, like what could be expected from post-detonation debris, with no chemical separation prior to analysis. 239 Pu + was reacted to form 239 Pu 16 O + , eliminating interferences derived from the sample matrix by measuring 239 Pu + intensity at m/z = 255 ( 239 Pu 16 O + ). To validate NO for 238 U 1 H + interference removal in environmental matrices, standard reference materials were diluted to 1 mg per gram of solution and spiked to 0.05 pg/g of 239 Pu and 1 µg/g 238 U (Pu/U = 5 x 10 -8 ). Measured 239 Pu concentrations were within 6% of the spiked value. In conclusion, these results demonstrate that reliable 239 Pu measurements can be made at levels relevant to nuclear forensics without the need for extensive chemical matrix separation prior to analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Paired Neural Network for Matching Experimental and Predicted Infrared Spectra

Here, we present a novel machine learning (ML)-based scoring technique for determining the similarity between experimental and predicted infrared (IR) spectra for identification purposes. IR spectroscopy is a powerful technique used to identify the molecular structure and composition of a sample by measuring the unique vibrational frequency pattern of the molecule’s functional groups. Molecular identifications are often made by comparing experimental and reference spectra. However, the limited number of reference spectra available in spectral libraries can confound the identification process. Alternative identification procedures rely on in silico techniques to simulate spectra for a wide range of molecules. However, scoring spectral similarity between an experimental query and computationally predicted reference remains a significant challenge. Our proposed ML-based scoring technique overcomes these barriers by accurately and efficiently determining spectral similarity.

Neural Network↗
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↗
DOE OSTI2022

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich data challenges existing data storage and access technologies, therefore precluding informatics advancements. Here we present MZA™ (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed by a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and efficient programmatic access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Atomistic simulations for investigation of substrate effects on lipid in-source fragmentation in secondary ion mass spectrometry

In beam-based ionization methods, the substrate plays an important role on the desorption mechanism of molecules from surfaces. Both the specific orientation that a molecule adopts at a surface and the strength of the molecule-surface interaction can greatly influence desorption processes, which in turn will affect the ion yield and the degree of in-source fragmentation of a molecule. In the beam-based method of secondary ion mass spectrometry (SIMS), in-source fragmentation can be significant and molecule specific due to the hard ionization method of using a primary ion beam for molecule desorption. To investigate the role of the substrate on orientation and in-source fragmentation, we have used atomistic simulations—molecular dynamics in combination with density functional theory calculations—to explore the desorption of a sphingolipid (palmitoylsphingomyelin) from a model surface (gold). We then compare SIMS data from this model system to our modeling findings. Using this approach, we found that the combined adsorption and binding energy of certain bonds associated with the headgroup fragments (C3H8N+, C5H12N+, C5H14NO+, and C5H15PNO4+) was a good predictor for fragment intensities (as indicated by relative ion yields). This is the first example where atomistic simulations have been applied in beam-based ionization of lipids, and it presents a new approach to study biointerfacial lipid ordering effects on SIMS imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Joint Modeling/Experimental Approach to Characterize Ionization and Fragmentation of SOA Molecules with CIMS

The Molecular Dynamics technique Collective variable hyperdynamics (CVHD) interfaced with density functional tight-binding at the GFN1-xTB and GFN2-xTB levels of electronic structure theory has been applied to the H + GAG positively charged peptide system to explore the fragmentation of this simple tripeptide under relatively low temperature/energy conditions. Unlike conventional chemical dynamics simulations which can only be performed up to hundreds of picoseconds when coupled with semi-empirical Hamiltonians, CVHD is able to capture the long-time dynamics of multiple proton hopping, the formation of reversible ring structures, and ester rearrangement of the H + GAG system prior to fragmentation. The CVHD method applied to H + GAG is also able to uncover alternative fragmentation pathways not considered previously such as glycine fragmentation on the N-terminal side of the charged peptide and new cyclic cationic species. These findings in the H + GAG case have implications for other peptide systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Computational Methods for Modeling Electrospray Microdroplet Chemistry for Improved Quantitative Mass Spectrometry

This project aimed at enhancing the quantitative analysis capabilities of electrospray ionization mass spectrometry (ESI-MS) by developing advanced computational methods. The primary focus was to integrate continuum and molecular dynamics simulations to study the behavior of microdroplets in the ESI process, from formation to evaporation. Through this research, we sought to bridge significant length and time scales to provide a comprehensive understanding of how analyte concentrations evolve from bulk solutions into gas-phase ions. This understanding is crucial for addressing challenges such as ionization efficiency, solvent effects, and ion suppression, which currently limit the accuracy of quantitative ESI-MS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗