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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Methods for Quantitative Thermal Analysis of Lithium Solid-State and Beyond Battery Safety

The use of differential scanning calorimetry (DSC) to measure the thermal behavior of individual components and electrolyte/electrode combinations is common. However, here we focus on DSC tests on an anode, cathode, and electrolyte (ACE) component combination over a temperature range that includes many of the phase transitions and key reactions (i.e., to 500 °C) that contribute to thermal runaway. This method can help quantify the complex reaction network in a full cell, thereby informing potential safety issues. Here, we used DSC heat flow data from a solid-state Li 0.43 CoO 2 +C+PVDF | LLZO | Li metal ACE sample and its components to quantify key factors affecting results. We focused on three areas: (1) ACE sample preparation and assembly in DSC pans, (2) DSC measurement parameters, and (3) heat flow analysis. Key points include the choice of component ratios (e.g., commercially relevant N:P capacity ratio), the importance of conductive carbon and binder, type of pan used, DSC ramp rate, and integration method used when dealing with broad and overlapping exothermic peaks. This work deepens the scientific basis and best practices for obtaining heat flow data from ACE samples for early-stage evaluation of solid-state and beyond battery safety.

25 ENERGY STORAGE↗
DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Transient local secondary structure in the intrinsically disordered C‐term of the Albino3 insertase

Albino3 (Alb3) is an integral membrane protein fundamental to the targeting and insertion of light‐harvesting complex (LHC) proteins into the thylakoid membrane. Alb3 contains a stroma‐exposed C‐terminus (Alb3‐Cterm) that is responsible for binding the LHC‐loaded transit complex before LHC membrane insertion. Alb3‐Cterm has been reported to be intrinsically disordered, but precise mechanistic details underlying how it recognizes and binds to the transit complex are lacking, and the functional roles of its four different motifs have been debated. Using a novel combination of experimental and computational techniques such as single‐molecule fluorescence resonance energy transfer, circular dichroism with deconvolution analysis, site‐directed mutagenesis, trypsin digestion assays, and all‐atom molecular dynamics simulations in conjunction with enhanced sampling techniques, we show that Alb3‐Cterm contains transient secondary structure in motifs I and II. The excellent agreement between the experimental and computational data provides a quantitatively consistent picture and allows us to identify a heterogeneous structural ensemble that highlights the local and transient nature of the secondary structure. This structural ensemble was used to predict both the inter‐residue distance distributions of single molecules and the apparent unfolding free energy of the transient secondary structure, which were both in excellent agreement with those determined experimentally. We hypothesize that this transient local secondary structure may play an important role in the recognition of Alb3‐Cterm for the LHC‐loaded transit complex, and these results should provide a framework to better understand protein targeting by the Alb3‐Oxa1‐YidC family of insertases.

Okoto, Patience S.↗
DOE OSTI2023

Analysis of gas storage and transport in Eagle Ford shale using pressure pulse decay measurements with He, Kr and CO 2

Detailed characterization of gas transport and storage in low-permeability and organic-rich shales is associated with an array of challenges due to the complex morphology of the pore space, representing a broad range of pore sizes, combined with the heterogeneous fabric of the shale matrix. These factors, and their interplay during gas transport and sorption, complicate a) the analysis of shale samples at the laboratory scale (~ up to a ft. In length), and b) the prediction of natural gas production and carbon sequestration potential at larger scales. Here, in this work, tandem experiments with inert (helium – He) and adsorbing (krypton - Kr and carbon dioxide - CO 2 ) gases were performed and analyzed to develop an efficient workflow for characterizing and modeling transport and sorption at the laboratory scale. In particular, pressure pulse-decay (PPD) measurements were conducted on an Eagle Ford shale core sample at room temperature using He, Kr, and CO 2 . PPD measurements with He (a non-sorbing gas) were employed to probe the overall porosity, including natural fractures, microcracks, mesopores, and micropores. A triple-porosity model (TPM) was adopted to interpret the gas transport in the shale sample: The pore space is represented by three interacting continua, including macropores (larger fractures), mesopores (including microcracks), and micropores. To facilitate the application of the TPM, a modified analytical approach is introduced to extract effective transport parameters (in terms of the characteristic time for transport at relevant porosity levels) directly from the PPD measurements with He. To validate the modeling approach, model parameters extracted from two He PPD measurements are demonstrated to provide excellent agreement with a 3rd He PPD measurement performed at a higher pressure. The effective transport parameters, extracted from the He experiments, were subsequently converted for application to the Kr and CO 2 PPD experiments, by accounting for relevant transport modes and differences in fluid properties. Excess adsorption isotherms were extracted from the equilibrium pressures of the Kr and CO 2 PPD experiments using the He pore volume as a baseline. These adsorption isotherms were then integrated into the TPM to predict combined gas transport and sorption for Kr and CO 2 , with transport coefficients translated from the He measurements. The predictions for Kr and CO 2 are demonstrated to be in excellent agreement with the experimental observations. This, in turn, demonstrates that the proposed analytical approach provides for an effective characterization of mass transfer rates in shales, that can be applied directly in a TPM representation of mass transfer and sorption.

58 GEOSCIENCES↗
DOE OSTI2023

A standardized quantitative analysis strategy for stable isotope probing metagenomics

ABSTRACT Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many DNA-SIP studies rely on 16S rRNA gene sequences to identify active taxa, but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA gene sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes and their level of isotopic enrichment were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytical models for identifying active taxa and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg , an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. IMPORTANCE Answering the questions, “who is eating what?” and “who is active?” within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. These questions can be pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganism’s taxonomic identity and genome composition while providing quantitative estimates of the microorganism’s isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Parameter inference with deep jointly informed neural networks

Abstract A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.

Humbird, Kelli D.↗
DOE OSTI2022

Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI

Abstract Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (N CCN ), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or N CCN decreases. Cloud depth affects RR the most while N CCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of N CCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scanning transmission x-ray microscopy (STXM) of plutonium oxide

Scanning transmission x-ray microscopy was used to examine plutonium oxide particles formed by the corrosion of δ-phase plutonium alloy under high-humidity conditions. O K-edge spectra collected from eight distinct particles displayed significant spectral differences, revealing heterogeneity in oxidation states within a single sample batch. Here, this variation suggests complex chemical environments and formation histories, which are important considerations for nuclear forensic investigations. These findings highlight both the potential of synchrotron-based x-ray microscopy for nondestructive, high-resolution analysis of nuclear materials and the need for expanded reference datasets to improve the interpretation and forensic utility of such measurements.

organic↗
DOE OSTI2018

Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations

The recovery of approximately sparse or compressible coefficients in a polynomial chaos expansion is a common goal in many modern parametric uncertainty quantification (UQ) problems. However, relatively little effort in UQ has been directed toward theoretical and computational strategies for addressing the sparse corruptions problem, where a small number of measurements are highly corrupted. Such a situation has become pertinent today since modern computational frameworks are sufficiently complex with many interdependent components that may introduce hardware and software failures, some of which can be difficult to detect and result in a highly polluted simulation result. In this paper we present a novel compressive sampling--based theoretical analysis for a regularized $\ell^1$ minimization algorithm that aims to recover sparse expansion coefficients in the presence of measurement corruptions. Our recovery results are uniform (the theoretical guarantees hold for all compressible signals and compressible corruptions vectors) and prescribe algorithmic regularization parameters in terms of a user-defined a priori estimate on the ratio of measurements that are believed to be corrupted. We also propose an iteratively reweighted optimization algorithm that automatically refines the value of the regularization parameter and empirically produces superior results. Lastly, our numerical results test our framework on several medium to high dimensional examples of solutions to parameterized differential equations and demonstrate the effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Design and analysis of actively-cooled, edge-transport diagnostic for long-pulsed operation in WEST

Next step fusion devices that will operate in steady-state will require complex plasma-facing components (PFCs) that can survive the harsh environment over long timescales not common in current devices. This will require robust plasma facing surfaces that are integrated with active cooling systems. In a collaboration between CEA and ORNL, a plasma-interacting diagnostic is being designed for the W Environment in Steady-state Tokamak (WEST) in Cadarache, France which requires plasma-facing protection like those needed for steady-state PFCs. This integrated diagnostic studies edge transport and impurity migration within WEST, and is planned to include imbedded temperature Langmuir probe sensors, as well as removeable sample slots for ex-situ surface analysis of plasma-material interactions. The entire assembly is expected to move into the plasma edge for periods up to 1000 s having an energy removeable capability of ∼6 kW and seeing a peak heat flux of more than 7 MW/m2. The assembly compliments WEST high fluence campaigns that plan for multiple 1000 s pulses. Within these specifications, the assembly will require a refractory metal plasma facing surface and integral cooling in order to function within the limited space allotted for such diagnostics. Because of the limited space and linear actuator needs, additive manufacturing of the high heat flux working end of the assembly is being considered which could allow for precision cooling-channels and lighter weight designs. Conceptual design along with simulation and analysis results will be presented for this complex diagnostic with novel PFCs.

Lumsdaine, Arnold↗
DOE OSTI2020

Persistent Protein Motions in a Rugged Energy Landscape Revealed by Normal Mode Ensemble Analysis

Proteins are allosteric machines that couple motions at distinct, often distant, sites to control biological function. Low-frequency structural vibrations are a mechanism of this long-distance connection and are often used computationally to predict correlations, but experimentally identifying the vibrations associated with specific motions has proved challenging. Spectroscopy is an ideal tool to explore these excitations, but measurements have been largely unable to identify important frequency bands. The result is at odds with some previous calculations and raises the question what methods could successfully characterize protein structural vibrations. Here we show the lack of spectral structure arises in part from the variations in protein structure as the protein samples the energy landscape. However, by averaging over the energy landscape as sampled using an aggregate 18.5 μs of all-atom molecular dynamics simulation of hen egg white lysozyme and normal-mode analyses, we find vibrations with large overlap with functional displacements are surprisingly concentrated in narrow frequency bands. These bands are not apparent in either the ensemble averaged vibrational density of states or isotropic absorption. However, in the case of the ensemble averaged anisotropic absorption, there is persistent spectral structure and overlap between this structure and the functional displacement frequency bands. We systematically lay out heuristics for calculating the spectra robustly, including the need for statistical sampling of the protein and inclusion of adequate water in the spectral calculation. The results show the congested spectrum of these complex molecules obscures important frequency bands associated with function and reveal a method to overcome this congestion by combining structurally sensitive spectroscopy with robust normal mode ensemble analysis.

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

Tetrafluoroboric Acid Digestion for Accurate Determination of Rare Earth Elements in Coal and Fly Ash by ICP-MS Analysis

Coal and coal-related fly ash often contain rare earth elements (REEs) that have the potential to be utilized as valuable mineral resources. Accurately determining the REE content in coal and fly ash is crucial for resource evaluation. The conventional approach involves using hydrofluoric acid (HF) to dissolve silicates and release REEs, which, however, prolongs the digestion process due to the additional step of complexing fluoride ions (F−) with boric acid (H3BO3). Determining the correct amount of H3BO3 for neutralization can be challenging, and in some instances, the binding of fluoride ions with certain lanthanides (Lns) hampers the accurate determination of all 14 naturally occurring rare earth elements in a single digestion batch by inductively coupled plasma mass spectrometry (ICP-MS). In this study, we present an alternative method that achieves the accurate determination of all 14 naturally occurring REEs using tetrafluoroboric acid (HBF4) followed by ICP-MS analysis. This approach eliminates the need for an F− complexing step. We tested this method on certified REE reference materials, including NIST 1632e (coal) and NIST 1633c (fly ash), as well as the REE geological reference material USGS AGV-1 (andesite). Our results demonstrated excellent recovery rates (relative standard deviation, RSD < ±10%), with a correlation coefficient (r2) exceeding 0.99. Using this method, we investigated the concentrations of all 14 REEs in coal and fly ash samples collected from various locations in the southwestern USA. This improved digestion technique streamlines the analysis process and enhances the accuracy of REE determination, facilitating a more comprehensive evaluation of REE-rich coal and fly ash deposits for resource exploration.

58 GEOSCIENCES↗
DOE OSTI2019

Transient Kinetic Experiments within the High Conversion Domain: The Case of Ammonia Decomposition

In the development of catalytic materials, a set of standard conditions is needed where the kinetic performance of many samples can be compared. This can be challenging when a sample set covers a broad range of activity. Precise kinetic characterization requires uniformity in the gas and catalyst bed composition. This limits the range of convecting devices to low conversion (generally <20%). While steady-state kinetics offer a snapshot of conversion, yield and apparent rates of the slow reaction steps, transient techniques offer much greater detail of rate processes and hence more information as to why certain catalyst compositions offer better performance. In this work, transient experiments in two transport regimes are compared: an advecting differential plug flow reactor (PFR) and a pure-diffusion temporal analysis of products (TAP) reactor. The decomposition of ammonia was used as a model reaction to test three simple materials: polycrystalline iron, cobalt and a bimetallic preparation of the two. These materials presented a wide range of activity and it was not possible to capture transient information in the advecting device for all samples at the same conditions while ensuring uniformity. We push the boundary for the theoretical estimates of uniformity in the TAP device and find reliable kinetic measurement up to 90% conversion. However, what is more advantageous from this technique is the ability to observe the time-dependence of the reaction rate rather than just singular points of conversion and yield. For example, on the iron sample we observed reversible adsorption of ammonia and on cobalt materials we identify two routes for hydrogen production. From the time-dependence of reactants and product, the dynamic accumulation was calculated. This was used to understand the atomic distribution of H and N species regulated by the surface of different materials. When ammonia was pulsed at 550 °C, the surface hydrogen/nitrogen, (H/N), ratios that evolved for Fe, CoFe and Co were 2.4, 0.25 and 0.3 respectively. This indicates that iron will store a mixture of hydrogenated species while materials with cobalt will predominantly store NH and N. While much is already known about iron, cobalt and ammonia decomposition, the goal of this work was to demonstrate new tools for comparing materials over a wider window of conversion and with much greater kinetic detail. As a result, this provides an approach for detailed kinetic discrimination of more complex industrial samples beyond conversion and yield.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Correlated Synthetic Time Series Generation using Fourier and ARMA

As the contribution of renewable energy grows in electricity markets, the complexity nuclear’s place within the energy mix increases, and likewise the need for robust simulation techniques. While decades of wind, solar, and demand pro- files can sometimes be obtained, this is too few samples to provide a statistically meaningful analysis of a system with baseload, peaker, and renewable generation. Synthetic time series generation presents itself as a suitable methodology to meet this need. One approach for synthetic series generation is training a model using Fourier series decomposition for seasonal patterns and Auto-Regressive Moving Average models (ARMA) to describe time-correlated statistical noise about the seasonal patterns. When combined, the Fourier plus ARMA (FARMA) model has been shown to provide an infinite set of independent, identically-distributed sample time series with the same statistical properties as the original data [1]. When considering an energy mix with renewable electricity production, several time series of energy, grid, and weather measurements are needed for each synthetic year modeled to statistically comprehend the efficiency of any given energy mix. These cannot be considered independent series in a given synthetic year. To capture and reproduce the correlations that might exist in the measured histories, the ARMA can further be extended as a Vector ARMA (VARMA). In the VARMA algorithm, covariance in statistical noise is captured both within a history as part of the autoregressive moving average, and with respect to the other variables in the time series. In this work the implementation of the Fourier VARMA in the RAVEN uncertainty quantification and risk analysis software framework [2] is presented, along with examples of correlated synthetic history generation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A consistent path for phase determination based on transmission electron microscopy techniques and supporting simulations

Here this work addresses aspects for the analysis of industrial relevant materials via transmission electron microscopy (TEM). The complex phase chemistry and structural diversity of these materials require several characterization techniques to be employed simultaneously; unfortunately, different characterization techniques often lack connection to yield a complete and consistent picture. This paper describes a continuous path, starting with the acquisition of 3D diffraction data – alongside classical high-resolution imaging techniques – and linking the structural characterization of hard metal industrial samples with energy-loss fine-structure simulations, quantitative electron energy-loss (EEL) and energy-dispersive X-ray (EDX) spectroscopy. Thereby, the compositional analysis of a MAX phase indicated an offset of the hydrogenic, theoretical sensitivity factors, originating from poorly-adjusted screening factors. In a next step, these results were matched against quantitative compositions and parameters obtained from X-ray spectroscopy data, carried out synchronously with EELS.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deformation-enhanced hierarchical multiscale structure heterogeneity in a Pd-Si bulk metallic glass

Here, the multiscale structures in a Pd 82 Si 18 binary bulk metallic glass before and after deformation were studied using electron microscopies, high-energy synchrotron X-ray diffraction, and small-angle scattering techniques. The experimental results revealed an enhancement of hierarchical structure heterogeneities on multiple length scales after deformation. Hierarchical multiple shear bands of high number density were observed after bending, introducing complex but periodically distributed residual strain. Pair distribution function analysis revealed that the connectivity of the short-range clusters on the medium-range scale determines the packing density difference between the tension side and the compression side in the sample after bending. In-situ synchrotron X-ray diffraction study also revealed a transformation of connection modes among short-range clusters under uniaxial tension and compression, which is consistent with those of triaxial tension/compression parts upon bending in Pd 82 Si 18 glassy alloys. The nanoscale heterogeneities for metallic glasses after deformation observed by small-angle scattering and transmission electron microscopy may be attributed to the nanoscale amorphous phase separation and interacting multiple shear bands enhanced by plastic deformation. Our findings suggested that the enhancement of hierarchical heterogeneous structure on multiple length scales may explain the excellent plasticity of Pd-Si glassy alloys, deepening the understanding of structure-property relation during plastic deformation in metallic glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone

The spatial distribution of organics in geological samples can be used to determine when and how these organics were incorporated into the host rock. Mass spectrometry (MS) imaging can rapidly collect a large amount of data, but ions produced are mixed without discrimination, resulting in complex mass spectra that can be difficult to interpret. Here, we apply unsupervised and supervised machine learning (ML) to help interpret spectra from time-of-flight-secondary ion mass spectrometry (ToF-SIMS) of an organic-carbon-rich mudstone of the Middle Jurassic of England (UK). It was previously shown that the presence of sterane molecular biomarkers in this sample can be detected via ToF-SIMS (Pasterski, M. J. et al., Astrobiology 2023, 23, 936). We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithms─k-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)─to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. In terms of predictive accuracy and balanced accuracy, KNN was the best performing model and RPART the worst. The feature importance, or the specific features of the ToF-SIM spectra used by the models to make classifications, cannot be determined for KNN, preventing posthoc model interpretation. Nevertheless, the feature importance extracted from the other models was useful for interpreting spectra. In conclusion, we determined that some of the organic ions used to classify biomarker containing spectra may be fragment ions derived from kerogen which is abundant in this mudstone sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗