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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimization of sporulation and purification methods for sporicidal efficacy assessment on Bacillus spores

Abstract Validating the efficacy of sporicidal agents is a critical step in current good manufacturing practices for disinfection requirements. A limitation is that the poor quality of spores can lead to false positive sporicidal results. The aim of this study was to explore optimal sporulation and purification methods in Bacillus spores. Spores of 7 Bacillus strains were produced in 5 different sporulation media. After density centrifugation, spore yields were measured by phase-contrast microscopy and enumeration assays. Effects of purification methods including heat, sonication and lysozyme, and maturation on spore qualities were determined by sodium hypochlorite sporicidal assay. Difco sporulation media was identified as the preferred sporulation medium for 4 out of 7 tested Bacillus strains. Sporulation rates in B. cereus, B. sphaericus, and B. thuringiensis were higher at 30°C than the rates at 37°C at a difference of 5%, 65%, and 20%, respectively. Bacillus licheniformis favored Mn2+-amended 10% Columbia Broth at 37°C for sporulation with 40–72% higher sporulation rates than other media. The maximum sporulation rates of B. cereus and B. thuringiensis were observed on double-strength Schaeffer's-glucose broth. All studied purification methods improved the spore purity with strain variations. However, intense heat (80°C for 20 min) and lysozyme (100 μg/mL) treatment impaired the spore quality of specific Bacillus strains by sensitizing them against sodium hypochlorite. The length of the maturation period had an impact on the spore resistance, and the most optimal maturation periods ranged from 7 to 21 days in Bacillus strains. The results of this study will pave the way for further evaluation of the sporicidal activity of disinfectants.

Biotechnology & Applied Microbiology↗
DOE OSTI2026

The ABCs of phase retrieval: Connecting the acronyms of scanning transmission electron microscopy

High-resolution scanning transmission electron microscopy (S/TEM) is an indispensable tool for characterizing the structure and properties of materials down to the atomic scale. Conventional S/TEM imaging, however, is limited by the phase problem, whereby the phase of the electron exit wave is lost upon detection. Recent advances in diffractive imaging and 4D-STEM have enabled a range of phase-retrieval techniques that computationally reconstruct the missing information encoded in the phase of the transmission function. These approaches offer improved dose efficiency and enhanced sensitivity to weakly scattering signals, extending quantitative imaging to beam-sensitive materials composed of light elements. In this work, we introduce the phase problem in electron microscopy and survey the diverse landscape of phase-retrieval techniques used in the field. Despite their many acronyms and algorithmic variations, these techniques share a common physical and mathematical foundation. We present a unified framework that connects these seemingly distinct methods, from parallax imaging and tilt-corrected bright-field (tcBF-STEM), to aberration-corrected bright-field (acBF-STEM), optimum bright-field (OBF-STEM) and single-sideband (SSB) ptychography, as well as first-moment integrated center of mass techniques (iCOM) and iterative ptychographic algorithms. Based on these insights, we discuss the opportunities and practical limitations of applying these methods across different materials systems, detector designs, and microscope configurations.Graphical abstractRepresentative electron microscopy configurations used for phase retrieval and diffractive imaging in S/TEM: (a) Zernike phase-contrast transmission electron microscopy (TEM), (b) small-convergence-angle four-dimensional scanning transmission electron microscopy (4D-STEM) for nanobeam-based phase reconstruction methods, and (c) large-convergence-angle 4D-STEM for ptychographic and related diffractive imaging techniques reviewed in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Coral and Seawater Metagenomes Reveal Key Microbial Functions to Coral Health and Ecosystem Functioning Shaped at Reef Scale

The coral holobiont is comprised of a highly diverse microbial community that provides key services to corals such as protection against pathogens and nutrient cycling. The coral surface mucus layer (SML) microbiome is very sensitive to external changes, as it constitutes the direct interface between the coral host and the environment. Here, we investigate whether the bacterial taxonomic and functional profiles in the coral SML are shaped by the local reef zone and explore their role in coral health and ecosystem functioning. The analysis was conducted using metagenomes and metagenome-assembled genomes (MAGs) associated with the coral Pseudodiploria strigosa and the water column from two naturally distinct reef environments in Bermuda: inner patch reefs exposed to a fluctuating thermal regime and the more stable outer reefs. The microbial community structure in the coral SML varied according to the local environment, both at taxonomic and functional levels. The coral SML microbiome from inner reefs provides more gene functions that are involved in nutrient cycling (e.g., photosynthesis, phosphorus metabolism, sulfur assimilation) and those that are related to higher levels of microbial activity, competition, and stress response. In contrast, the coral SML microbiome from outer reefs contained genes indicative of a carbohydrate-rich mucus composition found in corals exposed to less stressful temperatures and showed high proportions of microbial gene functions that play a potential role in coral disease, such as degradation of lignin-derived compounds and sulfur oxidation. The fluctuating environment in the inner patch reefs of Bermuda could be driving a more beneficial coral SML microbiome, potentially increasing holobiont resilience to environmental changes and disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Chemistry↗
DOE OSTI2026

Comparison of automated chemical-guided segmentation and human annotation of soil organic matter in X-ray microcomputed tomography imaging in contrasted soil types

Soil organic matter (OM) formation and persistence is strongly influenced by the spatial distribution of organic substrates and microscale soil heterogeneity by dictating OM accessibility to microorganisms. However, traditional size and/or density fractionation techniques disrupt aggregate architecture, eliminating spatial information needed to fully understand intra-aggregate OM distribution. To quantify three-dimensional OM spatial distribution and automate segmentation in X-ray microcomputed tomography (µCT) imaging without human annotation bias, we developed an iodine gas vapor (I2) based staining workflow that eliminates labor-intensive manual annotation while maintaining segmentation accuracy, using aggregates from four taxonomically diverse soils (Xerofluvent, Haploxeroll Sphagnofibrist, Palehumult) with an 8-fold range of soil organic carbon. Human annotation of 10 µCT slices by the experienced and inexperienced annotators resulted in variations up to 3% in the Dice similarity coefficient (DSC), reflecting a degree of inherent subjectivity of manual labeling. Such inconsistencies are expected to compound as the number of manually annotated slices increases. Dual-energy µCT imaging at 33.1 keV (below the iodine (I) K-edge) and 33.2 keV (above the I K-edge) was used to resolve aggregate microstructure following I2 staining. The automated image subtraction pipeline identified OM regions by the I Kedge induced brightness increases, achieving DSC values of 0.58–0.83 relative to an experienced annotator. Sensitivity analyses revealed that the reconstruction alpha value—optimized via the open-source tool TomocuPy—and the 3D registration slice count were the primary determinants of accuracy, providing a novel benchmark for dual-energy soil imaging. The pipeline without GPU acceleration achieved 9.6 to 43.2 times faster than manual annotation. Using GPU-accelerated image post-processing and affine transformation matrices, the pipeline successfully segmented OM elements for large-scale datasets (3232×3232 pixel, 2048 slices) within ~5200 s from raw file acquisition to segmented output. The high-throughput approach enables the quantification of OM spatial distribution across diverse and heterogeneous soil.

Soil microbial biomass↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Environmental controls on observed spatial variability of soil pore water geochemistry in small headwater catchments underlain with permafrost

Abstract. Soil pore water (SPW) chemistry can vary substantially across multiple scales in Arctic permafrost landscapes. The magnitude of these variations and their relationship to scale are critical considerations for understanding current controls on geochemical cycling and for predicting future changes. These aspects are especially important for Arctic change modeling where accurate representation of sub-grid variability may be necessary to predict watershed-scale behaviors. Our research goal is to characterize intra- and inter-watershed soil water geochemical variations at two contrasting locations in the Seward Peninsula of Alaska, USA. We then attempt to identify the key factors controlling concentrations of important pore water solutes in these systems. The SPW geochemistry of 18 locations spanning two small Arctic catchments was examined for spatial variability and its dominant environmental controls. The primary environmental controls considered were vegetation, soil moisture and/or redox condition, water–soil interactions and hydrologic transport, and mineral solubility. The sampling locations varied in terms of vegetation type and canopy height, presence or absence of near-surface permafrost, soil moisture, and hillslope position. Vegetation was found to have a significant impact on SPW NO3- concentrations, associated with the localized presence of nitrogen-fixing alders and mineralization and nitrification of leaf litter from tall willow shrubs. The elevated NO3- concentrations were, however, frequently equipoised by increased microbial denitrification in regions with sufficient moisture to support it. Vegetation also had an observable impact on soil-moisture-sensitive constituents, but the effect was less significant. The redox conditions in both catchments were generally limited by Fe reduction, seemingly well-buffered by a cache of amorphous Fe hydroxides, with the most reducing conditions found at sampling locations with the highest soil moisture content. Non-redox-sensitive cations were affected by a wide variety of water–soil interactions that affect mineral solubility and transport. Identification of the dominant controls on current SPW hydrogeochemistry allows for qualitative prediction of future geochemical trends in small Arctic catchments that are likely to experience warming and permafrost thaw. As source areas for geochemical fluxes to the broader Arctic hydrologic system, geochemical processes occurring in these environments are particularly important to understand and predict with regards to such environmental changes.

Conroy, Nathan Alec (ORCID:0000000305973373)↗
DOE OSTI2025

Time-reversed Young’s experiment: Deterministic, diffractionless second-order interference effect

The classic Young’s double-slit experiment exhibits first-order interference, producing alternating bright and dark fringes shaped by the diffraction effect of the slits. In contrast, here we demonstrate that its time-reversed configuration generates an ideal, deterministic second-order ‘ghost’ interference pattern free from diffraction and first-order effects with the use of only a position-fixed detector. The pattern’s size is governed by the dimensions of the ‘effectively extended light source’ formed by point emitter(s). Beyond highlighting the nonreciprocal physics between the two configurations, this system unlocks a range of novel phenomena inaccessible in traditional double-slit experiments. These include fully programmable, digitized interference fringe formations and the ability to align the pattern plane with the source plane on the same side of the setup. Remarkably, our proposed experiment achieves these outcomes without relying on nonclassical correlations or quantum entanglement. Here, by restoring time-reversal symmetry and eliminating diffraction, our theoretical analysis reveals that this approach offers exciting potential for advancing optical imaging and sensing technologies with improved transverse and longitudinal phase-shift sensitivity and resolution limit beyond current limitations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Redefining Resource Adequacy for Modern Power Systems: A Report of the Redefining Resource Adequacy Task Force

Today's rapidly increasing levels of wind, solar, storage, and load flexibility require the industry to rethink reliability planning and resource adequacy methods for modern power systems. Periods with a risk of shortfall often no longer coincide with peak demand - reliability risks are less about peak load and more about the daily setting of the sun, extended cloud cover, wind speeds, cold snaps, and heat waves. In addition, demand is increasingly flexible. Key resources are time-sensitive, as batteries need time to recharge and electricity customers can only be asked to provide demand response for just so long. And reliability failures are often correlated - with one another and with the weather. Two driving factors require the industry to reconsider its analytical approach for resource adequacy: (1) Chronological grid operations: The increasing importance of variable renewable resources (such as wind and solar) and of energy-limited resources (storage and demand response) make it essential to understand the full year of chronological operation of the grid. Specific attention must be paid to hourly, seasonal, and inter-annual resource variability. The sequence of the variability is key, as energy-limited resources such as batteries or demand response require either a preceding period or subsequent period of high production to be useful for grid reliability. (2) Correlated events: Historically, resource adequacy analysis focused on shortfalls caused by random, discrete mechanical failures of large generating units. In contrast, shortfalls today are often caused by multiple, correlated events caused by common weather patterns. Resource adequacy analysis must increasingly shift its focus to these correlated events. The redesign of resource adequacy methods will benefit from a set of guiding principles to better allow for sharing of insights and best practices, interregional resource coordination, and a smoother regulatory process for resource procurement. The objective of this report is to move this redesign forward. It provides an overview of key drivers changing the way resource adequacy needs to be evaluated, identifies shortcomings of conventional approaches, and outlines first principles for practitioners to consider as they adapt their approaches. The central message is: what got us here won't get us there.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Linking Hydrology and Dissolved Organic Matter Characteristics in a Subtropical Wetland: A Long-Term Study of the Florida Everglades

Dissolved organic matter (DOM) acts as an important biogeochemical component of aquatic ecosystems that controls nutrient cycling, influences water quality, and links terrestrial and oceanic carbon pools, yet long-term studies of how changing environmental drivers alter its abundance and composition are rare. Using a ten-year, spatially explicit dataset from Everglades National Park (ENP), a globally significant wetland, we investigated the relationships between DOM quality/quantity and hydrologic/climatic drivers along two contrasting marsh-estuarine transects based on generalized linear modeling and a cumulative sums analysis. Analyses revealed distinct spatial, seasonal, and interannual patterns in variability of DOC and optical properties. Landscape-scale seasonal patterns showed an enrichment in microbial-like and protein-like DOM during the dry season relative to the wet season. We found that, while some compositional constituents varied with the solar calendar, responsive to temperature and photoperiod, others varied with the hydrologic calendar. Independent water level and discharge effects indicated strong hydrologic control on DOM quality that differed between the two transects, evidencing differences in their connectivity to areas of high agricultural activity. Across all sites, a significant long-term increasing trend in the fluorescence index was observed, associated with a positive correlation with precipitation and also potential changes in agricultural inputs, with other features associated with drought and hurricanes. Lastly, the cumulative sums analysis revealed differences between the two transects in the sensitivity of DOM composition to decreased water levels associated with 30-year climate scenarios, with the less hydrologically dynamic transect exhibiting greater potential sensitivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil disturbance and invasion magnify CO 2 effects on grassland productivity, reducing diversity

Climate change, disturbance, and plant invasion threaten grassland ecosystems, but their combined and interactive effects are poorly understood. Here, in this study, we examine how the combination of disturbance and plant invasion influences the sensitivity of mixed-grass prairie to elevated carbon dioxide (eCO 2 ) and warming. We established subplots of intact prairie and disturbed/invaded prairie within a free-air CO 2 enrichment (to 600 ppmv) by infrared warming (+1.5°C day, 3°C night) experiment and followed plant and soil responses for 5 years. Elevated CO 2 initially led to moderate increases in biomass and plant diversity in both intact and disturbed/invaded prairie, but these effects shifted due to strong eCO 2 responses of the invasive forb Centaurea diffusa. In the final 3 years, biomass responses to eCO 2 in disturbed/invaded prairie were 10 times as large as those in intact prairie (+186% vs. +18%), resulting in reduced rather than increased plant diversity (-17% vs. +10%). At the same time, warming interacted with disturbance/invasion and year, reducing the rate of topsoil carbon recovery following disturbance. The strength of these interactions demonstrates the need to incorporate disturbance into predictions of climate change effects. In contrast to expectations from studies in intact ecosystems, eCO 2 may threaten plant diversity in ecosystems subject to soil disturbance and invasion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the mechanical behavior of a Nb 3 Sn superconducting coil during a quench: Two-dimensional finite element analysis of a quench heater protected magnet

New high-field accelerator magnets based on $Nb_3Sn$ superconductors are pushing the boundaries of magnet design and quench protection towards new limits. While their large stored energies and current densities result in a very challenging scenario for magnet protection, their great electromagnetic forces create also new requirements in terms of magnet design and stress management techniques. Furthermore, the strain sensitivity of $Nb_3Sn$ cables turns the electro-mechanical limits of the conductor into a parameter of the highest importance, where conductor degradation becomes a critical aspect in magnet operation. The coupling of all the above-mentioned considerations during quench is a case of special interest that adds further complexity to the design of $Nb_3Sn$ magnets. The objective of this paper is to provide a complete two-dimensional investigation of the coil and magnet structure mechanics during a quench event. Here, the analysis is performed using a combination of finite element codes that provide the necessary input for the mechanical study. The core of the modelling strategy relies on a thermal-electric model, whose results are directly used as loads in the mechanical simulation. In doing so, the stress evolution during and after a quench is obtained. We focus for this time in the analysis of a quench heater protected magnet, where dynamic effects are of less importance in contrast to other protection systems like CLIQ.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Unsteady Land-Sea Breeze Circulations in the Presence of a Synoptic Pressure Forcing

Unsteady land-sea breezes (LSBs) that result from time-varying surface temperature contrasts Δθ(t) are explored in the presence of a constant synoptic pressure forcing, M g , oriented from sea to land (α = 0°) or land to sea (α = 180°). Large eddy simulations reveal the development of four distinctive regimes, depending on the joint interaction between M g , α, and Δθ(t) in modulating the fine-scale dynamics. Time lags, computed as the shifts that maximize correlation coefficients of the velocity between the unsteady and the corresponding steady scenarios at Δθ = Δθ max , are found to be significant and to extend 2 hr longer for α = 0° compared to α = 180°. These diurnal dynamics result in nonequilibrium conditions that are significantly affected by the flow history, and that behave differently over the two patches for the different α’s. Turbulence is found to be out of equilibrium with the mean flow, and the mean itself is found to be out of equilibrium with the thermal forcing. The sea surface heat flux is consistently more sensitive than its land counterpart to the time-varying external forcing Δθ(t), and more so for synoptic forcing from land to sea (α = 180°). Hence, although the land reaches equilibrium faster, the sea patch is found to exert a stronger control on the turbulence-mean flow equilibrium response. Finally, the vertical velocity profile at the shore and shore-normal velocity transects at the first grid level are shown to encode the multiscale regimes of the LSBs evolution and can thus be used to identify these regimes using k-means clustering.

58 GEOSCIENCES↗
DOE OSTI2022

Supplementary Data for "Evaluation of the Economic Implications of Varied Pressure Drawdown Strategies Generated Using a Real-time, Rapid Predictive, Multi-fidelity Model for Unconventional Oil and Gas Wells" by Bello, K., Vikara, D., Sheriff, A., Viswanathan, H., Carr, T., Sweeney, M., O'Malley, D., Marquis, M., Vactor, R.T., and Cunha, L.

The Bello et al. study evaluates the impact of contrasting pressure drawdown on gas productivity and the resulting economics of a well in the Marcellus Shale of the Appalachian Basin. This research applies a techno-economic analysis approach to help identify potential ways pressure management strategies can be used to improve cumulative recovery of hydraulically fractured horizontal wells while maintaining project profitability. Gas production forecast outlook scenarios of the Marcellus Shale Energy and Environment Laboratory Laboratory's MIP-3H well were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning (PIML) workflow and 2) via traditional reservoir simulation in Computer Modeling Group’s (CMG) GEM Compositional & Unconventional Simulator. Cash flow and other economic metrics of interest were compiled on the production outlook using the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) Unconventional Shale Well Economic Model (UShWEM).The sheets within this Microsoft ExcelTM workbook provide the economic metric outputs for the baseline condition and the one-at-a-time (OAT) sensitivity analysis of UShWEM's input parameters for each of the production scenarios evaluated.

Fracture Network Model↗
DOE OSTI2022

“Tailing” Phenomenon During Polymer Propagation at The Milne Point Polymer Flood

Chemical flooding simulators traditionally assume that polymer retention follows the Langmuir isotherm, while fractional-flow calculations assume concentration-independent retention. Both models predict a delay in propagation of the polymer front (in proportion to the retention value), followed by a rapid rise in produced polymer concentration to the injected level. In contrast, laboratory retention studies for the Milne Point polymer flood (North Slope of Alaska) consistently show virtually no delay in polymer propagation, a rapid rise in produced polymer to 70-90% of the injected concentration, followed by produced concentration gradually approaching the injected value over many pore volumes. From a practical viewpoint, this behavior means that retention causes no significant delay in propagation of the polymer bank (and therefore the oil bank), but the effective viscosity and displacement efficiency are less than originally planned. This paper explores why this polymer “tailing” phenomenon occurs. During core floods, effluent was analyzed for brine-tracer concentration, viscosity, total organic carbon, and total nitrogen. The latter three items allowed three independent measures of polymer concentration and polymer retention. Experiments were performed with Milne Point core material, oil, brine, and conditions. Mechanistic experiments were also performed with packs of glass beads with controlled levels of various minerals. In Milne Point cores, the presence of the tailing phenomenon was not sensitive to flow rate, polymer concentration, core heterogeneity, or whether the core was preserved, cleaned of oil, or cleaned and resaturated with oil, or cleaned, re-saturated and aged with oil. The three independent measures of polymer concentration confirmed that the tailing phenomenon was not an artifact associated with polymer degradation or detection interferences. Tailing was also observed during mechanistic floods using glassbead packs when sufficient levels of illite or kaolinite were present. This observation was consistent with high levels of illite noted in Milne Point cores. The tailing was not noted during mechanistic floods that contained beads with montmorillonite, chlorite, calcium carbonate, dolomite, siderite, pyrite, or calcium sulfate. A model is proposed to account for the tailing phenomenon. This work suggests that mineralogy analysis (especially for illite and kaolinite) may reveal whether tailing should be accounted for during simulations of polymer propagation/retention in a given field application.

Seright, Randy↗
DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A Synchrotron-Based Vacuum Ultraviolet Photoionization Mass Spectrometer-Coupled Microreactor To Probe Thermocatalysis

Vacuum ultraviolet photoionization (VUV-PI) mass spectrometry offers an isomer-selective and universal ionization with minimal fragmentation detection of organics in complex chemical systems such as pyrolysis and combustion. Here, in this study, we report a state-of-the-art experimental setup of a universal catalytic microreactor combined with a molecular beam to investigate the thermocatalytic oxidation of a heterogeneous system relevant for probing reactions at gas–solid interfaces. In strong contrast to traditional off-line analytical methods, this technique is capable of identifying and quantifying short-lived species (radicals) as well as stable products to decipher initial reaction steps via the detection of nascent products. The thermocatalytic oxidative degradation of exo-tetrahydrodicyclopentadiene (JP-10), a high energy-density hydrocarbon fuel, over solid titanium–aluminum–boron reactive mixed metal nanopowder (Ti-Al-B RMNP) is exploited to showcase potential applications. Overall, some 59 nascent gas-phase products are identified via photoionization efficiency (PIE) curves, including oxygenated species and hydrocarbons comprising closed-shell molecules and radicals. The critical temperature for complete oxidative decomposition of JP-10 was lowered by 450 K from 1400 K to 950 K, indicating an efficient thermocatalytic action of Ti-Al-B nanoparticles on JP-10. The enabling of a universal chemical microreactor along with VUV-PI mass spectrometry broadens the applicability of this technique to hydrocarbon fuel oxidation and pyrolysis characterization. This isomer-selective sensitive probing along with the detection of radical transients makes the aforementioned technique superior to other conventional analytical techniques such as microflow tube and pyrolysis-gas chromatography coupled with mass spectrometry for investigating similar pyrolysis reactions and comprehensive quantification.

Paul, Dababrata [Univ. of Hawaii at Manoa, Honolul↗
DOE OSTI2020

Structural Transformation and Chemical Stability of a Shock-Compressed Insensitive High Explosive Single Crystal: Time-Resolved Raman Spectroscopy

Despite the considerable interest in insensitive high explosives (IHE) as a safer alternative to conventional high explosives, a good understanding of the low sensitivity of IHEs to shock initiation is lacking. In particular, real-time measurements to directly probe the molecular-level response of shock compressed IHE single crystals constitute an important need. To address this need, plate impact experiments were conducted to determine time-resolved changes in the Raman spectra of 1,1-diamino-2,2-dinitroethene (FOX-7) single crystals – a representative IHE crystal – shock compressed up to 20 GPa longitudinal stress. The Raman measurements examined vibrational frequencies from 800 cm-1 to 1500 cm-1 with 15 nanosecond time resolution and were conducted at several peak stresses. At 4 – 6 GPa, two new Raman peaks appeared, in addition to the original peaks, consistent with onset of the α' – ε structural transformation reported previously in static compression work. The measured spectra indicated completion of the transformation at 10 GPa. Raman data to 20 GPa showed neither additional transformations nor any indication of chemical decomposition. This finding, though consistent with recent continuum measurements, is in marked contrast to the chemical decomposition observed at lower stresses in shock compressed conventional high explosive single crystals. Our Raman results support the previous suggestion that strengthening of intra- and inter-molecular bonds, due to the α' – ε structural transformation, plays a significant role in the insensitivity of FOX-7 single crystals to shock initiation. In conclusion, the present work, in conjunction with previous static compression studies, provides the first experimental insight into the molecular-level response of a shock compressed IHE single crystal and can serve as a bench mark for theoretical studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗