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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Historical Land Management Alters New Soil Carbon Inputs by Annual and Perennial Bioenergy Crops

Bioenergy and bioproduct markets are expanding to meet demand for climate-friendly goods and services. Perennial biomass crops are particularly well suited for this goal because of their high yields, low input requirements, and potential to increase soil carbon (C). However, it is unclear how much C is allocated into belowground pools by perennial bioenergy crops and whether the belowground benefits vary with nitrogen (N) fertilizer inputs. Using in situ 13 C pulse-chase labeling, we tested whether the sterile perennial grass Miscanthus × giganteus (miscanthus) or annual maize transfers more photosynthetic C to belowground pools. The experiment took place at two sites in Central and Northwest (NW) Iowa with different management histories and two nitrogen (N) fertilizer rates (0 and 224 kg N ha −1 year −1 ) to determine if the fate of plant-derived soil C depends on soil fertility and crop type (perennial or annual). Maize allocated a greater percentage of total new 13 C to roots than miscanthus, but miscanthus had greater new 13 C in total and belowground plant biomass. We found strong interactions between site and most soil measurements—including new 13 C in mineral and particulate soil organic matter (SOM) pools—which appears to be driven by differences in historical fertilizer management. The NW Iowa site, with a history of manure inputs, had greater plant-available nutrients (phosphorus, potassium, and ammonium) in soils, and resulted in less 13 C from miscanthus in SOM pools compared to maize (approximately 64% less in POM and 70% less in MAOM). In more nutrient-limited soils (Central site), miscanthus transferred 4.5 times more 13 C than maize to the more stable mineral-associated SOM pool. Our results suggest that past management, including historical manure inputs that affect a site's soil fertility, can influence the net C benefits of bioenergy crops.

13C↗
DOE OSTI2022

RADAMS: Resilient and adaptive alert and attention management strategy against Informational Denial-of-Service (IDoS) attacks

Attacks exploiting human attentional vulnerability have posed severe threats to cybersecurity. In this work, we identify and formally define a new type of proactive attentional attacks called Informational Denial-of-Service (IDoS) attacks that generate a large volume of feint attacks to overload human operators and hide real attacks among feints. Here, we incorporate human factors (e.g., levels of expertise, stress, and efficiency) and empirical psychological results (e.g., the Yerkes-Dodson law and the sunk cost fallacy) to model the operators’ attention dynamics and their decision-making processes along with the real-time alert monitoring and inspection. To assist human operators in dismissing the feints and escalating the real attacks timely and accurately, we develop a Resilient and Adaptive Data-driven alert and Attention Management Strategy (RADAMS) that de-emphasizes alerts selectively based on the abstracted category labels of the alerts. RADAMS uses reinforcement learning to achieve a customized and transferable design for various human operators and evolving IDoS attacks. The integrated modeling and theoretical analysis lead to the Product Principle of Attention (PPoA), fundamental limits, and the tradeoff among crucial human and economic factors. Experimental results corroborate that the proposed strategy outperforms the default strategy and can reduce the IDoS risk by as much as 20%. Besides, the strategy is resilient to large variations of costs, attack frequencies, and human attention capacities. We have recognized interesting phenomena such as attentional risk equivalency, attacker’s dilemma, and the half-truth optimal attack strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Phosphorothioate-Based Site-Specific Labeling of Large RNAs for Structural and Dynamic Studies

Pulsed electron-electron double resonance (PELDOR) spectroscopy, X-ray scattering interferometry (XSI), and single-molecule Forster resonance energy transfer (smFRET) are molecular rulers that provide inter- or intramolecular pair-wise distance distributions in the nanometer range, thus being ideally suitable for structural and dynamic studies of biomolecules including RNAs. The prerequisite for such applications requires site-specific labeling of biomolecules with spin labels, gold nanoparticles, and fluorescent tags, respectively. Recently, site-specific labeling of large RNAs has been achieved by a combination of transcription of an expanded genetic alphabet containing A-T/G-C base pairs and NaM-TPT3 unnatural base pair (UBP) with posttranscriptional modifications at UBP bases by click chemistry or amine-NHS ester reactions. However, due to the bulky sizes of functional groups or labeling probes used, such strategies might cause structural perturbation and decrease the accuracy of distance measurements. Here, we synthesize an a-thiophosphorylated variant of rTPT3TP (rTPT3aS), which allows for post-transcriptional site-specific labeling of large RNAs at the internal a-phosphate backbone via maleimide-modified probes. Subsequent PELDOR, XSI, and smFRET measurements result in narrower distance distributions than labeling at the TPT3 base. The presented strategy provides a new route to empower the molecular rulers for structural and dynamic studies of large RNA and its complex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Historical land management alters new soil carbon inputs by annual and perennial bioenergy crops

Bioenergy and bioproduct markets are expanding to meet demand for climate friendly goods and services. Perennial biomass crops are particularly well suited for this goal because of their high yields, low input requirements, and potential to increase soil carbon (C). However, it is unclear how much C is allocated into belowground pools by perennial bioenergy crops and whether the belowground benefits vary with nitrogen (N) fertilizer inputs. Using in situ 13C pulse-chase labeling, we tested whether the sterile perennial grass Miscanthus × giganteus ( miscanthus ) or annual maize transfers more photosynthetic C to belowground pools. The experiment took place at two sites in Central and Northwest (NW) Iowa with different management histories and two nitrogen (N) fertilizer rates (0 and 224 kg N ha-1 yr-1) to determine if the fate of plant-derived soil C depends on soil fertility and crop type (perennial or annual). Maize allocated a greater percentage of total new 13C to roots than miscanthus, but miscanthus had greater new 13C in total and belowground plant biomass. We found strong interactions between site and most soil measurements – including new 13C in mineral and particulate soil organic matter (SOM) pools –which appear to be driven by differences in historical fertilizer management. The NW Iowa site, with a history of manure inputs, had greater plant-available nutrients (phosphorus, potassium, and ammonium) in soils, and resulted in less 13C from miscanthus in SOM pools compared to maize (approximately 64% less in POM and 70% less in MAOM). In more nutrient-limited soils (Central site), miscanthus transferred 4.5 times more 13C than maize to the more stable mineral-associated SOM pool. Our results suggest that past management, including historical manure inputs that affect a site’s soil fertility, can influence the net C benefits of bioenergy crops.

Keiser, Ashley [Stockbridge School of Agriculture,↗
DOE OSTI2025

Impact of nutritional history, prey quality, and quantity on grazing and photophysiological responses in the mixoplanktonic dinoflagellate Karenia brevis

The mixotrophic toxic dinoflagellate Karenia brevis forms red tides almost annually along Florida's Gulf coast. We hypothesize that the nutritional status and abundance of its prey, the picocyanobacterium Synechococcus , will affect its feeding rates and growth responses, with implications for bloom dynamics. This study investigated how prey nutritional quality and quantity (absolute and relative) impact grazing rates by K. brevis initially exponentially growing and in nitrogen (N)-limited conditions, and how grazing, in turn, affects the photophysiological responses of predator and prey. Prey quality was manipulated by providing Synechococcus grown under different ratios of N : phosphorus (P). Synechococcus quality did not significantly affect ingestion rates (measured as prey death rate) but grazing rates increased with increasing prey : grazer ratios (R 2 = 0.7). Compared to control, the growth of exponentially growing grazers doubled when Synechococcus was provided, whereas there was no growth enhancement when Synechococcus of varying qualities was provided to N-limited, chemostat-seeded grazers. Despite this doubled growth, 15 N labeling of the prey and nanoscale secondary ion mass spectrometry (nanoSIMS) detected low Synechococcus -N transfer into grazer biomass after 3 d (< 1% on a cell basis). This suggests the potential of grazers benefiting from alternative N sources (e.g., microbiome-N) or other constituents (e.g., vitamins or metals) not measured in this study. Prey photosynthetic efficiency declined under grazing conditions, demonstrating that grazers can directly affect prey abundance through grazing and indirectly affect prey photophysiology, potentially via allelopathy, supporting previous findings of an inverse relationship between grazers and prey along the Florida Gulf Coast.

Ahn, So Hyun [Univ. of Maryland, Cambridge, MD (Un↗
DOE OSTI2024

The ‘photosynthetic C 1 pathway’ links carbon assimilation and growth in California poplar

Although primarily studied in relation to photorespiration, serine metabolism in chloroplasts may play a key role in plant CO 2 fertilization responses by linking CO 2 assimilation with growth. Here, we show that the phosphorylated serine pathway is part of a 'photosynthetic C 1 pathway' and demonstrate its high activity in foliage of a C 3 tree where it rapidly integrates photosynthesis and C 1 metabolism contributing to new biomass via methyl transfer reactions, imparting a large natural 13 C-depleted signature. Using 13 CO 2 -labelling, we show that leaf serine, the S-methyl group of leaf methionine, pectin methyl esters, and the associated methanol released during cell wall expansion during growth, are directly produced from photosynthetically-linked C 1 metabolism, within minutes of light exposure. We speculate that the photosynthetic C 1 pathway is highly conserved across the photosynthetic tree of life, is responsible for synthesis of the greenhouse gas methane, and may have evolved with oxygenic photosynthesis by providing a mechanism of directly linking carbon and ammonia assimilation with growth. Although the rise in atmospheric CO 2 inhibits major metabolic pathways like photorespiration, our results suggest that the photosynthetic C 1 pathway may accelerate and represents a missing link between enhanced photosynthesis and plant growth rates during CO 2 fertilization under a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Impact of Small-Alkane Solvents on Polyolefin Hydrogenolysis over a Ruthenium Catalyst

Selective catalytic hydrogenolysis of polyolefins is a promising route to convert plastic waste into valuable liquid products, such as lubricants, waxes, and surfactants. However, the high viscosity of polymer melts imposes mass transfer limitations on this reaction. Solvents can mitigate these challenges, but their effects on reaction kinetics and product selectivity remain underexplored. Here, we systematically explore the effects of small n-alkanes and cycloalkanes on the hydrogenolysis of polyethylene and polypropylene over a Ru/TiO 2 catalyst. Using kinetic measurements and isotopic labeling, we show that n-octane at high mass fractions alters the mechanism from direct hydrogenation to solvent-mediated hydrogen transfer, reducing the rate of C–C bond cleavage. Longer alkanes further inhibit reactivity due to stronger surface binding. 1,4-Dimethylcyclohexane suppresses methane formation, favoring heavier products, while decalin likely forms surface-bound aromatics that poison the catalyst. Overall, alkane solvents modulate product selectivity and reduce the yield of methane byproduct, allowing for ∼35–40% selectivity to valuable C 20 -C 30 alkane products. This work highlights the complex impact of polymer–alkane mixtures on hydrogenolysis kinetics relevant to the design of commercial-scale plastic waste valorization processes.

Catalysts↗
DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
DOE OSTI2025

De Novo Design of Proteins That Bind Naphthalenediimides, Powerful Photooxidants with Tunable Photophysical Properties

De novo protein design provides a framework to test our understanding of protein function and build proteins with cofactors and functions not found in nature. Here, we report the design of proteins designed to bind powerful photooxidants and the evaluation of the use of these proteins to generate diffusible small-molecule reactive species. Because excited-state dynamics are influenced by the dynamics and hydration of a photooxidant’s environment, it was important to not only design a binding site but also to evaluate its dynamic properties. Thus, we used computational design in conjunction with molecular dynamics (MD) simulations to design a protein, designated NBP (NDI Binding Protein), that held a naphthalenediimide (NDI), a powerful photooxidant, in a programmable molecular environment. Solution NMR confirmed the structure of the complex. We evaluated two NDI cofactors in this de novo protein using ultrafast pump–probe spectroscopy to evaluate light-triggered intra- and intermolecular electron transfer function. Moreover, we demonstrated the utility of this platform to activate multiple molecular probes for protein labeling.

carbonyls↗
DOE OSTI2025

Polyethylene Upcycling to Liquid Alkanes in Molten Salts under Neat and External Hydrogen Source-Free Conditions

Development of facile approaches to convert plastic waste into liquid fuels under neat conditions is highly desired but challenging, particularly without noble metal catalysts and an external hydrogen source. Herein, highly efficient and selective polyethylene-to-gasoline oil (branched C 6 –C 12 alkanes) conversion was achieved under mild conditions (<170 °C) using commercially available AlCl 3 -containing molten salts as reaction media and to provide catalytic sites (no extra solvents, additives, or hydrogen feeding). The high catalytic efficiency and selectivity was ensured by the abundant active Al sites with strong Lewis acidity (comparable to the Al type in acidic zeolite) and highly ionic nature of the molten salts to stabilize the carbenium intermediates. Dynamic genesis of the Al sites was elucidated via time-resolved Al K-edge soft X-ray and 27 Al NMR, confirming the tricoordinated Al 3+ as active sites and its coordination with the as-generated alkene/aromatic intermediates. Further, the carbenium formation and polyethylene chain variation was illustrated by inelastic neutron scattering (INS) and an isotope-labeling experiment. Theoretical simulations further demonstrated the successive hydride abstraction, β-scission, isomerization, and internal hydrogen transfer reaction pathway with AlCl 3 as active sites. This facile catalytic system can further achieve the conversion of robust, densely assembled, and high molecular weight plastic model compounds to liquid alkane products in the diesel range.

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

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

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

Catalytic 1,3-Proton Transfer in Alkenes Enabled by Fe=NR Bond Cooperativity: A Strategy for p K a -Dictated Regioselective Transposition of C=C Double Bonds

Transition metal catalyzed alkene double bond transposition usually involves metal hydride intermediates. Despite significant advances in the design of catalysts that dictate product selectivity, control over substrate selectivity is less advanced and transition metal catalysts that selectively transpose double bonds in substrates containing multiple 1-alkene functionalities are rare. Herein, we report that the three-coordinate high spin (S = 2) Fe(II) imido complex [Ph 2 B( t BuIm) 2 Fe=NDipp][K(18-C-6)THF 2 ] (1-K(18-C-6)) catalyzes 1,3-proton transfer from 1-alkene substrates to afford 2-alkene transposition products. Mechanistic investigations involving kinetics, competition, and isotope labeling studies, supported by experimentally calibrated DFT computations, strongly support an unusual nonhydridic mechanism for alkene transposition that is enabled by the cooperative action of the iron center and basic imido ligand. As dictated by the pK a of the allylic protons, this catalyst enables the regioselective transposition of C=C double bonds in substrates containing multiple 1-alkenes. The high spin (S = 2) state of the complex allows a wide scope of functional groups to be tolerated, including those that are typical catalyst poisons, such as amines, N-heterocycles, and phosphines. Furthermore, these results demonstrate a new strategy for metal-catalyzed alkene transposition with predictable substrate regioselectivity.

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

Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation

The utility of transfer learning to improve the performance of deep learning in axon segmentation Data Data: All the input and labeled volumes tf-logs: Tensorflow logs, view with command "tensorboard --logdir [name of folder]" Model Weights: model_weights: the argument list under variable combo indicate 1) no oversampling, 2) no rotation, 3) no learn scheduler, and 4) flipping on all three dimensions, and the additional values indicate 5) elastic deformation percentage, 6) rotate deformation percentage, 7) layer setting , 8) learning rate, and 9) training/validation/test data division suffix (leave '' if not using suffix). Results: Output from inference segment_total_results_validation_final: All validation results and calculations segment_total_results: All test results and calculations Authors The modified code was created for a paper by: Marjolein Oostrom, Michael A. Muniak, Rogene Eichler West, Sarah Akers, Paritosh Pande, Moses Obiri, Wei Wang, Kasey Bowyer, Zhuhao Wu, Lisa Bramer, Tianyi Mao, Bobbie Jo Webb-Robertson The work is adapted from Github TrailMap, which was created by Albert Pun and Drew Friedmann Acknowledgments MO, RMEW, SA, MO, LB, BJWR were supported by the Laboratory Directed Research and Development at Pacific Northwest National Laboratory (PNNL), a Department of Energy facility operated by Battelle under contract DE-AC05-76RLO01830. WW, KB, and ZW were supported in part by a NIH/BRAIN Initiative Grant RF1MH128969. MAM and TM were supported by two NIH/BRAIN Initiative Grants R01NS104944, RF1MH120119 and NIH R01NS081071. This research is affiliated with the Pacific northwest bioMedical Innovation Co-laboratory (PMedIC) collaboration between OHSU and PNNL.

Oostrom, Marjolein T↗
DOE OSTI2025

Efficient 15 N hyperpolarization of [ 15 N 3 ]metronidazole antibiotic via spin-relayed pulsed SABRE-SHEATH

Signal Amplification by Reversible Exchange in SHield Enables Alignment Transfer to Heteronuclei (SABRE-SHEATH) is an NMR hyperpolarization technique that relies of the simultaneous exchange of parahydrogen and a to-be-hyperpolarized molecule on the metal center of a polarization-transfer catalyst in a microtesla magnetic field. Until recently, this method has been understood to perform hyperpolarization by establishing level anti-crossings between the nuclear spins of the parahydrogen derived hydrides (acting as a source of hyperpolarization) and those of the substrate. Recently, the application of highly non-intuitive pulse sequences (comprising pulses of microtesla DC fields) was predicted to hyperpolarize nuclear spins more efficiently than the canonical (static-field) SABRE-SHEATH approach. Here we show that by employing a basic “on-off” pulse sequence of rectangular microtesla pulses, it is possible to improve the hyperpolarization efficiency for SABRE-SHEATH of [ 15 N 3 ]metronidazole, an FDA-approved antibiotic (in non-enriched and non-hyperpolarized form) and potential hypoxia sensing molecule. Specifically, we demonstrate that 15N polarization of 18.5 % can be obtained in 80 s of parahydrogen bubbling parahydrogen through a solution containing 20 mM [ 15 N 3 ]metronidazole. In practice, (1.32 ± 0.14)-fold improvements in P 15N was obtained with the pulsed method described here compared to static field technique variant. These results show that pulsed SABRE-SHEATH was successfully applied to 15 N-labeled biologically relevant molecule. Moreover, we also demonstrate that although the pulsed SABRE-SHEATH sequence was designed for polarization transfer from parahydrogen derived hydrides to the metronidazole’s 15 N catalyst-binding site, all three 15 N sites of [ 15 N 3 ]metronidazole attained the hyperpolarized state. This spin-relayed polarization transfer becomes possible due to the 15 N relay network established by their spin-spin J-couplings. The feasibility of the spin-relayed polarization transfer is demonstrated here for the first time for pulsed SABRE-SHEATH (as opposed to the static-field SABRE-SHEATH reported previously) and it paves the way to broad applicability of the technique.

Hyperpolarization↗
DOE OSTI2025

Multi-Analytical Investigation of Arsenical Transfer and Remediation on Nineteenth-Century Green Books

Books containing heavy metals, specifically nineteenth-century green arsenical books, have been identified at Northwestern University Libraries, raising health and safety concerns related to handling. Copper acetoarsenite pigments, such as emerald green, were detected on book covers, decorative page edges, labels, and other components using noninvasive analytical techniques including X-ray fluorescence (XRF), Raman spectroscopy, and Fourier-transform infrared (FTIR) spectroscopy. Further examination of selected volumes using synchrotron radiation (SR) techniques revealed pigment migration, degradation, and arsenic transfer to adjacent books. This paper expands on initial findings through two related experiments. The first explored the transfer of arsenic using mechanical friction; Staedtler Mars® white vinyl erasers rubbed on arsenical books generated crumbs which were analyzed via scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM-EDX). Results confirmed the transfer of arsenic, copper, and lead, with decorative page edges being particularly prone to shedding arsenic onto other materials. The second experiment tested remediation methods on a book contaminated by prolonged exposure to an arsenical neighbor. Surface cleaning using erasers and a vacuum removed flecks of pigment but did not eliminate non-chromophoric arsenic as confirmed by SR analyses, which highlights its presence either as a degradation product embedded within the paper or present in the paper as part of its production process. Findings demonstrate the acute toxicity risk posed by arsenical books and support the need for safe handling protocols. However, materials with only trace levels of arsenic embedded during production may pose a lower risk of transfer. Cross contamination beyond prolonged direct contact appears limited. These results highlight critical considerations for library preservation practices and future research on arsenic in historical materials.

arsenic↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale that has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify highvalue data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies.

58 GEOSCIENCES↗