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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Carbon source–driven metabolic and regulatory remodeling defines phenomic states in Lipomyces starkeyi

Lipomyces is a genus of oleaginous yeasts with potential for contributing to reliable biomanufacturing supply chains. However, progress in advanced strain designs and engineering efforts are still constrained by a lack of understanding of the underlying molecular drivers of Lipomyces phenotypes. To address this gap, we collected a suite of multi-omic data to dissect how carbon source availability reshapes the metabolic network, lipid allocation, and regulatory architecture of Lipomyces starkeyi. We observed that glucose promotes biosynthetic and proliferative processes supported by abundant energy and carbon intermediates, xylose enhances redox-balancing mechanisms centered on the pentose phosphate pathway, and glycerol activates respiratory metabolism, ß-oxidation, and the glyoxylate cycle. Lipid species distributions remained consistent in both nitrogen replete and depleted conditions across the carbon sources, indicating robust production mechanisms. Regulatory protein identification and network analysis revealed glycerol-driven respiratory growth favors regulatory programs integrating stress tolerance, redox balance, and lipid-associated metabolism, whereas xylose growth activates compensatory transcriptional responses aimed at maintaining mitochondrial function. Nitrogen limitation modulates the strength of these responses but does not fundamentally alter their direction, reinforcing carbon source as the dominant driver of regulatory architecture. Taken together, this data enhances the understanding of Lipomyces molecular rearrangements and provides a foundation for further development of predictive phenotypic tools in this genus.

Biotechnology↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2025

Hydrogenation of ethylene over molybdenum–sulfur complexes supported on UiO-66

Development of supported single-site catalysts using small metal sulfide complexes could significantly help in the development of cost-effective catalytic materials to drive selective hydrogenation and hydrogenolysis. The goal of this study is to contribute to the development of metal sulfide catalysts by calculating the thermodynamics of a catalytic cyclic involving a metal organic framework functionalized by insertion of metal sulfide. Anchored metal sulfide complexes can potentially be designed with ligands with distinctly different electronic and catalytic properties for specific catalytic applications. Here we examine the hydrogenation of ethylene as a model. We use density functional theory to investigate molybdenum–sulfur complexes as active catalysts anchored on the metal–organic framework UiO-66 as a stable support. Our calculations show that the anchored complexes with more than two sulfur ligands are unfavorable for ethylene adsorption, so we study complexes with one or two sulfur ligands. Hydrogenation of the unsaturated carbon double bond requires the transfer of two hydrogen atoms, which can occur via heterolytic activation of hydrogen to form a Mo-hydride and a protonated sulfur – either by hydride transfer followed by proton transfer or via proton transfer followed by hydride transfer, and we find that both mechanisms proceed via two-state reactivity involving two spin states along the reaction path. Of the two catalysts studied in gas the phase, the MoS single-sulfur–ligand complex with lower oxidation states produces thermodynamically more favorable intermediates along the pathway for the first hydrogen transfer for both the hydride-first mechanism and the proton-first mechanism. As a result, the quantum mechanical calculations provide experimentally inaccessible partial atomic charges and geometries of the various intermediates encountered along the steps of the reaction mechanisms.

Kermani, Maryam Mansoori [University of Minnesota,↗
DOE OSTI2024

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis—nonadditive interactions between amino acid substitutions—and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences—a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

biocatalysis↗
DOE OSTI2025

Testing the limits of ITkPixV2: the ATLAS inner tracker pixel detector readout chip

The ITkPixV2 chip is the final production readout chip for the ATLAS Phase 2 Inner Tracker (ITk) upgrade at the upcoming High-Luminosity LHC (HL-LHC). Due to the extraordinarily high peak luminosity at the HL-LHC of 5 × 1034 cm-1 s-1, ITkPixV2 must meet significant increases in nearly all design requirements compared to the current ATLAS Inner Detector (ID), including a 10× increase in trigger rate, a 7.5× increase in hit rate, a 3× increase in radiation tolerance, and a 12.5× decrease in pixel current draw per unit area, all while maintaining a similar power per unit area as present pixel detectors. Here we present the first measurements of the ITkPixV2 chip operated at the limits of the full chip design requirements, including in particular a measurement of the activity-induced current of the chip as a function of increasing hit rate.

Le Pottier, L↗
DOE OSTI2025

Genetic Transfer in Action: Uncovering DNA Flow in an Extremophilic Microbial Community

ABSTRACT Horizontal genetic transfer (HGT) is a significant driver of genomic novelty in all domains of life. HGT has been investigated in many studies however, the focus has been on conspicuous protein‐coding DNA transfers that often prove to be adaptive in recipient organisms and are therefore fixed longer‐term in lineages. These results comprise a subclass of HGTs and do not represent exhaustive (coding and non‐coding) DNA transfer and its impact on ecology. Uncovering exhaustive HGT can provide key insights into the connectivity of genomes in communities and how these transfers may occur. In this study, we use the term frequency‐inverse document frequency (TF‐IDF) technique, that has been used successfully to mine DNA transfers within real and simulated high‐quality prokaryote genomes, to search for exhaustive HGTs within an extremophilic microbial community. We establish a pipeline for validating transfers identified using this approach. We find that most DNA transfers are within‐domain and involve non‐coding DNA. A relatively high proportion of the predicted protein‐coding HGTs appear to encode transposase activity, restriction‐modification system components, and biofilm formation functions. Our study demonstrates the utility of the TF‐IDF approach for HGT detection and provides insights into the mechanisms of recent DNA transfer.

Microbiology↗
DOE OSTI2025

Tunable Topological Phonon for Next-generation Quantum Transduction

This project aims to understand the critical factors that determine transduction performance of topological phonons across an oxide perovskite/tungsten diselenide heterojunction. We will investigate mechanisms of their propagation and interfacial coupling using modeling and machine learning approaches. Methods include density functional theory, molecular dynamics, numerical transport simulations, and active learning for building up a training dataset for force field development.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗
DOE OSTI2024

Dramatic changes in mitochondrial subcellular location and morphology accompany activation of the CO 2 concentrating mechanism

Dynamic changes in intracellular ultrastructure can be critical for the ability of organisms to acclimate to environmental conditions. Microalgae, which are responsible for ~50% of global photosynthesis, compartmentalize their Ribulose 1,5 Bisphosphate Carboxylase/Oxygenase (Rubisco) into a specialized structure known as the pyrenoid when the cells experience limiting CO 2 conditions; this compartmentalization is a component of the CO 2 Concentrating Mechanism (CCM), which facilitates photosynthetic CO 2 fixation as environmental levels of inorganic carbon (Ci) decline. Changes in the spatial distribution of mitochondria in green algae have also been observed under CO 2 limitation, although a role for this reorganization in CCM function remains unclear. We used the green microalga Chlamydomonas reinhardtii to monitor changes in mitochondrial position and ultrastructure as cells transition between high CO 2 and Low/Very Low CO 2 (LC/VLC). Upon transferring cells to VLC, the mitochondria move from a central to a peripheral cell location and orient in parallel tubular arrays that extend along the cell’s apico-basal axis. We show that these ultrastructural changes correlate with CCM induction and are regulated by the CCM master regulator CIA5. The apico-basal orientation of the mitochondrial membranes, but not the movement of the mitochondrion to the cell periphery, is dependent on microtubules and the MIRO1 protein, with the latter involved in membrane–microtubule interactions. Furthermore, blocking mitochondrial respiration in VLC-acclimated cells reduces the affinity of the cells for Ci. Overall, our results suggest that mitochondrial repositioning functions in integrating cellular architecture and energetics with CCM activities and invite further exploration of how intracellular architecture can impact fitness under dynamic environmental conditions.

CO2 concentrating mechanism↗
DOE OSTI2026

Three-State Electrochiroptical Switches Derived from Chiral Stable Carbenes

Chiral redox switches have been used to develop stimuli-responsive materials and organic electronics wherein small molecule chirality produces new functionality. Despite the widespread use of stable carbenes in redox-active materials and asymmetric synthesis, their integration into chiral redox switches remains largely unexplored. Herein, we show that chiral stable carbenes can be used to construct helically chiral overcrowded alkenes which function as three-state electrochiroptical switches. Redox-driven (de)aromatization triggers the reversible exchange of helical and axial chirality via a helically chiral π-radical cation. Due to dramatic changes in both electronic and geometric structure, including the inversion of helical chirality, each state exhibits distinct chiroptical properties. As a proof of concept, we demonstrate multiple cycles of electrochemical ON–OFF switching and sign inversion of the electronic circular dichroism response. Overall, this work establishes chiral stable carbenes as promising building blocks for chiral and redox-switchable materials.

Chirality↗
DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Nitrate-To-Ammonia Electroconversion at Neutral pH on Polycrystalline Vanadium Sulfide Derived from Vanadium Disulfide

The electrochemical nitrate reduction reaction (NO3RR) offers a pathway to produce NH3 for fuel and fertilizer from waste NO3-. In this work, a polycrystalline vanadium sulfide (VSx), which is derived from solvothermally grown and annealed VS2, is shown to exhibit excellent NO3RR activity (2.3 +- 0.6 mg.cm-2 geo..h-1 @ -0.92 VRHE) and Faradaic efficiency to NH4+ (69 +- 6% at -0.69 VRHE) in buffered neutral pH electrolyte containing 0.1 M NO3-. A variety of characterization techniques are leveraged to support the VSx assignment, including X-ray photoelectron spectroscopy, near-edge X-ray absorption fine structure spectroscopy, selected area electron diffraction, and X-ray diffraction measurements. The VS2 annealing step reduces the oxide character and generates VSx, which, based on the improved NO3RR activity, results in the creation of active sites for NO3- binding. To help shed light on NO3RR on VSx, VS2 is used as a model system, and a grand-canonical density functional theory (GC-DFT) investigation of VS2 shows strong evidence that S vacancies are active sites for NO3RR, where NO3- outcompetes H+ for adsorption at the S-vacancy sites. Moreover, GC-DFT results highlight a thermodynamically favorable reaction to generate NH4+ in an aqueous electrolyte at relevant cathodic potentials. As an annealed material, VSx may contain undersaturated V sites, which show an electronic structure similar to the theoretically calculated S-vacancy site of VS2, and these sites may contribute to the observed increase in NO3RR activity and selectivity for NH4+ on VSx versus unannealed VS2. Finally, kinetic isotope effect measurements suggest that the kinetic rate-limiting step of the NO3RR on VSx is not proton-coupled, indicating it may be the first electron transfer to adsorbed NO3*.

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

Nitrogen Deposition Weakens Soil Carbon Control of Nitrogen Dynamics Across the Contiguous United States

ABSTRACT Anthropogenic nitrogen (N) deposition is unequally distributed across space and time, with inputs to terrestrial ecosystems impacted by industry regulations and variations in human activity. Soil carbon (C) content normally controls the fraction of mineralized N that is nitrified ( ƒ nitrified ), affecting N bioavailability for plants and microbes. However, it is unknown whether N deposition has modified the relationships among soil C, net N mineralization, and net nitrification. To test whether N deposition alters the relationship between soil C and net N transformations, we collected soils from coniferous and deciduous forests, grasslands, and residential yards in 14 regions across the contiguous United States that vary in N deposition rates. We quantified rates of net nitrification and N mineralization, soil chemistry (soil C, N, and pH), and microbial biomass and function (as beta‐glucosidase (BG) and N ‐acetylglucosaminidase (NAG) activity) across these regions. Following expectations, soil C was a driver of ƒ nitrified across regions, whereby increasing soil C resulted in a decline in net nitrification and ƒ nitrified . The ƒ nitrified value increased with lower microbial enzymatic investment in N acquisition (increasing BG:NAG ratio) and lower active microbial biomass, providing some evidence that heterotrophic microbial N demand controls the ammonium pool for nitrifiers. However, higher total N deposition increased ƒ nitrified , including for high soil C sites predicted to have low ƒ nitrified , which decreased the role of soil C as a predictor of ƒ nitrified . Notably, the drop in contemporary atmospheric N deposition rates during the 2020 COVID‐19 pandemic did not weaken the effect of N deposition on relationships between soil C and ƒ nitrified . Our results suggest that N deposition can disrupt the relationship between soil C and net N transformations, with this change potentially explained by weaker microbial competition for N. Therefore, past N inputs and soil C should be used together to predict N dynamics across terrestrial ecosystems.

Nieland, Matthew A. [Stockbridge School of Agricul↗
DOE OSTI2025

Breaking the Linear Scaling Relations for the Oxygen Reduction Reaction with a Dual‐Atom Catalyst Composed of a MnFe‐Porphyrrole Aerogel

Bimetallic catalysts offer enhanced catalytic performance through synergistic interactions between the two metals, allowing them to break the linear scaling relations and reach high electrocatalytic activity. This study presents bimetallic aerogel-based catalyst synthesized as a covalent, three-dimensional framework containing neighboring iron and manganese sites. The aerogel structure provides a high surface area and porosity, facilitating an ultra-high active site density and efficient mass transport. The MnFe porphyrrole's unique structure is obtained by alternately linking Mn-porphyrin and Fe-corrole complexes. It exhibited outstanding performance with an onset potential of 0.99 V RHE . Comparative studies with a free-base Fe porphyrrole catalyst (E onset 0.97 V RHE ) revealed that while Mn incorporation led to only a slight improvement in half-cell performance, it resulted in significantly enhanced performance in anion exchange membrane fuel cell. The MnFe catalyst achieved an OCV of 0.97 V and a peak power density of 0.27 W cm −2 , outperforming the free-base Fe counterpart. Using density functional theory calculations, we show that the higher ORR activity of MnFe-porphyrrole is due to charge transfer between Mn and Fe atoms, which is absent in the reference free-base Fe-porphyrrole. These findings underscore the advantages of bimetallic catalysts in improving ORR activity and fuel cell efficiency by leveraging synergistic effects.

Aerogel↗
DOE OSTI2024

Dehydrogenated Polyethylene from Discarded Plastics as a Synthon for Functional Polyolefins

Many valuable specialty chemicals and drug candidates rely on synthesizing reactive intermediates to generate the final product(s). An approach that leverages C-H activation chemistry to achieve chemically active reactive handles for polymer diversification is desirable. Further, this report describes the synthesis and functionalization of dehydrogenated high-density polyethylene (HDPE) from discarded post-consumer plastic via acceptorless Ir-pincer-catalyzed non-oxidative dehydrogenation followed by acid-catalyzed hydroamination and hydroalkoxylation. The final products show significant material property changes with tunability and scalability through variation of the olefin content. This method highlights the potential value of dehydrogenated intermediates as platforms to enable carbon circularity and polyolefin end-of-life management strategies.

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

Hydrogen activation by rhodium under the cover of a copper oxide thin film

Here, the activation of reactants by catalytically active metal sites at metal-oxide interfaces is important for understanding the effect of metal-support interactions on nanoparticle catalysts and for tuning activity and selectivity. Using a combined experimental and theoretical approach, we studied the activation of H 2 and the effect of CO poisoning on isolated Rh atoms completely or partially covered by a copper oxide (Cu 2 O) thin film. Temperature-programmed desorption (TPD) experiments conducted in ultra-high vacuum (UHV) show that neither a partially nor a fully oxidized Cu 2 O layer grown on a Rh/Cu(111) single-atom alloy can activate hydrogen in UHV. However, in situ ambient pressure X-ray photoelectron spectroscopy (AP-XPS) experiments performed at elevated H 2 pressures reveal that Rh significantly accelerates the reduction of these Cu 2 O thin films by hydrogen. Remarkably, the fastest reduction rate is observed for the fully oxidized sample with all Rh sites covered by Cu 2 O. Both TPD and AP-XPS data demonstrate that these covered Rh sites are inaccessible to CO, indicating that Rh under Cu 2 O is active for H 2 dissociation but cannot be poisoned by CO. In contrast, an incomplete oxide film leaves some of the Rh sites exposed and accessible to CO, and hence prone to CO poisoning. Density functional theory calculations demonstrate that unlike many reactions in which hydrogen activation is rate limiting, the rate-determining step in the dissociation of H 2 on thin-film Cu 2 O with Rh underneath is the adsorption of H 2 on the buried Rh site, and once adsorbed, the dissociation of H 2 is barrierless. These calculations also explain why H 2 can only be activated at higher pressures. Together, these results highlight how different the reactivity of atomically dispersed Rh in Cu can be depending on its accessibility through the oxide layer, providing a way to engineer Rh sites that are active for hydrogen activation but resilient to CO poisoning.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Preparation of ultrathin sputtered gold films on palladium as efficient oxygen reduction electro-catalysts

Herein, we present the preparation of ultrathin gold (Au) films on different M (M = Pd, Pt, Rh, Ir, and Ru) electrodes (Au/M) using magnetron sputtering deposition for oxygen reduction reaction (ORR). The ORR electro-catalytic activities of prepared Au/M catalysts increase as follows: Au/Ru < Au/Ir < Au/Rh < Au/Pt < Au/Pd. Accordingly, the oxygen reduction catalytic behaviors of the Au-modified palladium (Au/Pd) thin films with various Au thicknesses (0.16, 0.24, 0.5, and 1 nm) are investigated in acidic environments. It is found that Au modifications can promote oxygen reduction performances due to the development of more catalytic active sites on the Pd catalyst through the existence of Au atoms. Noticeably, Au/Pd samples exhibit a connection in oxygen reduction ability as a function of Au thickness, with the ultrathin 0.16 nm Au/Pd being the most active ORR catalyst. Significantly, the 0.16 nm Au/Pd material demonstrates superior stability after 1000 potential cycles compared with the 0.16 nm Pt/Pd owing to the influence of Au coating and structurally stable surfaces. Therefore, surface modification with the deposition of Au films on Pd represents a favorable technique to boost both the ORR capability and stability of the electro-catalysts.

Magnetron sputtering↗
DOE OSTI2024

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗