Search NASA⌕ Search

SEARCH · Search NASA

Results for “optimal hypothesis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

Local Electric Field Effects on Water Dissociation in Bipolar Membranes Studied Using Core–Shell Catalysts

The local electric field strength is thought to affect the rate of water dissociation (WD) in bipolar membranes (BPMs) at the catalyst–nanoparticle surfaces. Here, we study core–shell nanoparticles, where the core is metallic, semiconducting, or insulating, to understand this effect. The nanoparticle cores were coated with a WD catalyst layer (TiO2 or HfO2) via atomic layer deposition (ALD), and the morphology was imaged with transmission electron microscopy. Irrespective of the core material, these core–shell catalysts displayed comparable WD overpotentials at optimal mass loading, despite the hypothesized differences in the electric field strength across the catalyst particle suggested by continuum electrostatic simulations. Substantial atomic interdiffusion between the core and shell was ruled out by X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and diffuse reflectance optical measurements. However, the optimal mass loading of catalyst was roughly 1 order of magnitude higher for the conductive and high dielectric core materials than for the low dielectric insulating cores. These findings are consistent with the hypothesis that electric field screening within the core material focuses the electric field drop between particles such that larger film thicknesses can be tolerated. Collectively, these data support the idea that it is the local electric field at the molecular level that controls proton-transfer rates and that the metal core/dielectric-shell constructs introduced here modulate that field. Further materials and synthetic design may enable optimization of the electric field strength across the proton-transfer trajectory at the material surface.

Sarma, Prasad V↗

Transgenic Mixed‐Linkage‐Glucan Enhancement Affects Root Characteristics and Decomposition in Soils of Contrasting Vegetation History

ABSTRACT Development of transgenic bioenergy sorghum [ Sorghum bicolor (L.) Moench] with increased contents of mixed‐linkage (1,3;1,4)‐β‐glucan (MLG) is an important step towards enhancing quality of bioenergy feedstocks. Since MLG‐enhancement leads to greater biomass digestibility, our overarching hypothesis is that root residues of MLG‐enhanced plants may be more readily decomposed in the soil, potentially creating new opportunities for optimizing soil carbon (C) sequestration, nutrient cycling, and overall agricultural sustainability. The study examined morphological, chemical, and enzymatic characteristics of fine and coarse roots of four bioenergy sorghum genotypes. Then, we incubated the roots within soils with contrasting vegetation histories while measuring C mineralization, microbial biomass C (MBC), and activity of hydrolytic enzymes and calculating vector length and vector angle enzymatic stoichiometry parameters. The results indicated that MLG‐enhancing transformations increased root total nitrogen (N) contents, decreased C/N ratios, and were associated with higher MLG concentrations in fine than in coarse roots. Incubations with transgenic roots led to 16%–38% higher MBC and 19%–41% lower microbial metabolic quotient (qCO 2 ). While enzyme activity differed markedly among the studied genotypes, it did not directly respond to MLG levels in root tissues. The increase in MBC without concurrent increases in C mineralization or hydrolytic enzyme activities in transgenic genotypes suggests that MLG enhancement promoted microbial anabolic retention of root‐derived C rather than stimulating catabolic decomposition. Enzymatic vector results indicated that these parameters reflect a variety of drivers behind microbial enzyme production, including availability of specific substrates, such as MLG here, and/or deficiency in specific nutrients, such as phosphorus (P). The study confirms the positive impacts from the roots of engineered MLG‐enhanced bioenergy plants on soil microbial activity and highlights the interactive influences on the MLG‐enhancement effects from root size and inherent soil properties.

Mahmoodabadi, Majid [Department of Plant, Soil, an↗

Acute Stent-Induced Endothelial Denudation: Biomechanical Predictors of Vascular Injury

Recent concern for local drug delivery and withdrawal of the first Food and Drug Administration-approved bioresorbable scaffold emphasizes the need to optimize the relationships between stent design and drug release with imposed arterial injury and observed pharmacodynamics. In this study, we examine the hypothesis that vascular injury is predictable from stent design and that the expanding force of stent deployment results in increased circumferential stress in the arterial tissue, which may explain acute injury poststent deployment. Using both numerical simulations and ex vivo experiments on three different stent designs (slotted tube, corrugated ring, and delta wing), arterial injury due to device deployment was examined. Furthermore, using numerical simulations, the consequence of changing stent strut radial thickness on arterial wall shear stress and arterial circumferential stress distributions was examined. Regions with predicted arterial circumferential stress exceeding a threshold of 49.5 kPa compared favorably with observed ex vivo endothelial denudation for the three considered stent designs. In addition, increasing strut thickness was predicted to result in more areas of denudation and larger areas exposed to low wall shear stress. We conclude that the acute arterial injury, observed immediately following stent expansion, is caused by high circumferential hoop stresses in the interstrut region, and denuded area profiles are dependent on unit cell geometric features. Such findings when coupled with where drugs move might explain the drug–device interactions.

Conway, Claire↗

Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure

ABSTRACT A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present a new method to reconstruct maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type l1-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time, these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artefacts of the reconstruction. Here, we present this new formalism, and demonstrate the method on simulations, before applying the developed formalism to two observational data sets of the Abell 520 cluster. Initial reconstructions of the Abell 520 catalogues reported the detection of an anomalous ‘dark core’ – an overdense region with no optical counterpart – which was taken to be evidence for self-interacting dark matter. In our Bayesian framework, it is found that neither Abell 520 data set can conclusively determine the physicality of such dark cores at $99{{\ \rm per\ cent}}$ confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data.

Price, M. A.↗

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Systems Analysis of the Physiological and Molecular Mechanisms of Sorghum Nitrogen Use Efficiency, Water Use Efficiency and Interactions with the Soil Microbiome (Final Report for DE-SC0014395)

The specific project objectives were to: 1) Conduct deep census surveys of root microbiomes concurrent with phenotypic characterizations of a diverse panel of sorghum genotypes across multiple years to define the microbes associated with the most productive lines under drought and low nitrogen conditions. 2) Associate systems-level genotypic, microbial, and environmental factors with improved sorghum performance using robust statistical approaches. 3) Develop culture collections of sorghum root/leaf associated microbes that recapitulate root-enriched sequences defined in the census. 4) Perform controlled environment experiments for in-depth characterization and hypothesis testing of G sorghum x G microbe x E interactions . Validate physiological mechanisms, map genetic loci for stress tolerance, and determine the persistence of optimal microbial strains under greenhouse and field conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Intercropping kura clover with prairie cordgrass mitigates soil greenhouse gas fluxes

Prairie cordgrass (PCG) ( Spartina pectinata Link) has a high tolerance to soil salinity and waterlogging, therefore, it can thrive on marginal lands. Optimizing the nitrogen (N) input is crucial to achieving desirable biomass production of PCG without negatively impacting the environment. Thus, this study was based on the hypothesis that the use of legumes such as kura clover ( Trifolium ambiguum M. Bieb.) (KC) as an intercrop with PCG can provide extra N to the crop reducing the additional N fertilizer and mitigating soil surface greenhouse gas (GHG) emissions. Specific objective of the study was to assess the impact of PCG managed with different N rates [0 kg N ha -1 (PCG-0N), 75 kg N ha -1 (PCG-75N), 150 kg N ha -1 (PCG-150N), and 225 kg N ha -1 (PCG-255N)], and PCG intercropped with KC (PCG-KC) on GHG fluxes and biomass yield. The experimental site was established in 2010 in South Dakota under a marginally yielding cropland. The GHG fluxes were measured from 2014 through 2018 growing seasons using the static chamber. Net global warming potential (GWP) was calculated. Data showed that cumulative CH 4 and CO 2 fluxes were similar for all the treatments over the study period. However, the PCG-KC, PCG-0N, and PCG-75N recorded lower cumulative N 2 O fluxes (384, 402, and 499 g N ha -1 , respectively) than the PCG-150N (644 g N ha -1 ) and PCG-255N (697 g N ha -1 ). The PCG-KC produced 85% and 39% higher yield than the PCG-0N in 2016 and 2017, respectively, and similar yield to the other treatments (PCG-75N, PCG-150N, and PCG-255N) in these years. Net GWP was 52% lower for the PCG-KC (112.38 kg CO 2 -eq ha -1 ) compared to the PCG-225N (227.78 kg CO 2 -eq ha -1 ), but similar to other treatments. Soil total N was 15%% and 13% higher under PCG-KC (3.7 g kg -1 ) than that under PCG-0N (3.2 g kg -1 ) and PCG-75N (3.3 g kg -1 ), respectively. This study concludes that intercropping prairie cordgrass with kura clover can enhance biomass yield and reduce fertilizer-derived N 2 O emissions and net global warming potential.

09 BIOMASS FUELS↗

A general theory of avian migratory connectivity

Birds exhibit a remarkable array of seasonal migrations. Despite much research describing migratory behaviour, the underlying forces driving how a species’ breeding and wintering populations redistribute each year, that is, migratory connectivity, remain largely unknown. Here, we test the hypothesis that birds migrate in a way that minimises energy expenditure while considering intraspecific competition for energy acquisition, by developing a modelling framework that simulates an optimal redistribution of individuals between breeding and wintering areas. Using 25 species across the Americas, we find that the model accurately predicts empirical migration patterns, and thus offers a general explanation for migratory connectivity based on first ecological and energetic principles. Our model provides a strong basis for exploring additional processes underlying the ecology and evolution of migration, but also a framework for predicting how migration impacts local adaptation across seasons and how environmental change may affect population dynamics in migratory species.

60 APPLIED LIFE SCIENCES↗

Optimizing the critical temperature and superfluid density of a metal-superconductor bilayer

A promising path to realizing higher superconducting transition temperatures 𝑇 c is the strategic engineering of artificial heterostructures. For example, quantum materials could, in principle, be coupled with other materials to produce a more robust superconducting state. Here, in this work, we add numerical support to the hypothesis that a strongly interacting superconductor weakened by phase fluctuations can boost its 𝑇 c by hybridizing the system with a metal. Using determinant quantum Monte Carlo, we simulate a two-dimensional bilayer composed of an attractive Hubbard model and a metallic layer in two regimes of the interaction strength −|𝑈|. In the strongly interacting regime, we find that increasing the interlayer hybridization 𝑡 ⊥ results in a nonmonotonic enhancement of 𝑇 c , with an optimal value comparable to the maximum 𝑇 c observed in the single-layer attractive Hubbard model, confirming trends inferred from other approaches. In the intermediate coupling regime, when −|𝑈| is close to the value associated with the maximum 𝑇 c of the single-layer model, increasing 𝑡 ⊥ tends to decrease 𝑇 c , implying that the correlated layer was already optimally tuned. Importantly, we demonstrate that the mechanism behind these trends is related to enhancement in the superfluid stiffness, as was initially proposed by Kivelson [Phys. B: Condens. Matter 318, 61 (2002)].

Zhang, Yutan [Univ. of California, Davis, CA (Unit↗

In situ quantification of fracture slip induced by hydraulic injections in a deep borehole: A comparison of two different borehole techniques

In situ measurements of fracture deformation during fluid injection are rare, yet essential for understanding the mechanical response of fractured rock. In this study, we evaluate the reliability of two methods by comparing their slip vector estimates: high-resolution borehole acoustic televiewer images captured before and after injection tests, and displacement data from a three-component borehole deformation probe recorded during the injections. Acoustic televiewer images capture only final in-plane displacement, whereas three-component borehole deformation measurements provide full 3D, transient fracture movement. Four injection tests in a fractured granitic rock mass along an inclined borehole at the Bedretto Underground Laboratory (Switzerland), beneath more than 1100 m of overburden, were analyzed. The two methods yielded consistent kinematics and comparable slip magnitudes, typically in the range of 0.2 – 0.6 mm. Angular differences between estimated slip directions ranged from 0.2° to 28°. For two test intervals with pre-existing fractures optimally oriented for slip, the measured slip vectors agreed with forward-modeled slip vectors based on the regional in situ stress field, supporting the Wallace–Bott hypothesis. Measurable shear displacement was identified in two test intervals containing fractures oriented nearly perpendicular to the least principal stress, including a newly formed hydraulic fracture. This demonstrates that even fractures unfavorably oriented for slip can undergo shear due to local stress perturbations around an inclined borehole.

Acoustic televiewer↗

Mechanistic Interpretations and Insights for the Oxidative Dehydrogenation of Propane via CO 2 over Cr 2 O 3 /Al 2 O 3 Catalysts

Oxidative dehydrogenation (ODH) of alkanes using carbon dioxide as a soft oxidant has recently emerged as a potentially attractive alternative to steam cracking for the production of light olefins. To elucidate reaction pathways and their dependence on the operating conditions, CO 2 -assisted propane dehydrogenation over a redox-active Cr 2 O 3 /Al 2 O 3 catalyst was examined in a packed bed reactor as a function of temperature, Cr 2 O 3 /CO 2 feed ratio, and residence time. Previous ODH studies have largely focused on CO 2 -rich conditions with the aim of preventing coke formation. However, at T = 600 °C the present study finds that the use of propane-rich conditions (1 ≤ C 3 H 8 /CO 2 ≤ 2.5) maximizes propylene production and selectivity while maintaining catalyst stability. In this work, it is postulated that the selective Mars van Krevelen dehydrogenation process is optimized at these ratios. Excess CO 2 apparently promotes nonselective dehydrogenation and dry reforming pathways that generate additional CO, adversely impacting catalyst stability via the Bouduard reaction. This hypothesis is supported by complementary investigations of the reverse water gas shift reaction and thermodynamic analysis. The findings and methodology presented here are likely applicable to related ODH processes with other alkanes and redox-active catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reductive quantum phase estimation

Estimating a quantum phase is a necessary task in a wide range of fields of quantum science. To accomplish this task, two well-known methods have been developed in distinct contexts, namely, Ramsey interferometry (RI) in atomic and molecular physics and quantum phase estimation (QPE) in quantum computing. We demonstrate that these canonical examples are instances of a larger class of phase estimation protocols, which we call reductive quantum phase estimation (RQPE) circuits. Here, we present an explicit algorithm that allows one to create an RQPE circuit. This circuit distinguishes an arbitrary set of phases with a smaller number of qubits and unitary applications, thereby solving a general class of quantum hypothesis testing to which RI and QPE belong. We further demonstrate a tradeoff between measurement precision and phase distinguishability, which allows one to tune the circuit to be optimal for a specific application. Published by the American Physical Society 2024

Papadopoulos, Nicholas J. C. (ORCID:00000002635700↗

Coordinated resource allocation to plant growth–defense tradeoffs

Summary Plant resource allocation patterns often reveal tradeoffs that favor growth (G) over defense (D), or vice versa. Ecologists most often explain G–D tradeoffs through principles of economic optimality, in which negative trait correlations are attributed to the reconciliation of fitness costs. Recently, researchers in molecular biology have developed ‘big data’ resources including multi‐omic (e.g. transcriptomic, proteomic and metabolomic) studies that describe the cellular processes controlling gene expression in model species. In this synthesis, we bridge ecological theory with discoveries in multi‐omics biology to better understand how selection has shaped the mechanisms of G–D tradeoffs. Multi‐omic studies reveal strategically coordinated patterns in resource allocation that are enabled by phytohormone crosstalk and transcriptional signal cascades. Coordinated resource allocation justifies the framework of optimality theory, while providing mechanistic insight into the feedbacks and control hubs that calibrate G–D tradeoff commitments. We use the existing literature to describe the coordinated resource allocation hypothesis (CoRAH) that accounts for balanced cellular controls during the expression of G–D tradeoffs, while sustaining stored resource pools to buffer the impacts of future stresses. The integrative mechanisms of the CoRAH unify the supply‐ and demand‐side perspectives of previous G–D tradeoff theories.

Monson, Russell K.↗

Effective Mechanisms for Improving Seed Oil Production in Pennycress (Thlaspi arvense L.) Highlighted by Integration of Comparative Metabolomics and Transcriptomics

Pennycress is a potentially lucrative biofuel crop due to its high content of long-chain unsaturated fatty acids, and because it uses non-conventional pathways to achieve efficient oil production. However, metabolic engineering is required to improve pennycress oilseed content and make it an economically viable source of aviation fuel. Research is warranted to determine if further upregulation of these non-conventional pathways could improve oil production within the species even more, which would indicate these processes serve as promising metabolic engineering targets and could provide the improvement necessary for economic feasibility of this crop. To test this hypothesis, we performed a comparative biomass, metabolomic, and transcriptomic analyses between a high oil accession (HO) and low oil accession (LO) of pennycress to assess potential factors required to optimize oil content. An evident reduction in glycolysis intermediates, improved oxidative pentose phosphate pathway activity, malate accumulation in the tricarboxylic acid cycle, and an anaplerotic pathway upregulation were noted in the HO genotype. Additionally, higher levels of threonine aldolase transcripts imply a pyruvate bypass mechanism for acetyl-CoA production. Nucleotide sugar and ascorbate accumulation also were evident in HO, suggesting differential fate of associated carbon between the two genotypes. An altered transcriptome related to lipid droplet (LD) biosynthesis and stability suggests a contribution to a more tightly-packed LD arrangement in HO cotyledons. In addition to the importance of central carbon metabolism augmentation, alternative routes of carbon entry into fatty acid synthesis and modification, as well as transcriptionally modified changes in LD regulation, are key aspects of metabolism and storage associated with economically favorable phenotypes of the species.

59 BASIC BIOLOGICAL SCIENCES↗

Acclimation of leaf respiration consistent with optimal photosynthetic capacity

Abstract Plant respiration is an important contributor to the proposed positive global carbon‐cycle feedback to climate change. However, as a major component, leaf mitochondrial (‘dark’) respiration ( R d ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of R d follows an optimal behaviour related to the need to maintain long‐term average photosynthetic capacity ( V cmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co‐ordination theory to predict the acclimation of R d to growth temperature via a link to V cmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co‐ordination theory predicts that field‐measured R d and V cmax accessed at growth temperature ( R d,tg and V cmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for R d and V cmax respectively. Data‐fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both R d and V cmax assessed at 25°C ( R d,25 and V cmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for R d acclimation to temperature that is simpler—and potentially more reliable—than the plant functional type‐based leaf respiration schemes currently employed in most ecosystem and land‐surface models.

Wang, Han↗

Likelihood-based interactive local docking into cryo-EM maps in ChimeraX

The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 Å. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located. In such cases, a local search using a map subvolume will be much faster because the search volume is smaller, and more sensitive because optimizing the search volume for the rotation-search step enhances the signal to noise. A Fourier-space likelihood-based local search approach, based on the previously published em_placement software, has been implemented in the new emplace_local program. Tests confirm that the local search approach enhances the speed and sensitivity of the computations. An interactive graphical interface in the ChimeraX molecular-graphics program provides a convenient way to set up and evaluate docking calculations, particularly in defining the part of the map into which the components should be placed.

59 BASIC BIOLOGICAL SCIENCES↗

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators (NOs) employ deep neural networks to learn the mappings between infinitedimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, we introduce a random sampling technique to be adopted during the training of DeepONet, aimed at improving the generalization ability of the model, while significantly reducing the computational time. The proposed approach targets the trunk network of the DeepONet model that outputs the basis functions corresponding to the spatiotemporal locations of the bounded domain on which the physical system is defined. While constructing the loss function, DeepONet training traditionally considers a uniform grid of spatiotemporal points at which all the output functions are evaluated for each iteration. This approach leads to a larger batch size, resulting in poor generalization and increased memory demands, due to the limitations of the stochastic gradient descent (SGD) optimizer. The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing the memory requirements during training, resulting in significant computational gains. We validate our hypothesis through three benchmark examples, demonstrating substantial reductions in training time while achieving comparable or lower overall test errors relative to the traditional training approach. Our results indicate that incorporating randomization in the trunk network inputs during training enhances the efficiency and robustness of DeepONet, offering a promising avenue for improving the framework’s performance in modeling complex physical systems.

Karumuri, Sharmila [Department of Civil & Systems ↗

Assess and Benchmark Magneto-Inertial Fusion (MIF) Scaling. Final Technical Report for the SNL/Rochester ALPHA Follow-on Project

This project was a follow-on to the Sandia National Laboratories (SNL) and the Laboratory for Laser Energetics (LLE) ARPA-E ALPHA project entitled “Demonstrating Fuel Magnetization and Laser Heating Tools for Low-Cost Fusion Energy”. The primary purpose of this follow-on project was to obtain additional data at the OMEGA facility to help better understand how MagLIF, a platform that has already demonstrated the scientific viability of magneto-inertial fusion, scales across a factor of 1000 in driver energy. A secondary aspect of this project was to extend simulations and analysis at SNL to cover a wider magneto-inertial fusion (MIF) parameter space and test scaling of those models across this wide range of input energies and conditions of the target. This work was successful in improving understanding of how key physics elements of MIF scales and improves confidence in setting requirements for fusion gain with larger drivers. The OMEGA experiments at the smaller scale verified the hypothesis that preheating the fuel plays a significant role in introducing wall contaminants that mix into the fuel and significantly degrade fusion performance. This contamination not only impacts target performance but the optimal input conditions for the target. However, analysis at the Z-scale showed that target performance at high preheat levels is limited by the Nernst effect, which advects magnetic flux from the hot spot, reducing magnetic insulation and consequently reduces the temperature of the fuel. The combination of MagLIF experiments at the disparate scales of OMEGA and Z along with a multiscale 3D simulation analysis has led to new insight into the physical mechanisms responsible for limiting target performance and provides important benchmarks to assess target scaling more generally for MIF schemes. Finally, in addition to the MagLIF related work, a semi-analytic model of liner driven Field Reversed Configuration (FRC) was developed that predicts the fusion gain for such systems. This model was also validated with 2D radiation magneto-hydrodynamic simulations and predicts that fusion gains of near unity could be driven by the Z machine.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗