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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Sea ice breakup and freeze-up indicators for users of the Arctic coastal environment

Abstract. The timing of sea ice retreat and advance in Arctic coastal waters varies substantially from year to year. Various activities, ranging from marine transport to the use of sea ice as a platform for industrial activity or winter travel, are affected by variations in the timing of breakup and freeze-up, resulting in a need for indicators to document the regional and temporal variations in coastal areas. The primary objective of this study is to use locally based metrics to construct indicators of breakup and freeze-up in the Arctic and subarctic coastal environment. The indicators developed here are based on daily sea ice concentrations derived from satellite passive-microwave measurements. The “day of year” indicators are designed to optimize value for users while building on past studies characterizing breakup and freeze-up dates in the open pack ice. Relative to indicators for broader adjacent seas, the coastal indicators generally show later breakup at sites known to have landfast ice. The coastal indicators also show earlier freeze-up at some sites in comparison with freeze-up for broader offshore regions, likely tied to earlier freezing of shallow-water regions and areas affected by freshwater input from nearby streams and rivers. A factor analysis performed to synthesize the local indicator variations shows that the local breakup and freeze-up indicators have greater spatial variability than corresponding metrics based on regional ice coverage. However, the trends towards earlier breakup and later freeze-up are unmistakable over the post-1979 period in the synthesized metrics of coastal breakup and freeze-up and the corresponding regional ice coverage. The findings imply that locally defined indicators can serve as key links between pan-Arctic or global indicators such as sea ice extent or volume and local uses of sea ice, with the potential to inform community-scale adaptation and response.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Proximal Galerkin: A Structure-Preserving Finite Element Method for Pointwise Bound Constraints

The proximal Galerkin finite element method is a high-order, low iteration complexity, nonlinear numerical method that preserves the geometric and algebraic structure of pointwise bound constraints in infinite-dimensional function spaces. This paper introduces the proximal Galerkin method and applies it to solve free boundary problems, enforce discrete maximum principles, and develop a scalable, mesh-independent algorithm for optimal design with pointwise bound constraints. This paper also introduces the latent variable proximal point (LVPP) algorithm, from which the proximal Galerkin method derives. When analyzing the classical obstacle problem, we discover that the underlying variational inequality can be replaced by a sequence of second-order partial differential equations (PDEs) that are readily discretized and solved with, e.g., the proximal Galerkin method. Throughout this work, we arrive at several contributions that may be of independent interest. These include (1) a semilinear PDE we refer to as the entropic Poisson equation; (2) an algebraic/geometric connection between high-order positivity-preserving discretizations and certain infinite-dimensional Lie groups; and (3) a gradient-based, bound-preserving algorithm for two-field, density-based topology optimization. The complete proximal Galerkin methodology combines ideas from nonlinear programming, functional analysis, tropical algebra, and differential geometry and can potentially lead to new synergies among these areas as well as within variational and numerical analysis. Open-source implementations of our methods accompany this work to facilitate reproduction and broader adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Convection-permitting hindcasting of diurnal variation of Mei-yu rainfall over East China with a global variable-resolution model

Two sets of 48-hr hindcast experiments for Mei-yu season using non-hydrostatic global variable-resolution model (MPAS-Atmosphere) with regional refinement at 4km resolution (V4km) and uniform 60km resolution (U60km) are first-time conducted to attempt at simulating the Mei-yu rainfall in 2015 over East China. The analysis focuses on the seasonal average of diurnal variation of precipitation amount (PA), intensity (PI), and frequency (PF). Both simulations reasonably reproduce the spatial distribution of PA. V4km is more skillful in simulating the spatial distributions and magnitudes of PF and PI. The diurnal cycle of Mei-yu rainfall shows a major early morning peak and a minor afternoon peak, contributed by precipitation during two sub-periods with distinct synoptic circulations. With strong Mei-yu in the first sub-period, the diurnal variation of PA is controlled by nocturnal southwesterly jet. V4km overestimates the morning peak mainly due to its bias in simulating boundary layer inertial oscillation. Although differences in PA between the simulations are small, U60km overestimates PF and underestimates PI. With weak Mei-yu in the second sub-period, the diurnal variation of PA is controlled by both synoptic circulation and local convection. At both resolutions, deviations in the large-scale circulation modulated by a few typhoons lead to positive biases in the morning peak of PA. After removing the typhoon impacts, V4km captures the observed diurnal cycle of PA well, while U60km significantly underestimates PA and PI particularly in the afternoon. Future studies focusing on advancing modeling of southwesterly jet and typhoons may further improve convection permitting simulation of Mei-yu rainfall.

Xu, Mingyue↗
DOE OSTI2020

The Discovery of the Long-Period, Eccentric Planet Kepler-88 d and System Characterization with Radial Velocities and Photodynamical Analysis

We present the discovery of Kepler-88 d (P{sub d} = 1403±14 days, Msini{sub d}=965±44 M{sub ⊕}=3.04±0.13 M{sub J}, e{sub d}=0.41±0.03) based on six years of radial velocity (RV) follow-up from the W. M. Keck Observatory High Resolution Echelle Spectrometer spectrograph. Kepler-88 has two previously identified planets. Kepler-88 b (KOI-142.01) transits in the NASA Kepler photometry and has very large transit timing variations (TTVs). Nesvorný et al. performed a dynamical analysis of the TTVs to uniquely identify the orbital period and mass of the perturbing planet (Kepler-88 c), which was later was confirmed with RVs from the Observatoire de Haute-Provence (OHP). To fully explore the architecture of this system, we performed photodynamical modeling on the Kepler photometry combined with the RVs from Keck and OHP and stellar parameters from spectroscopy and Gaia. Planet d is not detectable in the photometry, and long-baseline RVs are needed to ascertain its presence. A photodynamical model simultaneously optimized to fit the RVs and Kepler photometry yields the most precise planet masses and orbital properties yet for b and c: P{sub b}=10.91647±0.00014 days, M{sub b}=9.5±1.2 M{sub ⊕}, P{sub c}=22.2649±0.0007 days, and M{sub c}=214.0±5.3 M{sub ⊕}. The photodynamical solution also finds that planets b and c have low eccentricites and low mutual inclination, are apsidally anti-aligned, and have conjunctions on the same hemisphere of the star. Continued RV follow-up of systems with small planets will improve our understanding of the link between inner planetary system architectures and giant planets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Increased inflammation as well as decreased endoplasmic reticulum stress and translation differentiate pancreatic islets from donors with pre-symptomatic stage 1 type 1 diabetes and non-diabetic donors

Aims/hypothesis Progression to type 1 diabetes is associated with genetic factors, the presence of autoantibodies and a decline in beta cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting beta cells prior to the onset of type 1 diabetes. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet-autoantibody-positive organ donors with pre-symptomatic stage 1 type 1 diabetes (HbA1c ≤42 mmol/mol [6.0%]). We aimed to identify pathways in islets that are indicative of beta cell dysfunction. Methods Multiple islet sections were collected through laser microdissection of frozen pancreatic tissues from organ donors positive for single or multiple islet autoantibodies (AAb + , n=5), and age (±2 years)- and sex-matched non-diabetic (ND) control donors (n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to MS-based proteomics and analysed with label-free quantification followed by pathway and functional annotations. Results Analyses resulted in ~4500 proteins identified with low false discovery rate (<1%), with 2165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on only the donors with stage 1 type 1 diabetes who were positive for multiple autoantibodies (mAAb + , n=3) and genetic risk compared with their matched ND controls (n=3) for the final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different (unadjusted p<0.025, q<0.15) for mAAb + vs ND donor islets. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including a proteomics dataset from in vitro proinflammatory cytokine-treated human islets and single-cell RNA-seq datasets from AAb + individuals. Conclusions/interpretation In situ human islet proteome alterations in stage 1 type 1 diabetes centred around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation/HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on beta cell changes during type 1 diabetes progression and pathogenesis. Data availability The LC-MS raw datasets that support the findings of this study have been deposited in the online repository: MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) with accession no. MSV000090212.

Autoantibody-positive↗
DOE OSTI2020

A thin-walled composite beam model for light-weighted structures interacting with fluids

A thin-walled beam model is proposed for structures of variable cross-section, which can be either open or closed and includes multicellular cross-sections with either isotropic or orthotropic materials. The proposed model does not require any priori definition of cross-sectional warping which instead results from the solution of the problem. To achieve that a special deformation pattern is superimposed on the bending deformation described by Euler–Bernoulli beam theory. All sectional properties are automatically incorporated in the analysis as a result of the usual variational formulation of the system of equations. The proposed model is specifically designed to simulate the dynamics of wind/hydrokinetic turbine blade with low computational cost, especially in fluid–structure interaction (FSI) simulation. A number of test cases have been carried out to validate the proposed structural model which show good agreement between the results obtained her e and the solutions available in literature. Finally, FSI simulation of a hydrokinetic blade under field condition is carried out to illustrate the capability of the current thin-walled beam model in practice.

42 ENGINEERING↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
DOE OSTI2021

Long-term excess nitrogen fertilizer increases sensitivity of soil microbial community to seasonal change revealed by ecological network and metagenome analyses

Nitrogen (N) fertilizer has often been generously applied to increase crop biomass yield. Although the influences of inorganic N fertilizer on soil microbial communities have been widely studied, the effect of N fertilizer on microbial co-occurrence networks and its metagenome is largely unknown. Further, seasonal changes in microbial community responses to N addition have rarely been reported. In this study, three N fertilizer rates (0, 56, 196 kg N/ha) were applied annually in switchgrass (Panicum virgatum L.) grown for bioenergy production in the upper Midwest, USA. The soil microbiome was affected by both fertilizer and low pH in the 7th year of fertilization treatments. The microbial community structures were relatively stable during the growing season for each N fertilizer rate. However, the excess N fertilizer (196N) increased the seasonal variation of bacterial and fungal communities. Network analysis showed that the microbial interactions at the 196N treatment were more intense, with decreased bacteria-fungal interactions compared to 56N and 0N. Furthermore, this suggests that the microbial community became more sensitive to environmental change under the influence of long-term excess N fertilizer. Metagenomic analysis showed that the long-term excess N fertilizer promoted many metabolic processes, especially carbohydrate and amino acid related metabolism and Archaea mediated ammonia oxidation. However, N fertilizer also reduced many other traits, especially N 2 fixation and signal transduction, the latter of which may contribute to the decreased interactions between bacteria and fungi.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A microphysiological model of human trophoblast invasion during implantation

Abstract Successful establishment of pregnancy requires adhesion of an embryo to the endometrium and subsequent invasion into the maternal tissue. Abnormalities in this critical process of implantation and placentation lead to many pregnancy complications. Here we present a microenigneered system to model a complex sequence of orchestrated multicellular events that plays an essential role in early pregnancy. Our implantation-on-a-chip is capable of reconstructing the three-dimensional structural organization of the maternal-fetal interface to model the invasion of specialized fetal extravillous trophoblasts into the maternal uterus. Using primary human cells isolated from clinical specimens, we demonstrate in vivo-like directional migration of extravillous trophoblasts towards a microengineered maternal vessel and their interactions with the endothelium necessary for vascular remodeling. Through parametric variation of the cellular microenvironment and proteomic analysis of microengineered tissues, we show the important role of decidualized stromal cells as a regulator of extravillous trophoblast migration. Furthermore, our study reveals previously unknown effects of pre-implantation maternal immune cells on extravillous trophoblast invasion. This work represents a significant advance in our ability to model early human pregnancy, and may enable the development of advanced in vitro platforms for basic and clinical research of human reproduction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Mapping Critical Vulnerabilities in Natural Gas Pipeline Systems through Network Centrality and GIS Analytics

Natural gas plays a central role in the US energy landscape, providing 43% of electricity generation in 2023. Its exclusive recovery ability on pipelines for transmission underscores the importance of understanding the disruption recovery ability of this infrastructure. This study employs a network-based analytical framework integrating geographic information systems (GIS) with multiple centrality measures—betweenness, closeness, degree, and eigenvector—to pinpoint key segments and evaluate the structural robustness of the national pipeline network. Pipelines are grouped by System ID and Operator ID to capture variations across organizational and physical structures. The analysis reveals uneven patterns of network influence, where certain pipelines function as critical connectors or dominant hubs. Spatial mapping highlights geographic dependencies and potential chokepoints, offering a clear view of where targeted risk prevention measures would be most effective. The findings provide practical guidance for prioritizing maintenance, enhancing system robustness, and mitigating risks to ensure a stable and secure energy supply. Future research will expand the framework to incorporate dynamic operational data and real-time network behavior.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
DOE OSTI2022

An intermediate model for fitting triplet–triplet annihilation in phosphorescent organic light emitting diode materials

Triplet–triplet annihilation (TTA) is one of the primary contributors to efficiency roll-off and permanent material degradation in phosphorescent organic light-emitting diodes. Here, the two limiting case models typically used to quantify this quenching mechanism are multi-step Dexter and single-step Förster, which, respectively, assume ideal Fickian diffusion or perfect trapping of triplet excitons. For device-relevant guest doping levels (typically 5–12 vol. %), both significant diffusion of excitons and trapping due to spatial and energetic disorder exist, so neither conventional model fits experimental data well. We develop and validate an intermediate TTA model, which is a weighted average of the limiting cases of pure radiative decay (no TTA) and multi-step Dexter based TTA that returns an effective TTA rate constant and a parameter quantifying the portion of well-isolated excitons. Kinetic Monte-Carlo simulations and time-resolved photoluminescence measurements of an archetype host–guest system demonstrate that our intermediate model provides significantly improved fits with more realistic physical values, is more robust to variations in experimental conditions, and provides an analysis framework for the effects of trapping on TTA.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Impact of bar resonances in the velocity–space distribution of the solar neighbourhood stars in a self-consistent N -body Galactic disc simulation

ABSTRACT The velocity–space distribution of the solar neighbourhood stars shows complex substructures. Most of the previous studies use static potentials to investigate their origins. Instead we use a self-consistent N-body model of the Milky Way, whose potential is asymmetric and evolves with time. In this paper, we quantitatively evaluate the similarities of the velocity–space distributions in the N-body model and that of the solar neighbourhood, using Kullback–Leibler divergence (KLD). The KLD analysis shows the time evolution and spatial variation of the velocity–space distribution. The KLD fluctuates with time, which indicates the velocity–space distribution at a fixed position is not always similar to that of the solar neighbourhood. Some positions show velocity–space distributions with small KLDs (high similarities) more frequently than others. One of them locates at $(R,\phi)=(8.2\,\,\rm{\mathrm{kpc}}, 30^\circ)$, where R and ϕ are the distance from the galactic centre and the angle with respect to the bar’s major axis, respectively. The detection frequency is higher in the inter-arm regions than in the arm regions. In the velocity maps with small KLDs, we identify the velocity–space substructures, which consist of particles trapped in bar resonances. The bar resonances have significant impact on the stellar velocity–space distribution even though the galactic potential is not static.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Multi-phase Wireless Power Transfer with High Power Density Inductive Coils for Electric Drone Charging

Wireless charging of unmanned ground vehi-cles and aircraft has been proposed to increase charging reliability and security, allow for autonomous functionality, and either reduce battery size or increase continuous flight time. This paper proposes a three-phase Litz wire primary and a two-phase PCB secondary for high secondary-side power density considering misalignment tolerances, surface and volumetric power density, and coil sizing. Elec-tromagnetic 3D finite element analysis (FEA) simulations are conducted to study variation in mutual inductance and coupling coefficient with different secondary coil sizes and number of turns, horizontal and vertical misalignment between the primary and secondary, and a combination of both for a set of designs with the same rated power. Results indicate that receiver coils of reduced size relative to the primary can deliver rated power for increased power density and higher tolerance to misalignment.

Gastineau, Lucas [University of Kentucky]↗
DOE OSTI2022

Estimating Fast Charging Infrastructure Requirements to Fully Electrify Ride-Hailing Fleets Across the United States

Ride-hailing electrification is made challenging by a variety of factors, including limited driver access to overnight charging and a nascent network of public direct-current fast charging (DCFC). This article projects the DCFC infrastructure needed to electrify ride-hailing across 384 cities in the United States. Research leveraging a novel infrastructure projection tool, Electric Vehicle Infrastructure Tool for On-Demand Mobility Services model (EVI-OnDemand), estimates that 25,400 DCFC ports capable of charging vehicles at a mean power of 150 kW are required to electrify 1.45 million ride-hailing vehicles in the baseline scenario. Results show that ride-hailing vehicles require 17.5 DCFC ports per 1000 vehicles, three times that of personal electric vehicles (EVs). Infrastructure demand is also shown to vary across cities; 63% more DCFC ports are needed to support 1000 battery EVs in Chicago versus Miami. The analysis also includes sensitivity studies that explore variation in charging demand across input assumptions, finding that charging power, vehicle class, overnight charging access rates, and port utilization influence the network size needed. Projections vary from 18,600 to 41,300 DCFC ports across sensitivity scenarios, indicating that ride-hailing electrification will require significant expansion of access to fast charging even in the most optimistic scenarios, and investments into supporting assets can reduce the scale of the public charging expansion needed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Deciphering the shifting role of intrinsic and extrinsic drivers on moss decomposition in peatlands over a 5-year period

Bryophytes are largely responsible for globally significant carbon accumulation in peatland ecosystems. This accumulation is primarily caused by the slow decomposition of these mosses, which can be attributed to a combination of intrinsic (chemical) characteristics of decaying mosses and extrinsic (environmental) influences. Here we investigated the importance of intrinsic and extrinsic drivers of moss decomposition in peatlands and hypothesized that the early stages of decay will be driven primarily by intrinsic characteristics, while extrinsic drivers will become more important in the later phases of decomposition. We tested this hypothesis by placing litterbags of three moss types (Sphagnum divinum, Sphagnum angustifolium/fallax and Polytrichum sp.) into hummock and hollow microtopographies in a bog and a lawn microtopography in a nearby poor fen in northern Minnesota, USA. Decomposition was measured over a 5-year period and confirmed our hypothesis; while intrinsic and extrinsic variables were both important in driving decay rates over time, decay in the first year was primarily predicted by litter type, while extrinsic influences (e.g. peat temperature, moisture, nutrient limitation) that varied at peatland and microtopographic scales became more important by year 5. Though the nutrient (i.e. nitrogen [N] and phosphorus [P]) content of decaying mosses did not explain the variation in decay rates among litter types, analysis of N:P in moss litter suggested that litter-decaying microorganisms became increasingly P-limited over time. The majority of litter decomposition studies in peatlands are generally of short duration (< 3 years). Our study suggests that longer-term studies are needed to reveal the roles of multiple drivers of moss decay. Improving the mechanistic understanding of decomposition processes in peatlands will be critical to developing more robust representations of peatlands and their ecosystem processes in carbon and climate models.

29 poor fen↗
DOE OSTI2023

Consideration of Nonideal Detonation Regimes Influenced by Wave Modes in a Water-Cooled Rotating Detonation Engine Using OH* Chemiluminescence

Although inherently unstable, existing research in rotating detonation combustion supports its application in notionally steady processes resulting in greater availability compared to conventional, constant pressure combustion. Further improvements rely on a more in-depth understanding of system losses and identifying conditions which optimize device performance. Within this study, the presence and proportion of ideal and nonideal combustion regimes are compared across a variety of process conditions and wave modes. Here, large-scale data analysis seeks to summarize proportional heat release associated with commensal, parasitic, and detonative combustion averaged across individual traces of OH* chemiluminescent data acquired at the detonation plane. Means of regime partitioning based on the anatomy of the time-resolved OH* signal are proposed to ensure consistent analysis throughout the current and future studies concerning combustion regimes. Of particular interest is the possible influence of wave on the nonideal combustion in relative proportion to the desired detonation. Results showed improved percent detonation with increasing significance for the following trends: decreasing equivalence ratio, increasing wave count, decreasing wave velocity, and increasing detonation time. Increased wave number, brought on by decreased equivalence ratios and wave velocities, is thought to decrease fill region surface area, and therefore, decrease nonideal contact burning. Additional analysis is performed to consider possible trend variation due to the presence of stable galloping waves, which were found to have minimal influence on relative percent detonation values. The outcome of this study suggests operational states, which correspond to increased wave quantities for increased proportions of reactants consumed by the targeted detonative combustion regime.

42 ENGINEERING↗
DOE OSTI2024

Comparative analysis of KRAS4a and KRAS4b splice variants reveals distinctive structural and functional properties

KRAS, the most frequently mutated oncogene in human cancer, produces two isoforms, KRAS4a and KRAS4b, through alternative splicing. These isoforms differ in exon 4, which encodes the final 15 residues of the G-domain and hypervariable regions (HVRs), vital for trafficking and membrane localization. While KRAS4b has been extensively studied, KRAS4a has been largely overlooked. Our multidisciplinary study compared the structural and functional characteristics of KRAS4a and KRAS4b, revealing distinct structural properties and thermal stability. Position 151 influences KRAS4a’s thermal stability, while position 153 affects binding to RAF1 CRD protein. Nuclear magnetic resonance analysis identified localized structural differences near sequence variations and provided a solution-state conformational ensemble. Notably,KRAS4aexhibits substantial transcript abundance in bile ducts, liver, and stomach, with transcript levels approachingKRAS4bin the colon and rectum. Functional disparities were observed in full-length KRAS variants, highlighting the impact of HVR variations on interaction with trafficking proteins and downstream effectors like RAF and PI3K within cells.

Science & Technology - Other Topics↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗