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DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗

DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗

NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗

DOE OSTI2021

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1992

Tubular copper thrust chamber design study

The use of copper tubular thrust chambers is particularly important in high performance expander cycle space engines. Tubular chambers have more surface area than flat wall chambers, and this extra surface area provides enhanced heat transfer for additional energy to power the cycle. This paper was divided into two sections: (1) a thermal analysis and sensitivity study; and (2) a preliminary design of a selected thrust chamber configuration. The thermal analysis consisted of a statistical optimization to determine the optimum tube geometry, tube booking, thrust chamber geometry, and cooling routing to achieve the maximum upper limit chamber pressure for a 25,000 pound thrust engine. The preliminary design effort produced a layout drawing of a tubular thrust chamber that is three inches shorter than the Advanced Expander Test Bed (AETB) milled channel chamber but is predicted to provide a five percent increase in heat transfer. Testing this chamber in the AETB would confirm the inherent advantages of tubular chamber construction and heat transfer.

Masters, A. I.↗

NASA NTRS2001

Quantifying Residual Stresses by Means of Thermoelastic Stress Analysis

This study focused on the application of the Thermoelastic Stress Analysis (TSA) technique as a tool for assessing the residual stress state of structures. TSA is based on the fact that materials experience small temperature changes when compressed or expanded. When a structure is cyclically loaded, a surface temperature profile results which correlates to the surface stresses. The cyclic surface temperature is measured with an infrared camera. Traditionally, the amplitude of a TSA signal was theoretically defined to be linearly dependent on the cyclic stress amplitude. Recent studies have established that the temperature response is also dependent on the cyclic mean stress (i.e., the static stress state of the structure). In a previous study by the authors, it was shown that mean stresses significantly influenced the TSA results for titanium- and nickel-based alloys. This study continued the effort of accurate direct measurements of the mean stress effect by implementing various experimental modifications. In addition, a more in-depth analysis was conducted which involved analyzing the second harmonic of the temperature response. By obtaining the amplitudes of the first and second harmonics, the stress amplitude and the mean stress at a given point on a structure subjected to a cyclic load can be simultaneously obtained. The experimental results showed good agreement with the theoretical predictions for both the first and second harmonics of the temperature response. As a result, confidence was achieved concerning the ability to simultaneously obtain values for the static stress state as well as the cyclic stress amplitude of structures subjected to cyclic loads using the TSA technique. With continued research, it is now feasible to establish a protocol that would enable the monitoring of residual stresses in structures utilizing TSA.

Gyekenyesi, Andrew L.↗

DOE OSTI2020

Combustion of Silane-Hydrogen-Nitrous oxide Mixtures: Analysis of Laminar Flame Propagation and Condensed Products

The laminar burning rate, the explosion pressure, and the pressure rise coefficient have been measured for silane-nitrous oxide-argon mixtures using the spherically expanding flame technique in a constant volume combustion chamber. For these three parameters, the values obtained were higher than for hydrogen-nitrous oxide-argon and typical hydrocarbon-based mixtures. As an example, a maximum burning rate of 1800 g/m 2 s was measured at 101 kPa, whereas under similar conditions, a maximum burning rate around 950 g/m 2 s has been reported for hydrogen-nitrous oxide-argon mixtures. Here, the condensed combustion products formed in the silane-nitrous oxide-argon flames were sampled and characterized using electron micrograph, electronic diffraction, energy-dispersive spectroscopy, and X-ray powder diffraction. For all equivalence ratios, silica spherical particles with a mean diameter in the range 200-300 nm were observed. In addition, for mixtures with Φ ≥2.2, silicon nanowires were formed. X-ray diffraction experiments showed that the silicon nanowires are composed of a material referred to as “metal silicon” characterized by a cubic structure (lattice parameter: a = 5.425 Å) with the Fm-3m space group.

42 ENGINEERING↗

DOE OSTI2020

Expanded capabilities of the CarMa code in modeling resistive wall mode dynamics with 3-D conductors

In this work, an improved version of the CarMa code is presented, called CarMa-D, for the analysis of resistive wall modes (RWMs) in fusion devices, simultaneously considering the effects of volumetric three-dimensional conducting structures, plasma dynamics, toroidal rotation or drift-kinetic damping. Unlike static CarMa, CarMa-D does not rely on the simplifying assumptions such as neglecting the plasma mass, toroidal rotation and kinetic damping. The new coupling strategy is based on matrix-based Padé rational functions approximation of plasma a response. The arising mathematical model is formally equivalent to the original CarMa model, but with a higher number of degrees of freedom to model the dynamics of the plasma. CarMa-D overcomes the main limitations of the original CarMa, in particular: (i) the massless assumption for the plasma is removed, allowing modeling of global modes growing both on ideal kink time scales and in the typical RWM growth rate regime, with a suitable treatment of the model; (ii) the effects of toroidal plasma flow and drift kinetic damping can be included into the new model, providing a powerful tool to study macroscopic phenomena where both plasma dynamics and 3-D conducting structures play important roles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Airport Ground Support Equipment Analysis

Ground support equipment (GSE) plays a critical role in providing services to aircraft at arrival and departure gates, ensuring smooth and efficient airport operations. As the airline industry seeks to reduce costs and expand energy options, electric GSE (eGSE) is emerging as a promising solution. This project examines the integration of eGSE into airport operations, addressing key considerations such as vehicle deployment, charging infrastructure, and grid impacts. We developed a flexible, bottom-up modeling framework to evaluate energy demands and infrastructure requirements across more than 300 U.S. airports, providing actionable insights to support electrification planning and implementation. The study focuses on eight major types of GSE with commercially available electric counterparts: aircraft tractors, ground power units, baggage tractors, belt loaders, cargo loaders, catering trucks, lavatory trucks, and water trucks. Project results include annual charging load profiles, total energy consumption, and the charger and fleet requirements for each airport.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1974

Analysis of laminar flow between stationary and rotating disks with inflow

The laminar flow between a rotating and a stationary disk with inflow was analyzed. Solutions to the dimensionless governing equations are sought by expanding each of the velocity components in powers of inverse radius. The equations to leading order are those for the configuration with no inflow. The subsequent orders yield sets of linear ordinary differential equations. Solutions are obtained for the first two of these subsequent orders. The solutions indicate that inflow tends to increase the magnitude of the azimuthal velocity in the flow between the two disks and to decrease the torque on the rotating disk. For Prandtl number one, an energy integral is obtained which relates the temperature distribution to the velocity distribution for all Reynolds numbers and therefore eliminates the needs for separate solution of the energy equation.

Rohatgi, U.↗

DOE OSTI2018

Climate Change Impacts on Natural Sulfur Production: Ocean Acidification and Community Shifts

Utilizing the reduced-complexity model Hector, a regional scale analysis was conducted quantifying the possible effects climate change may have on dimethyl sulfide (DMS) emissions within the oceans. The investigation began with a review of the sulfur cycle in modern Earth system models. We then expanded the biogeochemical representation within Hector to include a natural ocean component while accounting for acidification and planktonic community shifts. The report presents results from both a latitudinal and a global perspective. This new approach highlights disparate outcomes which have been inadequately characterized via planetary averages in past publications. Our findings suggest that natural sulfur emissions (ESN) may exert a forcing up to 4 times that of the CO 2 marine feedback, 0.62 and 0.15 Wm –2 , respectively, and reverse the radiative forcing sign in low latitudes. Additionally, sensitivity tests were conducted to demonstrate the need for further examination of the DMS loop. As a result, the present work attempts to include dynamic ESN within reduced-complexity simulations of the sulfur cycle, illustrating its impact on the global radiative budget.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Kickstarting a New Era of Lunar Industrialization via Campaign of Lunar COTS Missions

To support the goals of expanding our human presence and current economic sphere beyond LEO, a new plan was constructed for NASA to enter into partnerships with industry to foster and incentivize a new era of lunar industrialization. For NASA to finally be successful in achieving sustainable human exploration missions beyond LEO, lessons learned from our space history have shown that it is essential for current program planning to include affordable and economic development goals as well as address top national priorities to obtain much needed public support. In the last 58 years of NASA's existence, only Apollo's human exploration missions beyond LEO were successful since it was proclaimed to be a top national priority during the 1960's. However, the missions were not sustainable and ended abruptly in 1972 due to lack of funding and insufficient economic gain. Ever since Apollo, there have not been any human missions beyond LEO because none of the proposed program plans were economical or proclaimed a top national priority. The proposed plan outlines a new campaign of low-cost, commercial-enabled lunar COTS (Commercial Orbital Transfer Services) missions which is an update to the Lunar COTS plan previously described. The objectives of this new campaign of missions are to prospect for resources, determine the economic viability of extracting those resources and assess the value proposition of using these resources in future exploration architectures such as Mars. These missions would be accomplished in partnership with commercial industry using the wellproven COTS Program acquisition model. This model proved to be very beneficial to both NASA and its industry partners as NASA saved significantly in development and operational costs, as much as tenfold, while industry partners successfully expanded their market share and demonstrated substantial economic gain. Similar to COTS, the goals for this new initiative are 1) to develop and demonstrate cost-effective, cis-lunar commercial services, such as lunar transportation, lunar mining and lunar ISRU operations; 2) enable development of an affordable and economical exploration architecture for future missions to Mars and beyond; and 3) to incentivize the creation of new lunar markets through use of lunar resources for economic benefit to NASA, commercial industry and the international community. These cost-effective services would not only enable NASA to economically and sustainably achieve its human exploration missions to the Moon, Mars and beyond but it would also kickstart a new era of lunar industrialization. This paper will describe the goals, objectives and approach for implementing this new campaign of missions. It will also describe the potential benefits and progress that can be accomplished with these low-cost, Lunar COTS missions. Lastly, a preliminary economic analysis approach is proposed for understanding the cost and potential return on investment in the use of lunar resources to reach the goal of lunar industrialization and an expanded and sustainable human presence into cis-lunar space and beyond.

Lunar Exploration↗

DOE OSTI2018

Development and Advanced Analysis of Dynamic and Static Casing Strain Monitoring to Characterize the Orientation and Dimensions of Hydraulic Fractures

Horizontal wells combined with multi-stage hydraulic fracturing have been applied to significantly increase production from low permeability formations, contributing to expanded total US production of oil and gas. Not all applications are successful, however. Field observations indicate that poorly designed or placed fracture stages in horizontal wells can result in significant well casing deformation and damage. In some instances, early fracture stages have deformed the casing enough so that it is not possible to drill out plugs in order to complete subsequent fracture stages. Improved fracture characterization techniques are required to identify potential problems early in the development of the field. Over the past decade, several new technologies have been presented as alternatives to characterize the fracture geometry for unconventional reservoirs. Monitoring dynamic casing strain and deformation during hydraulic fracturing represents one of these new techniques. The objective of this research is to evaluate dynamic and static strains imposed on a well casing by single and multiple stage fractures, and to use that information in combination with numerical inversion techniques to estimate fracture characteristics such as length, orientation and post treatment opening. GeoMechanics Technologies, working in cooperation with the Department of Energy, Small Business Innovation Research through DOE SBIR Grant No: DE-SC-0017746, is conducting a research project to complete an advanced analysis of dynamic and static casing strain monitoring to characterize the orientation and dimensions of hydraulic fractures. This report describes our literature review and technical approach.

04 OIL SHALES AND TAR SANDS↗

DOE OSTI2021

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗

DOE OSTI2025

Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.

Carpenter, Timothy S. [Lawrence Livermore National↗

DOE OSTI2023

A pyridoxal 5'-phosphate-dependent Mannich cyclase

Pyridoxal 5'-phosphate (PLP)-dependent enzymes catalyse a diverse range of chemical transformations. Despite their extraordinary functional diversity, no PLP-dependent enzyme is known to catalyse Mannich-type reactions, an important carbon–carbon bond-forming reaction in synthetic organic chemistry. Here we report the discovery of a biosynthetic enzyme LolT, a PLP-dependent enzyme catalysing a stereoselective intramolecular Mannich reaction to construct the pyrrolizidine core scaffold in loline alkaloids. Importantly, its versatile catalytic activity is harnessed for stereoselective synthesis of a variety of conformationally constrained α,α-disubstituted α-amino acids, which bear vicinal quaternary–tertiary stereocentres and various aza(bi)cyclic backbones, such as indolizidine, quinolizidine, pyrrolidine and piperidine. Furthermore, crystallographic and mutagenesis analysis and computational studies together provided mechanistic insights and structural basis for understanding LolT’s catalytic activity and stereoselectivity. As a whole, this work expands the biocatalytic repertoire of carbon–carbon bond-forming enzymes and increases our knowledge of the catalytic versatility of PLP-dependent enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Burns, Kory (ORCID:0000000158019909)↗

DOE OSTI2024

Genomes OnLine Database (GOLD) v.10: new features and updates

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

59 BASIC BIOLOGICAL SCIENCES↗