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DOE OSTI2022

Room-temperature polariton quantum fluids in halide perovskites

Abstract Quantum fluids exhibit quantum mechanical effects at the macroscopic level, which contrast strongly with classical fluids. Gain-dissipative solid-state exciton-polaritons systems are promising emulation platforms for complex quantum fluid studies at elevated temperatures. Recently, halide perovskite polariton systems have emerged as materials with distinctive advantages over other room-temperature systems for future studies of topological physics, non-Abelian gauge fields, and spin-orbit interactions. However, the demonstration of nonlinear quantum hydrodynamics, such as superfluidity and Čerenkov flow, which is a consequence of the renormalized elementary excitation spectrum, remains elusive in halide perovskites. Here, using homogenous halide perovskites single crystals, we report, in both one- and two-dimensional cases, the complete set of quantum fluid phase transitions from normal classical fluids to scatterless polariton superfluids and supersonic fluids—all at room temperature, clear consequences of the Landau criterion. Specifically, the supersonic Čerenkov wave pattern was observed at room temperature. The experimental results are also in quantitative agreement with theoretical predictions from the dissipative Gross-Pitaevskii equation. Our results set the stage for exploring the rich non-equilibrium quantum fluid many-body physics at room temperature and also pave the way for important polaritonic device applications.

42 ENGINEERING↗

DOE OSTI2024

Decadal Thermal Variability of the Upper Southern Ocean: Zonal Asymmetry

Abstract As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

Meteorology & Atmospheric Sciences↗

DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1993

Navier-Stokes Flow Field Analysis of Compressible Flow in a Pressure Relief Valve

The present study was motivated to analyze the complex flow field involving gaseous oxygen (GOX) flow in a relief valve (RV). The 9391 RV, pictured in Figure 1, was combined with the pilot valve to regulate the actuation pressure of the main valve system. During a high-pressure flow test at Marshall Space Flight Center (MSFC) the valve system developed a resonance chatter, which destroyed most of the valve body. Figures 2-4 show the valve body before and after accident. It was understood that the subject RV has never been operated at 5500 psia. In order to fully understand the flow behavior in the RV, a computational fluid dynamics (CFD) analysis is carried out to investigate the side load across the piston sleeve and the erosion patterns resulting from flow distribution around piston/nozzle interface.

Vu, Bruce T.↗

NASA NTRS2005

Liquid Hydrocarbons on Titan's Surface? How Cassini ISS Observations Fit into the Story (So Far)

Titan is the only satellite in our Solar System with a substantial atmosphere, the origins and evolution of which are still not well understood. Its primary (greater than 90%) component is nitrogen, with a few percent methane and lesser amounts of other species. Methane and ethane are stable in the liquid state under the temperature and pressure conditions in Titan s lower atmosphere and at the surface; indeed, clouds, likely composed of methane, have been detected. Photochemical processes acting in the atmosphere convert methane into more complex hydrocarbons, creating Titan s haze and destroying methane over relatively short timescales. Therefore, it has been hypothesized that Titan s surface has reservoirs of liquid methane which serve to resupply the atmosphere. Early observations of Titan s surface revealed albedo patterns which have been interpreted as dark hydrocarbon liquids occupying topographically low regions between higher-standing exposures of bright, water-ice bedrock, although this is far from being the only explanation for the observed albedo contrast. Observations made by the Imaging Science Subsystem during Cassini's approach to Saturn and its first encounters with Titan show the bright and dark regions in greater detail but have yet to resolve the question of whether there are liquids on the surface.

Turtle, E. P.↗

DOE OSTI2021

Entanglement rearrangement in self-consistent nuclear structure calculations

Background: Entanglement plays a central role in a diverse array of increasingly important research areas, including quantum computation, simulation, measurement, sensing, and communication. Extensive suites of investigations have been performed to better understand entanglement in atomic and molecular quantum many-body systems, while the exploration of entanglement in the structure of nuclei and their reactions is presently in its infancy. Purpose: The goal of this work is to begin investigating the entanglement properties of nuclei from first-principles nuclear many-body calculations. We attempt to identify common features and emergent structures of entanglement that could ultimately lead to new and natural many-body schemes. With an eye toward quantum accelerators in future hybrid-supercomputers, criteria for partitioning nuclear many-body calculations into quantum and classical components may provide advantages in future large-scale computations. Along the way we look for explanations of the relative success of phenomenological models such as the nuclear shell model, and for better ways to match to low-energy nuclear effective field theories and lattice QCD calculations to nuclear many-body techniques that are based upon entanglement. Method: We explore the entanglement between single-particle states in 4 He and 6 He. The patterns of entanglement emerging from different single-particle bases are compared, and possible links with the convergence of observables are explored, in particular, ground-state energies. The nuclear wave functions are obtained by performing active-space no-core configuration-interaction calculations using a two-body nucleon-nucleon interaction derived from chiral effective field theory. Entanglement measures within single-particle bases exhibiting different degrees of complexity are determined, in particular, harmonic oscillator (HO), Hartree-Fock (HF), natural (NAT) and variational natural (VNAT) bases. Specifically, single-orbital entanglement entropy, two-orbital mutual information, and negativity are studied. Results: The entanglement structures in 4 He and 6 He are found to be more localized within NAT and VNAT bases than within a HO basis for the optimal HO parameters we have worked with. In particular a core-valence structure clearly emerges from the full no-core calculation of 6 He. Here, the two-nucleon mutual information shows that the VNAT basis, which typically exhibits good convergence properties, effectively decouples the active and inactive spaces. Conclusions: Measures of one- and two-nucleon entanglement are found to be useful in analyzing the structure of nuclear wave functions, in particular the efficacy of basis states, and may provide useful metrics toward developing more efficient schemes for ab initio computations of the structure and reactions of nuclei, and quantum many-body systems more generally.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗

NASA NTRS1995

Applying AI tools to operational space environmental analysis

The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.

Krajnak, Mike↗

NASA NTRS2015

Predicting Clear-Sky Reflectance Over Snow/Ice in Polar Regions

Satellite remote sensing of clouds requires an accurate estimate of the clear-sky radiances for a given scene to detect clouds and aerosols and to retrieve their microphysical properties. Knowing the spatial and angular variability of clear-sky albedo is essential for predicting clear-sky radiance at solar wavelengths. The Clouds and the Earth's Radiant Energy System (CERES) Project uses the nearinfrared (NIR; 1.24, 1.6 or 2.13 micrometers), visible (VIS; 0.63 micrometers) and vegetation (VEG; 0.86 micrometers) channels available on the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) to help identify clouds and retrieve their properties in both snow-free and snow-covered conditions. Thus, it is critical to have reliable distributions of clear-sky albedo for all of these channels. In CERES Edition 4 (Ed4), the 1.24-micrometer channel is used to retrieve cloud optical depth over snow/ice-covered surfaces. Thus, it is especially critical to accurately predict the 1.24-micrometer clear-sky albedo alpha and reflectance rho for a given location and time. Snow albedo and reflectance patterns are very complex due to surface texture, particle shapes and sizes, melt water, and vegetation protrusions from the snow surface. To minimize those effects, this study focuses on the permanent snow cover of Antarctica where vegetation is absent and melt water is minimal. Clear-sky albedos are determined as a function of solar zenith angle (SZA) from observations over all scenes determined to be cloud-free to produce a normalized directional albedo model (DRM). The DRM is used to develop alpha(SZA=0 degrees) on 10 foot grid for each season. These values provide the basis for predicting r at any location and set of viewing & illumination conditions. This paper examines the accuracy of this approach for two theoretical snow surface reflectance models.

Chen, Yan↗

NASA NTRS2016

Digital Image Correlation for Performance Monitoring

Evaluating the health of a mechanism requires more than just a binary evaluation of whether an operation was completed. It requires analyzing more comprehensive, full-field data. Health monitoring is a process of non-destructively identifying characteristics that indicate the fitness of an engineered component. In order to monitor unit health in a production setting, an automated test system must be created to capture the motion of mechanism parts in a real-time and non-intrusive manner. One way to accomplish this is by using high-speed video and Digital Image Correlation (DIC). In this approach, individual frames of the video are analyzed to track the motion of mechanism components. The derived performance metrics allow for state-of-health monitoring and improved fidelity of mechanism modeling. The results are in-situ state-of-health identification and performance prediction. This paper introduces basic concepts of this test method, and discusses two main themes: the use of laser marking to add fiducial patterns to mechanism components, and new software developed to track objects with complex shapes, even as they move behind obstructions. Finally, the implementation of these tests into an automated tester is discussed.

Palaviccini, Miguel↗

NASA NTRS2016

Can Aerosol Direct Radiative Effects Account for Analysis Increments of Temperature in the Tropical Atlantic?

In the late 1990's, prior to the launch of the Terra satellite, atmospheric general circulation models (GCMs) did not include aerosol processes because aerosols were not properly monitored on a global scale and their spatial distributions were not known well enough for their incorporation in operational GCMs. At the time of the first GEOS Reanalysis (Schubert et al. 1993), long time series of analysis increments (the corrections to the atmospheric state by all available meteorological observations) became readily available, enabling detailed analysis of the GEOS-1 errors on a global scale. Such analysis revealed that temperature biases were particularly pronounced in the Tropical Atlantic region, with patterns depicting a remarkable similarity to dust plumes emanating from the African continent as evidenced by TOMS aerosol index maps. Yoram Kaufman was instrumental encouraging us to pursue this issue further, resulting in the study reported in Alpert et al. (1998) where we attempted to assess aerosol forcing by studying the errors of a the GEOS-1 GCM without aerosol physics within a data assimilation system. Based on this analysis, Alpert et al. (1998) put forward that dust aerosols are an important source of inaccuracies in numerical weather-prediction models in the Tropical Atlantic region, although a direct verification of this hypothesis was not possible back then. Nearly 20 years later, numerical prediction models have increased in resolution and complexity of physical parameterizations, including the representation of aerosols and their interactions with the circulation. Moreover, with the advent of NASA's EOS program and subsequent satellites, atmospheric aerosols are now monitored globally on a routine basis, and their assimilation in global models are becoming well established. In this talk we will reexamine the Alpert et al. (1998) hypothesis using the most recent version of the GEOS-5 Data Assimilation System with assimilation of aerosols. We will explicitly calculate the impact of aerosols on the temperature analysis increments in the tropical Atlantic and assess the extent to which inclusion of atmospheric aerosols have reduced these increments.

General Circulation Models↗

NASA NTRS1990

Macromolecular differentiation of Golgi stacks in root tips of Arabidopsis and Nicotiana seedlings as visualized in high pressure frozen and freeze-substituted samples

The plant root tip represents a fascinating model system for studying changes in Golgi stack architecture associated with the developmental progression of meristematic cells to gravity sensing columella cells, and finally to "young" and "old", polysaccharide-slime secreting peripheral cells. To this end we have used high pressure freezing in conjunction with freeze-substitution techniques to follow developmental changes in the macromolecular organization of Golgi stacks in root tips of Arabidopsis and Nicotiana. Due to the much improved structural preservation of all cells under investigation, our electron micrographs reveal both several novel structural features common to all Golgi stacks, as well as characteristic differences in morphology between Golgi stacks of different cell types. Common to all Golgi stacks are clear and discrete differences in staining patterns and width of cis, medial and trans cisternae. Cis cisternae have the widest lumina (approximately 30 nm) and are the least stained. Medial cisternae are narrower (approximately 20 nm) and filled with more darkly staining products. Most trans cisternae possess a completely collapsed lumen in their central domain, giving rise to a 4-6 nm wide dark line in cross-sectional views. Numerous vesicles associated with the cisternal margins carry a non-clathrin type of coat. A trans Golgi network with clathrin coated vesicles is associated with all Golgi stacks except those of old peripheral cells. It is easily distinguished from trans cisternae by its blebbing morphology and staining pattern. The zone of ribosome exclusion includes both the Golgi stack and the trans Golgi network. Intercisternal elements are located exclusively between trans cisternae of columella and peripheral cells, but not meristematic cells. In older peripheral cells only trans cisternae exhibit slime-related staining. Golgi stacks possessing intercisternal elements also contain parallel rows of freeze-fracture particles in their trans cisternal membranes. We propose that intercisternal elements serve as anchors of enzyme complexes involved in the synthesis of polysaccharide slime molecules to prevent the complexes from being dragged into the forming secretory vesicles by the very large slime molecules. In addition, we draw attention to the similarities in composition and apparent site of synthesis of xyloglucans and slime molecules.

NASA Program Space Biology↗

DOE OSTI2022

Understanding Solution State Conformation and Aggregate Structure of Conjugated Polymers via Small Angle X-ray Scattering

Donor-acceptor (D-A) conjugated polymers are high-performance organic electronic materials that exhibit complex aggregation behavior. Understanding the solution state conformation and aggregation of conjugated polymers is crucial for controlling morphology during thin-film deposition and the subsequent electronic performance. However, a precise multiscale structure of solution state aggregates is lacking. Here, we present an in-depth small-angle X-ray scattering (SAXS) analysis of the solution state structure of an isoindigo-bithiophene-based D-A polymer (PII-2T) in chlorobenzene and decane as our primary system. Modeling the system as a combination of hierarchical fibrillar aggregates mixed with dispersed polymers, we extract information about conformation and multiscale aggregation and also clarify the physical origin of features often observed but unaddressed or misinterpreted in small-angle scattering patterns of conjugated polymers. The persistence length of the D-A polymer extracted from SAXS agrees well with a theoretical model based on the dihedral potentials. Additionally, we show that the broad high q structure factor peak seen in scattering profiles can be attributed to lamellar stacking occurring within the fibril aggregates and that the low q aggregate scattering is strongly influenced by the polymer molecular weight. Overall, the SAXS profiles of D-A polymers in general exhibit a sensitive dependence on the co-existence of fibrillar aggregate and dispersed polymer chain populations. We corroborate our findings from SAXS with electron microscopy of freeze-dried samples for direct imaging of fibrillar aggregates. Finally, we demonstrate the generality of our approach by fitting the scattering profiles of a variety of D-A polymers based on thieno-isoindigo (PTII-2T), diketopyrrolopyrrole (DPP2T-TT, DPP-BTZ, PDPP2FT-C- 16 ), naphthalenediimide (P(NDI2OD-T2)), and a conjugated block copolymer P3HT-b-DPPT-T. The results presented here establish a picture of the D-A polymer solution state structure and provide a general method of interpreting and analyzing their scattering profiles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop

Abstract Background Despite their widespread distribution and ecological importance, protists remain one of the least understood components of the soil and rhizosphere microbiome. Knowledge of the roles that protists play in stimulating organic matter decomposition and shaping microbiome dynamics continues to grow, but there remains a need to understand the extent to which biological and environmental factors mediate protist community assembly and dynamics. We hypothesize that protists communities are filtered by the influence of plants on their rhizosphere biological and physicochemical environment, resulting in patterns of protist diversity and composition that mirror previously observed diversity and successional dynamics in rhizosphere bacterial communities. Results We analyzed protist communities associated with the rhizosphere and bulk soil of switchgrass (SG) plants ( Panicum virgatum ) at different phenological stages, grown in two marginal soils as part of a large-scale field experiment. Our results reveal that the diversity of protists is lower in rhizosphere than bulk soils, and that temporal variations depend on soil properties but are less pronounced in rhizosphere soil. Patterns of significantly prevalent protists groups in the rhizosphere suggest that most protists play varied ecological roles across plant growth stages and that some plant pathogenic protists and protists with omnivorous diets reoccur over time in the rhizosphere. We found that protist co-occurrence network dynamics are more complex in the rhizosphere compared to bulk soil. A phylogenetic bin-based null model analysis showed that protists’ community assembly in our study sites is mainly controlled by homogenous selection and dispersal limitation, with stronger selection in rhizosphere than bulk soil as SG grew and senesced. Conclusions We demonstrate that environmental filtering is a dominant determinant of overall protist community properties and that at the rhizosphere level, plant control on the physical and biological environment is a critical driver of protist community composition and dynamics. Since protists are key contributors to plant nutrient availability and bacterial community composition and abundance, mapping and understanding their patterns in rhizosphere soil is foundational to understanding the ecology of the root-microbe-soil system.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

Antennas Lower Cost of Satellite Access

Whether for scientific inquiry, weather forecasting, or public safety, the world relies upon the data gathered by satellite remote sensing. Some of NASA s most valuable work is in its remote sensing capabilities - the ability to retrieve data acquired at great distances - affording a height and scope not available from the ground. NASA satellites in low Earth orbit (LEO) monitor ocean health by taking large-scale pictures of phytoplankton blooms and measuring surface temperatures; snap photographs of full hurricanes from above, teaching researchers about how these giant storms form; and capture images of cloud formation and air pollution, all allowing researchers to further develop understanding of the planet s health. NASA remote sensing satellites also monitor shifts in the Earth s crust, analyze wind patterns around the world to develop efficient wind energy, help people around the world recover from natural disasters, and monitor diminishing sea ice levels. Just as researchers are more heavily relying on this data from space to conduct their work, the instruments carried on satellites are getting more sophisticated and capable of capturing increasingly complex and accurate measurements. The satellites are covering larger areas, from farther away, and generating more and more valuable data. The ground-based receivers for this wealth of satellite data have grown increasingly capable of handling greater bandwidth and higher power levels. They have also become less expensive, through a NASA research partnership, with the creation of a high-rate X-band data receiver system that is now in widespread use around the globe.

Source record↗

DOE OSTI2021

Engineering Forisome Scaffolds: Elucidating Spatial Self-Assembly Patterning of Bio-inorganic Complexes

Organisms can synthesize biomaterials incorporating an array of naturally occurring elements while overcoming challenges and insults. Although, it is known that most cellular biomaterials are synthesized in specialized cellular compartments, there are knowledge gaps about how organic/inorganic biomaterial synthesis is orchestrated inside cells. In addition, there is great potential in understanding how individual monomers can self-assembly into organized patterns to form responsive biomaterials. Forisomes are a natural responsive biomaterial found in legume plants that serve as a plug sieve element in the plant phloem that undergo anisotropic conformational changes by rapid (<1 s) ATP-independent from condensed spindle to plug-like form, triggered by the influx of Ca 2+ . Addressing principles of forisome synthesis and assembly will determine how biomaterials containing inorganic elements self-assemble and conduct chemical modification to produce biomaterials or undergo biomineralization. We employ transcription and translation (TXTL) using cell-free expression systems for forisome monomer expression, self-assembly, and pattern probing. We conducted experiments to precisely control forisome proteins synthesis of various monomers SEO1, SEO2, SEO3, and SEO4 to explore self- assembly. We demonstrate forisome self-assembly of the SEO monomers is possible and indicate unique monomer fluorescent labeling patterns that require additional analysis. We investigated locations and linkers for adding tetracysteine tag fluorophore probes to determine impacts of self-assembly and anisotropic conformational changes.

42 ENGINEERING↗

DOE OSTI2025

MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets, or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data is multi-dimensional and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries don’t allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to effortlessly sort and filter plots by categorical or numerical variables. MODE allows users to build and share these displays with several options for plot design and meta selection. In conclusion, the MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.

lipidomics↗

DOE OSTI2025

Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability

Snow plays a critical role in carbon cycling, vegetation dynamics, and permafrost hydrology at high latitudes by influencing surface energy exchange. Predicting snow distribution patterns is essential for understanding the evolution of Arctic ecosystems, yet scaling process-level knowledge to landscape predictions remains challenging. Here, we analyze snow depth (2019 and 2022), terrain elevation, and vegetation height from a watershed on the Seward Peninsula, Alaska, to examine how topography and shrubs shape snow redistribution across spatial scales. We find that snow depth is strongly coupled to terrain at scales below ∼60 m but becomes increasingly decoupled at larger scales. The topographic model of snow depth variation, which transforms terrain data to align with these scale-dependent snow patterns, is well correlated with local snow depth variations (linear fit R 2 > 0.5 for 85% of 100-m patches). A machine learning reconstruction of shrub canopy snow trapping reveals a simple exponential relationship between canopy structure and snow accumulation ( R 2 = 0.59), highlighting the combined influence of topography and vegetation on snow distribution. Together, these empirical relationships capture much of the observed snow variability in the watershed ( R 2 = 0.49, root mean square error (RMSE) = 30 cm), though systematic limitations persist in areas of strong scour and at coarser scales where wind-terrain interactions are more complex. These findings provide a framework for more efficient snow depth prediction and offer insights to improve snow-vegetation feedback representation in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗