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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Large spatiotemporal variability in aerosol properties over central Argentina during the CACTI field campaign

Abstract. Few field campaigns with extensive aerosol measurements have been conducted over continental areas in the Southern Hemisphere. To address this data gap and better understand the interactions of convective clouds and the surrounding environment, extensive in situ and remote sensing measurements were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign conducted between October 2018 and April 2019 over the Sierras de Córdoba range of central Argentina. This study describes measurements of aerosol number, size, composition, mixing state, and cloud condensation nuclei (CCN) collected on the ground and from a research aircraft during 7 weeks of the campaign. Large spatial and multiday variations in aerosol number, size, composition, and CCN were observed due to transport from upwind sources controlled by mesoscale to synoptic-scale meteorological conditions. Large vertical wind shears, back trajectories, single-particle measurements, and chemical transport model predictions indicate that different types of emissions and source regions, including biogenic emissions and biomass burning from the Amazon and anthropogenic emissions from Chile and eastern Argentina, contribute to aerosols observed during CACTI. Repeated aircraft measurements near the boundary layer top reveal strong spatial and temporal variations in CCN and demonstrate that understanding the complex co-variability of aerosol properties and clouds is critical to quantify the impact of aerosol–cloud interactions. In addition to quantifying aerosol properties in this data-sparse region, these measurements will be valuable to evaluate predictions over the midlatitudes of South America and improve parameterized aerosol processes in local, regional, and global models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign

Abstract. A high-resolution soot particle aerosol mass spectrometer (SP-AMS) was used to selectively measure refractory black carbon (rBC) and its associated coating material using both the ensemble size-resolved mass spectral mode and the event trigger single particle (ETSP) mode in Houston, Texas, in summer 2022. This study was conducted as part of the Department of Energy Atmospheric Radiation Measurement (ARM) program's TRacking Aerosol Convection interactions ExpeRiment (TRACER) field campaign. The study revealed an average (±1σ) rBC concentration of 103 ± 176 ng m−3. Additionally, the coatings on the BC particles were primarily composed of organics (59 %; 219 ± 260 ng m−3) and sulfate (26 %; 94 ± 55 ng m−3). Positive matrix factorization (PMF) analysis of the ensemble mass spectra of BC-containing particles resolved four distinct types of soot aerosol, including an oxidized organic aerosol (OOABC,PMF) factor associated with processed primary organic aerosol, an inorganic sulfate factor (SO4,BC,PMF), an oxidized rBC factor (O-BCPMF), and a mixed mineral dust–biomass burning aerosol factor with significant contribution from potassium (K-BBBC,PMF). Additionally, K-means clustering analysis of the single-particle mass spectra identified eight different clusters, including soot particles enriched in hydrocarbon-like organic aerosol (HOABC,ETSP), sulfate (SO4,BC,ETSP), two types of rBC, OOA (OOABC,ETSP), chloride (ClBC,ETSP), and nitrate (NO3,BC,ETSP). The single-particle measurements demonstrate substantial variation in BC coating thickness with coating-to-rBC mass ratios ranging from 0.1 to 100. The mixing state index (χ), which denotes the degree of homogeneity of the soot aerosol, varied from 4 % to 94 % with a median of 40 %, indicating that the aerosol population lies in between internal and external mixing but has large temporal and source type variability. In addition, a significant fraction of BC-containing particles, a majority enriched with oxidized organics and sulfate, exhibit sufficiently high κ values and diameters conducive to activation as cloud nuclei under atmospherically relevant supersaturation conditions. This finding bears significance in comprehending the aging processes of rBC-containing particles and their activation into cloud droplets. Our analysis highlights the complex nature of soot aerosol and underscores the need to comprehend its variability across different environments for accurate assessment of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Panel Session 128: Best Practices in Project Communications

This panel focused on a sustainable, holistic and integrated approach to structure nuclear waste management, disposal and remediation decisions. This panel had participation from internal and external stakeholders and demonstrate use of tools and techniques to effectively communicate factors that inform decisions. The desired outcome was to effectively communicate how risk is evaluated at complex sites, highlight holistic site level approaches that are protective of human health and the environment, and incorporate policy and technical concerns related to achieving alternative end points. This discussion included existing decision-making tools in conjunction with case studies. Panelists with presentations: Removing the Cloak of Secrecy from Legacy Cleanup at Los Alamos National Laboratory (David Nickless); Best Practice In Project Communications (Alan Paulley); Stakeholder and Public Engagement (Mark Lesinski); Stakeholder Engaged Structured Decision Making (Kelly Black)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Localized High Concentration Electrolytes for High Voltage Lithium-Metal Batteries: Correlation between the Electrolyte Composition and Its Reductive/Oxidative Stability

We demonstrate a first-principles screening methodology as an effective tool to explore electrolyte formulations for the new generation of high energy density rechargeable batteries. We study the liquid structure and electronic properties in dilute electrolytes, high concentration electrolytes (HCE), and localized high concentration electrolytes (LHCE), with focus on electrolyte formulations based on lithium bis(fluorosulfonyl)imide (LiFSI), dimethyl carbonate (DMC), and bis(2,2,2-trifluoroethyl) ether (BTFE) as a diluent. We describe the solvation complexes in the dilute electrolyte and explore structural changes triggered by the increase in lithium salt concentration for HCEs and the diluent effects in LHCEs. In HCE formulations, there is a 4-fold coordination environment of lithium-ions as in the dilute electrolyte, but the number of lithium-ion interactions with O atoms from FSI- anions dominates. In these solutions, the ability of the FSI- anions to interact with multiple lithium-ions allows complex 3D network formation and influences the reductive/oxidative behavior of the electrolyte. Interestingly, in LHCEs, the BTFE diluent molecules do not change the 3D solution structure when diluting the HCE formulation from 5.49 to 3.83 M. However, there is a composition threshold where the structural and electronic behavior may change. We show that diluting the HCE electrolyte with BTFE down to 1.77 M breaks the three-dimensional solution structure into an island-like solvation complex. We relate these structural changes to the electronic properties of the electrolytes finding a causal relationship between the reductive/oxidative behavior and the lithium-oxygen interaction mechanisms in the solvated complexes. The coordination with lithium-ions lowers the electrolyte LUMO and HOMO levels: the higher is the number of interactions with lithium-ions, the more likely the solvent molecule, FSI- anion, or diluent molecule is to be reduced and the less likely it is to become oxidized. The evolution of the solvated ion structure in HCE and LHCE suggests a close connection to a corresponding change in the lithium-ion transport mechanisms for these electrolytes.

anions, molecules, oxygen, solvents, electrolytes↗
DOE OSTI2020

TeraChem Cloud: A High-Performance Computing Service for Scalable Distributed GPU-Accelerated Electronic Structure Calculations

The encapsulation and commoditization of electronic structure arise naturally as interoperability, and the use of nontraditional compute resources (e.g., new hardware accelerators, cloud computing) remains important for the computational chemistry community. Here, we present TERACHEM CLOUD, a high-performance computing service (HPCS) that offers on-demand electronic structure calculations on both traditional HPC clusters and cloud-based hardware. The framework is designed using off-the-shelf web technologies and containerization to be extremely scalable and portable. Within the HPCS model, users can quickly develop new methods and algorithms in an interactive environment on their laptop while allowing TERACHEM CLOUD to distribute ab initio calculations across all available resources. This approach greatly increases the accessibility of hardware accelerators such as graphics processing units (GPUs) and flexibility for the development of new methods as additional electronic structure packages are integrated into the framework as alternative backends. Cost-performance analysis indicates that traditional nodes are the most cost-effective long-term solution, but commercial cloud providers offer cutting-edge hardware with competitive rates for short-term large-scale calculations. We demonstrate the power of the TERACHEM CLOUD framework by carrying out several showcase calculations, including the generation of 300,000 density functional theory energy and gradient evaluations on medium-sized organic molecules and reproducing 300 fs of nonadiabatic dynamics on the B800-B850 antenna complex in LH2, with the latter demonstration using over 50 Tesla V100 GPUs in a commercial cloud environment in 8 h for approximately $1250.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗
DOE OSTI2022

Robotic Planning under Uncertainty in Spatiotemporal Environments in Expeditionary Science

In the expeditionary sciences, spatiotemporally varying environments -- hydrothermal plumes, algal blooms, lava flows, or animal migrations -- are ubiquitous. Mobile robots are uniquely well-suited to study these dynamic, mesoscale natural environments. We formalize expeditionary science as a sequential decision-making problem, modeled using the language of partially-observable Markov decision processes (POMDPs). Solving the expeditionary science POMDP under real-world constraints requires efficient probabilistic modeling and decision-making in problems with complex dynamics and observational models. Previous work in informative path planning, adaptive sampling, and experimental design have shown compelling results, largely in static environments, using data-driven models and information-based rewards. However, these methodologies do not trivially extend to expeditionary science in spatiotemporal environments: they generally do not make use of scientific knowledge such as equations of state dynamics, they focus on information gathering as opposed to scientific task execution, and they make use of decision-making approaches that scale poorly to large, continuous problems with long planning horizons and real-time operational constraints. In this work, we discuss these and other challenges related to probabilistic modeling and decision-making in expeditionary science, and present some of our preliminary work that addresses these gaps. We ground our results in a real expeditionary science deployment of an autonomous underwater vehicle (AUV) in the deep ocean for hydrothermal vent discovery and characterization. Our concluding thoughts highlight remaining work to be done, and the challenges that merit consideration by the reinforcement learning and decision-making community.

Preston, Victoria↗
DOE OSTI2026

The scientific case for concurrent neutron and X-ray scattering and spectroscopy

The interrogation of materials with X-rays or neutrons to determine structure, energetics, and dynamics is fundamental to advancing physical and chemical materials science and enabling innovative material technologies. A persistent challenge in materials development is that progress depends on understanding structure and dynamics across multiple length and time scales in increasingly complex, multicomponent systems featuring interfaces, heterogeneity, and hierarchical organization. Despite rapidly growing demands on materials characterization, current experimental approaches are almost exclusively based on isolated X-ray or neutron scattering and spectroscopy, reflecting a paradigm largely unchanged for decades. To assess the scientific need for a new experimental paradigm, a 3-day workshop sponsored by the U.S. National Science Foundation (NSF) was held at the SpringHill Suites, San Jose, California, from June 2 to 4, 2022. The workshop brought together 70 national and international experts who critically evaluated opportunities enabled by concurrent neutron and X-ray (NeX) scattering, spectroscopy, and imaging experiments. The participants reached a clear consensus that establishing NeX capabilities is crucial for advancing the science of complex materials in the United States. This report illustrates the scientific drivers for NeX experiments through representative examples spanning biomaterials, energy materials, soft matter, nanomaterials, quantum materials, geoscience, and applied materials research. The complementarity of neutrons and X-rays is essential for robust model development and refinement, particularly in multiphase and multicomponent systems. While joint refinement of data from separate experiments is valuable, concurrent measurements uniquely eliminate uncertainties arising from sample evolution, environmental drift, and irreproducibility associated with experiments performed at different locations and times. Realizing NeX capabilities will require the development of new instrumentation, data analysis frameworks, and robust sample environments compatible with both neutron and X-ray probes. Addressing these challenges will enable unambiguous interpretation of complex materials behavior and open new frontiers in materials research.

X-ray↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Compositionally-driven surface nanostructuring on refractory compositionally complex alloys under low energy helium bombardment

Additive manufacturing enables user-defined control of the compositional complexity, opening new design spaces for complex concentrated alloys (CCAs). Refractory CCAs may offer enhanced performance in the divertor region of a fusion reactor environment where plasma-facing materials will be subject to high temperatures, low energy He and D particles, and 14 MeV neutrons. In this work, specimens with the nominal composition of NbTaMoTi, NbTaMo, NbTaTi, and NbTa were fabricated via directed energy deposition (DED) then bombarded with 40 eV He ions to a fluence of 2x10 26 m −2 at ∼ 1000 K. Post irradiation, the surface morphology and composition were examined with electron microscopy and x-ray photoelectron spectroscopy to offer information on the spatial distribution of surface nano-structuring. Sub-surface He bubbles driving the nano-structuring were examined with electron microscopy techniques. Analysis indicates the local composition directly influences the He bubble and tendril size, while the region with the highest complexity showed the shortest nano-structuring. Molecular Dynamics simulations complement the experimental results, showing comparable helium bubble growth and migration as a function of compositional complexity. This work demonstrates the compositional dependence of surface nanostructure formation of compositionally complex alloys, important for future design of complex plasma facing materials in fusion reactors.

Complex concentrated alloys↗
DOE OSTI2025

U 4+/5+/6+ in a Conserved Pseudotetrahedral Imidophosphorane Coordination Sphere

While several ligand systems support uranium across a range of oxidation states, spanning more than two oxidation states in a conserved coordination geometry is uncommon among structurally authenticated complexes. Imidophosphorane ligands significantly stabilize high-valent lanthanide and actinide complexes. Here, we report a series of homoleptic uranium imidophosphorane complexes, spanning the +4, +5 and +6 oxidation states in a four-coordinate pseudotetrahedral ligand field. The +6 oxidation state is accessible using a mild ferrocenium oxidant, yielding a rare example of U6+ in a pseudotetrahedral coordination environment. As the formal oxidation state increases, the U–N distances gradually contract, consistent with the Shannon ionic radii of U 4+/5+/6+ . Compared to reported complexes, the short U–N distances observed in the U 6+ complex are more comparable to dianionic imido ligands than monoanionic amido ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A multidimensional approach to quantum state tomography of photoelectron wavepackets

There is a growing interest in reconstructing the density matrix of photoelectron wavepackets, in particular in complex systems where decoherence can be introduced either by a partial measurement of the system or through coupling with a stochastic environment. To this end, several methods to reconstruct the density matrix, quantum state tomography protocols, have been developed and tested on photoelectrons ejected from noble gases following absorption of extreme ultraviolet (XUV) photons from attosecond pulses. It remains a challenge to obtain model-free, single scan protocols that can reconstruct the density matrix with high fidelities. Current methods require extensive measurements or involve complex fitting of the signal. Efficient single-scan reconstructions would be of great help to increase the number of systems that can be studied. We propose a new and more efficient protocol that is able to reconstruct the continuous variable density matrix of a photoelectron in a single time delay scan. It is based on measuring the coherences of a photoelectron created by absorption of an XUV pulse using a broadband infrared (IR) probe that is scanned in time and a narrowband IR reference that is temporally fixed to the XUV pulse. We illustrate its performance for a Fano resonance in He as well as mixed states in Ar arising from spin-orbit splitting. We show that the protocol results in excellent fidelities and near-perfect estimation of the purity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Non-additive microbial community responses to environmental complexity

Environmental composition is a major, though poorly understood, determinant of microbiome dynamics. Here we ask whether general principles govern how microbial community growth yield and diversity scale with an increasing number of environmental molecules. By assembling hundreds of synthetic consortia in vitro, we find that growth yield can remain constant or increase in a non-additive manner with environmental complexity. Conversely, taxonomic diversity is often much lower than expected. To better understand these deviations, we formulate metrics for epistatic interactions between environments and use them to compare our results to communities simulated with experimentally-parametrized consumer resource models. We find that key metabolic and ecological factors, including species similarity, degree of specialization, and metabolic interactions, modulate the observed non-additivity and govern the response of communities to combinations of resource pools. Our results demonstrate that environmental complexity alone is not sufficient for maintaining community diversity, and provide practical guidance for designing and controlling microbial ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

XANES reflects coordination change and underlying surface disorder of zinc adsorbed to silica

In this work, zinc K -edge X-ray absorption near-edge structure (XANES) spectroscopy of Zn adsorbed to silica and Zn-bearing minerals, salts and solutions was conducted to explore how XANES spectra reflect coordination environment and disorder in the surface to which a metal ion is sorbed. Specifically, XANES spectra for five distinct Zn adsorption complexes (Zn ads ) on quartz and amorphous silica [SiO 2(am) ] are presented from the Zn–water–silica surface system: outer-sphere octahedral Zn ads on quartz, inner-sphere octahedral Zn ads on quartz, inner-sphere tetrahedral Zn ads on quartz, inner-sphere octahedral Zn ads on SiO 2(am) and inner-sphere tetrahedral Zn ads on SiO 2(am) . XANES spectral analysis of these complexes on quartz versus SiO 2(am) reveals that normalized peak absorbance and K -edge energy position generally decrease with increasing surface disorder and decreasing Zn–O coordination. On quartz, the absorption-edge energy of Zn ads ranges from 9663.0 to 9664.1 eV for samples dominated by tetrahedrally versus octahedrally coordinated species, respectively. On SiO 2(am) , the absorption-edge energy of Zn ads ranges from 9662.3 to 9663.4 eV for samples dominated by tetrahedrally versus octahedrally coordinated species, respectively. On both silica substrates, octahedral Zn ads presents a single K -edge peak feature, whereas tetrahedral Zn ads presents two absorbance features. The energy space between the two absorbance peak features of the XANES K -edge of tetrahedral Zn ads is 2.4 eV for Zn on quartz and 3.2 eV for Zn on SiO 2(am) . Linear combination fitting of samples with a mixture of Zn ads complex types demonstrates that the XANES spectra of octahedral and tetrahedral Zn ads on silica are distinct enough for quantitative identification. These results suggest caution when deciphering Zn speciation in natural samples via linear combination approaches using a single Zn ads standard to represent sorption on a particular mineral surface. Correlation between XANES spectral features and prior extended X-ray absorption fine structure (EXAFS) derived coordination environments for these Zn ads on silica samples provides insight into Zn speciation in natural systems with XANES compatible Zn concentrations too low for EXAFS analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Polymer Canvas with the Stiffness of the Bone Matrix to Study and Control Mesenchymal Stem Cell Response

Reproducing in vitro the complex multiscale physical features of human tissues creates novel biomedical opportunities and fundamental understanding of cell–environment interfaces and interactions. While stiffness has been recognized as a key driver of cell behavior, systematic studies on the role of stiffness have been limited to values in the KPa–MPa range, significantly below the stiffness of bone. Here, in this work, a platform enabling the tuning of the stiffness of a biocompatible polymeric interface up to values characteristic of human bone is reported, which are in the GPa range, by using extremely thin polymer films on glass and cross-linking the films using ultraviolet (UV) light irradiation. It is shown that a higher stiffness is related to better adhesion, proliferation, and osteogenic differentiation, and that it is possible to switch on/off cell attachment and growth by solely tuning the stiffness of the interface, without any surface chemistry or topography modification. Since the stiffness is tuned directly by UV irradiation, this platform is ideal for rapid and simple fabrication of stiffness patterns and gradients, thus representing an innovative tool for combinatorial studies of the synergistic effect of tissue environmental cues on cell behavior, and creates new opportunities for next-generation biosensors, single-cell patterning, and lab-on-a-chip devices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Optimizing Cryo-Focused Pyrolysis GC/MS for Tracing Soil Organic Matter Across Diverse Ecosystems

The cycling of organic matter in terrestrial soils and sediments is central to a range of biogeochemical processes that regulate nutrient cycling, crop productivity, trace gas emissions, and contaminant transport. Pyrolysis-gas chromatography/mass spectrometry (py-GC/MS) is a powerful tool for characterizing bulk soil organic matter (SOM) at the molecular level. In this study, we used a cryo-focused py-GC/MS system to analyze soil samples from seven diverse ecosystems: vernal pool, prairie pothole, temperate forest, tropical forest, tundra, wildfire-affected boreal forest, and grassland. We addressed a key bottleneck in molecular-level SOM characterization by developing an automated data analysis pipeline to optimize py-GC/MS and complementary evolved gas analysis/mass spectrometry (EGA/MS) methods, incorporating advanced tools for peak deconvolution, developing a custom compound class library, and implementing fragmentation spectrum-based molecular networking for the first time. This improved workflow was applied to soil samples from all seven ecosystems, including multiple depths and density fractions. Our findings demonstrate that ecosystem type plays a dominant role in shaping compositional differences in SOM. We also identified trends in the source of SOM compounds (e.g., microbial vs plantderived) across soil depth and density fractions, which are critical for understanding persistence and turnover of SOM. Our molecular networking analysis indicated that although many compounds are widespread across ecosystems, others are restricted to specific environments, such as wetlands. This underscores the utility of molecular-level data in elucidating the complexity of SOM composition and the environmental drivers that shape it. Such molecular-level insights can deepen our knowledge of biogeochemical SOM cycles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Influence of Electronic Polarization on the Spectral Density

Accurate spectral densities are necessary for computing realistic exciton dynamics and nonlinear optical spectra of chromophores in condensed-phase environments, including multichromophore pigment–protein systems. However, due to the significant computational cost of computing spectral densities from first principles, requiring many thousands of excited-state calculations, most simulations of realistic systems rely on treating the environment as fixed-point charges. In this work, using a number of representative systems ranging from solvated chromophores to the photoactive yellow protein (PYP), we demonstrate that the quantum mechanical (QM) electronic polarization of the environment is key to obtaining accurate spectral densities and line shapes within the cumulant framework. We show that the QM environment can enhance or depress the coupling of fast chromophore degrees of freedom to the energy gap, altering the electronic–vibrational coupling and the resulting vibronic progressions in the absorption spectrum. In analyzing the physical origin of peaks in the spectral density, we identify vibrational modes that couple the electron and the hole as being particularly sensitive to the QM screening of the environment. For PYP, we reveal the need for careful determination of the appropriate QM region to obtain reliable spectral densities. Our findings suggest that the QM polarization of the environment can be crucial not just for excitation energies but also for electronic–vibrational coupling in complex systems with implications for the correct modeling of linear and nonlinear optical spectroscopy in the condensed phase as well as energy transfer in pigment–protein complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗