Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Spatial process”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simultaneous x-ray and neutron 4D tomographic study of drying-driven hydro-mechanical behavior of cement-based materials at moderate temperatures

The drying of cement-based materials is intimately related to their durability, which has significant economic, social and environmental repercussions. The evolution of the saturation of the pore network and the associated drying shrinkage are in fact leading causes of cracking and of the ensuing penetration of aggressive chemicals. This process is highly heterogeneous, due to the thermo-hydric spatial gradients developing in the material from the exposed surfaces to its core and because of local effects, driven by the intrinsically heterogeneous micro-structure (e.g., by the distribution of pores and aggregates). It follows that macroscopic, sample-scale measurements cannot fully disclose the complexity of the underlying processes. In the last few decades, significant advances in full-field techniques have allowed an unprecedented insight into these local processes. For cement-based materials, x-ray and neutron tomography lend themselves as ideal, and highly complementary, tools for the study of their thermo-hydro-mechanical behavior. Notably, the high sensitivity to density variations of x-ray imaging gives access to the developments of fractures, in 4D (3D + time). On the other hand, neutron tomography allows the study of the evolution of the moisture field in 4D, thanks to its high hydrogen sensitivity. The combination of these two techniques provides a unique insight in thermo-hydro-mechanical couplings, e.g., the effect of cracks on the water content field. This contribution presents novel 5D datasets (3D tomographies along time, plus truly simultaneous x-ray and Neutron rapid acquisitions) in-operando of a cement paste and of a concrete sample heated at moderate temperatures (up to 140{sup ∘}C). The analysis of this 5D data-set (once aligned in time and across modalities) allows for example a quantification of the 4D moisture profiles which were found to predict an overall water loss at hydric equilibrium coherent with the corresponding analytical analysis. In the cement paste sample, the x-ray dataset captures the evolution of an extensive cracking network, opening and propagation toward the core of the sample. A novel analysis procedure is here proposed which allows the extraction of these fractures and the analysis of their interplay with local drying as captured through neutron imaging. This for example reveals the depth of penetration of drying in the vicinity of the fractures along time, which is essential for the assessment and calibration of hydro-mechanical coupled models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimization of WAAM Process to Produce AUSC Components with Increased Service Life

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Longitudinal Patterns in Nitrate Revealed Through Drone‐Based Measurements in an Agriculturally Influenced Midwestern U.S. River

Nitrate concentrations in streams and rivers in the Midwestern United States are often elevated, reflecting the predominance of agriculture in the surrounding landscape. Recent advances in technology, including surface water drones and more precise sensors, provide opportunities to investigate nitrate dynamics with high spatial and temporal resolution. We deployed an aquatic drone, the AquaBOT, in a sixth-order, agriculturally influenced river to examine longitudinal patterns in water quality. Our goal was to measure the spatial and temporal heterogeneity in nitrate and nitrate removal processes and determine the influence of tributary inputs on main stem chemistry. We navigated the drone along a 12-km reach of the Des Moines River (Iowa, USA) nine times between June 2021 and August 2022. Across the deployments, mean nitrate concentration was positively related to discharge and was nearly two orders of magnitude higher in spring than summer. We observed contrasting patterns in main stem nitrate, which decreased downstream during some runs (e.g., 3.1–2.7 mg N L −1 in June 2021), demonstrating net nitrate uptake along the reach, and remained constant on other dates. Similarly, tributaries to the Des Moines had a varied influence on riverine nitrate. Tributaries either increased or decreased main stem nitrate concentrations depending on the tributary and the date. Nitrate removal rates were spatially and temporally variable but showed some consistency at the subreach (2 km) scale, with two subreaches often showing elevated rates of nitrate removal across dates. In conclusion, our study reveals nuanced heterogeneity in nitrate dynamics of the Des Moines River despite the homogeneity of agricultural land cover in the watershed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High-throughput homogenization of a quasi-Gaussian ultrafast laser beam using a combined refractive beam shaper and spatial light modulator

Efficiently shaping femtosecond, transverse Gaussian laser beams to flat-top beams with flat wavefronts is critical for large-scale material processing and manufacturing. Existing beam shaping devices fall short either in final beam homogeneity or efficiency. Here, we present an approach that uses refractive optics to perform the majority of the beam shaping and then uses a fine-tune device (spatial light modulator) to refine the intensity profile. For the beam that we selected, circularly asymmetric with intensity fluctuations, our method achieved a uniformity of 0.055 within 90% of the beam area at 92% efficiency. The optimization involved an iterative beam shaping process that converged to optimum within 10 iterations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Verification and validation of the open-source plasma fluid code: Zapdos

Zapdos is an open-source finite element plasma fluid solver based on the MOOSE multiphysics framework. This paper outlines Zapdos verification, benchmarking, and validation efforts for 1D and 2D RF capacitively coupled plasma discharge models for mid-range pressures (0.1 - 1 Torr). The verification process involved using the method of manufactured solutions to assess Zapdos spatial and temporal error convergence. L2 errors ranged from 10 -2 to 10 -4 , while the convergence’s slope were in agreement with the predicted slopes for the tested variable and time integration orders. The benchmarking process involved comparisons to previously results from the validated finite element code, LSODI. These works included 1D and 2D simulations for a range of plasma parameters (densities, temperatures, voltage, etc.). For the 1D cases, Zapdos and LSODI results were in very good agreement. In the 2D cases, variable behaviors matched, with slight discrepancies in peak values. The validation process involved comparisons to experimental works including electron density measurements by microwave interferometry and metastable density measurements by planar laser-induced fluorescence imaging. Results shown reasonable agreement at higher pressure, with results starting to diverge at low pressures. Furthermore, probable causes for this diverges are the limitation of the fluid assumption for plasmas at low pressure, or the need for more robust boundary conditions. Overall, Zapdos shown reasonable results for the verification, benchmarking, and validation efforts, and Zapdos can be downloaded at https://github.com/shannon-lab/zapdos.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2025

Initial Stage of Nanoscale Imaging in Positive Tone Extreme UV Photoresists: The Influence of the Polymer Sequence

Photolithographic patterning using extreme ultraviolet (EUV, 92.5 eV) light is a radiolytic process that initially forms electrons, radical cations, anions, and neutral radicals in the polymeric photoresist matrix. These species may participate in the chemical reactions that define the ultimate resolution of the printed image, and their concentrations and nanometer-scale stochastic variations in their formation influence printed image quality. Proposals have been made that polymer chain uniformity may be advantageous in reducing stochastics due to spatial inhomogeneities, and this aspect of radiolysis is examined in this work. We have simulated the initial subpicosecond stages of the imaging process for a series of photoresist films that are identical in composition but vary in their polymer chain structures. We use detailed, physically accurate stochastic reaction-diffusion calculations to evaluate the influence of defined sequence and random copolymer structures on radiolytic spur formation, i.e., a cluster of species formed by electron-polymer interactions that defines the initial spatial characteristic of the imaging process. Predictions of electron thermalization in the present work are shown to be consistent with the literature, indicating that our overall computational approach for ultrafast nanoscale processes is sound. The computational results show that the polymer sequence has no significant effect on the spur composition. This suggests that any potential imaging improvements to be gained by sequence control must originate from postimaging lithographic process steps.

Absorption↗
DOE OSTI2022

Fatigue crack growth resistance of a mesoscale composite microstructure Haynes 282 fabricated via electron beam melting additive manufacturing

Materials synthesis via additive manufacturing gives unprecedented control over the solidified microstructure. While a number of studies have demonstrated the ability to produce spatially varying microstructures, little work exists to understand the behavior of such “composite” materials. In this work, we utilized electron beam melting to process Ni-based superalloy Haynes 282 and produce compact tension samples with a spatially varying mesoscale structure. Fatigue crack growth experiments reveal that the crack growth rate is dependent on the degree of microstructural heterogeneity. This work demonstrates that the crack growth resistance can be tailored within a component using electron beam melting additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reaction heterogeneity in practical high-energy lithium–sulfur pouch cells

The lithium–sulfur (Li–S) battery is a promising next-generation energy storage technology because of its high theoretical energy and low cost. Extensive research efforts have been made on new materials and advanced characterization techniques for mechanistic studies. However, it is uncertain how discoveries made on the material level apply to realistic batteries due to limited analysis and characterization of real high-energy cells, such as pouch cells. Evaluation of pouch cells (>1 A h) (instead of coin cells) that are scalable to practical cells provides a critical understanding of current limitations which enables the proposal of strategies and solutions for further performance improvement. Herein, we design and fabricate pouch cells over 300 W h kg -1 , compare the cell parameters required for high-energy pouch cells, and investigate the reaction processes and their correlation to cell cycling behavior and failure mechanisms. Spatially resolved characterization techniques and fluid-flow simulation reveal the impacts of the liquid electrolyte diffusion within the pouch cells. We found that catastrophic failure of high-energy Li–S pouch cells results from uneven sulfur/polysulfide reactions and electrolyte depletion for the first tens of cycles, rather than sulfur dissolution as commonly reported in the literature. The uneven reaction stems from limited electrolyte diffusion through the porous channels into the central part of thick cathodes during cycling, which is amplified both across the sulfur electrodes and within the same electrode plane. A combination of strategies is suggested to increase sulfur utilization, improve nanoarchitectures for electrolyte diffusion and reduce consumption of the electrolytes and additives.

25 ENERGY STORAGE↗
DOE OSTI2020

High-speed x-ray phase contrast imaging and digital image correlation analysis of microscale shock response of an additively manufactured energetic material simulant

The performance of energetic materials subjected to dynamic loading significantly depends on their micro- and meso-scale structural morphology. The geometric versatility offered by additive manufacturing opens new pathways to tailor the performance of these materials. Additively manufactured energetic materials (AMEMs) have a wide range of structural characteristics with a hierarchy of length scales and process-inherent heterogeneities, which are hitherto difficult to precisely control. It is important to understand how these features affect AMEMs’ response under dynamic/shock loading. Therefore, temporally and spatially resolved measurements of both macroscopic behavior and micro- and meso-level processes influencing macroscopic behavior are required. In this paper, we analyze the shock compression response of an AMEM simulant loaded under several impact conditions and orientations. Furthermore, x-ray phase contrast imaging (PCI) is used to track features across the observed shock front and determine the linear shock velocity vs particle velocity equation of state, as well as to quantify the interior deformation fields via digital image correlation (DIC) analyses. Photon Doppler velocimetry is simultaneously used to measure the particle velocities of the specimens, which are consistent with those obtained from x-ray PCI. The DIC analyses provide an assessment of the average strain fields inside the material, showing that the average axial strain depends on the loading intensity and reaches as high as 0.23 for impact velocities up to 1.5 km/s. The overall results demonstrate the utility of x-ray PCI for probing “in-material” equation of state and interior strains associated with dynamic shock compression behavior of the AMEM simulant.

3D printing↗
DOE OSTI2023

Linking transportation agent-based model ($\mathrm{ABM}$) outputs with micro-urban social types ($\mathrm{MUSTs}$) via typology transfer for improved community relevance

The human relationship with transportation is shaped by social, economic, demographic, and urban form variables, or socio-spatial factors. The spatial dynamics of these are key to generating and interpreting outputs of transportation models that are most relevant for a community and the diverse mobility needs of its members. Here we present a typology transfer framework, grounded in socio-spatial dynamics shaping people's mobility, to take transportation-themed regional mobility model outcomes, in this case from two agent-based models (ABMs), and extrapolate them to other cities, with less time and resource intensity than new ABM development. The typology transfer process first identifies micro-urban social types (MUSTs) using socio-spatial factors, then defines city types based on spatial patterns of MUSTs to assess across which cities transfer results are likely to best hold. Lastly, a typology transfer multiplier matrix extrapolates a given variable, in our case the Mobility Energy Productivity (MEP) metric, to another city. The full process demonstration uses ABM results from Chicago (POLARIS model) and San Francisco (BEAM model), applying them to New York City. We discuss how MEP or other outputs can be appropriately estimated and used for integrated, human-centered mobility analysis. Key findings include that this MUST framework of user-defined dependent and independent variables allows tailoring ABM results and interpretations to specific community needs and data availability. Findings clarify that positive outcomes can be targeted towards user groups, based on sociospatial characteristics, using a typology approach, such as inclusive access to mobility choices, transportation affordability, and greater efficiency in resource use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Atomic force microscopy: Emerging illuminated and operando techniques for solar fuel research

Integrated photoelectrochemical devices rely on the synergy between components to efficiently generate sustainable fuels from sunlight. The micro- and/or nanoscale characteristics of the components and their interfaces often control critical processes of the device, such as charge-carrier generation, electron and ion transport, surface potentials, and electrocatalysis. Understanding the spatial properties and structure–property relationships of these components can provide insight into designing scalable and efficient solar fuel components and systems. These processes can be probed ex situ or in situ with nanometer-scale spatial resolution using emerging scanning-probe techniques based on atomic force microscopy (AFM). In this Perspective, we summarize recent developments of AFM-based techniques relevant to solar fuel research. We review recent progress in AFM for (1) steady-state and dynamic light-induced surface photovoltage measurements; (2) nanoelectrical conductive measurements to resolve charge-carrier heterogeneity and junction energetics; (3) operando investigations of morphological changes, as well as surface electrochemical potentials, currents, and photovoltages in liquids. Opportunities for research covered here include: (1) control of ambient conditions for performing AFM measurements; (2) in situ visualization of corrosion and morphological evolution of electrodes; (3) operando AFM techniques to allow nanoscale mapping of local catalytic activities and photo-induced currents and potentials.

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

BCARS Simulated Phantom Dataset for Evaluation of Processing Pipelines

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a powerful label-free biological imaging technique, but the raw signal requires careful processing. The vibrationally resonant (Raman) fingerprint signal is usually small compared with instrumental noise sources and the nonresonant background (NRB) inherent in the BCARS signal. Fortunately, the NRB exhibits a systematic phase relationship with the coherent Raman response, acting as a heterodyne amplifier for the weak fingerprint signal. Due to this heterodyne effect, the Raman response can be recovered quantitatively and invariantly across different instruments, provided the NRB shape is known. Even with heterodyne amplification, the amplitudes of fingerprint signal components are often comparable to system noise. Singular value decomposition (SVD), which utilizes spatial information, is often employed for additional noise filtering. Consequently, finding optimal processing parameters to properly distinguish the NRB and Raman responses and suppress noise in the complex BCARS signal requires a reference system that realistically represents the spectral and spatial properties of BCARS signals obtained from biological samples. We present a digital tissue phantom that meets these criteria as a tool for testing candidate signal processing pipelines. The digital phantom is generated with simulated hyperspectral Raman images having system-specific noise and background characteristics. Here, we analyze phantom datasets with differing background and signal-to-noise conditions to evaluate their impact on the performance of multiple signal processing pipelines. Specifically, we investigate the application of a Butterworth filter-based routine to directly estimate the NRB from the BCARS signal. Additionally, we evaluate a Lorentzian wavelet transform as an alternative to the Hilbert transform for extracting the Raman spectrum from the BCARS signal. While we demonstrate this phantom for BCARS, it can be used for any spectroscopic Raman imaging approach.

Dixon, Jessica Z. [Georgia Institute of Technology↗
DOE OSTI2022

Development of an Experimental Approach to Achieve Spatially Resolved Plant Root-Associated Metaproteomics Using an Agar-Plate System

Plant–microbe interactions in the rhizosphere play a vital role in plant health and productivity. The composition and function of root-associated microbiomes is strongly influenced by their surrounding environment, which is often customized by their host. How microbiomes change with respect to space and time across plant roots remains poorly understood, and methodologies that facilitate spatiotemporal metaproteomic studies of root-associated microbiomes are yet to be realized. Here, we developed a method that provides spatially resolved metaproteome measurements along plant roots embedded in agar-plate culture systems, which have long been used to study plants. Spatially defined agar “plugs” of interest were excised and subsequently processed using a novel peptide extraction method prior to metaproteomics, which was used to infer both microbial community composition and function. As a proof-of-principle, a previously studied 10-member community constructed from a Populus root system was grown in an agar plate with a 3-week-old Populus trichocarpa plant. Metaproteomics was performed across two time points (24 and 48 h) for three distinct locations (root base, root tip, and a region distant from the root). The spatial resolution of these measurements provides evidence that microbiome composition and expression changes across the plant root interface. Interrogation of the individual microbial proteomes revealed functional profiles related to their behavioral associations with the plant root, in which chemotaxis and augmented metabolism likely supported predominance of the most abundant member. This study demonstrated a novel peptide extraction method for studying plant agar-plate culture systems, which was previously unsuitable for (meta)proteomic measurements.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Synthetic Soil Aggregates: Bioprinted Habitats for High-Throughput Microbial Metaphenomics

The dynamics of microbial processes are difficult to study in natural soil, owing to the small spatial scales on which microorganisms operate and to the opacity and chemical complexity of the soil habitat. To circumvent these challenges, we have created a 3D-bioprinted habitat that mimics aspects of natural soil aggregates while providing a chemically defined and translucent alternative culturing method for soil microorganisms. Our Synthetic Soil Aggregates (SSAs) retain the porosity, permeability, and patchy resource distribution of natural soil aggregates—parameters that are expected to influence emergent microbial community interactions. We demonstrate the printability and viability of several different microorganisms within SSAs and show how the SSAs can be integrated into a multi-omics workflow for single SSA resolution genomics, metabolomics, proteomics, lipidomics, and biogeochemical assays. We study the impact of the structured habitat on the distribution of a model co-culture microbial community and find that it is significantly different from the spatial organization of the same community in liquid culture, indicating a potential for SSAs to reproduce naturally occurring emergent community phenotypes. The SSAs have the potential as a tool to help researchers quantify microbial scale processes in situ and achieve high-resolution data from the interplay between environmental properties and microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Spatially Mapping the CO 2 Alkaline Sorbent Diffuse Microenvironment Using Operando Raman Spectroscopy

When designing a chemical process, the local balance of transport and kinetics, collectively referred to as the diffuse microenvironment, plays a critical role in performance but is difficult to directly observe. This work demonstrates a method of two-dimensional spatial chemical mapping of the diffuse microenvironment in the context of alkali metal hydroxide direct air capture of carbon dioxide using a custom operando gas-absorption flow cell along with confocal Raman spectroscopy. Notably, we observe the concentration boundary layer near the gas–liquid interface and elucidate the interplay of carbonate and bicarbonate ions within it while inferring local hydroxide depletion through continuum modeling. These first of their kind observations provide a technique to compare the performance of direct air capture solvents based on diffuse microenvironment dynamics while also providing metrics important for air contactor design such as boundary layer thickness. Overall, this work showcases a new experimental platform to study interfacial diffuse microenvironments in and outside of the field of direct air capture of carbon dioxide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗