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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems

Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.

AI/ML↗
DOE OSTI2026

Mitchell FEED Study Summary Report (Unlimited Rights Version) (Rev. D)

Heidelberg Materials (HM), Sargent & Lundy (S&L), and Mitsubishi Heavy Industries (MHI) completed a Front End Engineering Design (FEED) study for deployment of an amine-based CO 2 capture system at the Mitchell Cement Plant in Indiana. The project includes compression of captured CO 2 to a supercritical state for geological sequestration. The flue gases from 7,000 mtpd clinker plant combined with auxiliary boiler flue gases into the capture system. Auxiliary boiler supplies steam for solvent regeneration. The study developed an AACE Class 3 capital cost estimate (+30%/-20%) at approximately 30% design completion to evaluate the technical and economic feasibility of capturing up to 2 million tonnes of CO 2 per year.

42 ENGINEERING↗
DOE OSTI2025

Soil porous microstructure control over soil organic matter mobility: A multimethod workflow for understanding chemistry-dependent organic matter binding in soil

Soil organic matter (SOM) has attracted a great deal of interest; particularly for its potential to mitigate human derived CO 2 emissions. Studies have demonstrated that SOM plays a critical role in carbon storage and CO 2 sequestration. However, the sorption properties of SOM, which influence its transport in pore water and stabilization within the soil, remain poorly understood. This study develops a workflow to: (1) examine compound-specific advective and diffusive transport and desorption behaviors, (2) quantify desorption rates through stop-flow and continuous-flow column experiments, and (3) evaluate the impact of soil microporosity on SOM mobility using high-resolution imaging and extractions. Intact core column experiments were conducted on Uncultivated (Natural) and Cultivated soil samples, both were arid soils, collected in Washington State. X-ray computed tomography was employed to measure porosity and pore connectivity, while Fourier-transform ion cyclotron resonance mass spectrometry was used to analyze SOM composition. The findings revealed that cultivation increased total carbon and nitrogen levels due to irrigation and fertilization, enhancing carbon capture potential in arid soils. In contrast, the Natural soil, characterized by higher porosity and connectivity, contained more oxidized carbon. Pore network analysis indicated that soil compaction in the Cultivated soil may lead to longer diffusion pathways, significantly influencing SOM transport and stability.

Hydraulic Properties↗
DOE OSTI2023

Mechanochemical formation of highly stable amorphous calcium carbonate in calcium silicates: Potential long-term storage of CO 2 in cement

Here, we have investigated mechanochemical reactions with the calcium silicate wollastonite to probe potential mechanisms of sequestration and long-term storage of CO 2 as mineral carbonates in cement pastes. Wollastonite, CaSiO 3 , was milled under ambient and 13 C enriched CO 2 atmospheres. Milling induced a structure change from monoclinic wollastonite-2M to triclinic-1T, consistent with high pressure treatment. The results from solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy shows this process forms an extraordinarily stable amorphous calcium carbonate (ACC) phase that remains despite a high temperature dehydration treatment (130 °C) and persists in the same form after 3 yrs. of storage at ambient conditions. Thermogravimetric analysis (TGA) of these samples indicates that ACC is produced at the similar solid concentrations in both CO 2 -enriched and ambient atmospheres. The observation of meta-stable ACC due to wollastonite carbonation may play an important role in further exploration and optimization of mineral carbonation reactions for CO 2 capturing cement formulations.

13C solid-state NMR↗
DOE OSTI2021

Greenhouse Gas Impact of Algal Bio-Crude Production for a Range of CO2 Supply Scenarios

Refined bio-crude production from hydrothermal liquefaction of algae holds the potential to replace fossil-based conventional liquid fuels. The microalgae act as natural carbon sequestrators by consuming CO2. However, this absorbed CO2 is released to the atmosphere during the combustion of the bio-crude. Thus, the life-cycle greenhouse gas (GHG) emissions of refined bio-crude are linked to the production and supply of the materials involved and the process energy demands. One prominent raw material is CO2, which is the main source of carbon for algae and the subsequent products. The emissions associated with the supply of CO2 can have a considerable impact on the sustainability of the algae-based refined bio-crude production process. Furthermore, the diurnal algae growth cycle complicates the CO2 supply scenarios. Traditionally, studies have relied on CO2 supplied from existing power plants. However, there is potential for building natural gas or biomass-based power plants with the primary aim of supplying CO2 to the biorefinery. Alternately, a direct air capture (DAC) process can extract CO2 directly from the air. The life-cycle GHG emissions associated with the production of refined bio-crude through hydrothermal liquefaction of algae are presented in this study. Different CO2 supply scenarios, including existing fossil fuel power plants and purpose-built CO2 sources, are compared. The integration of the CO2 sources with the algal biorefinery is also presented. The CO2 supply from biomass-based power plants has the highest potential for GHG reduction, with a GHG footprint of −57 g CO2 eq./MJ refined bio-crude. The CO2 supply from the DAC process has a GHG footprint of 49 CO2 eq./MJ refined bio-crude, which is very similar to the scenario that considers the supply of CO2 from an existing conventional natural gas-based plant and takes credit for the carbon utilization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CO2 Streams Captured From Industrial Processes and Corrosion of Steel Pipelines

Based on the knowledge accumulated in the last decade related to Carbon Capture and Storage (CCS) processes and the identified gaps in knowledge, our study identifies and quantifies the key issues that impact the corrosion of materials, specifically relating to the integrity of structures for the CO2 transport pipelines and provides a better understanding of the materials’ compatibility with CO2 steams with impurities. This will help to understand the corrosion risks associated with CO2 transport and injection and leverage existing infrastructure or rebuild new pipeline networks to transport CO2 with impurities for EOR or geological storage.

CO2 corrosion↗
DOE OSTI2025

Assembled peptoid crystalline nanomaterials as carbonic anhydrase mimics for promoted hydration and sequestration of CO 2

Carbonic anhydrase (CA) mimics have received significant attention due to their promising applications in the enhanced hydration and sequestration of CO 2 . Herein, we report the assembly of sequence-defined peptoids into crystalline nanomaterials with controlled microenvironment of active sites as CA mimics for promoted hydration and sequestration of CO 2 . By incorporating specific ligands into self-assembling peptoids and coordinating these ligands with metal cations, we synthesize a variety of crystalline nanosheets and nanotubes as efficient CA mimics comparable to natural bovine CA. Molecular dynamics simulations reveal the critical roles of peptoid-Zn 2+ binding energy and the active site local microenvironment on the catalytic performance of these CA mimics. CO 2 precipitation results show that these CA mimics promote the hydration and sequestration of CO 2 while retaining high thermal and chemical stabilities. This study offers essential guidance for the future design of high-performance CA-mimics suitable for applications in CO 2 capture and sequestration.

Chakma, Progyateg [Pacific Northwest National Labo↗
DOE OSTI2024

Metrics for quantifying the efficiency of atmospheric CO 2 reduction by marine carbon dioxide removal (mCDR)

Abstract Marine carbon dioxide removal (mCDR) is gaining interest as a tool to meet global climate goals. Because the response of the ocean–atmosphere system to mCDR takes years to centuries, modeling is required to assess the impact of mCDR on atmospheric CO 2 reduction. Here, we use a coupled ocean–atmosphere model to quantify the atmospheric CO 2 reduction in response to a CDR perturbation. We define two metrics to characterize the atmospheric CO 2 response to both instantaneous ocean alkalinity enhancement (OAE) and direct air capture (DAC): the cumulative additionality ( α ) measures the reduction in atmospheric CO 2 relative to the magnitude of the CDR perturbation, while the relative efficiency ( ϵ ) quantifies the cumulative additionality of mCDR relative to that of DAC. For DAC, α is 100% immediately following CDR deployment, but declines to roughly 50% by 100 years post-deployment as the ocean degasses CO 2 in response to the removal of carbon from the atmosphere. For instantaneous OAE, α is zero initially and reaches a maximum of 40%–90% several years to decades later, depending on regional CO 2 equilibration rates and ocean circulation processes. The global mean ϵ approaches 100% after 40 years, showing that instantaneous OAE is nearly as effective as DAC after several decades. However, there are significant geographic variations, with ϵ approaching 100% most rapidly in the low latitudes while ϵ stays well under 100% for decades to centuries near deep and intermediate water formation sites. These metrics provide a quantitative framework for evaluating sequestration timescales and carbon market valuation that can be applied to any mCDR strategy.

Yamamoto, Kana (ORCID:0009000731519234)↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24, 2024. Carbon capture and storage (CCS) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2021

Bioenergy for climate change mitigation: Scale and sustainability

Many global climate change mitigation pathways presented in IPCC assessment reports rely heavily on the deployment of biomass for bioenergy, often used in conjunction with carbon capture and storage (BECCS). We review the literature on bioenergy, including the modelling of bioenergy in integrated assessment models (IAMs) and bottom-up modelling and non-modelling studies on the implications of bioenergy use. We summarise the limitations of existing modelling studies and what is known about the potential co-benefits and adverse side-effects of bioenergy systems. We find that the implications of bioenergy supply on mitigation and other sustainability criteria are context dependent and influenced by feedstock, management regime, climatic region, scale of deployment and the counterfactual land use and energy system. However, due to limitations of the existing models, and uncertainty over the future context with respect to the many variables that influence availability of biomass and land resources, it is not possible to precisely quantify the sustainability implications for different scales of bioenergy implementation. Given these uncertainties, the dependence on large-scale deployment of bioenergy in mitigation scenarios carries risks. The deployment of bioenergy technologies and the evolution of biomass supply chains at a scale that achieves significant mitigation and carbon sequestration requires integrative policies, coordinated institutions and improved governance mechanisms. As a result, bioenergy, and the use of land to produce biomass, is an essential part of many climate mitigation strategies but there are limits to its use due to trade-offs with sustainability.

09 BIOMASS FUELS↗
DOE OSTI2026

Illinois Basin Regional DAC Hub TA-1: Feasibility (Phase-0) (Final Scientific/Technical Report)

The overall objective of this project was to complete a feasibility study for a Regional Direct Air Capture (DAC) Hub that encompasses Illinois, Indiana, and Kentucky. The geographic construct of this hub was based on the Illinois Basin – a geological area where at least seven (7) geological storage studies were conducted. The Hub was designed to assure a capacity to capture, store, and utilize at least 1,000,000 metric tons (tonnes) of CO2 from the atmosphere annually 1 metric tonnes per annum (MTA), starting from an initial capacity of 200,000 tonnes of CO2 per annum (KTA). The Hub also included:(1) clean energy providers to supply renewable energy to the DAC units; (2) geological storage facilities to inject and sequestrate the captured CO2; (3) CO2 utilization facilities that would uptake the captured CO2 and convert it into products; and (4) small business organizations that would assist in workforce development efforts for the region. This closeout report consolidates the tasks undertaken and accomplished as deliverables for the project. The project team had successfully completed all required tasks of phase 0a, confirming that the project is feasible based on the preliminary conceptual designs and operational requirements of the DAC systems. Of particular interest, the hub ownership structure was identified and was implemented in follow on projects that would transition the hub infrastructure to a build/operate phase.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ion Exchange Processes for CO 2 Mineralization Using Industrial Waste Streams: Pilot Plant Demonstration and Life Cycle Assessment

Abstract An attractive technique for removing CO 2 from the environment is sequestration within stable carbonate solids (e. g., calcite). However, continuous addition of alkalinity is required to achieve favorable conditions for carbonate precipitation (pH>8) from aqueous streams containing dissolved CO 2 (pH<4.5) and Ca 2+ ions. In this study, a pH‐swing process using ion exchange was demonstrated to process 300 L of produced water brine per day for CO 2 mineralization. Proton titration capacities were quantified for aqueous streams in equilibrium with gas streams at various concentrations of CO 2 (pCO 2 =0.03–0.20 atm) and at various flow rates (0.5–2.0 L min −1 ). Energy intensities for the process were determined to be between 30 and 65 kWh per tonne of CO 2 sequestered depending on the composition of the brine stream. A life cycle assessment was performed to analyze the net carbon emissions of the technology which indicated a net CO 2 reduction for pCO 2 ≥0.12 atm (−0.06–−0.39 kg CO 2 e per kg precipitated CaCO 3 ) utilizing calcium‐rich brines. The results from this study indicate the ion exchange process can be used as a scalable method to provide alkalinity necessary for the capture and storage of CO 2 in Ca‐rich waste streams.

Chemistry↗
DOE OSTI2024

Technological evolution of large-scale blue hydrogen production toward the U.S. Hydrogen Energy Earthshot

Hydrogen potentially has a crucial role in the U.S. transition to a net-zero emissions economy. Learning from large-scale hydrogen projects will boost technological evolution and innovation toward the U.S. Hydrogen Energy Earthshot. We apply experience curves to estimate the evolving costs of blue hydrogen production and to further examine the economic effect on technological evolution of the Inflation Reduction Act’s tax credits for carbon sequestration and clean hydrogen. Learning-by-doing alone can decrease the production cost of blue hydrogen. Without tax incentives, however, it is hard for blue hydrogen production to reach the cost target of $\$1$/kg H 2 . Here we show that the breakeven cumulative production capacity required for gas-based blue hydrogen to reach the $\$1$/kg H 2 target highly depends on tax credit, natural gas price, inflation rate, and learning rates. We make recommendations for hydrogen hub development and for accelerating technological progress toward the Hydrogen Energy Earthshot.

08 HYDROGEN↗
DOE OSTI2026

Design and demonstration of a direct air capture system with moisture-driven CO 2 delivery into aqueous medium

Two direct air capture (DAC) systems were designed and demonstrated to passively capture CO 2 from ambient air and use moisture to release the CO2 into an alkaline medium. A bench-scale system delivering ∼1 g CO 2 d –1 was demonstrated in a laminar flow hood, and a small pilot-scale system that could deliver ∼100 g CO 2 d –1 was operated outdoors in a 4.2 m 2 raceway pond. Novel elongated mesh-tube packets containing anion-exchange resin (AER) beads were found to reduce drying and CO 2 loading time 4.3-fold compared to larger mesh bags. Technoeconomic analysis (TEA) estimates the cost of capturing CO 2 into an alkaline solution, suitable for cultivating photosynthetic microorganisms, to be $\$229$ per tonne for a practical scenario based on current results and $\$72$ per tonne for an aspirational scenario considering improvements to sorbent capacity, hydrophobicity, and sorbent lifetime. TEA further estimates an additional $\$110$ per tonne to extract CO 2 from solution, purify it, and compress it to 15 MPa, suitable for sequestration. Furthermore, moisture-driven processes have the potential to use up to 87% less energy than thermal and/or vacuum swing DAC by using energy from water evaporation.

09 BIOMASS FUELS↗
DOE OSTI2023

Impacts of Permeability Heterogeneity and Background Flow on Supercritical CO 2 Dissolution in the Deep Subsurface

Motivated by CO 2 capture and sequestration (CCS) design considerations, we consider the coupled effects of permeability heterogeneity and background flow on the dissolution of a supercritical CO 2 lens into an underlying deep, confined aquifer. We present the results of a large-scale Monte Carlo simulation study examining the interaction of background flow rate and three parameters describing multi-Gaussian log-permeability fields: mean, variance, and correlation length. Hundreds of high-resolution simulations were performed using the PFLOTRAN finite volume software to model CO 2 dissolution in a kilometer-scale aquifer over 1,000 years. Predictive dimensionless scaling relationships relating CO 2 dissolution rate to heterogeneity statistics, Rayleigh (Ra) and Péclet (Pe) numbers were developed for both gravitationally dominated free convection to background flow-dominated forced convection regimes. An empirical criterion, Pe = Ra 3/4 , was discovered for regime transition. All simulations converged quickly to a quasi-steady, approximately linear dissolution rate. However, this rate displayed profound variability between permeability field realizations sharing the same heterogeneity statistics, even under mild permeability heterogeneity. In general, increased heterogeneity was associated with a lower mean and higher variance of dissolution rate, undesirable from a CCS design perspective. The relationship between dissolution rate and background flow was found to be complex and nonlinear. Dimensionless scaling relationships were uncovered for a number of special cases. Results call into question the validity of the Boussinesq approximation in the context of modest-to-high background flow rates and the general applicability of numerical simulations without background flow.

42 ENGINEERING↗
DOE OSTI2024

Forest carbon sequestration on the west coast, USA: Role of species, productivity, and stockability

Forest ecosystems store large amounts of carbon and can be important sources, or sinks, of the atmospheric carbon dioxide that is contributing to global warming. Understanding the carbon storage potential of different forests and their response to management and disturbance events are fundamental to developing policies and scenarios to partially offset greenhouse gas emissions. Projections of live tree carbon accumulation are handled differently in different models, with inconsistent results. We developed growth-and-yield style models to predict stand-level live tree carbon density as a function of stand age in all vegetation types of the coastal Pacific region, US (California, Oregon, and Washington), from 7,523 national forest inventory plots. We incorporated site productivity and stockability within the Chapman-Richards equation and tested whether intensively managed private forests behaved differently from less managed public forests. We found that the best models incorporated stockability in the equation term controlling stand carrying capacity, and site productivity in the equation terms controlling the growth rate and shape of the curve. RMSEs ranged from 10 to 137 Mg C/ha for different vegetation types. There was not a significant effect of ownership over the standard industrial rotation length (~50 yrs) for the productive Douglas-fir/western hemlock zone, indicating that differences in stockability and productivity captured much of the variation attributed to management intensity. Our models suggest that doubling the rotation length on these intensively managed lands from 35 to 70 years would result in 2.35 times more live tree carbon stored on the landscape. These findings are at odds with some studies that have projected higher carbon densities with stand age for the same vegetation types, and have not found an increase in yields (on an annual basis) with longer rotations. We suspect that differences are primarily due to the application of yield curves developed from fully-stocked, undisturbed, single-species, “normal” stands without accounting for the substantial proportion of forests that don’t meet those assumptions. The carbon accumulation curves developed here can be applied directly in growth-and-yield style projection models, and used to validate the predictions of ecophysiological, cohort, or single-tree style models being used to project carbon futures for forests in the region. Our approach may prove useful for developing robust models in other forest types.

Chisholm, Paul J. (ORCID:0000000238784707)↗