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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bipolar Membranes with an Electrospun 3D Junction

Freestanding bipolar membranes (BPMs) with an entirely new and transformative morphology were fabricated and characterized. The key disruptive design element was a water-splitting/water-generation junction layer of finite thickness composed of interwoven and interlocking electrospun anion-exchange polymer (AEP) fibers and cation-exchange polymer (CEP) fibers. Methods were developed to sandwich the 3D junction layer between two pre-formed dense AEP and CEP films., with the closure of all interfiber voids, where the processing steps are easily incorporated into a commercial roll-to-roll membrane manufacturing scheme. A series of membranes were made using different polymers and junction layers to identify the optimum morphology and composition for water-splitting and water-generation applications. For water splitting, the 3D junction BPMs worked remarkably well with operating current densities at/above 1.0 A/cm2 (10X greater than commercial BPMs) at a transmembrane voltage drop of only 1.1 V. In water generation mode, the 3D junction BPMs operated at 0.5 A/cm2, a world-record current density. The membranes were found to exhibit outstanding durability and can be manufactured at scale for low cost. The high operating current densities and stable long-term operation are due to the 3D junction layer design, where there is a high interfacial area for water splitting or water generation reactions and where the interlocking fibers prevent delamination of the outer films. These new BPMs are ideal candidate materials for both existing water-splitting electrodialysis separations and new electrochemical processes, such as large-scale direct air capture of CO 2 , reactors for CO 2 reduction, self-hydrating fuel cells, and redox flow batteries.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Investigation of the Effect of Framework Flexibility on CO 2 Adsorption in SIFSIX-3-Cu Using a Machine-Learned Force Field

Metal–organic frameworks (MOFs) offer promise as selective CO 2 sorbents, but successful MOF sorbent materials need high CO 2 binding affinity and selectivity for CO 2 over water. This work focuses on the use of machine-learned force fields (MLFFs) to model CO 2 adsorption in flexible MOFs, with a focus on SIFSIX-3-Cu, an anion-pillared MOF known for its high CO 2 affinity. A preliminary high-throughput screening of over 900 anion-pillared MOFs was performed using rigid UFF+DDEC6 force fields to predict zero-loading heats of adsorption for CO 2 and H 2 O. SIFSIX-3-Cu was selected for further computational study due to its predicted CO 2 heat of adsorption and experimental relevance. A DeePMD-based MLFF was trained to reproduce DFT (PBE+D3) energies and forces, with an iterative sampling scheme combining molecular dynamics, geometry optimization, random geometric insertion, and NVT Monte Carlo-based configuration generation to capture both attractive and repulsive regions of the potential energy surface. Flexibility of the MOF was explicitly included, contrasting with previous models that approximated the MOF as rigid. Hybrid Monte Carlo/molecular dynamics (MC/MD) simulations with the MLFF produced CO 2 adsorption isotherms in good agreement with experimental data at direct air capture (DAC) pressures (e.g., 40 Pa), in contrast to previous overestimations of CO 2 sorption by models with rigid structures. Bond and angle histogram analysis showed that MOF flexibility increased the variance of fluorine–fluorine diagonal distances at adsorption sites, resulting in a lower predicted sorption for flexible, asymmetric SIFSIX-3-Cu pore geometries compared to the rigid, symmetric DFT-optimized SIFSIX-3-Cu pore geometry. A detailed description of flexibility afforded by the MLFF resulted in an accurately predicted CO 2 uptake (0.88 mmol/g) at low pressure (40 Pa) compared to the experimentally measured value (1.24 mmol/g). In conclusion, these results underscore the importance of including framework flexibility when modeling adsorption phenomena in MOFs, particularly for low-pressure applications.

adsorption↗
DOE OSTI2024

Physical and Chemical Responses of Amidine-Containing Polymers in the Capture and Release of CO 2

Polymeric materials containing amidine motifs are of high interest due to their ability to reversibly capture and release CO 2 at ambient temperature. Here, in this study, we probe physical and chemical responses of styrene-based copolymers containing linear amidine motifs as functions of CO 2 and inert gas exposures and temperature. A copper-catalyzed azide–alkyne cycloaddition “click” reaction involving N′-propargyl-N,N-dimethylacetamidine is used to modify random copolymers, resulting in an array of linear amidine motifs along the chain backbone with the amount of CO 2 -active amidine controlled by the copolymer composition. Through thermogravimetric measurements, we demonstrate that the amidine-functionalized copolymers efficiently capture CO 2 upon exposure to a stream of CO 2 (at 27 °C) and release it at a slightly elevated temperature (50 °C) when exposed to an inert gas stream (N 2 ). In addition to displaying a maximum adsorption capacity of 22 wt % in the presence of pure CO 2 , the copolymers show composition-dependent direct air capture (DAC) behaviors. Small molecule analogs are used to definitively understand degradation via chemical hydrolysis of the amidine moiety, which leads to insolubility and a large reduction in CO 2 adsorption capacity (1.7 wt %). Neutron vibrational spectroscopy and DFT calculations confirm that CO 2 binds strongly to the amidine motif, inducing a strong bending of the CO 2 molecule from its linear geometry. The coupled insights into mechanisms and behaviors of CO 2 adsorption in amidine-functionalized polymers provides a foundation for future investigations of CO 2 -responsive polymers and soft materials to improve carbon capture and sequestration technologies.

Chun, Danielle J.↗
DOE OSTI2024

In Silico Screening of CO 2 –Dipeptide Interactions for Bioinspired Carbon Capture

Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO 2 concentrations. On an industrial scale, amine-based solvents are extensively employed for CO 2 capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by-products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO 2 . Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Furthermore, those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO 2 affinity. Density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT) computations were performed for the calculation of CO 2 interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO 2 binding, as well as design principles that can further enhance CO 2 capture through cooperative binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Emerging concepts in intermediate carbon dioxide emplacement to support carbon dioxide removal

Substantial upscaling of carbon dioxide capture is possible in the coming decades but existing solutions for storage are projected to be limited in annual capacity by as much as 2–10 GtCO 2 per year until mid-century. Temporary storage of CO 2 in a solid or liquid state could prove useful for filling this gap in capacity, until more permanent and ideally lucrative CO 2 sequestration options come online. There are several concepts for reversible solid-state and chemical CO 2 storage, but their advantages and limitations have yet to be reviewed in this context. This article focuses on the physical and chemical aspects of CO 2 storage via liquid and solid chemical carriers and sorbents, and gives an overview of the energetics around their use, as well as prospects for their future development. Exciting opportunities for coupling capture and medium to high maturity multi-year storage technologies could support carbon removal in the coming decades. Highlights of the analysis are the remarkable storage capacity of oxalic acid and formic acid (CO 2 -density of 1857 kg m –3 and 1152 kg m –3 , compared with condensed liquid CO 2 at 993–1096 kg m –3 , respectively), the relative scalability and compatibility of carbonate salts for stationary storage with direct air capture, and the potential promise of multiple carriers for CO 2 transportation. Solid sorbents do not achieve such ultra-high storage capacities, but could improve storage over compressed gas tanks on a capacity and energetics basis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Distributed Mafic Rock Resources for Carbon Mineralization in Arizona

Ex-situ carbon mineralization is a process by which CO2 is reacted with alkaline silicate minerals and rocks to produce stable carbonate materials, which can be used for other industrial processes. Arizona, U.S.A., hosts abundant surficial mafic rocks in three young volcanic fields, Geronimo-San Bernardino, San Francisco, and Springerville, and other distributed locations throughout the state. We created a Mafic Rock Resource Inventory (MRRI) that categorizes geochemical, physical, and textural characteristics of a diverse suite of surficial mafic rock samples and provide a benchmark reaction dataset parameterizing the temperature, pressure, and pH conditions best suited ex-situ mineralization in different rock types. MRRI data is publicly available online via a map-viewer. We establish two reaction condition sets, varied in temperature, pressure, and pH, where crystal-rich and glassy rocks reach maximum reaction extent and different carbonate phases are formed. Systematic ex-situ mineralization experiments on 21 diverse rock types show trends in geochemical, mineralogical, and reactivity behavior and establish maximum effective capture capacity. From this, scoria cones in three Arizona volcanic fields have a ~62 Gt effective CO₂ storage capacity with one of the fields having a ~42 Gt storage capacity in lava flows. Reactivity results have applications to alkaline mafic rock resources exposed globally, including producing additional effective storage capacity estimates and scaled commercialization of mafic rock ex-situ mineralization, should reaction extents be improved through advances in mineralization techniques. MRRI data were used to create a Direct Air Capture to Mineralization (DACM) systems model, technoeconomic analysis, and life-cycle assessment. These documents are presented as three appendices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

CO 2 Capture Characteristics of Hyperbranched Poly(alkylene imine): A Molecular Dynamics Simulation Approach

This study explores the CO 2 capture characteristics of hyperbranched poly(ethylenimine) (HB-PEI) and poly- (propyleneimine) (HB-PPI) through molecular dynamics simulations using density functional theory-calibrated force fields. Key features such as density, free volume, glass transition temperature, CO 2 /H 2 O distribution, and molecular diffusion are systematically investigated to elucidate structure−function relationships under dry and hydrated conditions. HB-PEI demonstrates a slightly higher density and lower free volume compared to HB-PPI yet shows superior CO 2 capture due to the high amine concentration. Glass transition analysis indicates a higher thermal mobility in HBPEI, enhancing the CO 2 diffusivity. Pair correlation and coordination analyses confirm a stronger affinity of CO 2 with primary and secondary amines, particularly in hydrated environments where water competes with CO 2 for binding sites. Despite its more compact structure, HB-PEI outperformed HB-PPI in CO 2 and H 2 O transport, as confirmed by higher diffusion coefficients across all hydration levels. These findings highlight a critical balance among polymer architecture, amine accessibility, and hydration in designing next-generation solid amine sorbents for efficient direct air capture applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Deep Learning Potential For Investigation of Chemical Conversion of CO2 at Air-Reactive Deep Eutectic Solvent Interface

Capturing CO2 from air is urgent to battle against the climate change. Traditional CO2 capture by alkaline solution or aqueous amine has high regeneration cost, equipment corrosion, and amine leakage. Deep eutectic solvents (DESs) are promising alternatives with low corrosion, non-toxicity, and biodegradable nature. Functionalized DESs can capture and convert CO2 at low partial pressures. Developing reactive DESs for efficient CO2 capture needs a foundational understanding of the reaction-transport coupling of CO2 at the air-DES interface. In this study, we develop a deep learning potential (DLP) to investigate the absorption and reaction of CO2 at the air-DES interface (DES: 1-ethyl-3-methylimidazolium 2-cyanopyrrolide ([Emim][2-CNpyr]) and ethylene glycol (EG)). The DLP model allows us to simulate chemical reactions at a lower computational cost with ab initio accuracy. By analyzing reaction free energy surfaces, molecular interactions among CO2, DES components and reaction products, and transport of CO2 through the interface, we identify three main reaction pathways of CO2 at the interface: forming carboxylate with [Emim], carbamate with [2-CNpyr], and carbonate with EG. The mechanistic understanding of CO2 chemisorption at the interface will facilitate the development of novel direct air capture technology using DESs.

Nguyen, Manh Tien↗
DOE OSTI2024

Pronounced reduction in the regeneration energy of potassium sarcosinate CO 2 capture solvent using TiO 2

Absorption-based CO 2 capture technologies face economic feasibility concerns due to the exceedingly high energy requirements of solvent regeneration. Among various proposed solutions, solid acid-aided solvent regeneration stands out as a promising approach. Studies have shown that solid materials containing Lewis and Brønsted acid sites can facilitate deprotonation of protonated amine and breakdown of carbamate molecules, which significantly increases CO 2 desorption rate and decreases regeneration energy of common solvents such as MEA and DEA. However, the influence of solid acids on alternate solvents such as amino acids is not well known. Here, we report the performance of TiO 2 for the regeneration of CO 2 -loaded aqueous potassium sarcosinate (K-Sar) solvent. K-Sar is an environmentally friendly amino-acid salt that provides high CO 2 absorption rates, making it a good candidate for both point-source and direct-air capture. TiO 2 is hydrothermally stable and contains high surface acid site concentration. Desorption of CO 2 from K-Sar starts at room temperature in the presence of TiO 2 , while such onset temperature is greater than 70°C for regeneration without TiO 2 . Further, at a temperature of 95°C, the maximum CO 2 desorption rate and cumulative CO 2 removal increase by 128% and 91%, respectively, in the presence of TiO 2 compared to the no TiO 2 case. The total regeneration energy could be reduced by ~ 50% with TiO 2 , showcasing the significant role this process can take in improving the commercial competitiveness of absorption-based CO 2 capture. Further characterization with XRD, SEM, and NMR concluded that neither the TiO 2 powder nor the solvent undergoes any physical or chemical degradation in the regeneration process, suggesting the potential of their long-term usability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Sequential polymer infusion into solid substrates (SPISS): Impact of processing on sorbent CO 2 adsorption properties

Solid sorbents made of small amine molecules and polyamines infused into mesoporous substrates are promising materials for CO 2 capture technologies. To date, their preparation is mainly based on wet infusion with focus on increasing amine content by varying the structure of the amine sorbent and designing solid substrates of various pore parameters. Less explored in the field are changes in processing to afford efficient CO 2 sorbents. In this study, branched poly(ethylenimine) (bPEI, M w = 800 Da) alone and blends with linear poly(propylenimine) (LPPI, M n = 6,700 Da) are infused into solid SBA-15 substrates by a method that varies the solution processing called sequential polymer infusion into solid substrates (SPISS). The reference 40 % bPEI-SBA-15 samples are split by two methods: split batch in dry suspension (SBD) and split batch in liquid suspension (SBL). Sequentially, alcoholic 10% of bPEI (non-blends) and 10% of LPPI (blends) solutions are introduced to afford the desired products. Under dry conditions, the resulting 50% bPEI-SBA-15 SBD & SBL sorbents display high CO 2 capacities up to 3.47 mmol CO 2 /g SiO 2 for simulated flue gas (10% CO 2 ) and 2.62 mmol CO 2 /g SiO 2 for direct air capture (DAC, 400 ppm CO 2 ). Under humid DAC conditions the CO 2 performance is further enhanced with an uptake of 4.62 mmol CO 2 /gSiO 2 and amine efficiency of 0.22 mmol CO 2 /mmol N. Subjected to extended temperature swing adsorption kinetic cycling (20 cycles), the SPISS samples display stable working capacities and retain over 70% (blends) and over 90% (non-blends) of their initial 12 h adsorption performance. LPPI is demonstrated to be an effective water sorption limiting agent using dynamic vapor sorption measurements. Solid state NMR techniques reveal important insights into the dynamics of the amine polymers confined into SBA-15 pores, as impacted by processing conditions. The results suggest that the conformation of the polymers is different depending on the processing method, displaying relatively tight (SBD) and loose (SBL) packing. Finally, the simple solution processing approaches presented here show that processing variations may guide the design of solid amine sorbents with desirable properties relevant for integration into CO 2 capture technologies.

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

HydraGNN_Predictive_GFM_2026 - Ensemble of predictive graph foundation models for atomistic materials modeling

This release contains data and parameters of HydraGNN-based graph foundation models trained as a result of the work published in the pre-print "Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data" by M. Lupo Pasini et al. (https://arxiv.org/abs/2604.15380). We jointly train on 16 open first-principles datasets (544+ million structures covering 85+ elements) using a multi-task architecture with per-dataset heads and a scalable ADIOS2/DDStore data pipeline. On Frontier, we execute six large-scale DeepHyper hyperparameter optimization campaigns in FP64 and promote the top-performing message-passing models to sustained 2,048-node training, yielding a PaiNN-based lead model. The version of HydraGNN used to generate the outputs provided in this release is HydraGNN v5.0 (https://github.com/ORNL/HydraGNN/releases/tag/v5.0) The list of datasets used for the training of the graph foundation model is the following: 1) Alexandria [1] 2) ANI1x [2] 3) MPTrj [3] 4) Open Catalyst 2020 (OC20) [4] 5) Open Catalyst 2022 (OC22) [5] 6) Open Catalyst 2025 (OC25) [6] 7) Open Direct ir Capture 2023 (ODAC23) [7] 8) Open Materials 2024 (OMat24) [8] 9) Open Molecules 2025 (OMol25) [9] 10) OMol25-neutral (subset of OMol25 that contains only molecules with zero total charge) 11) OMol25-non-neutral (subset of OMol25 that contains only molecules with non-zero total charge) 12) Open Polymers 2026 (OPoly2026) [10] 13) Nabla2DFT [11] 14) QCML [12] 15) QM7X [reference 13] 16) transition1x [14] Dataset references: [1] J. Schmidt et al., “A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals,” Scientific Data, vol. 9, p. 64, 2022. [2] J. S. Smith et al., “The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules,” Scientific Data, vol. 7, p. 134, 2020. [Online]. Available: https: //www.nature.com/articles/s41597-020-0473-z [3] A. Jain et al., “Commentary: The Materials Project: A materials genome approach to accelerating materials innovation,” APL Materials, vol. 1, no. 1, p. 011002, 07 2013. [Online]. Available: https://doi.org/10.1063/1.4812323 [4] L. Chanussot et al., “Open catalyst 2020 (oc20) dataset and community challenges,” ACS Catalysis, vol. 11, no. 10, pp. 6059–6072, 2021. [Online]. Available: https://doi.org/10.1021/acscatal.0c04525 [5] K. Tran et al., “Open catalyst 2022 (oc22) dataset and challenges for oxidation electrocatalysts,” ACS Catalysis, vol. 13, no. 5, pp. 3066–3084, 2023. [Online]. Available: https://doi.org/10.1021/acscatal.2c05426 [6] S. J. Sahoo et al., “The open catalyst 2025 (oc25) dataset and models for solid-liquid interfaces,” arXiv preprint arXiv:2509.17862, 2025. [Online]. Available: https://arxiv.org/abs/2509.17862 [7] A. Sriram et al., “The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture,” ACS Central Science, vol. 10, no. 5, pp. 923–941, 2024. [8] L. Barroso-Luque et al., “Open materials 2024 (omat24) inorganic materials dataset and models,” 2024. [Online]. Available: https://arxiv.org/abs/2410.12771 [9] D. S. Levine et al., “The open molecules 2025 (OMol25) dataset, evaluations, and models,” 2025. [Online]. Available: https://arxiv.org/abs/2505.08762 [10] D. S. Levine et al., The open polymers 2026 (OPoly26) dataset and evaluations,” arXiv preprint arXiv:2512.23117, 2025. [Online]. Available: https://arxiv.org/abs/2512.23117 [11] K. Khrabrov et al., “Nabla2dft: A universal quantum chemistry dataset of drug-like molecules and a benchmark for neural network potentials,” in NeurIPS 2024 Datasets and Benchmarks Track, 2024. [Online]. Available: https://openreview.net/forum?id=ElUrNM9U8c [12] S. Ganscha et al., “The QCML dataset, quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations,” Scientific Data, vol. 12, p. 406, 2025. [13] J. Hoja et al., “QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules,” Scientific Data, vol. 8, p. 43, 2021. [Online]. Available: https://www.nature.com/articles/s41597-021-00812-2 [14] M. Schreiner et al., “Transition1x - a dataset for building generalizable reactive machine learning potentials,” Scientific Data, vol. 9, p. 779, 2022. The folder "datasets_ADIOS2_format" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "datasets_ADIOS2_format" directory contains 2 sub-directories, one for the version "v1" of the datasets and one for the version "v2" of the datasets. The version "v1" of the datasets provides values of the total energy as they are extracted from the original data as it was released by the respective institutions. The version "v2" of the datasets provides values of the energy that have been realigned. The realignment was performed by training a linear regression model that predicts the total energy as a function of the chemical composition of the atomistic structure, and then subtract such prediction from the original value of the total energy. Both folders "v1" and "v2" contain 16 sub-directories, each corresponding to an ADIOS2-formatted dataset The folder "DeepHyper-results" contains the configurational files and model's parameters for all the 186 HPO trials that were successfully completed by the scalable hyperparameter optimization (HPO) runs on Frontier. The content of the folder "DeepHyper-results" I structured as follows: 1) task-list.txt: list of mpnn name, jobid, and deephyper task id 2) gfm_${MPNN}_${JOBID}_0.${TASKID}: run directory with checkpoint files 3) gfm_${MPNN}: deephyper summary directory (*.csv) for each specific MPNN type 4) deephyper-experiment-${JOBID}: output and error logs for each job The file "deephyper-sorted.csv" contains the details of each HydraGNN model built and tested by HPO, obtained by merging the (*.csv) filed from each HPO run executed. Out of all the HPO trials, we selected 10 to continue the training of the respective HydraGNN models. Due to limited computational budget available in the LRN070 allocation we could not complete the training till convergence for all these 10 selected models. The folder "models" contains multiple sub-folders, one per each HydraGNN model trained. Each model sub-folder contains the parameters of each HydraGNN model, with multiple checkpoint-restarts. The list of sub-folders are as follows: 1) multidataset_hpo-BEST1-fp64 2) multidataset_hpo-BEST2-fp64 3) multidataset_hpo-BEST3-fp64 4) multidataset_hpo-BEST4-fp64 5) multidataset_hpo-BEST5-fp64 6) multidataset_hpo-BEST6-fp64 7) multidataset_hpo-BEST7-fp64 8) multidataset_hpo-BEST8-fp64 9) multidataset_hpo-BEST9-fp64 10) multidataset_hpo-BEST10-fp64 Within each one of these folders, additional auxiliary log files are provided with descriptions about how the training proceeded. The lead PaiNN-model is contained inside "multidataset_hpo-BEST6-fp64". The file "mlp_branch_weights" contains the parameters of the multi-layer perceptron (MLP) used to reconcile the predictions of the 16 output decoding heads of the HydragNN architectures. The MLP takes in input the chemical composition of the atomistic structure and predicts averaging weights to linearly mix the predictions of each output decoding head toward consolidating them into a single one. The folder "1.1billion-structure-inference" contains 1.1 billion atomistic structures randomly generated. Each structures is associated with energy and forces predicted with the lead-PaiNN model combined with the MLP model for reconciliation of the multi-branch predictions generated by the 16 output decoding heads. The folder "1.1billion-structure-inference" contains 9,300 (*.tar.gz) subdirectories, one per Frontier compute node used to execute the inference at exascale. Once uncompressed, each (*.tar.gz) subdirectory contains an ADIOS2 (*.bp) file container, where each atomistic structure is stored as a PyTorch-Geometric Data object. The file "export_dataset_environment_variables.sh" contains the environment variables that need to be set before running the HydraGNN code to reproduce the results provided in this dataset release. The code that can be used to load the ADIOS2 files, load HydraGNN models, and run inference is available at: https://github.com/ORNL/HydraGNN/releases/tag/v5.0

36 MATERIALS SCIENCE↗
DOE OSTI2023

Gradient Amine Sorbents for Low Vacuum Swing CO 2 Capture at Ambient Temperature

The growing concerns over CO 2 emissions have led to the development of various methods for CO 2 capture. CO 2 capture by amine-based sorbents has been achieved by temperature (thermal) swing adsorption (TSA) process, pressure swing adsorption (PSA) process, and temperature pressure or vacuuming swing adsorption (TPSA or TVSA) process for CO 2 capture. Amine sorbents for energy-efficient TSA, PSA, VSA, TPSA or TVSA CO 2 capture should possess a capability which allow adsorbed CO 2 to desorb and amine sorbent to be regenerated at low temperatures or low vacuums. Development of such amine sorbents would significantly decrease energy consumption and sorbent degradation during the regeneration step. The objective of this project is to develop a low vacuum swing adsorption (VSA) process for the capture of CO 2 from air. The focus is on the development of amine sorbent which allows CO 2 to adsorb in the form of weakly adsorbed CO 2 . The weakly adsorbed CO 2 species can be collected from the sorbent by applying a low vacuum at ambient temperature. Specifically, no heating (i.e., thermal energy) is needed for regeneration of amine sorbents. This novel sorbent allows VSA to be operated at ambient temperature without a significant energy demand. This process eliminates the energy-intensive heating and cooling process in temperature swing adsorption (TSA) process. Ambient temperature operation could prolong the lifetime of sorbent and minimize the maintenance cost. Extensive sorbent studies with in-situ infrared spectroscopy have revealed the modifications of conventional amine sorbents with additives can increase the fraction of weakly adsorbed CO 2 . The amine sorbents with loaded CO 2 can be regenerated in part by the following approaches: (i) mild heating to temperatures below 70 °C, (ii) flowing purging gas over modified amine sorbents at temperatures below 40 °C, (iii) vacuuming at pressure below 0.2 atm and temperatures below 40 °C. The performance of these modified amine sorbents has been tested in a 60 grams VSA unit at ambient temperatures. The results show that weakly adsorbed CO 2 which can be desorbed from modified amine sorbents with purging gas at 100 cc/min can also be evacuated at a vacuum of 1 psia at room temperature. A preliminary techno- economic analysis indicates the major cost of the VSA process with modified amine sorbents stems from the operation of vacuum pumps. Thus, developing an excellent sealing with minimum leakage for the VSA unit is the most critical task for further the development of this VSA technology.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Enhancing CO 2 Transport Across a PEEK‐Ionene Membrane and Water‐Lean Solvent Interface

Abstract Efficient direct air capture (DAC) of CO 2 will require strategies to deal with the relatively low concentration in the atmosphere. One such strategy is to employ the combination of a CO 2 ‐selective membrane coupled with a CO 2 capture solvent acting as a draw solution. Here, the interactions between a leading water‐lean carbon‐capture solvent, a polyether ether ketone (PEEK)‐ionene membrane, CO 2 , and combinations were probed using advanced NMR techniques coupled with advanced simulations. We identify the speciation and dynamics of the solvent, membrane, and CO 2 , presenting spectroscopic evidence of CO 2 diffusion through benzylic regions within the PEEK‐ionene membrane, not spaces in the ionic lattice as expected. Our results demonstrate that water‐lean capture solvents provide a thermodynamic and kinetic funnel to draw CO 2 from the air through the membrane and into the bulk solvent, thus enhancing the performance of the membrane. The reaction between the carbon‐capture solvent and CO 2 produces carbamic acid, disrupting interactions between the imidazolium (Im + ) cations and the bistriflimide anions within the PEEK‐ionene membrane, thereby creating structural changes through which CO 2 can diffuse more readily. Consequently, this restructuring results in CO 2 diffusion at the interface that is faster than CO 2 diffusion in the bulk carbon‐capture solvent.

Walter, Eric D.↗
DOE OSTI2021

Potential Early Markets for Fusion Energy

We examine potential early markets for fusion energy and their projected cost targets, based on analysis and synthesis of many relevant, recent studies and reports. Seeking to provide guidance to ambitious fusion developers aspiring to enable commercial deployment before 2040, we examine cost requirements for fusion-generated electricity, process heat, and hydrogen production based on today’s market prices but with various adjustments relating to possible scenarios in 2035, such as ‘‘business-as-usual,’’ high renewables penetration, and carbon pricing up to 100 $/tCO 2 . Key findings are that fusion developers should consider focusing initially on high-priced global electricity markets and consider including integrated thermal storage, depending on techno-economic factors, in order to maximize revenue and compete in markets with high renewables penetration. Process heat and hydrogen production will be tough early markets for fusion, but may open up to fusion as markets evolve and if fusion’s levelized cost of electricity falls below 50 $/MWh e . Finally, we discuss potential ways for a fusion plant to increase revenue via cogeneration (e.g., desalination, direct air capture, or district heating) and to lower capital costs (e.g., by minimizing construction times and interest or by retrofitting coal plants).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Net-zero CO 2 by 2050 scenarios for the United States in the Energy Modeling Forum 37 study

The Energy Modeling Forum (EMF) 37 study on deep decarbonization and high electrification analyzed a set of scenarios that achieve economy-wide net-zero carbon dioxide (CO 2 ) emissions in North America by mid-century, exploring the implications of different technology evolutions, policies, and behavioral assumptions affecting energy supply and demand. Here, for this paper, 16 modeling teams reported resulting emissions projections, energy system evolution, and economic activity. This paper provides an overview of the study, documents the scenario design, provides a roadmap for complementary forthcoming papers from this study, and offers an initial summary and comparison of results for net-zero CO 2 by 2050 scenarios in the United States. We compare various outcomes across models and scenarios, such as emissions, energy use, fuel mix evolution, and technology adoption. Despite disparate model structure and sources for input assumptions, there is broad agreement in energy system trends across models towards deep decarbonization of the electricity sector coupled with increased end-use electrification of buildings, transportation, and to a lesser extent industry. All models deploy negative emissions technologies (e.g., direct air capture and bioenergy with carbon capture and storage) in addition to land sinks to achieve net-zero CO 2 emissions. Important differences emerged in the results, showing divergent pathways among end-use sectors with deep electrification and grid decarbonization as necessary but not sufficient conditions to achieve net zero. These differences will be explored in the papers complementing this study to inform efforts to reach net-zero emissions and future research needs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Modeling hydrogen markets: Energy system model development status and decarbonization scenario results

Hydrogen can be used as an energy carrier and chemical feedstock to reduce greenhouse gas emissions, especially in difficult-to-decarbonize markets such as medium- and heavy-duty vehicles, aviation and maritime, iron and steel, and the production of fuels and chemicals. Significant literature has been accumulated on engineering-based assessments of various hydrogen technologies, and real-world projects are validating technology performance at larger scales and for low-carbon supply chains. While energy system models continue to be updated to track this progress, many are currently limited in their representation of hydrogen, and as a group they tend to generate highly variable results under decarbonization constraints. Here, the present work provides insights into the development status and decarbonization scenario results of 15 energy system models participating in study 37 of the Stanford Energy Modeling Forum (EMF37), focusing on the U.S. energy system. The models and scenario results vary widely in multiple respects: hydrogen technology representation, scope and type of hydrogen end-use markets, relative optimism of hydrogen technology input assumptions, and market uptake results reported for 2050 under various decarbonization assumptions. Most models report hydrogen market uptake increasing with decarbonization constraints, though some models report high carbon prices being required to achieve these increases and some find hydrogen does not compete well when assuming optimistic assumptions for all advanced decarbonization technologies. Across various scenarios, hydrogen market success tends to have an inverse relationship to success with direct air capture (DAC) and carbon capture and storage (CCS) technologies. While most model-scenario combinations predict modest hydrogen uptake by 2050 – <10 million metric tons (MMT) – aggregating the top 10 % of market uptake results across sectors suggests an upper range demand potential of 42–223 MMT. The high degree of variability across both modeling methods and market uptake results suggests that increased harmonization of both input assumptions and subsector competition scope would lead to more consistent results across energy system models. The wide variability in results indicates strongly divergent conclusions on the role of hydrogen in a decarbonized energy future.

08 HYDROGEN↗
DOE OSTI2027

Nodal capacity expansion planning with flexible large-scale load siting

We propose explicitly incorporating large-scale load siting into a stochastic nodal power system capacity expansion planning model that concurrently co-optimizes generation, transmission, and storage expansion. The potential operational flexibility of some of these large loads is also taken into account by considering them as consisting of a set of tranches with different reliability requirements, which are modeled as a constraint on expected served energy across operational scenarios. We implement our model as a two-stage stochastic mixed-integer optimization problem with cross-scenario expectation constraints. To overcome the challenge of scalability, we build upon existing work to implement this model on a high performance computing platform and exploit scenario parallelization using an augmented Progressive Hedging Algorithm. The algorithm is implemented using the bounding features of mpisppy, which have shown to provide satisfactory provable optimality gaps despite the absence of theoretical guarantees of convergence. We test our approach and assess the value of this proactive planning framework on total system cost and reliability metrics using realistic testcases geographically assigned to San Diego and South Carolina, with datacenter and direct air capture facilities as large loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Technology, technology, technology: An integrated assessment of deep decarbonization pathways for the Canadian oil sands

As a party to the Paris Agreement, Canada has an ambitious climate target of net-zero emissions by 2050. The country also holds the world's third largest oil reserves in the Alberta oil sands. Given increasing emissions from the oil sands sector, achieving Canada's net-zero target requires significant oil sands decarbonization. If, while phasing out fossil fuels, there is still a demand for Canadian oil sands, then the decarbonization of the resource production process becomes crucial. In this study, we use an enhanced version of the Global Change Analysis Model (GCAM) with a detailed unconventional oil sector for Canada, including mining and in situ resources. We ask, what is the future of the oil sands sector in deeply decarbonized global and Canadian economies? We address this question under four mitigation scenarios with varying global net-zero GHG emissions constraints, three additional representative lower carbon extraction technologies available for the oil sands sector, as well as global direct air capture (DAC) deployment. We find that lower carbon technology deployment allows a 20%–44% increase in oil sands production by 2050 for scenarios with net-zero GHG emissions in 2100 or 2075. DAC helps maintain oil sands production in the most ambitious global decarbonization scenario (net-zero GHG by 2050), without which low international oil demand makes Canadian oil sands production uncompetitive. Canadian oil sands production thus depends highly on the availability of lower carbon extraction technologies and international oil demand, which to a certain extent relies on the availability and global deployment of negative emissions technologies.

04 OIL SHALES AND TAR SANDS↗