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

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization. There is a lack of understanding on the impact of spatial scale, semantic granularity, and geographic context on POI-based land use modeling, particularly large-scale land use modeling. In this study, we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions. We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources. Then, by combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

58 GEOSCIENCES↗
DOE OSTI2021

Fermionic systems for quantum information people

The operator algebra of fermionic modes is isomorphic to that of qubits, the difference between them is twofold: the embedding of subalgebras corresponding to mode subsets and multiqubit subsystems on the one hand, and the parity superselection in the fermionic case on the other. We discuss these two fundamental differences extensively, and illustrate these through the Jordan–Wigner representation in a coherent, self-contained, pedagogical way, from the point of view of quantum information theory. Our perspective leads us to develop useful new tools for the treatment of fermionic systems, such as the fermionic (quasi-)tensor product, fermionic canonical embedding, fermionic partial trace, fermionic products of maps and fermionic embeddings of maps. We formulate these by direct, easily applicable formulas, without mode permutations, for arbitrary partitionings of the modes. It is also shown that fermionic reduced states can be calculated by the fermionic partial trace, containing the proper phase factors. We also consider variants of the notions of fermionic mode correlation and entanglement, which can be endowed with the usual, local operation based motivation, if the parity superselection rule is imposed. We also elucidate some other fundamental points, related to joint map extensions, which make the parity superselection inevitable in the description of fermionic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multimodal representation learning for predicting molecule–disease relations

Motivation: Predicting molecule–disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule–molecule, molecule–disease and disease–disease semantic dependencies can potentially improve prediction performance. Methods: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule–disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. Results: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. Availability and implementation: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Learning nuclear cross sections across the chart of nuclides with graph neural networks

We explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section data into a latent space using either variational autoencoders (VAEs) or implicit neural representations (INRs). Then, we train graph neural networks (GNNs) on the resulting embeddings to predict missing values across the nuclear chart by leveraging the topological structure of neighboring isotopes. We demonstrate accurate cross section predictions within a 9 × 9 block of missing nuclei. We also find that the optimal GNN training strategy depends on the type of latent representation used, with VAE embeddings performing best under end-to-end optimization in the original space, while INR embeddings achieve better results when the GNN is trained only in the latent space. Furthermore, using clustering algorithms, we map groups of latent vectors into regions of the nuclear chart and show that VAEs and INRs can discover some of the neutron magic numbers. These findings suggest that deep-learning models based on the representation encoding of cross sections combined with graph neural networks hold significant potential in augmenting nuclear theory models, e.g., by providing reliable estimates of covariances of cross sections, including cross-material covariances.

Machine learning↗
DOE OSTI2024

Impacts of Spatial Resolution in a High-Fidelity Capacity Expansion Model: An ERCOT Case Study

Capacity expansion models are important tools in examining the evolution of the electric power sector. Embedded in these tools are many modeling choices with consequential impacts on computational burden and associated analysis. In this study, we adjust the spatial resolution of the Regional Energy Deployment System (ReEDS) to understand the implications of higher-fidelity modeling on energy system projections and model solve times. The native ReEDS regions capture the contiguous United States in 134 balancing areas whereas the regions in the higher-resolution version are defined by over 3,000 U.S. counties. Using both resolutions, we conduct a case study of the Texas Interconnection (The Electric Reliability Council of Texas [ERCOT]) to explore differences in model projections and to inform appropriate applications of high spatial resolution in a large-scale, applied capacity expansion model.

county↗
DOE OSTI2020

Thermal Analysis of Large Area Additive Manufacturing Resistance Heating Composites for Out of Oven/Autoclave Applications

Additive Manufacturing (AM) of carbon fiber (CF) reinforced composite has received growing attention because of the design flexibility, superior mechanical properties, improved thermal properties, and weight reduction. Autoclave tooling was proven to be a successful application for large scale AM technology. The capital cost, and cost associated with heating, and cycle time in a conventional autoclave process is relatively high. Thus, an innovative design of AM mold with an efficient heating scheme is essential. This study represents an innovative method of the resistive heating of composite molds which does not require a room size oven for heating during the curing processing. Therefore, it has the potential to reduce the operating cost drastically. For the design validation and feasibility study, we performed a numerical analysis of the wire embedded and AM mold parts. The goal of this study is to determine and optimize the thermal behavior of the printed mold with embedded wire technology. It is anticipated that the larger distance between the embedded wires along the printing direction (z-direction) increase the cold spot, on the other hand, a close distance of the wire can create the unwanted localize heating, thus melting. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) was used for the simulation purpose. Thermal characterization was set to 100°C to avoid the thermal deformation or bulging on the part surface.

billah, Kazi↗
DOE OSTI2023

THERMAL ANALYSIS AND DESIGN OF SELF-HEATING MOLDS USING LARGE-SCALE ADDITIVE MANUFACTURING FOR OUT-OF-AUTOCLAVE APPLICATIONS

Autoclave processing is a commonly used state-of-the-art fiber-reinforced composite manufacturing technology, albeit high capital cost, long cycle times and high energy consumption. Alternatively, out-of-autoclave processing reduces the initial and operating costs while producing composite structures with similar quality as that of autoclave parts. Additive Manufacturing (AM) the scaled-up molds for out-of-autoclave process using carbon fiber (CF) reinforced composite offers design flexibility, enhanced mechanical, and thermal properties in addition to reduction in weight and cost. However, heating of these molds using an oven is still expensive and necessitates an energy-efficient heating process. In this study, resistive heating through heating elements embedded within fiber reinforced composite molds is used as an efficient heating mechanism. The goal is to design wire embeddings and determine the optimal heat flux density to achieve a target uniform temperature of 1000C across the mold surface. To this end, numerical analyses were performed to evaluate the temperature distribution across the composite mold surface for a given wire placement and mold configuration. Constant thermal properties of the 20 wt.% short CF reinforced acrylonitrile butadiene styrene (ABS) were used in the thermal analysis. Time taken to reach the steady state temperature was also estimated. Design guidelines for wire embeddings were included to enable efficient manufacturing of fiber-reinforced composites through out-of-autoclave molds.

Pokkalla, Deepak↗
DOE OSTI2022

Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements

An emerging use-case of machine learning (ML) is to train a model on a high-performance system and deploy the trained model on energy-constrained embedded systems. Neuromorphic hardware platforms, which operate on principles of the biological brain, can significantly lower the energy overhead of a machine learning inference task, making these platforms an attractive solution for embedded ML systems. In this paper, we present a design-technology tradeoff analysis to implement such inference tasks on the processing elements (PEs) of a Non-Volatile Memory (NVM)-based neuromorphic hardware. Through detailed circuit-level simulations at scaled process technology nodes, we show the negative impact of technology scaling on the information-processing latency, which impacts the quality-of-service (QoS) of an embedded ML system. At a finer granularity, the latency inside a PE depends on 1) the delay introduced by parasitic components on its current paths, and 2) the varying delay to sense different resistance states of its NVM cells. Based on these two observations, we make the following three contributions. First, on the technology front, we propose an optimization scheme where the NVM resistance state that takes the longest time to sense is set on current paths having the least delay, and vice versa, reducing the average PE latency, which improves the QoS. Second, on the architecture front, we introduce isolation transistors within each PE to partition it into regions that can be individually power-gated, reducing both latency and energy. Finally, on the system-software front, we propose a mechanism to leverage the proposed technological and architectural enhancements when implementing a machine-learning inference task on neuromorphic PEs of the hardware. Evaluations with a recent neuromorphic hardware architecture show that our proposed design-technology co-optimization approach improves both performance and energy efficiency of machine-learning inference tasks without incurring high cost-per-bit.

42 ENGINEERING↗
DOE OSTI2025

Position-Enhanced Gradient Attack (PEGA) on Medical Language Models

Federated Learning (FL) enables collaborative training of language models on sensitive clinical notes without sharing the data. However, this paradigm is vulnerable to gradient inversion attacks that can reconstruct private data from shared gradients. We find that state-of-the-art attacks are less effective in the medical domain, failing to overcome the unique challenges posed by its specialized vocabulary and unstructured format. To address this, we introduce the Position-Enhanced Gradient Attack (PEGA), a novel attack that makes gradients position-aware by optimizing token and position embeddings simultaneously. PEGA employs two key innovations: a periodic sorting of positional embeddings to resolve token order ambiguity and a late-stage embedding replacement strategy to correct hard-to-recover critical tokens. To evaluate the leakage of sensitive data more directly, we also propose the Unified PHI-Recall (UPHI), a new metric measuring the recovery of Protected Health Information. Experiments on the MIMIC-III dataset show that PEGA significantly outperforms leading attacks like TAG and LAMP, particularly in its ability to reconstruct identifiable patient information, exposing a more severe and nuanced privacy risk in federated medical NLP.

Xu, Nuo [University of Minnesota]↗
DOE OSTI2025

ChemEcho v1.0

ChemEcho is a tool that converts tandem mass spectra into embeddings used to build machine learning (ML) models with fully explainable predictions. It provides an API for transforming raw tandem mass spectral data into embeddings, along with functions for training and validating ML models. Additionally, it includes utilities for retrieving and cleaning training data. ChemEcho is broadly applicable in ML pipelines that use tandem mass spectra for a variety of tasks, such as chemical classification or bioactivity mining. While there are existing methods to generate embeddings from fragmentation data, ChemEcho's approach ensures that predictions remain interpretable, enabling experts to evaluate results and generate hypotheses about the underlying data.

Harwood, Thomas [Lawrence Berkeley National Labora↗
DOE OSTI2021

Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope

From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).

Bilheux, Hassina↗
DOE OSTI2022

Experimental Investigation of Low-Frequency Distributed Acoustic Strain-Rate Responses to Propagating Fractures

Summary Low-frequency distributed acoustic sensing (LF-DAS) responds to strain changes due to far-field fracture propagation. To better understand the LF-DAS response to fracture propagation, we performed laboratory-scale hydraulic fracture experiments with embedded optical strain sensors. The objectives of this research are to generate hydraulic fractures of known geometry, measure the strain response along the embedded fiber-optic cable comparable to LF-DAS measurements, and use the results to inform the interpretation of field-derived LF-DAS data. The experiments were conducted in unconfined transparent cubic blocks with a dimension of 8 in. on each side. The block was made of transparent epoxy to visualize the fracture propagation. Fiber-optic sensing cables were embedded in the block at different distances to the source of injection. We injected dyed water through an injection tubing to generate a transverse, radial fracture along an initial flaw. An optical interrogator recorded the response of offset fiber Bragg grating (FBG) strain sensors normal to the plane of the fracture. The strain data were visualized on a waterfall plot, akin to visualizations of field-derived LF-DAS data. Dimensional analysis was used to scale the laboratory results to field conditions. We compared the evolution of the strain response at the fiber-optic cable, injection pressure, and rate with known fracture geometry. The measured strains were compared with Sneddon’s (1946) linear elastic solution for a penny-shaped crack and found to follow this behavior. The generated radial fractures in transparent media can be modeled with Sneddon’s linear elastic radial fracture model and a Mode I critical stress intensity factor. The LF-DAS characteristic response of a narrowing region of extension surrounded by compression was exhibited as a fracture that approached and intersected the fiber-optic cable. The experimentally derived strain and strain-rate waterfall plots with known fracture geometry, injection rate, and pressure response provide insight in understanding LF-DAS responses in the field. Furthermore, we developed a method to estimate fracture geometry evolution from the fiber-optic strain data and validated the method against the experimental data.

Engineering↗
DOE OSTI2021

Reactor Pressure Vessel Fracture Mechanics Development and Concrete Application Testing for Grizzly

The Grizzly code is being developed to address degradation issues in nuclear reactor structures and components. For light-water reactors, Grizzly currently has capabilities to simulate degradation processes and their effects on structural integrity in two key areas: reactor pressure vessels (RPVs) and reinforced concrete structures. This report documents improvements made to Grizzly’s ability to address both of these structural systems. For RPVs, the reduced-order models (ROMs) used in fracture mechanics calculations have been expanded to allow their application over a broader range of the parameter space than was permitted by the previous models. The ROMs currently used in Grizzly for the evaluation of flaws that are fully embedded within the RPV (as opposed to surface-breaking flaws) are based on a model that is known to be conservative, indicating higher stress intensity factors than would be obtained from direct simulations. A more accurate model that eliminates these excess conservatisms has been recently included in the American Society of Mechanical Engineers Boiler and Pressure Vessel Code but was not applicable for flaws near the RPV surface, which is where the most critical flaws are usually located. That model has recently been extended for increased applicability in this near-surface region. The ROMs for embedded flaws in the Grizzly code have been expanded to include these recent extensions, which permit their use in a much broader set of cases than previously possible. Direct 3D simulations have been used to check these ROMs and have shown good agreement in most cases, although there are still some cases that need further investigation. There are considerable benefits to using these these more accurate and less conservative ROMs for embedded flaws. On a benchmark probabilistic fracture mechanics problem tested here, the conditional probability of fracture initiation computed for a population of flaws in a single plate in an RPV decreased by over a factor of 3. To address aging in reinforced concrete structures, a capability to simulate multiple degradation mechanisms, including alkali-silica reaction and radiation-induced volumetric expansion has been developed in Grizzly over the past several years. This had previously been demonstrated on laboratory-scale specimens but not on full-scale nuclear concrete structures with reinforcement. To demonstrate the applicability of Grizzly to the analysis of large-scale structures of interest, a full 3D model of a representative reinforced concrete structure, including a complex arrangement of reinforcing bars, was developed and demonstrated in Grizzly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Printed Strain Gauges for Anthropomorphic Test Dummies for Vehicle Crash Testing

In Phase 1 of the technical collaboration between Oak Ridge National Laboratory (ORNL) and Humanetics, additive manufacturing was studied as a method to produce high strain – strain gauges that are embedded in an additively manufactured (AM) anthropomorphic crash test dummy (ATD) to provide high fidelity data to significantly improve vehicle safety. In this study commercially available conductive filament was acquired and the mechanical properties were tested for use in this application. The design space of embedded printed strain gauges was explored with multiple configurations printed and tested for performance. The testing resulted in finding that commercially available filament materials were unable to meet the performance requirements. Finally, an extensive literature survey was performed on advanced manufacturing techniques and novel high strain conductive materials to manufacture strain gauges that can be embedded in to the AM ATD. Humanetics have taken the literature survey and have reached out to material suppliers such as Techmer to provide custom blends of materials for testing. Humanetics will continue to build off of the research that was performed and test custom materials for use in AM ATDs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Doped Lanthanum Chromite-refractory Based Composites Sensors for High Temperature Monitoring in Harsh Environments Systems

In order to test and monitor the operational stability and conditions of various energy, and manufacturing systems and their components, development of accurate sensors capable of operating at temperatures in excess of 1000 °C in various environments for long durations is required. In addition, many of these harsh-environment systems do not permit sensors to be directly inserted, so these sensors need to be embedded into thermal protective materials. In this work were developed high-temperature embedded thermocouples composed of various doped lanthanum chromites-refractory based composites synthesized by a mixed-oxide route and directly embedded into a Al2O3 based refractory. The electrical properties were specifically manipulated by altering the level of percolation of the conductive phase within the refractory constituent. Chemical stability of sensors was determined by phase development analysis by X-ray diffraction and X-ray photoelectron spectroscopy; Seebeck coefficients were determined by thermoelectric voltages measurements at temperature up to 1400 oC.

Perovskite LaCrO3, High-temperature conductivity, ↗
DOE OSTI2025

CIE Curriculum Guide (V.2.0)

The Cyber-Informed Engineering (CIE) Curriculum Guide offers a comprehensive framework, guidance, and resources for integrating CIE into university-level engineering programs and related educational activities. The primary goal is to help educators adopt CIE principles into their teaching to produce future engineers and technicians who understand digital risks in modern engineered systems, thereby addressing the nation’s infrastructure resilience needs. This guide outlines practical integration examples, links to resources to accelerate CIE adoption, and shares insights from partner academic institutions on various implementation strategies. CIE is a framework for embedding engineered controls that mitigate the impact of cyber-attacks in any cyber-physical system used in critical energy infrastructure and other sectors. Developed by the U.S. Department of Energy’s Office of Cybersecurity, Energy Security, and Emergency Response (CESER), the National Cyber-Informed Engineering Strategy emphasizes embedding CIE into formal education, training, and credentialing. This guide supports this strategic objective by providing examples of integrating CIE concepts into engineering curricula, from class activities to new courses and certificate programs. The importance of educating cyber-informed engineers is underscored by the evolving cybersecurity threats facing engineered systems. As industrial control systems (ICS) increasingly incorporate digital technologies, the responsibility for security extends to both cyber professionals and engineers. CIE addresses critical gaps in designing and protecting physical systems with digital components against cyber risks, ensuring engineers consider digital risk throughout the engineering design lifecycle. Currently, engineering education does not routinely include cyber-informed principles, highlighting a gap in addressing modern engineering system risks. This guide advocates for updating engineering curricula to include digital risk management as a fundamental element. By doing so, future engineers will be equipped to design resilient systems that mitigate digital risks from the outset. Through this guide, engineering faculty can integrate CIE into their curricula, bridging the gap between digital risk and engineering. This approach prepares a cyber-informed workforce capable of safeguarding the cyber-physical systems crucial to national security and public welfare. By embedding CIE into education and training, institutions can produce engineers and technicians who can effectively mitigate cyber impacts throughout the engineering design lifecycle, resulting in more secure critical infrastructures.

42 - ENGINEERING↗
DOE OSTI2026

Thriving in the Carbon-Aware Market: How to Account for Emissions in the Era of Carbon-Centered Trade Policies

Emerging global policies, such as the European Union's enacted Carbon Border Adjustment Mechanism and similar policies under development in Canada, Australia and the United Kingdom, will place a premium on goods traded into their territories with higher embedded emissions than those produced domestically. U.S. manufacturers could stand to benefit from such policies; given the investments U.S. industry has made to reduce the energy and emission intensities of its operations. For example, the overall GHG intensity of U.S. steel production in 2019 was ~0.96 t CO2/t steel, less than half that of China (~1.97 t CO2/t steel), and bested only by Italy. To realize these benefits, transparent, accurate, interoperable and accepted embedded emissions accounting and calculation methods are required. Achieving this requires overcoming challenges related to data availability, boundary definitions, and product definitions among others, both at individual facilities as well as through value chains. We will present technical findings on methodology considerations and data-availability constraints for determining the emissions of traded goods, using steel as a pilot and leveraging publicly available data. Issues such as determining the appropriate scope for emissions accounting, implications of the specificity of product chosen, emissions allocation in multi-product facilities, and enumeration of emissions for products manufactured across multiple facilities will be discussed. By sharing the results of our efforts, we aim to inform the development and execution of embedded emissions accounting methods from a technical perspective such that U.S. manufacturers can thrive in emerging global markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Direct Air Capture Using Trapped Small Amines in Hierarchical Nanoporous Capsules on Porous Electrospun Fibers (Final Technical Report)

This report summarizes the carbon capture research and development conducted by The State University of New York at Buffalo (UB) and GTI Energy (GTI) for award “DE-FE0031969: Direct Air Capture Using Trapped Small Amines in Hierarchical Nanoporous Capsules on Porous Electrospun Fibers” sponsored by the U.S. Department of Energy (DOE). The objective of this project is to develop an innovative sorbent structure of trapped small amines in HNC embedded in PEF for DAC. This involves tailoring both sorbent and PEF materials to achieve a compact system for DAC with high capacity for CO 2 at concentrations typically available in air and at near ambient conditions. An innovative sorbent structure of trapped small amines in hierarchical nanoporous capsules (HNC) embedded in porous electrospun fibers (PEF) was developed for direct air capture (DAC). This involves tailoring both sorbent and PEF materials to achieve a compact system for DAC with high capacity for CO 2 at concentrations typically available in air and at near ambient conditions. An interfacial polymerization process was developed, which utilized loaded amines inside mesoporous silica and trimesoyl chloride (TMC) dissolved in organic solvents as the precursors, to generate a polyamide (PA) coating layer on mesoporous silica and thus trap amines. Reaction conditions, including TMC concentration, organic solvents, reaction time, etc., for interfacial polymerization were optimized to effectively trap loaded amines, and cyclic heating-cooling operation was conducted to evaluate the coating quality. Larger pore volume mesoporous silica was also synthesized to increase amine loading and thus increase CO 2 capacity. The optimized sorbent material exhibited CO 2 capacity as high as 4.88 mmol/g under humid DAC conditions and negligible loss (<1%) during 10 cyclic heating-cooling operations. The optimized PA-coated sorbent also showed fast adsorption and desorption kinetics, with <20% t1/2 increase compared to uncoated sorbent. PEF fabrication conditions, including organic solvents for dissolving core and shell polymers, voltage, distance from the nozzle to the collection panel, etc. were adjusted to better incorporate HNC. After incorporating the optimized sorbent material into PEF, the structured sorbent had a CO 2 capacity of approximately 4.0 mmol/g under humid DAC conditions, with capacity loss of 0.17% per cycle and t1/2 increase less than 10%. A techno-economic analysis (TEA) for the process design for a DAC system based on our developed sorbent structure of trapped small amines in HNC embedded in PEF was conducted. The process design included process description and major equipment sizing and energy and mass balances in addition to scale-up research results and estimated capture cost. Aspen Adsorption Simulator was used to fit the experimentally measured breakthrough curves and extract equilibrium and kinetic data of the optimized sorbent. Our results indicated that for a DAC plant with CO 2 productivity of 3,000 tonne/year, the levelized cost of CO 2 capture was $\$$612/tonne, with the largest contribution of 44.33% from the fixed operation cost. Increasing CO 2 productivity, while maintaining similar fixed operation cost, is expected to significantly reduce the CO 2 capture cost. A sensitivity study was also conducted to understand the influence of total plant cost, sorbent cost, CO 2 concentration in the feed, sorbent mat lifetime, sorbent regeneration electricity, and adsorption blower pressure drop on the levelized cost of CO 2 capture, revealing a capture cost range of $\$$520-870/tonne.

36 MATERIALS SCIENCE↗