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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development of High Bs Fe-Ni Based Metal Amorphous Nanocomposite by Optimization of Glass Forming Ability

A growing interest in electric vehicles challenges soft magnetic materials to improve efficiency and power density. Metal amorphous nanocomposites (MANCs) have lower coercivities and eddy current losses, allowing for greater efficiency and higher switching frequency. The later allows for high motor speeds and improved power density. However, commercially available and laboratory demonstrated MANCs have shortcomings. Fe-based MANCs, such as FINEMET have excellent magnetic properties, but mechanical properties limit their application in high speed electric motors (HSMs). Fe-Co based MANCs, such as HITPERM, have high saturation inductions and good mechanical properties, but elevated magnetostrictive losses. Co-based MANCs and more recent Fe-Ni based MANCs have low losses and good mechanical properties, but lower saturation inductions (1-1.2 T), that limit power density in HSMs. Recently, much work has explored improving saturation induction in MANCs by increasing the content of magnetic elements, which comes at the cost of glass-forming ability (GFA). These efforts where generally limited to trial and error testing. Additionally, all work to date has focused on Fe-based alloys. In this work, a method of using Thermocalc simulation to locate near-eutectic compositions has been applied to Fe-Ni based alloys. Minima in liquidus temperature and solidification range were found for a ternary composition range and used to identify compositions that retain good GFA as the percentage of magnetic elements is increased. The (Fe 70 Ni 30 ) x (B-Si-Nb) 100-x alloy system for x=82% and x=85% was explored by Thermocalc simulations. This is an increase in magnetic element content compared to previously developed x=80% alloys. 3 compositions in the x=82% system, and 1 alloy in the x=85% system where identified and successfully cast as amorphous ribbon. The amorphous nature of the ribbon was confirmed by a bend test and XRD. Magnetic testing was performed by PPMS to measure saturation induction and Curie temperature of the amorphous material. The Curie temperatures increased to 407-438 °C for the x=82% alloys, and 462 °C for the x=85% alloy. Saturation induction increased to 1.28-1.36 T for the x=82% alloy and 1.48 T for the x=85% alloy, which is a significant improvement over the ~370 °C Curie temperature and 1.2 T saturation of previous Fe-Ni alloy. Crystallization behavior was studied by XRD for the x=85% alloy, showing that crystallization process follows a 2-step process of Amorphous → BCC+FCC+Amorphous → BCC+FCC+Fe 3 B+Fe 23 B 6 . Post annealing magnetic properties were studied by strip testing, and saturation induction as high as 1.48 T was seen after optimal annealing. TEM was used to study structure of as cast and annealed material. 2 alloys with x=82% compositions were identified with an optimal crystalline size of 10-20 nm, while the x=85% alloy had this structure in the as cast state. This opens the possibility of using the alloy in the as cast state, without requiring annealing. In conclusion, alloys with good GFA and improved magnetic properties were identified by Thermocalc simulation.

Krimer, Yuval↗
DOE OSTI2021

Numerical Simulations of Cryogenic Hydrogen Cooling in Vortex Tubes with Smooth Transitions

Improving efficiency of hydrogen cooling in cryogenic conditions is important for the wider applications of hydrogen energy systems. The approach investigated in this study is based on a Ranque-Hilsch vortex tube (RHVT) that generates temperature separation in a working fluid. The simplicity of RHVT is also a valuable characteristic for cryogenic systems. In the present work, novel shapes of RHVT are computationally investigated with the goal to raise efficiency of the cooling process. Specifically, a smooth transition is arranged between a vortex chamber, where compressed gas is injected, and the main tube with two exit ports at the tube ends. Flow simulations have been carried out using STAR-CCM+ software with the real-gas Redlich-Kwong model for hydrogen at temperatures near 70 K. It is determined that a vortex tube with a smooth transition of moderate size manifests about 7% improvement of the cooling efficiency when compared vortex tubes that use traditional vortex chambers with stepped transitions and a no-chamber setup with direct gas injection.

08 HYDROGEN↗
DOE OSTI2023

Solarize Fairbanks BRITE: Facilitating Efficient, Resilient Homes in Cold Climates

Solarize Fairbanks began an annual Solarize campaign in the Interior Alaska city in 2020, located in IECC climate zone 8, with a goal to increase the number of solar PV panels in the community. The campaign provides peer support, education, bulk purchase discounts, and simplified installation of solar PV technologies for homes, businesses, and nonprofits. In 2021, the campaign began offering energy audits to building and homeowners with a bulk discount; however, building owners were responsible for pursuing next steps on their own. In 2022, a diverse team of local, state, and federal partners formed a team to create a process to further facilitate energy efficiency improvements alongside solar PV technology. The resulting project, Solarize Fairbanks - Building Resilience for the Interior (BRITE) aims to build out an efficiency component over 3 years. If implemented, it will be the first efficiency add-on to a solarize campaign in Alaska. In year one, the team conducted energy audits of four nonprofits located in the cold climate of Interior Alaska and is providing technical assistance and fundraising for the nonprofits to pursue the recommended retrofits. These audits provided insights on the types of retrofits that could be expected to increase efficiency, comfort, and resiliency of buildings, including LED lighting retrofits, increased envelope insulation, improved building controls, and air source heat pump technology. A pre- and post-retrofit analysis will provide further insight on the energy savings and other benefits of the retrofits. It will also inform the offerings in the following years of the BRITE add-on to Solarize campaigns. In this presentation, program implementers will review the past campaigns of Solarize Fairbanks, summarize the energy efficiency and resiliency analyses of the nonprofit buildings, cover future plans for Solarize Fairbanks BRITE, and provide recommendations for other communities pursuing similar programs.

Alaska↗
DOE OSTI2026

Balance of Plant Modeling and Real-Time Hardware-in-the-Loop Integration with the Microreactor Automated Control System

The advent of novel microreactor technology has driven a focused effort to explore safety and efficiency improvements that can be achieved through the use of automated system control. Development of control strategies, especially for initial demonstration, requires an adequate surrogate environment to safely research failure modes and control integration with realistic hardware delay. However, efficiency gains from control strategies are improved when the scope of controller action is expanded to include system-level dynamics such as downstream heat extraction and mass flow. For this reason, a balance-of-plant (BOP) model of a representative microreactor system has been developed using the TRANsient Simulation Framework of Reconfigurable Models library in Modelica. This model captures a reactor and primary NaK coolant loop that represent corresponding system components of the Microreactor Applications Research Validation and EvaLuation (MARVEL) design as well as a secondary coolant loop and heat extraction representative of the Microreactor Agile Non-Nuclear Experimental Test Bed (MAGNET). This model configuration allows for hardware-in-the-loop (HIL) integration with microreactor automated control system (MACS) hardware in real time through a Python-based gRPC client. Real-time simulation of model performance with emulated hardware and communication delay suggests that under independent proportional-integral-derivative control of BOP model drum dynamics and downstream heat extraction, stable power load following is achievable. A slight delay in load following, filtering of high-frequency dynamics, and localized temperature fluctation suggest room for improvement through the development of higher-level control strategies. The simulated coupling of the MAGNET facility lays the groundwork for future digital twin analysis with a coupled MACS-MAGNET HIL demonstration.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗
DOE OSTI2022

Medium Voltage Integrated Drive and Motor

The objective of this program was to design, build, and test a medium voltage (4,160VAC) high speed permanent magnet machine (HSPMM) and variable speed drive (VSD) that incorporates next generation 10kV silicon carbide (SiC) modules. The use of a HSPMM allows for high efficiency, small footprint, maintenance free operation while the SiC modules enable higher voltage operating conditions, improved efficiency, and small footprint.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates

Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS algorithms have been developed to enhance SPH efficiency, the potential efficiency gains from modern computation hardware remain underexplored. Here, this study investigates the impact of GPU parallel architecture, low-precision computing on GPUs, and GPU memory management on NNPS efficiency. Our approach employs a GPU-accelerated mixed-precision SPH framework, utilizing low precision float-point 16 (FP16) for NNPS while maintaining high precision for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates of particles within background cells. Our testing results show three significant speedup rounds for CPU-based NNPS algorithms. The first comes from parallel GPU computations, with up to a 1000x efficiency gain. The second is achieved through low-precision GPU computing, where the proposed FP16-based RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based approach on GPUs. By optimizing GPU memory bandwidth utilization, the efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as demonstrated in an example with 1 million particles. Our code is released at https://github.com/pnnl/lpNNPS4SPH.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Aerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth Masks

The emergence of a pandemic affecting the respiratory system can result in a significant demand for face masks. This includes the use of cloth masks by large sections of the public, as can be seen during the current global spread of COVID-19. However, there is limited knowledge available on the performance of various commonly available fabrics used in cloth masks. Importantly, there is a need to evaluate filtration efficiencies as a function of aerosol particulate sizes in the 10 nm – 10 µm range, which is particularly relevant for respiratory virus transmission. We have carried out these studies for several common fabrics including cotton, silk, chiffon, flannel, various synthetics, and their combinations. While the filtration efficiencies for various fabrics when a single layer was used ranged from 5-80% and 15-95% for particle sizes <300 nm and >300 nm respectively, the efficiencies improved when multiple layers were used, and when using a specific combination of different fabrics. Filtration efficiencies of the hybrids (such as cotton-silk, cotton-chiffon, cotton-flannel) was >80 % (for particles <300 nm) and >90 % (for particles >300 nm). We speculate that the enhanced performance of the hybrids is likely due to the combined effect of mechanical and electrostatic-based filtration. Cotton, the most widely used material for cloth masks performs better at higher weave densities (i.e., threads per inch) and can make a significant difference in filtration efficiencies. Our studies also imply that gaps (as caused by an improper fit of the mask) can result in over a 60% decrease in the filtration efficiency, implying the need for future cloth mask design studies to take into account issues of “fit” and leakage, while allowing the exhaled air to vent efficiently. Overall, we find that combinations of various commonly available fabrics used in cloth masks can potentially provide significant protection against the transmission of aerosol particles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Abstract Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A Comparison of Novel Woodstove Catalytic Combustors to Improve Low Temperature Light off and Catalyst Stability at High Temperatures and Corrosive Environments, Including Stability of Catalytic Combustors used in Coastal Environments

Wood heaters with catalytic combustors often emit higher levels of particulate matter (PM) and volatile organic compounds (VOCs) at startup before the catalytic combustor is at operational temperature. This project aims to advance wood heater catalytic combustor technology by studying degradation mechanisms in failed field catalysts and using those insights to develop an improved catalytic combustor. Findings will provide critical insights into the degradation mechanisms of wood heater catalytic combustors and inform the design of catalysts that deliver improved efficiency, durability, and performance compared to current commercial products. The outcomes of this project will not only improve catalytic combustor technology but also contribute to operational guidelines to extend catalyst lifetimes.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

De-noising drift chambers in CLAS12 using convolutional auto encoders

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency. In this paper, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising raw hits from drift chambers in the CLAS12 detector. To the best of our knowledge, this is the first time CAE is employed to perform such an operation in this field. During the de-noising phase, it is important to remove as much noise as possible while retaining the valid hits to avoid losing crucial information about the experiment. Here, we show that using CAE, it is possible to remove noise hits while retaining up to 94% of valid tracks for a beam current of 110nA while for lower beam currents (45-55nA), we get up to 98% efficiency. Studies on experimental conditions with increasing noise show that CAE performs better than conventional tracking algorithms in isolating hits belonging to tracks. Specifically, the de-noising algorithm results in tracking efficiency improvements greater than 15%, in real data production procedures with nominal conditions, and up to two times better efficiency in synthetically generated data with high luminosity conditions (90-110nA), indicating that machine learning can lead to significantly shorter times for conducting physics experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparative study of machine learning techniques for post-combustion carbon capture systems

Computational analysis of countercurrent flows in packed absorption columns, often used in solvent-based post-combustion carbon capture systems (CCSs), is challenging. Typically, computational fluid dynamics (CFD) approaches are used to simulate the interactions between a solvent, gas, and column's packing geometry while accounting for the thermodynamics, kinetics, heat, and mass transfer effects of the absorption process. These simulations can then be used explain a column's hydrodynamic characteristics and evaluate its CO 2 -capture efficiency. However, these approaches are computationally expensive, making it difficult to evaluate numerous designs and operating conditions to improve efficiency at industrial scales. In this work, we comprehensively explore the application of statistical ML methods, convolutional neural networks (CNNs), and graph neural networks (GNNs) to aid and accelerate the scale-up and design optimization of solvent-based post-combustion CCSs. We apply these methods to CFD datasets of countercurrent flows in absorption columns with structured packings characterized by several geometric parameters. We train models to use these parameters, inlet velocity conditions, and other model-specific representations of the column to estimate key determinants of CO 2 -capture efficiency without having to simulate additional CFD datasets. We also evaluate the impact of different input types on the accuracy and generalizability of each model. We discuss the strengths and limitations of each approach to further elucidate the role of CNNs, GNNs, and other machine learning approaches for CO 2 -capture property prediction and design optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2024

Linac_Gen: Integrating Machine Learning and Particle-in-Cell Methods for Enhanced Beam Dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in Linacs over traditional methods, through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of Linac systems, as evidenced in its application to Fermilab's PIP-II Linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in linacs over traditional methods through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of linac systems, as evidenced in its application to Fermilab's PIP-II linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

SAM Code Development for Source Term Modeling in Fluoride-salt-cooled High-temperature Reactors

The SAM code is under development and supported by DOE-NE’s Nuclear Energy Advanced Modeling and Simulation program as a modern system-level modeling and simulation tool for advanced non-light water reactor safety analyses. These advanced reactor concepts incorporate novel and improved approaches to achieve safety and economic feasibility. Many concepts leverage higher operating temperatures for improved efficiency with a variety of coolants and structural materials to support those needs. Such design choices may also pose unique radiological source term risks. There are continuous efforts to incorporate new physics and phenomena relevant to advanced reactor concepts, including capabilities to enable system-level source term modeling. The Pebble-Bed Fluoride-salt-cooled High-temperature Reactor (PB-FHR) is a promising candidate among advanced nuclear reactor concepts with its improved passive safety characteristics and high thermal efficiency. In addition to past efforts to support the development and utilization of SAM for PB-FHR safety analysis, the species transport modeling capabilities in SAM have been extended to simulate source term phenomena in concepts like the PB-FHR. The PB-FHR concept utilizes pebble-form TRISO fuel and FLiBe salt coolant to provide robust barriers to the release of almost all fission products and radiological source terms. However, tritium poses a unique radiological risk due to its significant production from neutron interactions with 6 Li and 9 Be in the FLiBe as well as its high mobility at elevated temperatures where it can permeate through structural metals. Secondly, the use of graphite at elevated temperatures poses the risk of oxidation damage to any structural components or fuel pebbles that are exposed to accidental air ingress. This report summarizes progress made in modeling these phenomena in SAM, which leverages the system-level multi-physics thermal hydraulic simulation to support an effective engineering-scale source term modeling capability. A tritium transport model is developed to simulate the various transport pathways in FHR and MSR concepts and is presented through verification and validation examples as well as demonstrations simulating experimental test loops and reference plant FHR models. A graphite oxidation model is also introduced to calculate local oxidation rates and is presented with initial validation results.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Optimization of energy requirements for CO 2 post-combustion capture process through advanced thermal integration

The energy optimization modeling work described here was performed to determine efficiency improvements that could be achieved for existing coal-fired power plants to retrofit a partial CO 2 capture from the post-combustion flue gas for carbon sequestration through thermal integration. The work presented includes optimization of the mono-ethanol amine (MEA)-based post-combustion CO 2 capture to reduce energy requirements that could be achieved at existing power plants by thermal integration of the steam turbine cycle, boiler, CO 2 compression train and post-combustion CO 2 capture process to offset efficiency and capacity losses that would be incurred by retrofit or implementation of post-combustion CO 2 capture. Furthermore, partial CO 2 capture, involving treatment of less than 100% of the flue gas leaving the plant and modular design of the CO 2 scrubbing system, was also investigated. Thermal integration of the steam turbine cycle with boiler and CO 2 compression train improved cycle and plant performance and offset, in part, the negative effects of post-combustion CO 2 capture. The best-analyzed integration options improved gross power output by 5% and net unit efficiency by 1.57%, relative to the conventional MEA process. Operating with 40% CO 2 capture increased gross power output by 11.6–14% (depending on the MEA thermal integration option), relative to the conventional MEA integration and 90% CO 2 capture. The improvement in net unit performance is larger compared to the improvement in turbine cycle performance because of the CO 2 compression work, which is also reduced by partial CO 2 capture.

42 ENGINEERING↗
DOE OSTI2025

Efficient single-photon emission via quantum-confined charge funneling to quantum dots

Quantum light sources, particularly single-photon emitters (SPEs), are critical for quantum communications and computing. Among them, III-V semiconductor quantum dots (QDs) have demonstrated superior SPE metrics, including near-unity brightness, high photon purity, and indistinguishability, making them especially suitable for quantum applications. However, their overall quantum efficiency—determined by a product of the internal, excitation, and outcoupling efficiencies—remains limited, primarily due to low (typically below 0.1%) excitation efficiency. To mitigate the low efficiency under non-resonant pumping, here we realize liquid droplet etched GaAs QDs in a microscale 3D AlGaAs charge-carrier funnel. The funnel channels charge carriers to the QD and enhances the overall emission efficiency by over one order of magnitude while preserving the SPE behavior. We reveal that a modified energy landscape around the QD leads to the excitation efficiency improvement. These energy landscape-modified QDs can be operated with optical excitation up to 10 μm away, raising the promise of efficient electrically driven QD SPEs for quantum information systems.

Park, Sanghyeok [Sandia National Laboratories (SNL↗
DOE OSTI2025

Graphene reinforced UHMWPE fibers

Thermoplastic polymers are increasingly used in electric vehicles, hydrogen fuel cell vehicles, and other decarbonization applications due to their lightweight and formability. Higher-strength polymers are needed to supplant metals in the vehicle structure, thereby reducing mass and improving efficiency. Ultra-high molecular weight polyethylene (UHMWPE) fibers possess one of the highest strength-to-weight ratios of technical polymers, and further improvement via reinforcement by nanofillers, such as graphene, will expand their performance envelope. Here, in this work, UHMWPE/graphene nanocomposite fibers were gel spun and characterized for their morphological, microstructural, thermal, and mechanical properties. The addition of a low fraction of graphene improved the tensile strength of the fibers by 25% and tensile modulus by 32%. Differential scanning calorimetry showed an increase in melting temperature and degree of crystallinity, which indicates improved coordination of the molecular chains induced by the addition of graphene. The reinforcement also affected the cross-sectional shape of the fibers; the aspect ratio of the fibers’ elliptical shape declined with increasing graphene content showing the skeletal effect of the graphene nanofillers in the polymer matrix. The reinforcing effect of graphene declined above a threshold concentration, and theoretical modeling was applied to demonstrate that increased agglomerates led to reduced properties. This work demonstrates a simple, effective method to produce graphene-reinforced UHMWPE fibers and lays a foundation for understanding the potential for leveraging graphene to form ultra-high-performance nanocomposite fibers for myriad engineering applications.

36 MATERIALS SCIENCE↗