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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Boosting the sodium storage behaviors of carbon materials in ether-based electrolyte through the artificial manipulation of microstructure

The porous carbon blacks rationally designed by a facile yet efficient NH 3 thermal etching route have been investigated as anode materials in an ether-based electrolyte for sodium-ion batteries. The as-synthesized CBN35 carbon black with a 35% weight loss after NH3 thermal etching exhibited a large specific charge capacity of 352 mAh g -1 at 50 mA g -1 and a superior rate capability of 101 mAh g -1 at 16000 mA g -1 , due to its highest microporosity, an appropriate surface area, a desirable microstructure, and a promising hybrid intercalation mechanism. Impressively, even cycled at 1600 mA g -1 over 3200 cycles, an outstanding reversible capacity of 103 mAh g -1 with a negligible 0.0162% capacity loss per cycle can still be achieved. Based on the multimodal characterizations including the structural probes of phase evolution for carbon materials, the electrochemical techniques, and the surface-sensitive XAS measurements, the exceptional electrochemical properties should stem from several merits of modified carbon black system. While the particular microporous structure provides relatively more accessible sodium storage sites, a novel hybrid intercalation mechanism in ether-based electrolyte would incorporate the sodium ion insertion into the disordered structure with the solvated sodium ion species co-intercalation into the graphitic phase. In addition to the diffusion-controlled redox reactions, the noticeable surface-induced pseudocapacitive reactions also significantly contribute to the charge storage upon sodiation and guarantee the rapid migrations of sodium ions/solvated compounds. In conclusion, this system further features a controlled emergence of a robust but thin solid electrolyte interphase layer, which could suppress the side reactions of active electrode with reactive electrolyte, maintain the fragile porous structure upon cycling, and facilitate the migrations of sodium ions and solvated sodium ion compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dynamic Performance Enhancement for Nonlinear Stochastic Systems Using RBF Driven Nonlinear Compensation with Extended Kalman Filter

In this paper, a novel hybrid control method is proposed to enhance the control performance of the PI based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. Firstly, to enhance the tracking performance of the PI controller where the PI parameters are fixed in many actual control systems, the compensative signal is formed using the extended Kalman filter(EKF) based state estimator and driven by the radial basis function (RBF) neural network based compensator. In addition, the weights of RBF is trained to minimize the entropy criterion of tracking error as the process is subjected to non-Gaussian disturbances. Meanwhile, since the precise statistical property of noises is hard to obtain for many industrial processes, the kernel density estimation (KDE) technique is employed in this paper to estimate the entropy. The convergence of RBF network is discussed and the stability of the resulted closed-loop hybrid control system is analyzed in mean square sense. Finally, a numerical example and a practical system testing are given to illustrate the effectiveness of the proposed control method.

stochastic systems, PI Controller, Entropy, Extend↗
DOE OSTI2025

Characterization of Gramicidin A in Triblock and Diblock Polymersomes and Hybrid Vesicles via Continuous Wave Electron Paramagnetic Resonance Spectroscopy

Studying membrane proteins in a native environment is crucial to understanding their structural and/or functional studies. Often, widely accepted mimetic systems have limitations that prevent the study of some membrane proteins. Micelles, bicelles, and liposomes are common biomimetic systems but have problems with membrane compatibility, limited lipid composition, and heterogeneity. To overcome these limitations, polymersomes and hybrid vesicles have become popular alternatives. Polymersomes form from amphiphilic triblock or diblock copolymers and are considered more robust than liposomes. Hybrid vesicles are a combination of lipids and block copolymers that form vesicles composed of a mixture of the two. These hybrid vesicles are appealing because they have the native lipid environment of bilayers but also the stability and customizability of polymersomes. Gramicidin A was incorporated into these polymersomes and characterized using continuous wave electron paramagnetic resonance (CW-EPR) and transmission electron microscopy (TEM). EPR spectroscopy is a powerful biophysical technique used to study the structure and dynamic properties of membrane proteins in their native environment. Spectroscopic studies of gramicidin A have been limited to liposomes; in this study, the membrane peptide is studied in both polymersomes and hybrid vesicles using CW-EPR spectroscopy. Lineshape analysis of spin-labeled gramicidin A revealed linewidth broadening, suggesting that the thicker polymersome membranes restrict the motion of the spin label more when compared to liposome membranes. Statement of Significance: Understanding membrane proteins’ structures and functions is critical in the study of many diseases. In order to study them in a native environment, membrane mimetics must be developed that can be suitable for obtaining superior biophysical data quality to characterize structural dynamics while maintaining their native functions and structures. Many currently widely accepted methods have limitations, such as a loss of native structure and function, heterogeneous vesicle formation, restricted lipid types for the vesicle formation for many proteins, and experimental artifacts, which leaves rooms for the development of new biomembrane mimetics. The triblock and diblock polymersomes and hybrid versicles utilized in this study may overcome these limitations and provide the stability and customizability of polymersomes, keeping the biocompatibility and functionality of liposomes for EPR studies of membrane proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Application of ultrathin TiO 2 layers in solar energy conversion devices

Abstract A vital pursuit in solar energy research is to achieve high efficiency and long‐term stability in energy conversion devices. Coating or sandwiching an ultrathin layer of TiO 2 onto active solar components has been demonstrated to be a remarkably effective and facile strategy to improve their stability. The TiO 2 film acts as a protection layer and enhances solar energy conversion efficiency by passivating surface recombination sites or facilitating electron transport. To date, numerous efforts have been devoted to applying ultrathin TiO 2 layers in various kinds of solar energy conversion devices. In this study, we review deposition techniques, characterization methods of the resulting ultrathin TiO 2 layer, as well as its applications in dye‐sensitized solar cells, organic–inorganic hybrid solar cells, and photoelectrochemical cells.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Information-Guided Dynamic Nuclear Polarization

A spin ensemble in thermal equilibrium continuously undergoes random fluctuations analogous to those observed in Brownian motion, a process known as spin noise. Here, we investigate the dynamics of a system comprising a spin-1 paramagnetic center and a hyperfine-coupled spin-1/2 nucleus in the vicinity of a level crossing. We theoretically show that nuclear spins polarize efficiently under the combined action of thermal fluctuations and a closed-loop feedback protocol. The latter articulates periodic observations of the electronic magnetization and radio-frequency pulses connecting hybrid states with opposite nuclear spin alignment. Since nuclear polarization emerges from electronic spin fluctuations, not spin order, this microwave-free technique generically benefits from warmer, not colder, operation temperatures. Furthermore, because the spin dynamics at play near a level crossing is rather insensitive to the absolute value of the magnetic field, our work promises opportunities for high-field dynamic nuclear polarization, which is difficult to attain through present methods.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

X-ray spectroscopic investigation of crystal fields in Ce 2 Rh 1 − x Ir x In 8 heavy fermions

The higher dimensionality in the crystal fields of the Ce 2 M In 8 ( M = Rh , Ir ) compounds and its interplay with hybridization and disorder are key ingredients to understand the complex phase diagrams by this family, which have been explored extensively by macroscopic techniques. Here, we present an investigation of the crystal-electric field schemes of Ce 2 Rh 1 − x Ir x In 8 using x-ray absorption spectroscopy. Our full multiplet calculations for the 4 f 1 configuration of Ce 3 + to describe the temperature-dependent linear dichroism in Ce 2 M In 8 are consistent with a Γ 7 1 = 1 − α 2 · | ∓ 3 2 〉 − | α | · | ± 5 2 〉 ground state containing a predominant | ± 3 / 2 〉 contribution that increases further with x . This enhancement is believed to favor superconductivity in Ce-based heavy fermion materials, observed in previous results in the Ce M In 5 family. Our recent observations shed light on the unexpected emergence of the ambient-pressure superconducting dome in the center of the composition phase diagram and its subsequent suppression on the Ir-rich side due to the early onset of fluctuations associated with the structurally more disordered state, inferred from previous neutron magnetic diffraction experiments. Published by the American Physical Society 2024

Christovam, D. S. (ORCID:0000000250698107)↗
DOE OSTI2025

Bringing Different Views Together: A Hybrid Cooperative Perception Framework for Connected Autonomous Vehicles

Cooperative perception will be essential for connected autonomous vehicles to enhance object recognition and optimize path planning by sending data information about the surrounding environment. However, an inherent challenge in existing systems is the high bandwidth cost of transmitting information in real-time, which restricts cooperative perception’s practicality. Here, this work presents a hybrid cooperative perception fusion framework aimed at mitigating this issue by optimizing data transmission according to available bandwidth or through data reduction techniques. Our methods ensure that vehicles can rapidly transmit high-confidence data without overwhelming the network. Experimental results indicate that our methodology substantially diminishes data transmission sizes while maintaining object detection accuracy. For cooperative perception in autonomous vehicle systems, our approach provides a scalable and effective way to get past the bandwidth barrier.

Carrillo, Dominic [Univ. of North Texas, Denton, T↗
DOE OSTI2024

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost–benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Klein, Luan C. (ORCID:000000016306574X)↗
DOE OSTI2023

Advancement of hybrid fluid-kinetic modeling for HEDP and ICF science

We report on the development progress of a hybrid fluid-kinetic code for simulating fluids and plasmas in a wide range of environments, such as laser–matter interactions, inertial confinement fusion, magnetic confinement fusion, and pulsed power. The suite of numerical tools under development utilizes heterogeneous computer architectures and leverages the benefits of particle–based simulation techniques. By working to combine the kinetic particle-in-cell (PIC) model with a particle-based fluid simulation technique, such as smoothed particle hydrodynamics, we are developing a flexible framework capable of accurately modeling complex flows within and between kinetic and fluid regimes. The TriForce code is under development as a C++ framework for parallel, 3D, particle-based, hybrid fluid-kinetic plasma simulations. The fluid half of TriForce will be based upon the meshless smoothed-particle-hydrodynamics (SPH) approach, well-suited for shear, mixing, and turbulence, whereas the kinetic half resembles a traditional particle-in-cell (PIC) code; other particle-based approaches to fluid modeling that do use a mesh are also possible to use and are under investigation. Maxwell’s electromagnetic field equations are solved either via explicit or implicit algorithms or approximated via resistive magnetohydrodynamics (MHD) using an Ohm’s law and resulting induction equation (extended MHD is under development). A primary goal of enabling direct comparisons, from the same code, between results from the variants of MHD and implicit electromagnetic solutions is to improve our fundamental understanding of systems with magnetic fields. The code is under development to recover results from both radiation-MHD and fully kinetic codes in those limits, and is continuing to be developed from other follow-on grants to operate in between where both descriptions may co-exist and interact. For certain applications, it is desired for a simulation to contain fluid ions and electrons as well as kinetic ions and electrons. Typically, it is too computationally intensive to model a full-scale ICF or HEDP experiment fully kinetically since many cycles are expended with very small time steps on modeling the fluid part of a material that is well treated by the fluid approximation. In this case, many traditional PIC particles can be replaced with a single fluid particle representing the thermal part of the distribution function, and there are fewer needed kinetic particles, which describe the non-thermal part and can be sub-cycled relative to the fluid particle advance. Furthermore, a pure fluid code may, depending on the problem, simply lack many physically important details that are beyond the scope of its reduced approximations and assumptions. In this report, we summarize the objectives achieved in the development of the collisional and kinetic half of the code, and the physics problems to which the code has been applied in the areas of advanced and innovative fusion concepts, pulsed power, and magneto-inertial fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Production of polyacrylonitrile/ionic covalent organic framework hybrid nanofibers for effective removal of chromium(VI) from water

Hexavalent Cr(VI) found in industrial wastewater is a proven carcinogen which causes serious health issues in humans around the world. This study presents a novel method to enhance the Cr(VI) oxyanion removal from wastewater by polyacrylonitrile (PAN) nanofibers through incorporation of a guanidinium-based ionic covalent organic framework (BT-DG) in the nanofibers structure. In this work, simple electrospinning technique was employed to produce PAN nanofibers and BT-DG was synthesized through condensation between benzene-1,3,5-tricarbaldehyde and N,N'-diaminoguanidine monohydrochloride. In-situ polymerization of BT-DG onto PAN nanofibers resulted in generation of hybrid PAN-BT-DG nanofibers. This modified PAN-BT-DG was characterized by obtaining its point of zero charge (PZC), differential scanning calorimeter (DSC), scanning electron microscopy (SEM) morphology and surface elements and oxidation states by X-ray photoelectron spectroscopy (XPS). PAN-BT-DG exhibited positive surface charge below pH 4, making it an outstanding adsorbent, for Cr(VI) removal. Cr(VI) adsorption onto PAN-BT-DG followed pseudo second order kinetics and adsorption data fitted well to Freundlich isotherm model. Highest Cr(VI) removal was obtained at 55 °C with a maximum Langmuir adsorption capacity of 173 mg/g at pH 3. Kinetic studies revealed that Cr(VI) adsorption onto PAN-BT-DG is endothermic and thermodynamically feasible. Desorption studies were conducted on PAN-BT-DG using 1 M NaOH as the stripping solvent and PAN-BT-DG exhibited excellent regeneration after five consecutive cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Decoding diffraction and spectroscopy data with machine learning: A tutorial

This Tutorial provides a step-by-step guide on how to apply supervised machine-learning techniques to analyze diffraction and spectroscopy data. This Tutorial details four models—a reconstruction-focused model, a regression-focused model, a hybrid reconstruction/regression model, and a multimodal model—that use x-ray diffraction profiles and vibrational density of states spectra to predict various microstructural descriptors. In this Tutorial, we cover data pre-processing steps, constructions of the models via dimensionality reduction and regression, training, and analysis of these models. Comparisons of the model’s performance are provided, highlighting the strength and weakness of the various approaches utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulating strongly correlated molecules with a superconducting quantum processor

Many of the biggest challenges in expanding the nation’s access to clean and low-cost energy resources are fundamentally chemistry or materials challenges. An important case is the development of new catalysts for the up-conversion of cheap and readily available materials such as methane or water into materials suitable for use as a fuel such as methanol or oxygen. To understand and exploit such processes, computer simulations of chemical reactions provide a natural complement to experimental studies. Unfortunately, most catalytic reactions involve so-called “strongly correlated” molecules which are notoriously difficult to study with simulation algorithms that can be executed on existing (classical) computers. The recent growth in quantum information science offers an alternative potential route for simulating these difficult systems. As a result, an increasing number of computational chemists are becoming interested in quantum computing. At the same time, quantum information scientists have identified chemistry simulation as a possible first demonstration of a quantum computer providing an improvement over a classical computer. The objective of this project is to accurately simulate strongly correlated molecules on a quantum processor. To meet the high challenges of this objective, new hybrid quantum/classical algorithms will be co-designed with advanced quantum gate developments and computed on customized quantum hardware. Some of the developed techniques will be transferable to study other molecular systems, while the project as a whole will help define better strategies for advancing the quantum simulation of matter more generally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Topological superconductivity in skyrmion lattices

Atomic manipulation and interface engineering techniques have provided an intriguing approach to custom-designing topological superconductors and the ensuing Majorana zero modes, representing a paradigm for the realization of topological quantum computing and topology-based devices. Magnet-superconductor hybrid (MSH) systems have proven to be experimentally suitable to engineer topological superconductivity through the control of both the complex structure of its magnetic layer and the interface properties of the superconducting surface. Here, we demonstrate that two-dimensional MSH systems containing a magnetic skyrmion lattice provide an unprecedented ability to control the emergence of topological phases. By changing the skyrmion radius, which can be achieved experimentally through an external magnetic field, one can tune between different topological superconducting phases, allowing one to explore their unique properties and the transitions between them. In these MSH systems, Josephson scanning tunneling spectroscopy spatially visualizes one of the most crucial aspects underlying the emergence of topological superconductivity, the spatial structure of the induced spin–triplet correlations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Multimodal Characterization of Crystal Structure and Formation in Rubrene Thin Films Reveals Erasure of Orientational Discontinuities

Abstract Multimodal multiscale characterization provide opportunities to study organic semiconducting thin films with multiple length scales, across multiple platforms, to elucidate crystallization mechanisms of the various microstructures that impact functionality. With polarized scanning transmission X‐ray and 4D‐scanning transmission electron microscopy, hybrid crystalline structures in rubrene thin films in which large crystalline domains surround a common nucleus and transition to a spherulite morphology at larger radii is observed. These high‐resolution techniques reveal how azimuthal orientational discontinuities at smaller radii are erased as spherulite morphology takes hold. In situ crystallization in the films with optical microscopy is also captured, discovering the importance of considering the initial temperature increase of a film during thermal annealing over the crystallization timescale. This kinetic information of the radial crystallization rate and of corresponding film heating kinetics is used to estimate the temperature at which the larger crystalline regions transition into a spherulite. By combining the results obtained from the different characterization modes, it is learned that thermal conditions can sensitively affect the crystallization of rubrene and other organic thin films. The observations suggest opportunities for more complex temperature‐dependent processing to maximize hybrid structures’ functionality in organic thin films and demonstrate that multimodal studies deepen the understanding of structure‐function dynamics.

Tan, Jenna A.↗
DOE OSTI2026

Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Arid and semi-arid landscapes↗
DOE OSTI2023

Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy

Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity, even in ultra-thin film form. However, studying the weak response from ultra-thin film requires improved measurement sensitivity. In general, resonance-enhanced piezoresponse force microscopy (PFM) has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance spectrum. However, an iterative approach, such as traditional least squares (LS) fitting, is sensitive to noise and can result in the misunderstanding of weak responses. In this study, we developed the deep neural network (DNN) hybrid with deep denoising autoencoder (DDA) and principal component analysis (PCA) to extract resonance information. The DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm, allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf 0.5 Zr 0.5 O 2 thin films. Our hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Natural evolutionary strategies for variational quantum computation

Abstract Natural evolutionary strategies (NES) are a family of gradient-free black-box optimization algorithms. This study illustrates their use for the optimization of randomly initialized parameterized quantum circuits (PQCs) in the region of vanishing gradients. We show that using the NES gradient estimator the exponential decrease in variance can be alleviated. We implement two specific approaches, the exponential and separable NES, for parameter optimization of PQCs and compare them against standard gradient descent. We apply them to two different problems of ground state energy estimation using variational quantum eigensolver and state preparation with circuits of varying depth and length. We also introduce batch optimization for circuits with larger depth to extend the use of ES to a larger number of parameters. We achieve accuracy comparable to state-of-the-art optimization techniques in all the above cases with a lower number of circuit evaluations. Our empirical results indicate that one can use NES as a hybrid tool in tandem with other gradient-based methods for optimization of deep quantum circuits in regions with vanishing gradients.

Anand, Abhinav (ORCID:0000000280812310)↗
DOE OSTI2021

Hybrid of multi‐dimensional fillers for thermally enhanced polyamide 12 composites fabricated by selective laser sintering

Abstract Selective laser sintering (SLS) is an additive manufacturing technology that can provide a novel strategy to manufacture complex polymer‐based composites with tailored properties. In this study, polyamide 12 (PA12) composite powders based on Al 2 O 3 , boron nitride (BN), and carbon nanotubes (CNTs) were prepared via a two‐step mixing approach. Morphology characterization techniques indicated that the fillers were uniformly dispersed in the PA12 matrix as expected. Under the optimum 3D‐printing parameters, the fabrication of SLS parts with high filler loading could be obtained. With 40 wt% Al 2 O 3 , 8 wt% BN, and 2 wt% CNT hybrid fillers, the thermal conductivity of PA12 composites could reach 1.09 W/mK. It could be attributed to the synergistic effect of multi‐dimensional fillers to form dense and continuous thermal conduction paths. The factors influencing the performance of PA12 composites such as melting and crystallization behaviors, dielectric properties, and mechanical properties were also discussed. The results show that these composites have favorable dielectric and mechanical properties. Overall, the PA12 composites fabricated by SLS technology in this study exhibit excellent thermal management ability.

Yuan, Yue↗