Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “feature fusion”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Forest aboveground biomass estimation through integration of sentinel-2 and PALSAR-2 time series: assessing models trained on GEDI and field inventory benchmarks

Accurate and spatially explicit forest Aboveground Biomass (AGB) mapping through remote sensing is critical for quantifying terrestrial carbon stocks and informing effective forest management strategies. However, AGB estimation in dense forests with complex terrain remains challenging due to satellite sensor signal saturation problem (saturation issue occurs in high biomass forests), structural complexity, and limited ground truth for calibration. This study presents a novel framework that integrates multi-temporal Sentinel-2 optical imagery, ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data, and topographic variables with explainable Machine Learning to map AGB across mountainous forests within subtropical and temperate oceanic climate zones of Mexico. We evaluate the effects of temporal granularity and sensor synergy by comparing multiple temporal inputs and sensor configurations (Sentinel-2, PALSAR-2, and their fusion), and assess model performance using two reference datasets: NASA GEDI LiDAR-derived biomass and Mexico’s National Forest and Soil Inventory (INFyS). Our results showed that models trained on INFyS consistently outperformed those trained on GEDI, highlighting limitations in GEDI’s reliability in biomass estimates within this study region. Furthermore, the integration of Sentinel-2 and PALSAR-2 provided improved predictions compared to single-sensor models, particularly when combined with temporally explicit yearly statistics. The best-performing model, which was trained on INFyS data, and considered both Sentinel-2 and PALSAR-2 yearly statistics, as well as topographic variables, achieved an R2 of 0.64, RMSE of 51.10 Mg/ha, and relative RMSE (rRMSE) of 58.69%. Explainable ML analysis identified Sentinel-2 spectral indices and topographic features as key predictors, while PALSAR-2 metrics provided complementary information, partially mitigating saturation effects in high-biomass areas. Specifically, integrating both sensors substantially improved AGB estimation in high biomass forest (≥200 Mg/ha), yielding 98% gains over optical-only model, with resulting estimates exceeding GEDI L4B by 29% and ESA-CCI-BIOMASS by 174%. Terrain-stratified analysis indicated close agreement with GEDI in low-slope areas, with increasing divergence as slope steepness increased, while estimates remained consistently higher than ESA-CCI-BIOMASS across all slope classes. The proposed approach advances multi-sensor fusion and temporal feature engineering for AGB mapping using open-access satellite datasets, providing a scalable and reproducible framework for annual biomass monitoring in topographically complex mountainous forests. The resulting 25 m resolution biomass product has the potential to provide spatially detailed information for forest monitoring and may support applications in carbon accounting and forest management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mechanisms behind impurity spreading in a channeled, long-leg divertor configuration

One suggested benefit of long-leg, tightly baffled divertor configurations, a common feature included in the designs of many next-generation fusion devices, is access to a delocalized “spread” of impurity radiation along the leg, allowing for a substantial radiation region while maintaining both sufficient distance from the core to prevent pollution of the plasma fuel and avoiding local overheating of the plasma-facing materials. However, it is unclear whether this increased connection length and divertor volume afforded by the long-leg configuration is compatible with impurity transport, and whether it will enable this desired impurity radiation spread at all. Here, the SOLPS 4.3 code is used to assess the distribution of nitrogen and neon impurity radiation in a long-leg, tightly baffled divertor geometry. Further, there is a strong correspondence between the distribution of impurity radiation in the long leg and impurity recycling on the divertor side walls with both the nitrogen simulation set and the neon simulation set, suggesting that impurity spreading along these long legs is due to the interplay between the cross-field transport and associated neutralization of the impurity on the material surfaces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

First result of boronization assisted by the ICWC on EAST with full metal wall

Boron (B), a low-Z (atomic number) material, has been widely utilized in wall conditioning to improve plasma performance in fusion devices. In 2023, boronization was successfully conducted on EAST featuring an ITER-like tungsten divertor and fully metallic first wall. The process employed predischarge coating with carborane (C 2 B 10 H 12 ) as the working material, assisted by ion cyclotron wall conditioning (ICWC). After one time 12 g boronization, it was found the thickness of B film was approximately 120 nm. Post-boronization observations indicated that substantial hydrogen (H) release during initial plasma discharges compared with the consumed W/B wall, attributed to H co-deposition during the ICWC-boronization processing, which led to uncontrollable divertor neutral pressure and plasma density. The H/(H + D) ratio demonstrated a gradual reduction from ∼85% to 30% over more than 1850 s of deuterium plasma, with a cumulative injected energy of 2325 MJ. The B coating significantly enhanced the stored energy in plasma and improved confinement performance. The stored energy in plasma showed an increase of about 20%, primarily due to a reduction in impurity radiation, including oxygen (O) and heavy impurities such as tungsten (W), iron (Fe), and copper (Cu). The effective ion charge (Z eff ) decreased from 2.3 to 2.0. Following ICWC-boronization, the line-integrated radiation profile decreased by nearly 35% in the plasma core, plasma density and electron temperature exhibited an increase of ∼7% and 12% due to enhanced wall fueling and reduced impurity radiation. The lifetime of boronization, as evaluated by the line emissions from boron and other impurity radiation, was about 1700 s of deuterium plasma, with a cumulative injected energy of 2125 MJ on EAST. These findings provide significant insights for evaluating ICWC-boronization applicability in ITER with full W wall structure.

EAST↗
DOE OSTI2026

Enabling On-Demand Aerospace Component Manufacturing: Topology Optimization of GE Engine Bracket and Fabrication Using Metal FFF

Additive Manufacturing (AM) offers advantages over conventional manufacturing processes, particularly by reducing the number of parts produced through multistage combined technologies, but these often result in low manufacturing yields or require post-processing. AM facilitates the production of complex geometries with fine features, overhangs, and lattice structures. For instance, Laser Powder Bed Fusion (LPBF) technology enables the fabrication of intricate parts that can be easily post-processed by removing residual powder. Laser powder bed AM technologies are widely discussed in the literature due to their design freedom in creating complex geometries, with and without the need for support generation. However, rapid solidification due to a thermal gradient in the build direction, which leads to the formation of columnar grains and warpage, is one of the challenges. To address this challenge, we propose layer-by-layer metal FFF technology, followed by the debinding and sintering process, as an alternative to powder- and laser-based approaches. Furthermore, design for additive manufacturing (DfAM) principles are discussed to minimize the need for support generation and enable easy post-processing, thereby improving surface finish to meet high tolerances in fabricating components for aerospace and healthcare applications.

Singh, Abhishek [University of Michigan, Ann Arbor↗
DOE OSTI2025

Stacked-mosaic amplifier and diode delivery concept for kJ-class inertial fusion energy laser driver

We report on a laser amplifier architecture designed as a modular element for scaling to multi-megajoule laser facilities intended for inertial fusion energy (IFE) power plants. This kilojoule-class module features a stacked mosaic of gain media integrated with a diode delivery system for efficient optical pumping of the mosaic-structured gain medium, enabling high-repetition-rate operation at high wall plug efficiencies, necessary for an IFE driver. Details of a diode delivery system capable of pumping the mosaic architecture are presented. Comprehensive numerical modeling demonstrates that the stacked-mosaic approach reduces transverse gain by five orders of magnitude compared to a conventional full-aperture Yb:YAG based amplifier, substantially suppressing transverse amplified spontaneous emission (TASE) and enabling enhanced longitudinal energy extraction. Detailed analysis of thermal management and wavefront distortion in a 4 × 4 mosaic array indicates that temperature gradients and thermally induced aberrations are effectively controlled using gas cooling and commercially available phase-plate and deformable mirror technologies. We further discuss the applicability of a stacked-mosaic architecture to direct-drive IFE schemes, where broad spectral bandwidth is critical, and its compatibility with frequency conversion modules for up-conversion to blue wavelengths. Finally, an example point design for a 10 kJ, 10 Hz Yb:YAG module operating at 175 K with wall-plug efficiency exceeding 10 % is presented, underscoring the feasibility of this approach for next-generation high-energy lasers for IFE drivers. The results establish the stacked-mosaic amplifier as a scalable, robust platform not only for IFE but also for a broad range of advanced scientific and industrial laser applications.

Lasers↗
DOE OSTI2024

Preliminary Design of Ironless Linear Induction Motors for ITER MSE Shutter Actuators

In this article, the shutters need to be closed or opened during the operation and calibration period time for the fusion diagnostic systems, such as the International Thermonuclear Experimental Reactor (ITER) motional stark effect (MSE). Standard electric motors and actuators will not work in a strong magnetic field environment due to the presence of the magnetic field of fusion reactors. The innovative linear induction motor (LIM) with an ironless feature overcomes this kind of challenge and can be used for any application that requires controlled motion with a large stroke in the magnetic field environment. It consists of a high-electrical conductivity plate like copper and three-phase motor windings with nonferrous or stainless-steel stators. The conductor plate attached with the shutter is simply driven by the underneath three-phase linear windings to achieve linear motion. The motor drive can be controlled remotely by a controller using the electrical connection, so no sensitive electronic components are located in the harsh environment where the motor itself is located. The design requirements and test facility have been described. Several 3-D transient Maxwell electromagnetic (EM) models with different locations of three-phase linear motor windings and shutter stators have been analyzed and evolved to meet any applications allowed in the harsh environment inside the vacuum vessel of fusion reactors. The preliminary design results are presented in this article.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

LLNL FESP Theory Highlights: August 2024

The 2024 ABOUND SciDAC and BOUT++ combined workshop was held August 5-9 th 2024 at the University of California Livermore Collaboration Center (UCLC) in Livermore. Bringing together leading scientists and researchers from across the globe, this pivotal event focused on advancing plasma physics and boundary plasma dynamics within the context of fusion energy research. Key discussions throughout the meeting highlighted significant advancements in the BOUT++ framework, including enhanced simulations of small Edge Localized Modes (ELMs) and the initiation of integrating the integration of the 5D GEM gyrokinetic turbulence core code with the 2D SOLPS-ITER boundary transport code. These developments are crucial for managing heat loads in fusion reactors and supporting the longevity of plasma-facing components. The event also featured a session on Inter-SciDAC Collaborations, where principal investigators from multiple U.S. FES SciDAC tokamak projects explored opportunities for cross-collaboration. Additionally, the meeting showcased cutting-edge advancements in GPU acceleration and AI/ML technologies, poised to drive the next generation of fusion research. In his closing remarks, Dr. Xueqiao Xu emphasized the importance of the collaborative efforts and discussions that took place, noting their potential to shape future breakthroughs in fusion energy. The event underscored the global nature of the BOUT++ collaboration, with contributions from over 57 institutions worldwide. The 2024 BOUT++ and ABOUND Joint Hybrid Meeting continues to drive forward the research and innovations needed to achieve fusion energy, setting the stage for future collaboration and discovery.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Development and validation of fully open-source R2S shutdown dose rate capabilities in OpenMC *

We present the first fully open-source capabilities for shutdown dose rate (SDR) calculations of fusion energy facilities based on the Rigorous 2-Step (R2S) methodology. These capabilities have been implemented in the OpenMC Monte Carlo particle transport code, building on its existing capabilities while also leveraging new features that have been added to the code to support SDR calculations, such as decay photon source generation. Each of the individual physics components in the R2S workflow—neutron transport, activation, decay photon source generation, and photon transport—have been verified through code-to-code comparisons with MCNP6.2 and FISPACT-II 4.0. These comparisons generally demonstrate excellent agreement between codes for each of the physics components. The full cell-based R2S workflow was validated by performing a simulation of the first experimental campaign from the Frascati Neutron Generator (FNG) ITER dose rate benchmark problem from the Shielding INtegral Benchmark Archive and Database (SINBAD). For short cooling times, the dose calculated by OpenMC agrees with the experimental measurements within the stated experimental uncertainties. For longer cooling times, an overprediction of the shutdown dose was observed relative to experiment, which is consistent with previous studies in the literature. Altogether, these features constitute a combination of capabilities in a single, open-source codebase to provide the fusion community with a readily-accessible option for SDR calculations and a platform for rapidly analyzing the performance of fusion technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Latent code-based fusion: A Volterra neural network approach

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces. The so-called self-representation embedding of the latent codes leads to a simplified fusion which is driven by a similarly constructed decoding. The Volterra Filter architecture achieved reduction in parameter complexity is primarily due to controlled non-linearities being introduced by the higher-order convolutions in lieu of generalized activation functions. Experimental results on two different datasets have shown a significant improvement in the clustering performance for VNNs auto-encoder over conventional Convolutional Neural Networks (CNNs) auto-encoder. In addition, we also show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a robust classification performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deuterium trapping mechanisms in reduced activation ferritic martensitic steels and their correlation with mechanical strengthening

Development of high-strength materials often involves introduction of additional strengthening microstructures that also serve as tritium trapping sites. Such additions in fusion material development could degrade the fuel efficiency in fusion reactors and raise radiological concerns. The contribution of individual microstructure features in hydrogen trapping must be evaluated to ensure fuel efficiency and radiological safety. This study explores the mechanistic origins of deuterium trapping in reduced-activation ferritic–martensitic steels and its correlation to mechanical strengthening. A series of model alloys and engineering steels were fabricated and subjected to different heat treatments to control deuterium trapping site density. Deuterium retention was evaluated using D 2 gas charging and thermal desorption spectroscopy, focusing on the role of grain boundary, dislocation, M 23 C 6 precipitates, and TiC precipitates. Multiscale microstructure characterization and synchrotron X-ray diffraction were performed to characterize microstructure, which was correlated to the deuterium retention property. Results show that TiC precipitates exhibit the highest deuterium trapping capacity, followed by M 23 C 6 precipitates. Dislocation and grain boundary demonstrate the lowest and similar efficiencies. Furthermore, the relationship of trapping quantity and mechanical strengthening of these microstructure features was quantified, demonstrating that TiC precipitates offer highest deuterium trapping per unit of mechanical strengthening.

Retention↗
DOE OSTI2025

Strategies for Fabricating Molybdenum Structures Using Laser Powder Bed Fusion

Advances in manufacturing techniques are viewed as enabling technologies for development of high performance nuclear fuel forms that couple high uranium density with improvements to key properties such as thermal conductivity unattainable through conventional fabrication routes. Additive manufacturing (AM) enables the fabrication of complex fuel geometries that are difficult or impossible to achieve using conventional manufacturing methods. Melting-based AM processes, such as laser powder bed fusion (LPBF), provide high geometric resolution (>200 µm depending on the feature) across a variety of metal alloys, including those suitable for high-temperature fuel cladding applications, such as Nb, W, and Mo. Molybdenum is particularly attractive due to its high thermal conductivity, low thermal expansion, and excellent mechanical stability at elevated temperatures. However, its high melting temperature and brittle nature at low temperatures pose significant challenges during LPBF processing. Rapid solidification inherent to LPBF induces high residual stresses, often leading to post-solidification cracking, which limits the manufacturability of Mo components via this method.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A Comparison of Solidification Structures and Submicroscale Cellular Segregation in Rapidly Solidified Stainless Steels Produced via Two-Piston Splat Quenching and Laser Powder Bed Fusion

Fusion-based additive manufacturing techniques leverage rapid solidification (RS) conditions to create parts with complex geometries, unique microscale/nanoscale morphological features, and elemental segregation. Three custom composition stainless steel alloys with varying chromium equivalence to nickel equivalence ratio (Cr eq /Ni eq ) between 1.53 and 1.95 were processed using laser powder bed fusion (LPBF) and/or two-piston splat quenching (SQ) to produce solidification rates estimated between 0.4 and 0.8 m/s. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to collect high-resolution images, electron backscatter diffraction (EBSD) phase identification, and measure cellular segregation. Similar features were observed in both LPBF and SQ samples including phase and microstructure, nanoscale oxide particles, cell size, and segregation behavior. However, dislocation pileup was observed along the cell boundaries only in the LPBF austenite solidified microstructure. Targeted adjustment of the SQ feedstock Cr and Ni concentrations, within the ASTM A240 specification for 316L resulted in no observable impact on the cell size, oxide particle size, or magnitude of segregation. Also, the amount of Ni segregation in the ferrite solidified microstructures did not significantly differ, regardless of Cr/Ni eq or processing technique. Here, SQ is demonstrated as capable of simulating RS rates and microstructures similar to LPBF for use as an alternative screening tool for new RS alloy compositions.

42 ENGINEERING↗
DOE OSTI2020

Comprehensive characterization of N- and O- glycosylation of SARS-CoV-2 human receptor angiotensin converting enzyme 2

The emergence of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created the need for development of new therapeutic strategies. Understanding the mode of viral attachment, entry and replication has become a key aspect of such interventions. The coronavirus surface features a trimeric spike (S) protein that is essential for viral attachment, entry and membrane fusion. The S protein of SARS-CoV-2 binds to human angiotensin converting enzyme 2 (hACE2) for entry. Herein, we describe glycomic and glycoproteomic analysis of hACE2 expressed in HEK293 cells. We observed high glycan occupancy (73.2 to 100%) at all seven possible N-glycosylation sites and surprisingly detected one novel O-glycosylation site. To deduce the detailed structure of glycan epitopes on hACE2 that may be involved in viral binding, we have characterized the terminal sialic acid linkages, the presence of bisecting GlcNAc and the pattern of N-glycan fucosylation. We have conducted extensive manual interpretation of each glycopeptide and glycan spectrum, in addition to using bioinformatics tools to validate the hACE2 glycosylation. Our elucidation of the site-specific glycosylation and its terminal orientations on the hACE2 receptor, along with the modeling of hACE2 glycosylation sites can aid in understanding the intriguing virus-receptor interactions and assist in the development of novel therapeutics to prevent viral entry. Here, the relevance of studying the role of ACE2 is further increased due to some recent reports about the varying ACE2 dependent complications with regard to age, sex, race and pre-existing conditions of COVID-19 patients.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Enhancing microsegregation during rapid directional solidification through ternary microalloying

The nano-cellular dendritic microstructure formed during rapid directional solidification in powder bed fusion additive manufacturing creates unique properties such as simultaneous improvement in strength and ductility. However, process control of microsegregation features remains challenging due to low sensitivity of critical solidification mechanisms to process parameters. This study leverages microalloying to achieve large changes in dendrite composition, microstructure, and interdendritic zone width during laser powder bed fusion without modifying process parameters. CALPHAD simulations predict that the addition of Zr significantly steepens the solidus line of the dilute Cu-Cr alloy system, leading to enhanced Cr rejection into the melt and greater than 95% reduction in solubility of Cr in the solidified Cu matrix. Experimental validation using time-of-flight secondary ion mass spectrometry and Kelvin probe force microscopy reveals that the ternary alloy containing 0.01 wt% Zr exhibited wider interdendritic regions compared to the binary, a significantly higher number of Cr-rich particles within interdendritic regions, near-complete ejection of oxygen impurities from the matrix, and greater nanoscale work function contrast. These features indicate more aggressive Cr segregation in the presence of Zr and a purer Cu matrix and provide a potentially robust method for engineering the nano-cellular dendritic solidification microstructure.

CALPHAD↗
DOE OSTI2023

Radiation Damage Analysis of FNSF Components Using McCad and MCNP

The Fusion Energy System Studies Fusion Nuclear Science Facility (FESS-FNSF) concept represents a transitional step between ITER and a commercial fusion power plant. The FNSF is a conceptualized D-T fueled tokamak with 518 MW of fusion power that has been extensively used to explore and optimize design features. The energetic 14.1-MeV neutrons can produce significant localized heating and activations, and can cause damage to plasma-facing components, which can determine maintenance/outage scheduling needs and also impact the lifetime of the device as a whole. This study illustrates a neutronics analysis that was conducted on a 22.5-degree symmetric sector of the FNSF with the goal of understanding the neutron heating and radiation damage that can be characterized by quantifying the displacements per atom (dpa). Concurrently, this study also focused on the development of analysis capabilities by converting a three-dimensional computer-aided design model of the FNSF into MCNP6.2 input using the McCad code. Accordingly, some confirmatory results on tritium production and the tritium breeding ratio (TBR) are provided to support model validation. The results produced by MCNP6.2 simulations showed that the highest heating and damage occurred in the outboard region, which concentrated approximately 290 MW of the total nuclear heating, in contrast to 97 MW within the inboard region. These results are consistent with previous studies that employed earlier versions of the FNSF concept and different modeling approaches. Furthermore, this study also provides additional details on neutron wall loading, as well as total heating from neutrons and gammas, results which show the total heating of the device (16 sectors) is approximately 477.83 ± 0.80% MW, indicating a neutron energy multiplication factor of 1.15. Additionally, the capability to calculate hydrogen and helium production, as well as dpa, is illustrated. Finally, the neutronics effects of using alternative materials to tungsten carbide were evaluated for the vacuum vessel, low-temperature shield, and structural ring components, which showed that compounds like YH 2 , Mg(BH 4 ) 2 , and ZrH 2 could reduce the total heating on the magnet and also reduce the TBR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Aqueous Self‐Assembly of Cylindrical and Tapered Bottlebrush Block Copolymers

The self‐assembly of amphiphilic bottlebrush block copolymers (BCPs), featuring backbones densely grafted with two types of side chains, is less well understood compared to linear BCPs. In particular, the solution self‐assembly of tapered bottlebrush BCPs—cone‐shaped BCPs with hydrophilic or hydrophobic tips—remains unexplored. This study investigates eight tapered and four cylindrical bottlebrush BCPs with varied ratios of hydrophobic polystyrene (PS) and hydrophilic poly(acrylic acid) (PAA) side chains, synthesized via sequential addition of macromonomers using ring‐opening metathesis polymerization (SAM‐ROMP). Self‐assembled nanostructures formed in water were analyzed using cryogenic transmission electron microscopy, small‐angle neutron scattering, and dynamic light scattering. Most BCPs generated multiple nanostructures with surface protrusions, including spherical micelles, cylindrical micelles, and vesicles, alongside transitional forms like ellipsoids and semi‐vesicles. Coarse‐grained molecular dynamics simulations supported the experimental findings, which revealed two distinct self‐assembly pathways. The first involved micelle fusion, producing elliptical and cylindrical aggregates, sometimes forming Y‐junctions. The second pathway featured micelle maturation into semivesicles, which developed into vesicles or large compound vesicles. This work provides the first experimental evidence of vesicle formation via semivesicles in bottlebrush BCPs and demonstrates the significant influence of cone directionality on self‐assembly behavior in these cone‐shaped polymeric amphiphiles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗