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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗
DOE OSTI2024

A Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function

A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.

09 BIOMASS FUELS↗
DOE OSTI2025

Empowering Lineworkers: The Case for Active Exoskeletons in Utility Work

Exoskeletons have evolved from early medical prototypes to advanced systems capable of addressing physical demands in various industries. This report explores the potential of active exoskeleton technology within the utility sector, focusing on its application for linemen who face significant risks of work-related musculoskeletal disorders (WMSDs). By analyzing existing literature on exoskeletons across industries such as construction, manufacturing, and military, the study identifies a gap in utility-specific applications. Task-specific design features like gravity compensation, limb support, and advanced safety measures, improve exoskeletons’ potential to alleviate physical strain, reduce workplace injuries, and enhance productivity. This review emphasizes the need for targeted research and development to optimize exoskeleton designs for the utility sector to provide benefits for workers, companies, and the broader community.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Overview on Current Activities of Conduction-Cooled SRF Accelerators and their Applications

When Nb3Sn was reintroduced to the SRF community as an alternative to pure niobium, one key motivation has been to reduce the cryogenic requirements of new and existing accelerators by shifting from 2 K to 4 K operation. Meanwhile, a variety of implementations beyond research machines are being explored. The combination of Nb3Sn with conventional cryocoolers, enabling cryogen-free operation, has paved the way for the development of compact, standalone systems suitable for applications far beyond research, such as enhancing the durability of synthetics via crosslinking or sterilizing food and medical equipment, as well as environmental cleanup when it comes to decontaminating liquid and solid waste material. So, while fundamental R&D continues to refine Nb3Sn resonators, exploring improvements such as replacing the niobium substrate with copper, parallel research efforts are investigating how the increased beam power provided by SRF could expand the commercial use of electron beams. This presentation aims to deliver a comprehensive overview of ongoing research efforts to harness the benefits of SRF through Nb3Sn and conduction cooling.

Vennekate, John [Thomas Jefferson National Acceler↗
DOE OSTI2024

Impurity-enhanced core valence luminescence via Zn-doping in cesium magnesium chlorides

Scintillators with faster timing capabilities are currently in high demand for use in radiation detection systems in the fields of nuclear and medical physics. The limited number of suitable materials that meet the performance criteria of next generation detection systems presents an opportunity for discovery of new fast scintillator materials. In this work, the effects of doping several ultrafast core-valence luminescent (CVL) scintillators with divalent Zn is explored. Three compounds are investigated – CsMgCl 3 , Cs 2 MgCl 4 , and Cs 3 MgCl 5 – and single crystals of each doped with 5 mol% Zn are grown via the Bridgman method. Additionally, mixing across the full range of concentrations (from 0 % to 100 % Zn) is explored in the Cs 2 Mg 1-x Zn x Cl 4 and Cs 3 Mg 1-x Zn x Cl 5 systems. For low concentrations of Zn, light yields of all three compounds are enhanced (by up to ~60 %) compared to the pure crystals, achieving what we believe to be the brightest known CVL, CsMgCl 3 :Zn 5 % (3400 ± 170 ph/MeV light yield). More importantly, Zn doping does not affect the ultrafast timing properties, with each composition maintaining a single-component decay time around 1–3 ns. A sub-100 ps coincidence time resolution (CTR) is also achieved with CsMgCl 3 :Zn 5 %. The results of this work reveal a new avenue towards obtaining brighter CVL materials, which could open up possibilities for more advanced ultrafast scintillators to be discovered moving forward.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Geant4 Visualization

Geant4 offers versatile visualization tools to study detector geometry, particle trajectories, and hits. These tools support both interactive and batch modes (e.g., vis.mac). With a unified interface, Geant4 integrates various graphics systems such as G4VisExecutive, OpenGL, Qt, HepRApp, DAWN, VTK, and ToolsSG. Advanced examples, including the ICRP145 Human Phantoms and the gMocren tool, highlight its applications in medical research and simulation and as well as radiation protection and shielding studies. These features make Geant4 a robust framework for exploring particle interactions and supporting domain-specific use cases.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

An Evaluation and Qualification of U.S.-Based Research Reactors for Irradiation Capabilities Supporting Advanced Nuclear Systems

Irradiation experiments are a prerequisite for evaluating nuclear reactor system designs, analyzing the performance of these systems, and obtaining licenses. Likewise, irradiation facilities are necessary for producing the radioisotopes used in industrial and medical applications. Recent developments in modeling and simulation capabilities and advancements in computational resources have further enabled the design of irradiation experiments for evaluating radiation-induced phenomena and determining nuclear fuel, material, and system design and safety criteria pertaining to both normal and accident scenarios. These computational tools and models require comprehensive experimental datasets acquired under prototypic radiation conditions—for exploring material and system performance under the uniquely harsh environments found in nuclear reactors—to enable verification and validation for qualification and licensing purposes. However, qualification of irradiation experimental facilities, primarily research and test reactors (RTRs), necessitates that their performance be evaluated based on the irradiation environment (e.g. flux, power, testing capabilities) using an appropriate scoring matrix. Although many university campus RTRs are available for research and development (R&D) activities and initiatives, this study focuses on evaluating and qualifying the irradiation facilities (mostly RTRs) within the United States that are suitable for advanced nuclear fuel, material, and system irradiation experiments aimed at establishing operational-performance limits and informing component and fuel designs so as to improve operational efficiencies and mitigate proliferation vulnerabilities, as well as for radioisotope production aimed at multipurpose applications. As a result, the findings of the present study support the acceleration of nuclear fuel and material qualifications, thus hastening new and advanced nuclear energy system demonstrations and radioisotope production efforts by using extended R&D.

irradiation experiment↗
DOE OSTI2025

Relativistic Laser Plasma Interactions At The Highest Intensities

High energy density science (HEDS) explores the nature of matter under extreme conditions of temperature and pressure. It is of fundamental importance and has many applications such as facilitating imaging with ions, neutrons, x-rays, and gamma rays with new applications being developed, including materials processing and medical therapies. In this project, we used high power, ultrashort pulse lasers to reach HEDS conditions. We have shown that low-cost, liquid crystal film based, double plasma mirror systems can be used to greatly improve laser pulse contrast while still maintaining high power and excellent spatial mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2025

Enhanced Neutron and γ-Ray Detection via 6 Li Substitution in Undoped and Tl-Doped Zero-Dimensional Perovskite Cs 3 Cu 2 I 5 Scintillators

Radiation detectors are crucial in a wide variety of research and commercial applications, such as oil and gas exploration, medical imaging, nuclear nonproliferation, and homeland security. Neutron and gamma-ray detectors are fundamental components in portal monitors at ports and border crossings, bolstering national security against radiological threats. This study presents a dual-mode scintillator, undoped and Tl-doped 6 Li-Cs 3 Cu 2 I 5 , and demonstrates its potential as a promising material for simultaneous thermal neutron and gamma-ray detection. We explore the Bridgman growth of both undoped and thallium doped Li → Cu and Li → Cs substitutional systems with various Li doping levels and assess their impact on scintillation properties. Under 662 keV gamma-ray excitation, the undoped crystals had light yields up to 35,900 ph/MeV, with energy resolutions down to 4.5%. The Tl-doped crystals performed better than the undoped crystals with light yields peaking at 65,900 ph/MeV and energy resolutions as low as 3.5%. When exposed to a moderated 252 Cf excitation source, our crystals had light yields between 102,900 and 167,200 photons per thermal neutron capture, with a full energy thermal neutron peak reaching 3 MeV in gamma equivalent energy. Pulse shape discrimination studies reveal well-separated gamma and neutron events, resulting in Figure-Of-Merit (FOM) as high as 3.7. Furthermore, these findings highlight the potential of Li-doped Cs 3 Cu 2 I 5 as a viable candidate for next-generation dual-mode scintillators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Science & Technology - Other Topics↗
DOE OSTI2024

Synchrotron-based diffraction-enhanced imaging and diffraction-enhanced imaging combined with CT X-ray imaging systems to image seeds at 30 keV

Utilized the upgraded Synchrotron-based non-destructive Diffraction-enhanced imaging and Diffraction-enhanced imaging coupled with CT X-ray imaging systems to image the chickpea seeds, to enhance the contrast in plant root architecture, visibility of fine structures of root architecture growth and some aspects of physiology at acceptable level. DEI-CT images were acquired with 30 keV synchrotron X-rays. A series of DEI-CT slices were assembled together, to form a 3D data set. DEI-CT images explored more structural information and morphology. Noticed detailed anatomical, physiological observations, and contrast mechanisms. Furthermore, with these systems, some of the complex plant traits, root morphology, growth of laterals and subsequent laterals can be visualized directly.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Numerical simulations of liquid jetting with solid inclusions

The dynamics of finite-sized particles in fluids, and their influence on the overall flow, are of great interest across several industrial, environmental, and medical fields. In the context of inkjet printing, the presence of solid inclusions can be either intentional, as in additive manufacturing, or unintentional, as in standard printing processes. These inclusions can strongly impact the jetting process, causing effects such as jet asymmetry, bubble entrapment, and the formation of satellite droplets. Understanding and controlling particle behavior is therefore essential, particularly to predict how and when particles are ejected over multiple jetting cycles. It is therefore critical to develop reliable models that allow for a deeper understanding of the complex interplay between particle and fluid during the whole printing process. To address this, we present a tailored implementation of the Color-Gradient multicomponent Lattice Boltzmann Method for fully resolved three-dimensional (3D) simulations of multicycle liquid jetting with particles. Our method supports realistic parameter settings aligned with industrial inkjet systems, and we provide both qualitative and quantitative validation against experimental data. Additionally, we introduce a simplified model based on the Stokes drag law, in which solid particles are represented as point particles and do not influence the fluid flow. Despite this limitation, the model offers a computationally efficient means to explore the vast parameter space typically encountered in industrial applications, allowing, e.g., identifying critical ejection regions and estimating the number of cycles required for particle release. These qualitative insights are valuable for guiding and complement fully two-way coupled simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗
DOE OSTI2024

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Single‐Cell Nanodroplet Processing Proteomics Pipeline for Analysis of Human‐Derived Microglia

Single-cell omics tools provide unique insights into heterogeneous cell populations and their responses to stimuli. For example, single-cell RNA sequencing has identified several transcriptionally distinct populations of microglia, which are resident immune cells of the central nervous system (CNS) that are responsive to CNS injury, infection, and neurodegeneration. To date, single-cell studies of microglia have focused on RNA-sequencing or cytometry by time of flight (CyTOF), which provide indirect readouts of protein abundance or quantification of a limited number of targets. Herein, we present a workflow based on FACS-assisted isolation, cryopreservation, and nanodroplet-based processing for single-cell mass spectrometry proteomics analysis of the postmortem human brain cortex-derived microglia. From a single microglial cell, 1039 proteins could be identified on average. As a proof-of-principle, we applied single-cell proteomics for exploring the heterogeneity of brain microglia at the cellular level. This pilot proteomics data partially recapitulates the prior microglia subtypes. Specifically, we determined that mitochondrial proteins, in particular members of NADH dehydrogenase (Complex I), cytochrome b-c1 (Complex III), cytochrome c oxidase (Complex IV), F1-ATPase (Complex V), and Na+/K+-ATPase complex, drive variation across microglia. This pipeline offers the potential for identifying functionally and analytically relevant protein targets for microglia in Alzheimer's disease and other neurological disorders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2026

Bio-distribution and deposition of wildfire smoke chemicals into olfactory bulb and brain of rats after intranasal instillation

Epidemiological and experimental studies suggest wildfire smoke is a potential contributor to neurological dysfunction and associated with neuroinflammation. Using doses comparable to those encountered during intense wildfire events (200-300 μg/m 3 ), we explore the absorption, distribution, metabolism, and elimination (ADME) and pharmacokinetics of representative members of major chemical classes (acid, phenol, PAH, aldehyde) in inhaled wood smoke condensates. Male Sprague Dawley rats were intranasally instilled with smoldering eucalyptus woodsmoke extract (WSE) reconstituted in saline spiked with 14 C-labeled palmitic acid (PA), benzo[a]pyrene (B[a]P), catechol (CAT) or benzaldehyde (BZ). Serum was collected from 5 min to 2 weeks after exposure and tissues were collected at 0.5, 2, 4, 24 h and 2 weeks after exposure. Urine was collected over the 24 h exposure. Tissues were collected, rinsed in PBS and analyzed by accelerator mass spectrometry (AMS) for 14C-labeled chemicals. PA and B[a]P entered circulation slowly, reached maximum concentration (C max ) near 15 ng/mL at 2 h, and had circulating concentrations near 1/3 C max 24 h after exposure. CAT and BZ rapidly entered circulation and were mostly cleared at 2 h. Excess 14 C from all four chemicals was detected in olfactory bulb and brain over the first 24 h but only PA (or its metabolites) was retained in olfactory bulb, brain and kidney at 2 weeks post exposure. All excess 14 C was cleared from the lung at 2 weeks. Metabolite analysis of urine (CAT, BZ and B(a)P) dosed samples did not detect any parent compound. CAT and BZ were rapidly cleared. The slower uptake and clearance of PA or B[a]P or their reactive metabolites when dosed with WSE in brain and olfactory bulb potentially provide greater opportunity for inflammatory response. This study suggests that wildfire smoke chemicals can enter the brain directly from the nasal cavity to the olfactory bulb and via systemic circulation.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗