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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An investigation of the multi-mode Richtmyer-Meshkov instability at a gas/HE interface using Pagosa

Here, in this work, we present a hydrocode Pagosa and explore the Richtmyer-Meshkov Instability (RMI) at an air/high explosive (HE) interface for the first time that is important but has not received much attention yet in the high explosive safety field. Thus, the presented Pagosa can be expected to predict the whole deflagration-to-detonation transition (DDT) process in future. In Pagosa, spatial discretization is implemented on cubic staggered grids by computing different variables at the vertex and the cell center, respectively, a special operator-splitting technique is employed to reduce the computational cost, and an artificial viscosity is added to handle the discontinuous shock waves in our simulations. To quantitatively evaluate the capability of Pagosa to solve these kinds of instabilities, the single mode Rayleigh-Taylor instability (RTI) and the multimode RMI at an air/SF 6 interface are first performed, respectively. The Pagosa results are compared with the related numerical solutions in the existing references and the experimental result. Moreover, a theoretical derivation of growth of RTI is also provided based on our numerical method. Subsequently, we explore the multi-mode RMI at an air/HE interface as well as the effects of several factors using Pagosa. Numerical results show that Pagosa is a powerful toolset to generate the right structures and the amplitude of RTI and RMI at an air/SF 6 interface. The solid HE can be penetrated by a strong shock wave and forms RMI deformations. The RMI at an air/HE interface behaves very different than at an air/SF 6 interface, periodic, decreased oscillation is observed due to material character, and is very sensitive to the initial simulation settings, that is, a tiny change in physical quantities will lead to a remarkable RMI structure, which is also observed in a shock bubble interaction. The findings in this work are significant, and will present a new insight for the high explosive field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Autonomous continuous flow reactor synthesis for scalable atom-precision

With new instrumentation design, robotics, and in-operando hyphenated analytical tool automation, the intelligent discovery of synthesis pathways is becoming feasible. It can potentially bridge the gap for the scale-up of new materials. In this article, we review current progress and describe a new system that uses an autonomous continuous flow chemistry framework to translate high-quality lead molecules and materials to quantities that can meet scalability demands. At the core is a continuous flow synthesis platform that can design its viable synthesis pathway to a particular molecule or material and then autonomously carry it out. This is realized by integrating: (1) A workflow/architecture for multimode chemical/materials characterization in-line. The in-line characterization modes are NMR, ESR, IR, Raman, UV-Vis, GC-MS, and HPLC, along with ex-situ modes for X-Ray and neutron scattering; (2) Integration for feedback/analysis/data storage of the control variables; (3) A core software stack that includes deep learning and reinforcement learning alongside quantum chemistry and molecular dynamics; (4) On-demand compute architectures that parse calculations to compute resources needed which include light-weight edge, mid-level edge (NVIDA DGX-2), and high-performance computing. We demonstrate preliminary results on how this autonomous reactor system can enhance our ability to deliver deuterated materials, copolymers, and site-substituted molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Addition of transient kinetics capabilities to an infrared reflection absorption spectroscopy system through synchronized gas pulsing and data acquisition

Surface science methodologies for understanding thermodynamic aspects of surface processes are at an advanced level. However, instrumentation and approaches for extracting kinetic parameters from elementary steps are far less accessible. In this work, we present an approach combining the use of a fast gas pulsing valve synchronized with data acquisition to enable surface transient kinetics studies using infrared reflection absorption spectroscopy. This methodology applies to the study of reversible processes and borrows concepts and ideas from molecular beam scattering and temporal analysis of products. Here, a temporal resolution of ~67 ms is achieved, and this is illustrated through the study of CO adsorption and desorption on a Pd(111) crystal in the presence and absence of background O 2 . The same approach can be extended to other surface spectroscopies, such as X-ray photoelectron spectroscopy, to obtain spectra with high temporal resolution and signal-to-noise ratio and enable future multimodal surface transient kinetic studies aiming at elucidating reaction mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization

Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.

Defect project↗
DOE OSTI2021

A closer look at corrosion of steel reinforcement bars in concrete using 3D neutron and X-ray computed tomography

Corrosion of reinforcing bars constitutes the largest threat to the durability of concrete structures. Thus, several studies have investigated the nature of the corrosion products, most using post-mortem analyses. However, corrosion products evolve when in contact with oxygen, hindering result interpretation. This work presents instead a state-of-the-art, non-destructive 3D method for the assessment of corrosion of embedded reinforcements. Multimodal neutron and X-ray tomography was used to observe, non-destructively, the characteristics of the corrosion products in two concrete samples, with the aim of investigating possible benefits of the use of this technique for reinforced concrete structures. One sample was naturally corroded, extracted from an 81-year-old bridge, the other was corroded via the galvanostatic method, resulting in corrosion-induced cracks. Quantitative and qualitative data was acquired, including the iron-to-rust volumetric ratio in macroscopic interfacial voids and the thickness of the corrosion layer at the steel concrete interface. The iron-to-rust volumetric ratio corresponded to large, soluble, corrosion products, forming in environments with low availability of oxygen for both samples.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probing ethane phase changes in bead pack via high-field NMR spectroscopy

Phase equilibria analysis in two-phase systems requires discernment of the individual phases as well as the interfacial region. The phase behavior of fluids in constraint geometries have been studied through several techniques. High-Field Nuclear Magnetic Resonance (HF-NMR) can be used to detect fluid phases, i.e. gas, liquid, or supercritical fluid, in the bulk or under confinement. In solution-state HF-NMR, each phase exhibits a distinct chemical shift that correlates to the phase density. In turn, the emergence of multimodal in NMR spectra reveals the coexistence of multiple phases in fluid systems. Our results for ethane show that HF-NMR can enable tracking phase changes in the bulk, but also in macro-scale (10–100's μm in pore size) porous systems at various pressure and temperature conditions, even when the system us out of equilibrium. Finally, these results creates potential opportunities to investigate phase changes influenced by confinement in porous systems, though additional steps are necessary.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Operando methods: A new era of electrochemistry

One of the grand challenges facing electrochemistry is to directly resolve the complex nature of (electro)catalyst active sites and capture real-time “movies” of reaction dynamics, i.e. “watching chemistry in action”. The need for such fundamental understanding has stimulated the development of operando/in situ methods, which have greatly enhanced our ability to identify activity descriptors of electrocatalysts and establish structure–property relationships of energy materials. Here, this review summarizes the frontiers of operando electrochemical liquid-cell scanning transmission electron microscopy and correlative synchrotron X-ray methods, which are complementary tools to comprehensively investigate reaction dynamics across multiple spatiotemporal scales. In an effort to encourage greater adoption of advanced operando methods by the general electrochemistry community, this review points out the need to benchmark electrochemistry in confined and heterogenous liquid environment with minimal beam-induced damage. We anticipate that multimodal operando methods will become indispensable for understanding interfacial reaction mechanisms for the broad chemistry and energy materials communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Toward more-robust, AI-enabled subsurface seismic imaging for geotechnical applications

Non-invasive seismic imaging has the potential to cost-effectively evaluate large volumes of subsurface material to inform geotechnical site investigation. However, seismic imaging using full waveform inversion (FWI) requires significant computational time and is dependent on an initial starting model. As a result, FWI has not yet been widely adopted into geotechnical practice. Previous efforts, on relatively simple two-layered models, indicate that data-driven artificial intelligence (AI) models may be as effective as FWI at predicting 2D images of shear wave velocity (V s ). Furthermore, the AI model predictions can be made almost instantaneously after data acquisition and do not require an initial starting model. We examine the generality of these findings by developing a new AI model for subsurface seismic imaging, whereby we make several notable contributions. First, we architect a multimodal AI model that combines time- and frequency-domain representations of the seismic wavefield to predict a 50 m by 20 m subsurface image of V s . Second, we developed a new diverse dataset of 100,000 images with their corresponding seismic wavefields to train the AI model. Third, we propose four physics-informed data augmentations for data-driven seismic imaging. Fourth, we develop two prediction consistency tests to evaluate the model’s performance when the true subsurface is unknown. Our final model, which has been made publicly available, is capable of predicting a subsurface V s image from a single seismic wavefield with an average, mean absolute percent error (MAPE) of 24 %. The predictive model is applied to a field dataset and shown to be consistent with local geology and shear-wave refraction measurements from the same location.

Artificial intelligence↗
DOE OSTI2021

Effects of fiber dosage, loading orientation and stress on frequency response of enhanced Carbon Nano-Fiber Aggregates

Nanomaterial additives have been broadly used in cement and concrete-based sensors to measure the stress and strain in the structure. Most of the studies focused on measuring the resistivity of sensors with Direct Current (DC) or relatively low-frequency. Here in this work, the impedance responses of enhanced Carbon Nano-Fiber Aggregates (CNFAs) at different frequencies are rigorously studied and utilized as a tool for real-time structural health monitoring (SHM). The CNFA impedance measurements at different measurement frequencies provide an extra dimension (frequency dimension) to allow the characterization of different phenomena and multimodal measurements simultaneously. First, a comprehensive physical model is established to understand the frequency responses of the CNFA impedance. Second, COMSOL Multiphysics is used to simulate the frequency responses of the CNFA impedance, which provides more insights into the electric field and current density distribution. The impedance of CNFAs decreased with the increased dosage of carbon nanofibers (CNFs). Third, experimental studies are reported in detail. The electrical impedance variation (EZV) of the CNFA in parallel orientation is 151% higher than that of perpendicular orientation at the frequency of 2612 Hz and uniaxial compression stress of 4.65 MPa. The strain-EZV curve obtained from the response spectrum is linear for frequencies ranging from 98 Hz to 463.9 kHz. The strain-EZV curve for 5625 Hz has a linear fit with the gauge factor of 147.78. At 1000 Hz, the CNFA exhibited a repeatable behavior up to 9.35 MPa and detection limit up to the stress of 18.62 MPa.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advances in mass spectrometry-enabled multiomics at single-cell resolution

We report biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Editorial overview: Unlocking the secrets of nongenetic plasticity, one cell at a time

Cellular noise, the non-genetic variability observed among isogenic cells, arises from factors such as growth conditions, aging, and stochastic gene expression, influencing cell stress-response, metabolism, morphology, and size. Here, such plasticity, while critical for adaptation, often goes unnoticed with traditional population-averaging biotechnologies that inevitably mask cell-specific variations and prompting the question, "What else might we be missing". However, recent breakthroughs in optical imaging, microfluidics, and omics, are beginning to uncover the complexity of cellular plasticity. This special is-sue highlights some of these breakthroughs, with key contributions including innovations in multimodal chemical imaging, label-free microscopy, spatial and temporal omics, and droplet-based microfluidics. Collectively, these cutting-edge tools provide unprecedent-ed insights into non-genetic cell-to-cell variability, enhancing our understanding of cellu-lar plasticity and its implications for health, energy, and ecology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accelerating Markov Chain Monte Carlo sampling with diffusion models

Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global fit of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalising flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Interfaces in all solid state Li-metal batteries: A review on instabilities, stabilization strategies, and scalability

With technological advancements in electrochemical energy storage systems increasing at a spectacular rate, batteries equipped with a lithium anode hold the key towards unlocking high energy densities. While lithium-ion batteries with layered anodes (e.g. graphite) and liquid organic electrolytes have been ubiquitous in portable electronics, electric vehicles, and grid applications, all solid-state batteries that use the combination of a lithium anode and a solid-state electrolyte (SSE) will further advance the present technology. The underlying challenge that limits the successful development of all solid-state batteries (ASSBs) is dictated largely by the highly reactive interfaces at the anode/SSE and the cathode/SSE interface. In this comprehensive review, we present an overview of the following: (i) characterization of the electrode/SSE interface via multimodal characterization, which include X-ray-, electron-, neutron-, optical-, and computation-based methods: (ii) parasitic reactions that occur from chemical, mechanical or electrochemical instabilities and interfacial engineering strategies for improving the stability of these interfaces classified by the class of inorganic SSEs (sulfide-, NASICON-, and garnet-type SSEs); (iii) laboratory-to-industry scale processing perspectives of SSEs; (iv) scalability and manufacturing aspects of current interfacial strategies; and (v) the prospects of all ASSBs within the context of extreme fast charging capability. Here, this review seeks to highlight key efforts in the field of ASSBs, by focusing particularly on stabilizing the electrode/SSE interfaces, which will help to bridge fundamental studies to technological relevance.

25 ENERGY STORAGE↗
DOE OSTI2023

Synchrotron-based X-ray fluorescence microscopy mapping the ionome of a toxic freshwater cyanobacterium

Harmful algal blooms (HABs) pose a major environmental concern across the globe. In abundance, cyanobacteria, or so-called green-blue algae can produce extremely dangerous cyanotoxins that harm humans and animals. This study focused on the mapping and distribution of intracellular macro-and micronutrients of the widespread freshwater cyanobacteria Microcystis aeruginosa (M. aeruginosa). Towards a better understanding of trace metal uptake and homeostasis throughout the cell cycle, we quantitatively mapped the spatial distribution of the elements P, K, Fe, Ca, Zn, Mn, and Cu across the ultrastructure of frozen-hydrated single cells using state-of-theart X-ray nanofluorescence imaging at the Advanced Photon Source (APS) at Argonne National Laboratory. Further, bulk cellular nutrient and trace metal content correlated well with the total intracellular elemental content in individual cells obtained by quantitative synchrotron X-ray fluorescence measurements. Multi-dimensional mappings showed P and K atoms colocalized as discrete semicircular hotspots that were analyzed with respect to their stoichiometry. Elevated Cu and Ca concentrations were detected along division plane of cells. P and K were found to have similar spatial elemental distribution with about 65% and 69% of the total cellular P and K, respectively, located at the hotspots. The P and K colocalization were refined further using nanotomography, showing a K envelope surrounding the P core. Inorganic P and organic P compounds were specified using solution-state 31 P nuclear magnetic resonance (NMR) spectroscopy from M. aeruginosa. Of the total extracted P determined by 31 P NMR spectroscopy, 47% were found to be nucleotides while only 11% were polyphosphates. Multimodal X-ray imaging provides a better understanding of intracellular biochemical processes in cyanobacteria, helping us monitor and combat an emerging environmental threat.

31P NMR spectroscopy↗
DOE OSTI2024

Mesoscale interplay among composition heterogeneity, lattice deformation, and redox stratification in single-crystalline layered oxide cathode

Single-crystalline layered oxide materials for lithium-ion batteries are featured by their excellent capacity retention over their polycrystalline counterparts, making them sought-after cathode candidates. Their capacity degradation, however, becomes more severe under high-voltage cycling, hindering many high-energy applications. It has long been speculated that the interplay among composition heterogeneity, lattice deformation, and redox stratification could be a driving force for the performance decay. The underlying mechanism, however, is not well-understood. In this study, we use X-ray microscopy to systematically examine single-crystalline NMC particles at the mesoscale. This technique allows us to capture detailed signals of diffraction, spectroscopy, and fluorescence, offering spatially resolved multimodal insights. Focusing on early high-voltage charging cycles, we uncover heterogeneities in valence states and lattice structures that are inherent rather than caused by electrochemical abuse. These heterogeneities are closely associated with compositional variations within individual particles. Our findings provide useful insights for refining material synthesis and processing for enhanced battery longevity and efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Production, fuel properties and combustion testing of an iso-olefins blendstock for modern vehicles

With the increasing pressure to decarbonize the transportation sector, exploring strategies that can reduce emissions from light-duty vehicles (LDV) has become critical. Bioblendstocks that allow for higher engine efficiency and fuel economy could complement vehicle electrification and help reach carbon neutrality by 2050. In this context, the potential of a mixture of iso-olefins as a bioblendstock was investigated for multimode boosted spark-ignition (SI)/advanced compression ignition (ACI) engine operation designed to achieve higher overall vehicle fuel economy. By establishing the relationship between the molecular structure of iso-olefins and research octane number (RON), octane sensitivity (S) (i.e., the difference between RON and motor octane number [MON]), and phi-sensitivity a dimethyl-hexenes rich olefins mixture (DMHROM) was identified as a preferred blendstock for SI/ACI combustion engines. Here, a pathway for DMHROM production from biomass-derived ethanol was developed and scaled up. More than 1 gallon of DMHROM blendstock was produced for fuel properties assessment including engine testing. Measurements in a Cooperative Fuel Research Engine showed that the DMHROM blendstock possesses a RON of 94 and S of 13.5, and blends synergistically. Rapid compression machine tests coupled with single-cylinder gasoline direct injection engine measurements demonstrated the 20 vol.% DMHROM blend has higher phi-sensitivity than an olefin-free gasoline base fuel and a typical California Reformulated Gasoline Blendstock for Oxygenate Blending (CARBOB) gasoline fuel. These results demonstrate the potential of DMHROM for improving gasoline fuel performance and quality for operation under ACI conditions. The effectiveness of the aftertreatment system in mitigating emissions was verified and showed that the pure DMHROM blendstock and 20 vol.% blend would not increase non-methane organic gases, NO x , and carbon monoxide (CO) emissions. The DMHROM blendstock was found to slightly decrease sooting tendency when added to a gasoline-base fuel (i.e., ~6% reduction at 20 vol.% blending level). Oxidation stability and lubricant compatibility were both confirmed for the 20 vol.% blend. Overall, these results demonstrate that dimethyl-hexenes have potential for improving engine efficiency and fuel economy while meeting emissions regulations and ASTM specifications for gasoline fuel.

09 BIOMASS FUELS↗
DOE OSTI2021

Use of Multi-Site Radiation Therapy for Systemic Disease Control

Metastatic cancer is a heterogeneous entity, some of which could benefit from local consolidative radiation therapy (RT). Although randomized evidence is growing in support of using RT for oligometastatic disease, a highly active area of investigation relates to whether RT could benefit patients with polymetastatic disease. This article highlights the preclinical and clinical rationale for using RT for polymetastatic disease, proposes an exploratory framework for selecting patients best suited for these types of treatments, and briefly reviews potential challenges. The goal of this hypothesis-generating review is to address personalized multimodality systemic treatment for patients with metastatic cancer. The rationale for using high-dose RT is primarily for local control and immune activation in either oligometastatic or polymetastatic disease. However, the primary application of low-dose RT is to activate distinct antitumor immune pathways and modulate the tumor stroma in efforts to better facilitate T cell infiltration. We explore clinical cases involving high- and low-dose RT to demonstrate the potential efficacy of such treatment. We then group patients by extent of disease burden to implement high- and/or low-dose RT. Patients with low-volume disease may receive high-dose RT to all sites as part of an oligometastatic paradigm. Subjects with high-volume disease (for whom standard of care remains palliative RT only) could be treated with a combination of high-dose RT to a few sites for immune activation, while receiving low-dose RT to several remaining lesions to enhance systemic responses from high-dose RT and immunotherapy. We further discuss how emerging but speculative concepts such as immune function may be integrated into this approach and examine therapies currently under investigation that may help address immune deficiencies. The review concludes by addressing challenges in using RT for polymetastatic disease, such as concerns about treatment planning workflows, treatment times, dose constraints for multiple-isocenter treatments, and economic considerations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗