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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The causal relationship between melt pool geometry and energy absorption measured in real time during laser-based manufacturing

During laser powder bed fusion additive manufacturing, laser power absorption is governed by a pro -tean pool of molten metal that can present as a highly reflective surface, a deeply absorbing cavity, or some amalgamation thereof. These melt pool dynamics have been linked to defect creation, porosity, and surface finish quality. Although these are therefore critical for determining final part quality, their in-stantaneous influence on laser absorption have only been explored through simulation. To date, direct real-time observations have been elusive due to the locally extreme environment. In this work, we fo-cus a laser on Ti-6Al-4V powder and bare plate while quantifying the time-dependent, absolute energy absorption by monitoring omnidirectional backscattered laser intensity. We also simultaneously record the projective melt pool geometries with high-speed synchrotron x-ray imaging. We find that laser ab-sorption strongly reflects the stability of the vapor depression over a wide range of applied laser powers, oxygen content in the processing atmosphere, and with the presence of powder. During laser scanning of a powder bed surface, we find a significant absorption reduction after 400 mu s due to a dramatic change in the vapor depression aspect ratio-an event known to create porosity. Furthermore, as several industrial scan strate-gies necessitate thousands of these events during a build, their identification and control is of significant practical importance. Lastly, a normalized enthalpy model is demonstrated to be effective in quantifying the relationship between the laser absorption and cavity depth, even under transient conditions. In addi-tion to providing vital quantitative data for simulation calibration, the correlation of melt pool geometry with laser absorption during realistic processing conditions suggests the use of a total backscattered light detection system for real-time process control.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An elastin-like polymer targeting vascular endothelial growth factor receptor-1 reduces survival in serum-starved endothelial cells

Peptides often exhibit biological activity that depends on the context in which they are displayed and delivered. Understanding and controlling these contextual effects on peptide function is critical for designing targeted and responsive peptide-based biomaterials and therapeutics. Genetically engineered protein polymers such as elastin-like polypeptides (ELPs) can incorporate bioactive peptide motifs and are attractive candidates for biomaterials used in tissue engineering and targeted drug delivery. They also present an opportunity for investigating and modulating cell signaling pathways by presenting a peptide ligand in various defined chemical and physical environments. Vascular endothelial growth factor receptor-1 (VEGFR1) signaling plays important and complex roles in cell survival and angiogenesis, but polymeric materials that interact with this signaling axis are scarce. In this study, a novel genetically engineered elastin-like polymer that targets VEGFR1 is characterized. This polymer, termed R1B-ELP, binds to human endothelial cells in a manner dependent on its VEGFR1-targeting motif and, based on cell proliferation and cytotoxicity assays, demonstrates activity consistent with disrupting pro-survival signaling necessary for endothelial cell function under conditions of environmental stress. Notably, these findings indicate that ELP fusion alters the functional behavior of the targeting peptide. Modulators of VEGFR1 signaling have potential applications in basic studies of angiogenesis as well as in therapeutic applications targeting vascular or inflammatory diseases.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Catalyzing the future: recent advances in chemical synthesis using enzymes

Biocatalysis has the potential to address the need for more sustainable organic synthesis routes. Pro-tein engineering can tune enzymes to perform in cascade reactions and for efficient synthesis of en-antiomerically enriched compounds, using both natural and new-to-nature reaction pathways. This review highlights recent achievements in biocatal-ysis, especially the development of novel enzymatic syntheses to access versatile small molecule inter-mediates and complex biomolecules. Biocatalytic strategies for the degradation of persistent pollu-tants and approaches for biomass valorization are also discussed. Here, the transition of chemical synthesis to a greener future will be accelerated by imple-menting enzymes and engineering them for high performance and new activities.

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

Rewiring yeast metabolism for producing 2,3-butanediol and two downstream applications: Techno-economic analysis and life cycle assessment of methyl ethyl ketone (MEK) and agricultural biostimulant production

Rising concerns for sustainability and global climate change have driven the development of sustainable pro- duction pathways for biofuels and chemicals from lignocellulosic biomass via integrated biological and chemical processes. We constructed an engineered Saccharomyces cerevisiae capable of producing 2,3-butanediol (2,3-BDO) from glucose without accumulating ethanol and glycerol, which hinder downstream processing of 2,3-BDO, through extensive metabolic reprogramming. Specifically, we introduced heterologous 2,3-BDO biosynthetic enzymes and deleted the major isozymes of ethanol and glycerol biosynthetic enzymes. In addition, we introduced an NAD + regenerating Pyruvate-Malate (PM) cycle and enhanced the NAD + regenerating capability of the PM cycle to resolve the redox imbalance from the deletion of ethanol and glycerol production pathways. The resulting engineered yeast produced 109.9 g/L of 2,3-BDO with a productivity of 1.0 g/L/h and a yield of 0.36 g/ g glucose in a fed-batch fermentation. We also conducted techno-economic analysis (TEA) and life cycle assessment (LCA) of the production of methyl ethyl ketone (MEK) through catalytic dehydration of 2,3-BDO. A TEA based on the experimental results indicated that the minimum product selling price (MPSP) was estimated to be $1.90/kg. Regarding cradle-to-grave LCA, 100-year global warming potential (GWP100) and fossil energy consumption (FEC) were found to be 0.37 kg CO 2 eq/kg and 3.1 MJ/kg, respectively. These results demonstrated the feasibility of cost-competitive and sustainable bio-based MEK production via yeast fermentation. In addition, we explored the possibility of using the fermentation broth containing 2,3-BDO as a biostimulant inducing drought tolerance in plants. As a result, the yeast 2,3-BDO fermentation broth can induce drought tolerance in Arabidopsis thaliana without a complicated purification process.

2,3-butanediol (2,3-BDO)↗
DOE OSTI2023

Reinforced double-threaded slide-ring networks for accelerated hydrogel discovery and 3D printing

Traditionally, slide-ring gels are stretchable but soft as a result of an elasticity-stretchability trade-off. Herein, we introduce a new approach to breaking this trade-off and creating reinforced slide-ring networks with mobile crosslinkers. Our approach involves the construction of a polyethylene glycol double-threaded γ-cyclodextrin-based pro-slide-ring crosslinker that serves as a modular component for 3D printing and copolymerization. The resulting crystalline-domain-reinforced slide-ring hydrogels, or CrysDoS-gels, exhibit both high elasticity and high stretchability. The modular synthesis allows for high-throughput synthesis of CrysDoS-gels, generating a large amount of data for structure-property analysis. Here, by employing data science techniques, such as machine learning and linear regression, not only were we able to identify which chemical components influence the mechanical properties of CrysDoS-gels, but this analysis also aided in the discovery of better-performing CrysDoS-gels. Finally, we demonstrate the potential application of the newly discovered CrysDoS-gels as sensing devices by 3D printing them as stress sensors with high sensitivity and a broad detection range.

3D-printing↗
DOE OSTI2020

Effects of fuel composition and octane sensitivity on polycyclic aromatic hydrocarbon and soot emissions of gasoline–ethanol blend surrogates

The sooting propensity of a fuel is closely coupled with the fuel composition and chemistry. A detailed understanding of their effects is, therefore, needed to develop next-generation fuels which can minimize particulate emissions. With this overarching goal, the present work numerically investigates the effects of fuel composition and octane sensitivity (S) on polycyclic aromatic hydrocarbons (PAH) and soot emissions, for four-component gasoline-ethanol blend surrogates comprising isooctane, n-heptane, toluene, and ethanol. A partially-premixed counterflow flame is chosen as the canonical configuration for this study and simulations are performed using CHEMKIN-Pro-employing a kinetic mechanism developed by Park et al. (2017). In addition, a, detailed soot model based on the sectional method is used to capture the spatial characteristics of soot emissions. The kinetic mechanism and soot model are validated using available experimental data for various targets. A total of 86 TPRF-E mixtures, spanning a wide range of concentration of each component, and a wide range of S are analyzed. The effect of each non-paraffinic fuel component on the resultant PAH and soot emissions is investigated. PAH and soot emissions are found to vary significantly depending upon the blend composition. Additionally, based on the parametric sweeps, a regression analysis is carried out to identify global parameters that govern the formation of PAHs and soot. The analysis shows that both toluene content and S have a prominent effect on the formation of PAHs and soot, with toluene content having a stronger impact. Moreover, larger PAHs have higher dependency on toluene content and S. Furthermore, a detailed analysis is carried out to understand the physical and chemical phenomena associated with the observed trends of PAH and soot emissions.

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

A wide range experimental and kinetic modeling study of the oxidation of 2,3-dimethyl-2-butene: Part 1

2,3-Dimethyl-2-butene (TME) is a potential fuel additive with high research octane number (RON) and octane sensitivity (S), which can improve internal combustion engine performance and efficiency. However, the combustion characteristics of TME have not been comprehensively investigated. Thus, it is essential to study the combustion characteristics of TME and construct a detailed chemical kinetic model to describe its combustion. In this paper, two high-pressure shock tubes and a constant-volume reactor are used to measure ignition delay times and laminar flame speeds of TME oxidation. Further, the ignition delay times were measured at equivalence ratios of 0.5, 1.0, and 2.0 in “air”, at pressures of 5 and 10 bar, in the temperature range of 950 – 1500 K. Flame speeds of the TME/ “air” mixtures were measured at atmospheric pressure, at a temperature of 325 K, for equivalence ratios ranging from 0.78 to 1.31. Two detailed kinetic mechanisms were constructed independently using different methodologies; the KAUST TME mechanism was constructed based on NUIGMech1.1, and the MIT TME mechanism was built using the Reaction Mechanism Generator (RMG). Both mechanisms were used to simulate the experimental results using Chemkin Pro. In the present work, reaction flux and sensitivity analyses were performed using the KAUST mechanism to determine the critical reactions controlling TME oxidation at the conditions studied.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Estimating geographic origins of corn and soybean biomass for biofuel production: A detailed dataset

Sustainable fuel initiatives in the United States such as the Environmental Protection Agency’s Renewable Fuel Stan- dard and the Department of Energy’s Sustainable Aviation Fuel Grand Challenge have increased the production of corn ethanol and soybean biodiesel. However, the lack of precise information regarding biomass sourcing at a localized level has hindered accurate understanding of both biofuel costs and environmental impact of these production pathways. By harnessing the power of geospatial analysis and leveraging United States Department of Agriculture (USDA) crop cen- sus data, this dataset fills this critical knowledge gap. This dataset offers a novel estimation of geospatial biomass sourc- ing for biofuel production in the United States by synthe- sizing 2017 USDA crop census data, biorefinery data from the United States Energy Information Administration, and publicly available information about biomass sourcing for biofuel production. This dataset provides a detailed under- standing of biomass use for first generation biofuel pro- duction, enabling stakeholders to make informed decisions about resource allocation, investment strategies, and infras- tructure development. Furthermore, the county-level gran- ularity of the dataset allows for increased fidelity in the techno-economic assessments and life-cycle analyses of first- generation biofuels in the United States.

09 BIOMASS FUELS↗
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2023

Structure-guided design of direct-acting antivirals that exploit the gem-dimethyl effect and potently inhibit 3CL proteases of severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) and middle east respiratory syndrome coronavirus (MERS-CoV)

The high morbidity and mortality associated with SARS-CoV-2 infection, the etiological agent of COVID-19, has had a major impact on global public health. Significant progress has been made in the development of an array of vaccines and biologics, however, the emergence of SARS-CoV-2 variants and breakthrough infections are an ongoing major concern. Furthermore, there is an existing paucity of small-molecule host and virus-directed therapeutics and prophylactics that can be used to counter the spread of SARS-CoV-2, and any emerging and re-emerging coronaviruses. Here, we describe herein our efforts to address this urgent need by focusing on the structure-guided design of potent broad-spectrum inhibitors of SARS-CoV-2 3C-like protease (3CL pro or Main protease), an enzyme essential for viral replication. The inhibitors exploit the directional effects associated with the presence of a gem-dimethyl group that allow the inhibitors to optimally interact with the S4 subsite of the enzyme. Several compounds were found to potently inhibit SARS-CoV-2 and MERS-CoV 3CL proteases in biochemical and cell-based assays. Specifically, the EC50 values of aldehyde 1c and its corresponding bisulfite adduct 1d against SARS-CoV-2 were found to be 12 and 10 nM, respectively, and their CC50 values were >50 μM. Furthermore, deuteration of these compounds yielded compounds 2c/2d with EC50 values 11 and 12 nM, respectively. Replacement of the aldehyde warhead with a nitrile (CN) or an α-ketoamide warhead or its corresponding bisulfite adduct yielded compounds 1g, 1e and 1f with EC50 values 60, 50 and 70 nM, respectively. High-resolution cocrystal structures have identified the structural determinants associated with the binding of the inhibitors to the active site of the enzyme and, furthermore, have illuminated the mechanism of action of the inhibitors. Overall, the high Safety Index (SI) (SI=CC50/EC50) displayed by these compounds suggests that they are well-suited to conducting further preclinical studies.

3-Chymotrypsin-like protease (3CLpro)↗
DOE OSTI2024

Electrification of residential and commercial buildings integrated with hybrid renewable energy systems: A techno-economic analysis

Here, this study assesses the techno-economic viability of city-scale building electrification, comparing a base case scenario with conventional HVAC systems to an electrified scenario with heat pump systems. EnergyPlus simulations reveal a 28% increase in annual electricity consumption in the electrified scenario, shifting the peak consumption from summer to winter with a 49% surge. Hybrid renewable energy systems (HRESs) are then evaluated using HOMER Pro, considering renewable penetration from 0% to 100%. The net present cost (NPC) of the base case and electrified scenarios range from $\$10.34$ to $\$24.38$ billion and $\$8.79$ to $\$50.31$ billion, respectively. Sensitivity analysis explores the impact of carbon tax, fuel price, and renewable subsidies on HRESs in the electrified scenario. Results indicate that applying a carbon tax up to $\$120$/tonne of CO 2 increases the optimum renewable fraction from 5% to 26%, while higher fuel prices and renewable subsidies further enhance the feasibility by shifting the optimum renewable fraction to 60% and 54%, respectively, demonstrating the interplay between economic factors and renewable integration in electrified urban contexts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Decarbonizing via disparities: Problematizing the relationship between social identity and solar energy transitions in the United States

As solar adoption across the United States continues to grow, so do the gaps between rural and urban communities in how they choose to embrace these technologies, leading to serious questions of social justice and equity by researchers and policymakers alike. While recent studies have examined the racial and social justice elements of solar adoption alongside institutions' role in shaping pro-solar policies, codes, and code enforcement, an opportunity exists to discuss how the place and composition of the body politic in terms of race/ethnicity and rurality exists. This paper establishes a methodology for examining location and body politic composition concerning adopting all types of solar (residential, non-residential, utility-scale), utilizing the State of Georgia as a case study. Results indicate that the approach yields useful and informative findings; namely, there is a significant difference in adopting non-residential and utility-scale solar between rural and urban counties. We conclude by discussing further opportunities to expand on this analysis and the impact of assessing solar adoption in terms of value alignment between a body politic and the policies that shape the adoption of sustainable energy technologies. Finally, combining solar adoption information for the State of Georgia with Census data, this study compares solar adoption trends across counties--grouped by urban/rural classification and racial and ethnic majority.

14 SOLAR ENERGY↗
DOE OSTI2022

Enhancing methane production of anaerobic digested sewage sludge by advanced wet oxidation & steam explosion pretreatment

Anaerobic digestion (AD) of sewage sludge is associated with lowered carbon conversion efficiency due to the recalcitrance of microbial cells in the Waste Activated Sludge (WAS) from the aeration basins with 50 % of the dry weight still being present after AD. The effect of Advanced Wet Oxidation & Steam Explosion (AWOEx) pretreatment on the methane yield at variable temperatures (165–200 °C), residence time (15–45 min) and oxygen dosage (1%–10% based on VS concentration) was tested in this study. Central composite design (JMP® Pro) was used to design 19 runs of AWOEx pretreatment of dewatered digested sewage sludge (DADSS) at different operating conditions. After pretreatment the DADSS was fed to 19 bioreactors operated in semi-continues mode at 37 °C. The highest average methane yields of 183 mL/g VS and 170 mL/g VS were found for the bioreactors receiving DADSS pretreated at 165 °C with a retention time of 15 min and 10 % O2 (Condition 1), and, 182.5 °C with a retention time of 15 min and 5.5 % O2 (Condition 2). This corresponds to an increase in the methane production of 156.2 % and 140.5 % compared to the methane production from the DADSS without pretreatment.

09 BIOMASS FUELS↗
DOE OSTI2024

A chemical kinetic analysis of knock propensity of methanol-to-gasoline fuel

Production of low carbon gasoline-like fuels such as methanol-to-gasoline (MTG) is a promising approach to achieve rapid greenhouse gas emission reduction of the transportation sector. Despite the fact that gasoline that meets the ASTM D4814 standard for automotive spark-ignition engine fuel can be readily produced from these processes, it is unclear how the composition of MTG may affect engine performance and emissions. Here, in this paper, a surrogate for an MTG is used to numerically study the effects of gasoline composition on knock propensity and on the sensitivity of knock to thermal and fuel stratification, to oxygen dilution and to nitric oxide from exhaust gas recirculation of residual gases. Simulations were performed in ANSYS CHEMKIN-PRO using a comprehensive chemical kinetic mechanism for gasoline surrogates, and results of the MTG surrogate were compared against those of a petroleum-based regular E10 gasoline, termed PACE-20. A premium-grade MTG fuel was also formulated by adding ethanol to the MTG surrogate, and results were compared against those of four premium-grade, gasoline-like fuels representative of future alternative gasoline formulations. Surrogates and mechanism were evaluated by comparison against experimental engine data, and the model showed high accuracy at stoichiometric conditions (mean absolute error of ignition timing equal to 1.46 crank angle degrees) but larger deviations at lean conditions (mean absolute error of ignition timing equal to 5.52 crank angle degrees). Despite the fact that the MTG surrogate has a RON 1.1 units higher than that of PACE-20, it may show higher knock propensity at medium temperature conditions due to a less intense NTC behavior. MTG autoignition was more temperature- and equivalence ratio-sensitive than that of PACE20, suggesting that MTG can benefit more from naturally-occurring thermal stratification or from induced fuel stratification of the end gas to mitigate knock intensity. The sensitivity of autoignition reactivity to oxygen dilution and to NO concentration was higher for MTG than for regular gasoline at medium loads, but the opposite trend was observed at high loads due to the effect of pressure on the low-temperature chemistry of regular gasoline. Approximately 14 % vol ethanol content was required to upgrade the octane rating of MTG from regular grade to premium grade. Adding 13.6 % vol ethanol made the fuel autoignition less sensitive to both oxygen dilution and NO content (ignition time varies approx. 17 % and 50 % less with oxygen dilution and NO addition, respectively, when adding ethanol at high engine loads).

02 PETROLEUM↗
DOE OSTI2021

Role of mixed conducting Pr 0.1 Gd 0.1 Ce 0.8 O 1.9-δ barrier layer on the promotion of SOFC performance

The cathode activity in a solid oxide fuel cell can be promoted by introducing various catalysts to reduce its polarization resistance towards oxygen reduction, and thus improve cell performance. Here, in this work, the La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 (LSCF) cathode surface is modified by the infiltration of Pr 6 O 11 and the power density at 0.8 V and 750 °C is improved by 21%. Moreover, by replacing the traditional barrier layer Gd 0.2 Ce 0.8 O 1.9 with mixed conducting Pr 0.1 Gd 0.1 Ce 0.8 O 1.9 (PGCO), the power density increases by 38%. The ohmic resistance is dramatically reduced by applying the PGCO interlayer. The distribution of relaxation time was used to analyze the mechanism for which the polarization resistance was decreased, attributing to the mixed conduction nature in PrO x . An increase of power density, ~0.358 W/cm 2 (71%) at 0.8 V, is achieved with the implementation of both surface modification and buffer layer engineering.

08 HYDROGEN↗
DOE OSTI2022

Metal-supported solid oxide fuel cells operating with reformed natural gas and sulfur

The performance, long-term durability, and thermal cycling tolerance of metal supported solid oxide fuel cells (MS-SOFCs) operating with natural gas reformate fuels is assessed. Symmetric MS-SOFCs with composite SDC-Ni anode catalysts and PrO x cathode catalysts are operated with simulated natural gas steam-reformate and partial-oxidation-reformate fuels with 1 ppm and 5 ppm of sulfur. Cells are operated for 1000 h with initial degradation rates similar to humidified H 2 , and initial performance differences attributed to the lower H 2 concentration in reformate fuels. Additionally, cells tolerate many aggressive thermal cycles with sulfur present, with minimal impact on performance. Post mortem analysis suggests that Ni particle coarsening and Cr deposition are sources of degradation, while carbon and sulfur deposition are not observed. Overall, MS-SOFCs operate successfully with reformed natural gas.

08 HYDROGEN↗
DOE OSTI2023

Towards deep computer vision for in-line defect detection in polymer electrolyte membrane fuel cell materials

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. Here we explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Analysis of Patient-Reported Outcome Utilization Within National Clinical Trials Network Cooperative Group Radiation Oncology Trials Over the Past 2 Decades

When treating cancer, both quantity and quality of life are valuable, though oncology trials have long placed greater emphasis on the former. The goal of this work was to evaluate how patient-reported outcomes (PROs) have been incorporated into radiation therapy trials within the National Clinical Trials Network over the last 2 decades to measure quality of life and to assess how PRO data have been disseminated in publications upon trial conclusion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗