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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SURFACE ROUGHNESS REDUCTION AND PERFORMANCE OF Nb3Sn FILM FOR SRF APPLICATIONS

Nb3Sn offers better RF performance (Q and Eacc) than Nb at any given temperature because of its superior superconducting properties. The potential deployment of Nb3Sn-coated SRF cavities at 4 K, delivering similar performance to that of Nb cavities at 2 K, will be a transformational technology to enable new classes of SRF accelerator applications. A clean and smooth surface can enhance the performance of the Nb3Sn-coated cavity, typically, the attainable acceleration gradient. The reduction of surface roughness is often linked with a correlative reduction in average coating thickness and grain size. A new approach to enhance the nucleation centers using the pre-deposition of Sn on Nb is introduced for surface roughness reduction. Besides Sn supply's careful tuning, the temperature profiles were varied to engineer the coating surface with reduced roughness as low as ~40 nm in 20 µm × 20 µm AFM scans, one-third that of the typical coating. Electropolishing of coated sample shows surface a clear surface smoothening. A few sets of coating parameters were used to coat 1.3 GHz sin-gle-cell cavities to understand the effects of roughness variation on the RF performance. This presentation discusses ways to reduce surface roughness with results from a systematic analysis of the samples and Nb3Sn-coated single-cell cavities.

Pudasaini, Uttar↗
DOE OSTI2021

Catalytic Reduction of Residual Oxygen for the Purification of Carbon Dioxide Streams from Pressurized Oxy-Combustors

Pressurized oxy-combustion (OC) is regarded as an energy-efficient alternative to atmospheric OC. To utilize the captured carbon dioxide (CO2) from pressurized OC for enhanced oil recovery or carbon storage in geological formations, the CO2 product stream must meet the required purity specifications. One major impurity in OC flue gases is residual O2 (e.g., 3 vol%). In this study, a catalytic approach has been investigated to remove the residual O2 through a reduction reaction with injected methane (CH4) in a reverse-flow fixed-bed (RFFB) reactor. The performance was tested for a supported copper (Cu)-based catalyst synthesized by incipient wetness impregnation and a cobalt-manganese (CoMn) oxide catalyst synthesized by a coprecipitation method. A commercial Cu-based catalyst was also studied as the reference. Experimental results revealed that under the reverse-flow operation, the temperature profiles in the reactor were symmetrical on both sides of the catalyst zone and were more homogenous than those under the conventional one-direction-flow operation. Among the tested catalysts, the CoMn catalyst was the most reactive for promoting the reduction of O2 with CH4, able to attain >95% O2 removal at 475 °C and a gas hourly space velocity (GHSV) of 18,000 h-1 with a feed gas containing 3 vol% O2. Under all experiments, the concentrations of carbon monoxide (CO) were lower than 30 ppmv in the effluent gas streams. The reaction between O2 and CH4 could be depicted by using the Mars–van Krevelen redox mechanism based on a two-step reduction–oxidation reaction pathway.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Technological cost-reduction pathways for attenuator wave energy converters in the marine hydrokinetic environment.

This report considers and prioritizes the primary potential technical cost reduction pathways for offshore wave activated body attenuators designed for ocean resources. This report focuses on technical research and development costreduction pathways related to the device technology rather than environmental monitoring or permitting opportunities. Three sources of information were used to understand current cost drivers and develop a prioritized list of potential cost reduction pathways: a literature review of technical work related to attenuators, a reference device compiled from literature sources, and a webinar with each of three industry device developers. Data from these information sources were aggregated and prioritized with respect to the potential impact on the lifetime levelized cost of energy, the potential for progress, the potential for success, and the confidence in success. Results indicate the five most promising cost reduction pathways include advanced controls, an optimized structural design, improved power conversion, planned maintenance scheduling, and an optimized device profile.

Bull, Diana L↗
DOE OSTI2013

ADVANTG An Automated Variance Reduction Parameter Generator

The AutomateD VAriaNce reducTion Generator (ADVANTG) software automates the generation of variance reduction (VR) parameters for continuous-energy Monte Carlo simulations of fixed-source neutron, photon, and coupled neutron-photon transport problems using MCNP5 (X-5 Monte Carlo Team 2003). ADVANTG generates space- and energy dependent mesh-based weight-window bounds and biased source distributions from three dimensional (3-D) discrete ordinates (SN) calculations that are performed by the Denovo package (Evans et al. 2010). The deterministic calculations can be performed using multiple cores and/or processors (e.g., on multi-core desktop systems and clusters). The final variance reduction parameters are output in a format that can be used with unmodified versions of MCNP. The primary objective of the development of ADVANTG has been to reduce both the user effort and the computational time required to obtain accurate and precise tally estimates across a broad range of challenging transport application areas.

ADVANTG↗
DOE OSTI2014

Energy-Saving Melting and Revert Reduction Technology (E-SMARRT): Final Summary Report

Energy-Saving Melting and Revert Reduction Technology (E-SMARRT) is a balanced portfolio of R&D tasks that address energy-saving opportunities in the metalcasting industry. E-SMARRT was created to: • Improve important capabilities of castings • Reduce carbon footprint of the foundry industry • Develop new job opportunities in manufacturing • Significantly reduce metalcasting process energy consumption and includes R&D in the areas of: • Improvements in Melting Efficiency • Innovative Casting Processes for Yield Improvement/Revert Reduction • Instrumentation and Control Improvement • Material properties for Casting or Tooling Design Improvement The energy savings and process improvements developed under E-SMARRT have been made possible through the unique collaborative structure of the E-SMARRT partnership. The E-SMARRT team consisted of DOE’s Office of Industrial Technology, the three leading metalcasting technical associations in the U.S: the American Foundry Society; the North American Die Casting Association; and the Steel Founders’ Society of America; and SCRA Applied R&D, doing business as the Advanced Technology Institute (ATI), a recognized leader in distributed technology management. This team provided collaborative leadership to a complex industry composed of approximately 2,000 companies, 80% of which employ less than 100 people, and only 4% of which employ more than 250 people. Without collaboration, these new processes and technologies that enable energy efficiencies and environment-friendly improvements would have been slow to develop and had trouble obtaining a broad application. The E-SMARRT R&D tasks featured low-threshold energy efficiency improvements that are attractive to the domestic industry because they do not require major capital investment. The results of this portfolio of projects are significantly reducing metalcasting process energy consumption while improving the important capabilities of metalcastings. Through June 2014, the E-SMARRT program predicts an average annual estimated savings of 59 Trillion BTUs per year over a 10 year period through Advanced Melting Efficiencies and Innovative Casting Processes. Along with these energy savings, an estimated average annual estimate of CO2 reduction per year over a ten year period is 3.56 Million Metric Tons of Carbon Equivalent (MM TCE).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Solid Oxide Fuel Cell Cathodes: Unraveling the Relationship Between Structure, Surface Chemistry and Oxygen Reduction

In this work we have considered oxygen reduction reaction on LSM and LSCF cathode materials. In particular we have used various spectroscopic techniques to explore the surface composition, transition metal oxidation state, and the bonding environment of oxygen to understand the changes that occur to the surface during the oxygen reduction process. In a parallel study we have employed patterned cathodes of both LSM and LSCF cathodes to extract transport and kinetic parameters associated with the oxygen reduction process.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2016

Development of U isotope fractionation as an indictor or U(VI) reduction in uranium plumes

This is the final report for a university research project that advanced development of a new technology for identifying chemical reduction of uranium contamination in groundwater at the Rifle Field Challenge site. Reduction changes mobile hexavalent uranium into immobile U(IV). The stable isotope ratio (238U/235U) measurements of U using multicollector ICP-mass spectrometry were performed to understand the chemical reduction and sorption processes during various field experiments. In addition laboratory experiments were performed to better understand the isotopic fractionations. The main objectives of this project were completed during the project period and two peer-reviewed articles were published to disseminate the information gained.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2015

Geothermal Risk Reduction via Geothermal/Solar Hybrid Power Plants. Final Report

There are numerous technical merits associated with a renewable geothermal-solar hybrid plant concept. The performance of air-cooled binary plants is lowest when ambient temperatures are high due to the decrease in air-cooled binary plant performance that occurs when the working fluid condensing temperature, and consequently the turbine exhaust pressure, increases. Electrical power demand is generally at peak levels during periods of elevated ambient temperature and it is therefore especially important to utilities to be able to provide electrical power during these periods. The time periods in which air-cooled binary geothermal power plant performance is lowest generally correspond to periods of high solar insolation. Use of solar heat to increase air-cooled geothermal power plant performance during these periods can improve the correlation between power plant output and utility load curves. While solar energy is a renewable energy source with long term performance that can be accurately characterized, on shorter time scales of hours or days it can be highly intermittent. Concentrating solar power (CSP), aka solar-thermal, plants often incorporate thermal energy storage to ensure continued operation during cloud events or after sunset. Hybridization with a geothermal power plant can eliminate the need for thermal storage due to the constant availability of geothermal heat. In addition to the elimination of the requirement for solar thermal storage, the ability of a geothermal/solar-thermal hybrid plant to share a common power block can reduce capital costs relative to separate, stand-alone geothermal and solar-thermal power plant installations. The common occurrence of long-term geothermal resource productivity decline provides additional motivation to consider the use of hybrid power plants in geothermal power production. Geothermal resource productivity decline is a source of significant risk in geothermal power generation. Many, if not all, geothermal resources are subject to decreasing productivity manifested in the form of decreasing production fluid temperature, flow rate, or both during the life span of the associated power generation project. The impacts of geothermal production fluid temperature decline on power plant performance can be significant; a reduction in heat input to a power plant not only decreases the thermal energy available for conversion to electrical power, but also adversely impacts the power plant efficiency. The impact of resource productivity decline on power generation project economics can be equally detrimental. The reduction in power generation is directly correlated to a reduction in revenues from power sales. Further, projects with Power Purchase Agreement (PPA) contracts in place may be subject to significant economic penalties if power generation falls below a specified default level. While the magnitude of PPA penalties varies on a case-by-case basis, it is not unrealistic for these penalties to be on the order of the value of the deficit power sales such that the utility may purchase the power elsewhere. This report evaluates the use of geothermal/solar-thermal hybrid plant technology for mitigation of resource productivity decline, which has not been a primary topic of investigation in previous analyses in the open literature.

14 SOLAR ENERGY↗
DOE OSTI2013

Center for BioBased Binders and Pollution Reduction Technology

Funding will support the continuation of the Center for Advanced Bio-based Binders and Pollution Reduction Technology Center (CABB) in the development of bio-based polymers and emission reduction technologies for the metal casting industry. Since the formation of the center several new polymers based on agricultural materials have been developed. These new materials have show decreases in hazardous air pollutants, phenol and formaldehyde as much as 50 to 80% respectively. The polymers termed bio-polymers show a great potential to utilize current renewable agricultural resources to replace petroleum based products and reduce our dependence on importing of foreign oil. The agricultural technology has shown drastic reductions in the emission of hazardous air pollutants and volatile organic compounds and requires further development to maintain competitive costs and productivity. The project will also research new and improved inorganic binders that promise to eliminate hazardous emissions from foundry casting operations and allow for the beneficial reuse of the materials and avoiding the burdening of overcrowded landfills.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

State Approaches to Demand Reduction Induced Price Effects: Examining How Energy Efficiency Can Lower Prices for All

Effects (DRIPE) as a real, quantifiable benefit of energy efficiency and demand response programs. DRIPE is a measurement of the value of demand reductions in terms of the decrease in wholesale energy prices, resulting in lower total expenditures on electricity or natural gas across a given grid. Crucially for policymakers and consumer advocates, DRIPE savings accrue not only to the subset of customers who consume less, but to all consumers. Rate-paying customers realize DRIPE savings when price reductions across an electricity or natural gas system are passed on to all retail customers as lower rates (depending upon regulation and market structure, residual savings may be wholly or partially retained by utilities). DRIPE savings, though seemingly small in terms of percent price reductions or dollars per household, can amount to hundreds of millions of dollars per year across entire states or grids. Therefore, accurately assessing DRIPE benefits can help to ensure appropriate programs are designed and implemented for energy efficiency measures. This paper reviews the existing knowledge and experience from select U.S. states regarding DRIPE (including New York and Ohio), and the potential for expanded application of the concept of DRIPE by regulators. Policymakers and public utility commissions have a critical role to play in setting the methodology for determining DRIPE, encouraging its capture by utilities, and allocating DRIPE benefits among utilities, various groups of customers, and/or society at large. While the methodologies for estimating DRIPE benefits are still being perfected, policymakers can follow the examples of states such as Maryland and Vermont in including conservative DRIPE estimates in their resource planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

U.S. electric power sector transitions required to achieve 80% reductions in economy-wide greenhouse gas emissions: Results based on a state-level model of the U.S. energy system

The United States has articulated a deep decarbonization strategy for achieving a reduction in economy-wide greenhouse gas (GHG) emissions of 80% below 2005 levels by 2050. Achieving such deep emissions reductions will entail a major transformation of the energy system and of the electric power sector in particular. , This study uses a detailed state-level model of the U.S. energy system embedded within a global integrated assessment model (GCAM-USA) to demonstrate pathways for the evolution of the U.S. electric power sector that achieve 80% economy-wide reductions in GHG emissions by 2050. The pathways presented in this report are based on feedback received during a workshop of experts organized by the U.S. Department of Energy’s Office of Energy Policy and Systems Analysis. Our analysis demonstrates that achieving deep decarbonization by 2050 will require substantial decarbonization of the electric power sector resulting in an increase in the deployment of zero-carbon and low-carbon technologies such as renewables and carbon capture utilization and storage. The present results also show that the degree to which the electric power sector will need to decarbonize and low-carbon technologies will need to deploy depends on the nature of technological advances in the energy sector, the ability of end-use sectors to electrify and level of electricity demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Using learning curves on energy-efficient technologies to estimate future energy savings and emission reduction potentials in the U.S. iron and steel industry

Increasing concerns on non-sustainable energy use and climate change spur a growing research interest in energy efficiency potentials in various critical areas such as industrial production. This paper focuses on learning curve aspects of energy efficiency measures in the U.S iron and steel sector. A number of early-stage efficient technologies (i.e., emerging or demonstration technologies) are technically feasible and have the potential to make a significant contribution to energy saving and CO 2 emissions reduction, but fall short economically to be included. However, they may also have the cost effective potential for significant cost reduction and/or performance improvement in the future under learning effects such as ‘learning-by-doing’. The investigation is carried out using ISEEM, a technology oriented, linear optimization model. We investigated how steel demand is balanced with/without the availability learning curve, compared to a Reference scenario. The retrofit (or investment in some cases) costs of energy efficient technologies decline in the scenario where learning curve is applied. The analysis also addresses market penetration of energy efficient technologies, energy saving, and CO 2 emissions in the U.S. iron and steel sector with/without learning impact. Accordingly, the study helps those who use energy models better manage the price barriers preventing unrealistic diffusion of energy-efficiency technologies, better understand the market and learning system involved, predict future achievable learning rates more accurately, and project future savings via energy-efficiency technologies with presence of learning. We conclude from our analysis that, most of the existing energy efficiency technologies that are currently used in the U.S. iron and steel sector are cost effective. Penetration levels increases through the years, even though there is no price reduction. However, demonstration technologies are not economically feasible in the U.S. iron and steel sector with the current cost structure. In contrast, some of the demonstration technologies are adapted in the mid-term and their penetration levels increase as the prices go down with learning curve. We also observe large penetration of 225kg pulverized coal injection with the presence of learning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Subsurface Environmental Modeling

Subsurface environmental systems are open and complex, in which intricate hydrologic, microbiologic, and geochemical processes occur and interact at multiple scales. Understanding and predicting system responses to natural forces (e.g., climate changes) and human activities (e.g., contaminant remediation and CO 2 sequestration) is indispensable for managing water resources, cleaning subsurface contamination at many sites of the Department of Energy (DOE), and providing expert analysis to inform policy-making on long-term stewardship of nuclear waste disposal and CO 2 storage sites. Groundwater reactive transport modeling is necessary for understanding and predicting the complex subsurface environmental system. However, as complex systems are never completely predictable, model predictions of the subsurface system are inherently uncertain, and uncertainty is one of the greatest obstacles in simulating the subsurface environment. Quantifying predictive uncertainty of the complex system is critical to test model predictive power, support decision-making, and guide experimental design and data collection for uncertainty reduction. This project tackled two major obstacles to efficient and effective quantification and reduction of predictive uncertainty in simulating the complex subsurface environment. One obstacle is that existing methods of uncertainty quantification are too fragmented and incomplete for understanding and predicting the complex subsurface environment as a whole. The other obstacle is that existing methods of uncertainty quantification are computationally demanding and may not be applicable to groundwater reactive transport modeling that is always computationally expensive. The above two obstacles to uncertainty quantification directly affect the design of data collection schemes for uncertainty reduction. In this project, we developed a comprehensive Bayesian framework to remove the first obstacle. The second obstacle was tackled by using the sparse grid methods, which were originally developed and analyzed for numerical integration in the field of applied mathematics. We integrated the sparse grid methods into the Bayesian framework seamlessly, which led to a sparse-grid-based method of Bayesian uncertainty analysis. The developed computational Bayesian framework has been applied to a number of subsurface environmental problems through collaborating with scientists at the Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The applications demonstrated that the computational Bayesian framework is general and compatible with any models and numerical codes of subsurface modeling. It is thus can be applied to gain insights for subsurface environmental modeling. This project helped the PI achieve his career goal to advance the state-of-the-art computational methods in uncertainty quantification for subsurface environmental modeling. This project supported two doctoral students, two master students, and one post-doc. The two doctoral students, after their graduation, worked as post-docs at the Pacific Northwest National Laboratory and the Oak Ridge National Laboratory. The project produced a total of 29 journal articles (four under review), one book chapter, and a number of conference proceedings and abstracts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Nuclear Data Dimension Reduction

Our nuclear data consist of 30 energy group cross sections as well as nubar and PFNS. They are sampled from a Gaussian distribution with correlations shown in Figure 1. A few of these samples are shown in Figure 2. The purpose of this section is to describe how we can reduce the dimension of the nuclear data for purposes of uncertainty quantification. Sections 2 and 3 describe why dimension reduction is useful, as well as our approach to dimension reduction. When using these nuclear data as inputs to the PARTISN code, some of the resulting $k_{eff}$ values are unrealistic. For instance, when PARTISN is simulating the Jezebel critical assembly, some of the nuclear data yield $k_{eff}$ far from one. We would like to weight the nuclear data combinations that yield more realistic output higher, while downweighting those that result in unrealistic output. Our approach to this is described in Section 4. Throughout the other sections, we will merely refer to a prior distribution of nuclear data, which is not constrained by the Jezebel experiment, and a posterior distribution of nuclear data, which is conditioned on satisfactory performance for the Jezebel experiment. For instance, the correlation matrix for the reweighted (posterior) nuclear data is shown in Figure 3, along with the difference from the prior correlation matrix. Finally, we will describe dimension reduction results in Section 5.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

This exploratory analysis characterizes the balance-of-system (BOS) cost reduction opportunity for small, commercial, medium, and large distributed wind systems. To do this, we used the National Renewable Energy Laboratory’s (NREL’s) Land-based Balance of System Systems Engineering (LandBOSSE) model (Eberle et al. 2019). This model calculates the capital expenditures (CapEx) associated with installation and the system components (e.g., foundation and electrical infrastructure) other than the rotor nacelle assembly and tower. Then, building on the cost reduction potential assessment, we provide a more qualitative evaluation of prospective technology innovation concepts that may enable realization of those cost reductions for distributed wind systems.

17 WIND ENERGY↗
DOE OSTI2022

Universal Solvent Viscosity Reduction via Hydrogen Bonding Disruptors

Liquid Ion Solutions LLC (DBA RoCo Global) in partnership with Carnegie Mellon University and Carbon Capture Scientific LLC, has performed lab-scale development and evaluation of novel additives that lower the viscosity of water-lean amine solvents for post-combustion carbon dioxide capture. This project focuses on developing additives that minimize the formation of long-range electrostatic and hydrogen bonding (HB) networks, decreasing the solvent viscosity, improving diffusion, and improving the process economics. The project objectives included: 1) performing computer simulation to understand the molecular interactions of the additive molecules in water-lean CO 2 capture solvents, 2) design and synthesis of HB disruptors additives, 3) performance testing with additive molecules on model amine solvents, and 4) demonstration of the effectiveness of the optimized additives in the presence of synthetic flue gas. To meet the abovementioned objectives, the project team utilized a holistic approach that combines molecular simulation, experimental testing, and economic analysis studies. The project team developed ab initio molecular model and then perform computer simulation to develop relationship between hydrogen bonding, viscosity, and performed quantitative analysis of additive on the viscosity of the solvent. The team completed computational comparative study on a range of organic functional groups such as ethers, esters, cyclic carbonates, alkanes, and ammonium salts for their effect on viscosity gaining key insights into molecular interactions and the impact of various functional groups and molecular shapes on viscosity. Assisted with molecular simulation insights, the project team conducted additive synthesis and testing, including a proof-of-concept study, additive screening, optimization, and synthetic flue gas testing. The experimental proof-of-concept study proved that the hydrogen bonding acceptors result in significant decrease of viscosities. Detailed additive screening (exploring various functionalities and molecular structures) has been performed. Several promising additives showed excellent reduction in viscosity (30-41%) at 5% additive loading, and over 50% viscosity reduction at 10% additive loading for the model solvents. The team also performed complex screening studies on additive loadings and mixing effect among additives using the design of experiments. Based on multiple screening experiments, one additive-solvent candidate was down-selected for synthetic flue gas testing. A 100-hour continuous absorption/desorption study was conducted under simulated flue gas using a lab-scale continuous capture and separation system. No degradation (for both solvent and additive) was observed based on the GC results of the solvent samples collected from the continuous study. The team conducted preliminary engineering analyses and cost-benefit analyses to quantify the potential economic benefits of the additive approach for solvent viscosity reduction. Based on the experimental data, CO 2 capture cost savings from the capital and operating cost savings are estimated at $\$$4.7/tonne and $\$$0.3/tonne CO 2 captured, respectively. Considering the additive cost, the net benefit is estimated to be between $\$$4.32~$\$$4.86/tonne CO 2 captured.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance-reduction techniques that directly bias particle direction have historically received limited attention despite being useful for many radiation transport applications such as those in which radiation travels through large regions with a low probability of colliding. One such technique is known as DXTRAN (short for deterministic transport) in the MCNP Monte Carlo radiation transport code. Until now, effectively applying DXTRAN in calculations is largely based on empirical observations of computational performance. Optimal DXTRAN parameters are identified through manual iteration. This work develops new mathematical descriptions of the DXTRAN variance-reduction process and demonstrates a new automated variance-reduction method that applies these mathematical formulations to determine the optimal application of DXTRAN in a given problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

SURFACE ROUGHNESS REDUCTION AND PERFORMANCE OF Nb3Sn FILM FOR SRF APPLICATIONS

Nb3Sn offers better RF performance (Q and Eacc) than Nb at any given temperature because of its superior superconducting properties. The potential deployment of Nb3Sn-coated SRF cavities at 4 K, delivering similar performance to that of Nb cavities at 2 K, will be a transformational technology to enable new classes of SRF accelerator applications. A clean and smooth surface can enhance the performance of the Nb3Sn-coated cavity, typically, the attainable acceleration gradient. The reduction of surface roughness is often linked with a correlative reduction in average coating thickness and grain size. A new approach to enhance the nucleation centers using the pre-deposition of Sn on Nb is introduced for surface roughness reduction. Besides Sn supply's careful tuning, the temperature profiles were varied to engineer the coating surface with reduced roughness as low as ~40 nm in 20 µm × 20 µm AFM scans, one-third that of the typical coating. Electropolishing of coated sample shows surface a clear surface smoothening. A few sets of coating parameters were used to coat 1.3 GHz sin-gle-cell cavities to understand the effects of roughness variation on the RF performance. This presentation discusses ways to reduce surface roughness with results from a systematic analysis of the samples and Nb3Sn-coated single-cell cavities.

Pudasaini, Uttar↗