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Results for “Reusable”
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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A reusable neural network pipeline for unidirectional fiber segmentation
Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.
A portable and reusable sensor system based on graphene for real-time and sensitive detection of lead ions in water
Long-term exposure to Pb 2+ can cause irreversible damage to the nervous, cardiovascular, and reproductive systems. Therefore, developing a fast and sensitive detection system capable of monitoring minuscule concentrations of Pb 2+ is essential. In this study, we demonstrated a fully portable sensor system enabling rapid, sensitive, and real-time monitoring of Pb 2+ . The sensor system adopted the remote-gate field-effect transistor (RGFET) detection scheme and was easy to operate, even for non-experts. The sensor system comprised two printed circuit boards (PCBs): a sensor PCB with a remote-gate electrode and an analyzer PCB with a metal-oxide-semiconductor field-effect transistor (MOSFET) transducer and peripheral electronics to manage sensor signals. To achieve a high sensitivity for Pb 2+ , we utilized graphene ink drop-casted on the sensor PCB as a sensing membrane. The graphene film was easy to deposit and remove, enabling the sensor PCB to be reused multiple times. The sensor system was further linked to a smartphone application that instantly monitors the sensor response, allowing for rapid point-of-use detection. The sensor exhibited a high sensitivity of 21.7% when the limit of detection (LOD) value of 1 nM (∼0.2 ppb) was detected, and the typical detection time for each sample was approximately 60 seconds. This portable sensor system advances sensing technologies and could potentially supplement expensive, laborious conventional sensing equipment.
Toward Findable, Accessible, Interoperable and Reusable (FAIR) Photovoltaic System Time Series Data.
Abstract not provided.
Ultraselective sequestration of Li + and Mg 2+ from brines via a reusable polyoxoniobate-based ion sponge
Lithium (Li) and magnesium (Mg) are designated as critical mineral materials (CMM) due to their essential roles in clean energy technologies. However, extracting high-purity Li + from brine remains a formidable challenge owing to the presence of Mg 2+ , a physicochemical similar ion that often exists in excess. Here, we introduce a polyoxoniobate-based “Mg-PONb sponge” that enables ultraselective and rapid Li + /Mg 2+ separation across an exceptionally broad range of Mg/Li ratios (0.02 to 200.63). This framework achieves >99.9% Mg 2+ removal with negligible Li + loss in under 1 min, yielding Li + /Mg 2+ selectivity values exceeding 5000. The sponge demonstrates excellent recyclability, maintaining >99% Mg 2+ rejection and Li + permeability across five regeneration cycles without structural degradation. Mechanistic investigations reveal that selective Mg 2+ capture originates from strong coordination with terminal oxygens on the PONb cluster, driving rapid formation of porous Mg-PONb frameworks. This work presents a generalizable, scalable strategy for Li + /Mg 2+ separation and offers a sustainable path toward enhanced Li and Mg recovery from complex brine sources.
Assembling Reusable Genetic Modules for Efficient Biofuel Production from Marine Macroalgae
This project investigated microbial biodiversity capable of degrading algal polysaccharides to investigate potential for biofuel production. This project uncovered the role of ecological interactions among closely related strains of bacteria necessary for degradation of common polysaccharides, which had not been previously appreciated. In addition new bacterial isolates and genes were identified that can be used in future projects aimed at biofuel production. Finally, these enzymes were characterized for their efficiency under a range of environmental parameters, and encapsulated for future use in genetic engineering studies.
Designing FAIR Workflows at OLCF: Building Scalable and Reusable Ecosystems for HPC Science
High Performance Computing (HPC) centers, such as the Oak Ridge Leadership Computing Facility (OLCF), provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with unique hardware configurations, specialized software environments, and elevated security re quirements that differ substantially from what most users encounter on their local systems. As a result, users often develop customized digital artifacts that are tightly coupled to the specific configuration of a given HPC center. Although necessary, this practice can lead to significant duplication of effort as multiple users independently create similar solutions to common problems.
A standards perspective on genomic data reusability and reproducibility
Genomic and metagenomic sequence data provides an unprecedented ability to re-examine findings, offering a transformative potential for advancing research, developing computational tools, enhancing clinical applications, and fostering scientific collaboration. However, effective and ethical reuse of genomics data is hampered by numerous technical and social challenges. The International Microbiome and Multi’Omics Standards Alliance (IMMSA, https://www.microbialstandards.org/) and the Genomic Standards Consortium (GSC, https://gensc.org) hosted a 5-part seminar series “A Year of Data Reuse” in 2024 to explore challenges and opportunities of data reuse and reproducibility across disparate domains of the genomic sciences. Addressing these challenges will require a multifaceted approach, including common metadata reporting, clear communication, standardized protocols, improved data management infrastructure, ethical guidelines, and collaborative policies that prioritize transparency and accessibility. We offer strategies to enable responsible and technically feasible data reuse, recognition of data reproducibility challenges, and emphasizing the importance of cross-disciplinary efforts in the pursuit of open science and data-driven innovation.
Magnetically Recoverable and Reusable Titanium Dioxide Nanocomposite for Water Disinfection
A bifunctional magnetic Fe3O4@SiO2@TiO2 or MS-TiO2 antimicrobial nanocomposite was prepared based on simple sol-gel methods with common equipment and chemicals. Reaction pH was found to influence the TiO2 upload in the nanocomposite. The alkaline condition produced the greatest TiO2 upload, while the acidic condition the least. Annealing at 300 °C turned the as-synthesized amorphous TiO2 into one with high content of anatase, the most photoactive form of TiO2. Irradiated by 365 nm UV light, a sample of 30 mg/mL of annealed nanocomposite containing 12.6 wt.% Ti was shown to be able to completely eradicate 104 CFU/mL of the laboratory-grown E. coli within 25 min, 25 min faster than the control when the 365 nm UV light was employed alone. The nanocomposite demonstrated consistent antimicrobial performance over repeated uses and was easily recoverable magnetically due to its high magnetization value (33 emu/g). Additionally, it was shown to reduce the bacterial count in a real surface water sample containing 500–5000 CFU/mL of different microbes by 62 ± 3% within 30 min. The irradiating 365 nm UV light alone was found to have generated little biocidal effect on this surface water sample. The nanocomposite is promising to serve as an effective, safe, and eco-friendly antimicrobial agent, especially for surface water disinfection.
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
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Reusable Verification Components for High-Energy Physics readout ASICs
Verification is a critical aspect of designing front-end (FE) readout ASICs for High-Energy Physics (HEP) experiments. These ASICs share several similar functional features, resulting in similar verification objectives, which can be addressed using comparable verification strategies. This contribution presents a set of re-usable verification components for addressing common verification tasks, such as clock generation, reset handling, configuration, as well as hit and fault injections. The components were developed as part of the CHIPS initiative and they have been successfully used in the verification of multiple HEP ASICs.
Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform
The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.
Rapid photooxidation and removal of As(III) from drinking water using Fe-Mn composite oxide
Fe-Mn composite oxide (FMO) is widely applied to the oxidation and removal of As(III) from water. However, As(III) can directly reduce manganese oxides, decreasing the oxidation capacity or reusability and thereby greatly limiting the applicability of FMO. Here, the oxidation capacity and reusability of FMO for As(III) were efficiently improved by light radiation, and the effect of typical coexisting ions (SO 4 2– and Ca 2+ ) on the removal of As(III) was also studied. O 2 •– produced from excited manganese oxide and ligand-to-metal charge transfer in iron oxide-As(III) complex enhanced As(III) oxidation and removal under light radiation. At an initial As(III) concentration of 1000 μg L –1 , the total As concentration was respectively decreased to 11.5, 1.5 and 4.4 μg L –1 under darkness, UV light and sunlight at 180 min, and could be reduced to below the guideline limitation of drinking water (10 μg L –1 ) within 40 and 60 min under UV light and sunlight, respectively. SO 4 2– exhibited negligible effect on As removal efficiency because FMO had obviously lower adsorption capacity and selectivity for SO 4 2– than for As(V). The adsorption of coexisting Ca 2+ on manganese oxide decreased the negative charge on the FMO surface, thereby improving As(III) adsorption and oxidation. FMO exhibited excellent reusability, and a total As removal efficiency of 99.1% was still maintained after five cycles of an adsorption-desorption process under UV light. Finally, this work elucidates the photochemical oxidation and removal mechanism of FMO for As(III), and proposes a low-cost and efficient method for the detoxification of As(III)-contaminated drinking water.