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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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NLR and Plug will collaborate on process development and quality control methodologies for proton exchange fuel cell manufacturing.
Errors can occur at any level during the replication and transcription of genetic information. Genetic mutations derived mainly from replication errors have been extensively studied. However, fundamental details of transcript errors, such as their rate, molecular spectrum, and functional effects, remain largely unknown. To globally identify transcript errors, we applied an adapted rolling-circle sequencing approach to Escherichia coli, Bacillus subtilis, Agrobacterium tumefaciens, and Mesoplasma florum, revealing transcript-error rates 3 to 4 orders of magnitude higher than the corresponding genetic mutation rates. The majority of detected errors would result in amino-acid changes, if translated. With errors identified from 9929 loci, the molecular spectrum and distribution of errors were uncovered in great detail. A G→A substitution bias was observed in M. florum, which apparently has an error-prone RNA polymerase. Surprisingly, an increased frequency of nonsense errors towards the 3' end of mRNAs was observed, suggesting a Nonsense-Mediated Decay-like quality-control mechanism in prokaryotes.
To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the Escherichia coli cell envelope and apply it to several proteins from an OMP biogenesis pathway. Among the top confident hits obtained from screening ~1500 envelope proteins, we find not only expected interactions but also unexpected ones with profound implications. Subsequently, we predict atomic structures for these protein complexes. These structures, typically of high confidence, explain experimental observations and lead to mechanistic hypotheses for how a chaperone assists a nascent, precursor OMP emerging from a translocon, how another chaperone prevents it from aggregating and docks to a β-barrel assembly port, and how a protease performs quality control. This work presents a general strategy for investigating biological pathways by using structural insights gained from deep learning-based predictions.
A sensor system may be configured for continuous operation in a low resource environment and/or in extreme environmental conditions. The sensor system may have sufficient processing capabilities to provide scientific computing for pre-processing, quality control, statistical analysis, event classification, data compression and corrections (e.g., spikes in the data), autonomous decisions and actions, triggering other nodes, and information assurance functions that provide data confidentiality, data integrity, authentication, and non-repudiation. The hardware may have both mesh networking and satellite and cellular communication capability, and may be available for relatively low cost. Such a network provides the flexibility to have potentially any number of nodes be completely independent from one another. Thus, the network may scale across a diverse terrain.
Tristructural-isotropic (TRISO) particle fuel is an advanced, mature technology for nuclear energy generation. Inherent containment of radionuclides within the fuel obviates the need for expensive reactor containment buildings; however, this benefit comes at the cost of increased complexity in fabrication relative to other nuclear fuel forms. In addition, rigorous fuel characterization and quality control efforts are required to ensure that the credited radionuclide containment within the fuel is realized.
Additive manufacturing (AM) has the ability to produce parts with complex geometries and internal features, however, for demanding applications such as the automotive and aerospace industries, it is crucial that the parts can meet the demanding functional and geometric requirements. Quality control for AM parts focuses on nondestructive methods of testing, but many of the current methods are expensive and time-consuming. The research presented in this report explores various methods of nondestructive evaluation (NDE) using dynamic analysis on stainless steel parts produced with selective laser melting (SLM). Methods include, but are not limited to, frequency response functions (FRF), impedance-based measurements, and scanning laser doppler vibrometry. Additionally, mode shape analysis was performed in MATLAB and FEA simulations were used for comparison with experimental results. The results indicate that dynamic analysis has the potential to be a feasible method of defect detection and NDE in AM parts and future work should focus on refining these methods, such as optimizing test parameters to improve sensitivity to defects.
The Proton Improvement Plan-II (PIP-II, [1]) linac will include 35 Single Spoke Resonators type 2 (SSR2). A total of eight pre-production SSR2 jacketed cavities will be procured and five installed in the first pre-production cryomodule. The mechanical design of the jacketed cavity has been finalized and it will be presented in this paper along with fabrication and processing experience. The importance of interfaces, quality controls and procurement aspects in the design phase will be remarked as well as lessons learned during the fabrication process. Furthermore, development studies will be presented together with other design validation tests.
TRISO development background and particle design US DOE AGR Program Fuel fabrication and quality control Irradiation testing Post-irradiation examination and high-temperature safety testing
As identified through stakeholder dialogue, Weatherization Assistance Program (WAP) Grantees and Subgrantees face challenges in hiring and retaining field staff in the following positions: Retrofit Installers/Technicians (RITs), Crew Leaders (CLs), Energy Auditors (EAs), and Quality Control Inspectors (QCIs). To gain a baseline understanding of workforce needs, NREL collected voluntary workforce data from May 4-June 30, 2022. This presentation includes a qualitative analysis of this data, key takeaways, and an introduction to new tools supporting WAP workforce development.
To investigate the contributing factors of variance in the measurement of an iodine 127 (127I) sample, we implement a nested random effects analysis of variance (ANOVA). Historically, the reported uncertainty on a measurement of 127I has been obtained by methods of forward uncertainty propagation because there is typically only one replicate of a given sample for which to estimate the uncertainty. When samples are processed there are several types of quality control (QC) standards analyzed along-side the unknown samples with two to five replicates for each. Assuming the variance observed in these replicate QC standards is representative of that of the unknown samples, we use these data in a nested random effects ANOVA to estimate the total uncertainty of a measurement. We demonstrate this approach with two sets of measurements from Idaho National Laboratory and compare the results with the forward uncertainty propagation approach. Variance component estimates for the coarsest level of the nesting structure were most imprecise because replicates were most limited at these levels. We find that the results largely agree between forward propagation and ANOVA, and the greatest contributors of variance are due to instrument variation and chemical processing, with human processing being among the smallest contributors. This analysis provides reassurance that the reported uncertainties using forward propagation are reasonable and the process is well controlled. We propose a future designed experiment to increase replicates at the coarsest level of the hierarchy to improve estimates of these variance components.
Building energy modeling (BEM) has been widely used by researchers, regulators, and engineers to quantify building energy performance. Quality assurance (QA) and quality control (QC) of the model's performance are essential parts of such analysis. Currently, QA/QC is done in a manual and ad-hoc manner, which is tedious, error-prone, and time-consuming when QA/QC a large number of models. To solve these challenges, we propose a a dAta-driveN buIlding perforMance verificATion framEwork (ANIMATE), which conducts automated output-based verification of building operations requirements (especially for time-series output-based verification of control requirements). While this framework was developed for verifying energy model performance, it can be extended for other applications such as BEM software testing and performance verification of real buildings in the field.
Neutrinos began as theoretical, massless particles, and since their first detection they have continued to be the subject of various experiments. One such experiment is DUNE, which is a long baseline neutrino experiment with the goal of studying neutrino properties, such as neutrino oscillation parameters. In this work, two projects were completed, one dealing with the hardware of DUNE and the other dealing with neutrino simulations. For the hardware project, we designed a quality control method for testing adaptor boards which make up part of the Far Detector circuit boards. This method was completed and prototyped, however was not implemented as planned due to funding changes. For the simulation project, we simulated ντ , νµ, and νe CC interactions using two simulation methods (stand alone GENIE and a full detector simulation) in order to begin to understand what a ντ CC event would look like in the Far Detector and create an initial list of kinematic properties that can be used for this purpose. We found that there is not a complete understanding of what is occurring in the full detector simulation. We also found that there is promise in using kinematic properties to be able to differentiate ντ CC events, however properties beyond those covered here, as well as a more accurate simulation, will be required for any definitive statistical statements.
Carbon fiber characterization processes are described that include multi-condition Raman spectroscopy-based examination combined with multivariate data analyses. Methods are a nondestructive material characterization approach that can provide predictions as to carbon fiber bulk physical properties, as well as identification of unknown carbon fiber materials for quality control purposes. The framework of the multivariate analysis methods includes a principal component-based identification protocol including comparison of Raman spectral data from an unknown carbon fiber with a data library of multiple principal component spaces.
This presentation describes the overall process of the U.S. Nuclear Regulatory Commission (NRC)operating experience (OpE) program to estimate probabilistic risk assessment parameters, the estimations of initiating event frequencies, the component unreliability, and the quality assurance and quality control activities for the OpE program. This presentation can be used for training, workshop, or knowledge transfer purpose.
Measurements of peculiar velocities in the local Universe are a powerful tool to study the nature of dark energy at low ($z < 0.1$) redshifts. Here we present the largest single set of $z<0.1$ peculiar velocity measurements to date, obtained using the Fundamental Plane (FP) of galaxies in the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). We describe the photometric and spectroscopic selection criteria used to define the sample, as well as extensive quality control checks on the photometry and velocity dispersion measurements. Additionally, we perform detailed systematics checks for the many analysis parameters in our pipeline. Our DESI DR1 catalogue contains FP-based distances and peculiar velocities for $98,292$ unique early-type galaxies, increasing the total number of $z < 0.1$ FP distances ever measured by a factor of $\sim2$. We achieve a precision of $26\%$ random error in our distance measurements which is comparable to previous surveys. A series of companion DESI papers use the distances and peculiar velocities presented in this paper to measure cosmological parameters.
A novel and advanced approach of growing zinc oxide nanowires (ZnO NWs) directly on single-walled carbon nanotubes (SWCNTs) and graphene (Gr) surfaces has been demonstrated through the successful formation of 1D–1D and 1D–2D heterostructure interfaces. The direct two-step chemical vapor deposition (CVD) method was utilized to ensure high-quality materials’ synthesis and scalable production of different architectures. Iron-based universal compound molecular ink was used as a catalyst in both processes (a) to form a monolayer of horizontally defined networks of SWCNTs interfaced with vertically oriented ZnO NWs and (b) to grow densely packed ZnO NWs directly on a graphene surface. We show here that our universal compound molecular ink is efficient and selective in the direct synthesis of ZnO NWs/CNTs and ZnO NWs/Gr heterostructures. Heterostructures were also selectively patterned through different fabrication techniques and grown in predefined locations, demonstrating an ability to control materials’ placement and morphology. Several characterization tools were employed to interrogate the prepared heterostructures. ZnO NWs were shown to grow uniformly over the network of SWCNTs, and much denser packed vertically oriented ZnO NWs were produced on graphene thin films. Such heterostructures can be used widely in many potential applications, such as photocatalysts, supercapacitors, solar cells, piezoelectric or thermal actuators, as well as chemical or biological sensors.
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