Thermal fluids with high specific heat capacity through reversible Diels-Alder reactions
Not Available
42 ENGINEERING↗
SEARCH · Engineering Papers
Results for “Engineering science”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Preactivated zeolite nanosheet plate-tiled membrane on porous PVDF film: Synthesis and study of proton-selective ion conduction
Preactivated MFI zeolite nanosheet plates (ZNPs) with large areas (~2.0 × 2.0 μm 2 ) and nanometer thicknesses (~60 nm) were prepared directly in liquid-dispersed state. The individual ZNP was a stack of 4-nm-thick single crystalline zeolite nanosheets (ZNs) interlinked by Si–O–Si bonds between neighboring ZN surfaces. The dispersed open-pore ZNPs allowed formulating suspensions for tiling ZNP membranes on polymer substrates without requiring post-coating activation. A pinhole-free ZNP-tiled (ZNPT) membrane has been achieved on macroporous PVDF film by a self-repairing vacuum-assisted filtration coating method. The resultant ZNPT layer was ~500 nm-thick consisting of about 7 ZNP layers with inter-ZNP width and inter-ZNP entrance porosity around 9 nm and 2%, respectively. The ZNPT-PVDF membrane exhibited high selectivity to proton transport over vanadyl ion and low resistances to proton conduction in aqueous solutions. The membrane also demonstrated to function as an efficient ion separator for the vanadium redox flow battery. The reported synthesis of preactivated ZNP suspension may overcome the major hurdle to realizing polymer-supported ZN membranes, which is the inefficiency of existing methods in obtaining readily dispersible open-pore ZNs. Finally, the ZNPT-PVDF membrane can be a more affordable and sustainable alternative to the Nafion-based ion separation membranes.
36 MATERIALS SCIENCE↗
X-ray-Based Techniques to Study the Nano–Bio Interface
X-ray-based analytics are routinely applied in many fields, including physics, chemistry, materials science, and engineering. The full potential of such techniques in the life sciences and medicine, however, has not yet been fully exploited. We highlight current and upcoming advances in this direction. We describe different X-ray-based methodologies (including those performed at synchrotron light sources and X-ray free-electron lasers) and their potentials for application to investigate the nano–bio interface. The discussion is predominantly guided by asking how such methods could better help to understand and to improve nanoparticle-based drug delivery, though the concepts also apply to nano–bio interactions in general. We discuss current limitations and how they might be overcome, particularly for future use in vivo.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
ReclaimingSTEM: A healing-centered counterspace model for inclusive science communication and policy training
The dominant U.S. cultural norms shape science, technology, engineering, and math (STEM), and in turn, these norms shape science communication, further perpetuating oppressive systems. Despite being a core scientific skill, science communication research and practice lack inclusive training spaces that center marginalized identities. We address this need with a healing-centered counterspace grounded in the key principles of inclusive science communication: ReclaimingSTEM. ReclaimingSTEM is a science communication and science policy training space that centers the experiences, needs, and wants of people from marginalized communities. ReclaimingSTEM problematizes and expands the definitions of “what counts” as science communication. We organize ReclaimingSTEM with intentionality, emphasizing inclusion at every part of the process. Since initiating in 2018, five ReclaimingSTEM workshops have been held in multiple locations, both in-person and virtually, reaching more than 700 participants from all over the globe. In this paper, we share our model for ReclaimingSTEM, reflections of workshop participants and speakers, barriers faced during organizing, and recommendations for creating truly inclusive practices in science communication spaces.
99 GENERAL AND MISCELLANEOUS↗
Systemic racial disparities in funding rates at the National Science Foundation
Concerns about systemic racism at academic and research institutions have increased over the past decade. Here, we investigate data from the National Science Foundation (NSF), a major funder of research in the United States, and find evidence for pervasive racial disparities. In particular, white principal investigators (PIs) are consistently funded at higher rates than most non-white PIs. Funding rates for white PIs have also been increasing relative to annual overall rates with time. Moreover, disparities occur across all disciplinary directorates within the NSF and are greater for research proposals. The distributions of average external review scores also exhibit systematic offsets based on PI race. Similar patterns have been described in other research funding bodies, suggesting that racial disparities are widespread. The prevalence and persistence of these racial disparities in funding have cascading impacts that perpetuate a cumulative advantage to white PIs across all of science, technology, engineering, and mathematics.
99 GENERAL AND MISCELLANEOUS↗
Laboratory Directed Research & Development: FY23 Annual Report
Sandia is a federally funded research and development center (FFRDC) focused on developing and applying advanced science and engineering capabilities to mitigate national security threats. This is accomplished through the exceptional staff leading research at the Labs and partnering with universities and companies. Sandia’s LDRD program aims to maintain the scientific and technical vitality of the Labs and to enhance the Labs’ ability to address future national security needs. The program funds foundational, leading-edge discretionary research projects that cultivate and utilize core science, technology, and engineering (ST&E) capabilities. Per Congressional intent (P.L. 101-510) and Department of Energy (DOE) guidance (DOE Order 413.2C, Chg 1), Sandia’s LDRD program is crucial to maintaining the nation’s scientific and technical vitality
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Laboratory Directed Research & Development: FY22 Annual Report
Sandia is a federally funded research and development center (FFRDC) focused on developing and applying advanced science and engineering capabilities to mitigate national security threats. This is accomplished through the exceptional staff leading research at the Labs and partnering with universities and companies. Sandia’s LDRD program aims to maintain the scientific and technical vitality of the Labs and to enhance the Labs’ ability to address future national security needs. The program funds foundational, leading-edge discretionary research projects that cultivate and utilize core science, technology, and engineering (ST&E) capabilities. Per Congressional intent (P.L. 101-510) and Department of Energy (DOE) guidance (DOE Order 413.2C, Chg 1), Sandia’s LDRD program is crucial to maintaining the nation’s scientific and technical vitality.
99 GENERAL AND MISCELLANEOUS↗
RESEARCH Laboratory Directed Research & Development FY24 Annual Report
Sandia is a federally funded research and development center (FFRDC) focused on developing and applying advanced science and engineering capabilities to mitigate national security threats. This is accomplished through the exceptional staff leading research at the Labs and partnering with universities and companies. Sandia’s LDRD program aims to maintain the scientific and technical vitality of the Labs and to enhance the Labs’ ability to address future national security needs. The program funds foundational, leading-edge discretionary research projects that cultivate and utilize core science, technology, and engineering (ST&E) capabilities. Per Congressional intent (P.L. 101-510) and Department of Energy (DOE) guidance (DOE Order 413.2C, Chg 1), Sandia’s LDRD program is crucial to maintaining the nation’s scientific and technical vitality.
99 GENERAL AND MISCELLANEOUS↗
Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper
Abstract Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.
Computer Science↗
2018 LDRD Annual Report (Argonne National Laboratory)
Argonne National Laboratory’s Laboratory Directed Research and Development (LDRD) program encourages the development of novel technical concepts, enhances the Laboratory’s research and development (R&D) capabilities, and enables pursuit of strategic laboratory goals. Argonne’s LDRD projects are proposal based and peer reviewed, supporting ideas that require advanced exploration so they can be sufficiently developed to pursue support through normal programmatic channels. Among the aims of the projects supported by the LDRD program are the establishment of engineering proofs of principle, assessment of design feasibility for prospective facilities, development of instrumentation or computational methods or systems, and discoveries in fundamental science and exploratory development. All LDRD projects have demonstrable ties to one or more of the science, energy, environment, and national security missions of the U.S. Department of Energy (DOE) and its National Nuclear Security Administration (NNSA), and many are also relevant to the missions of other federal agencies that sponsor work at Argonne. A natural consequence of the more “applied” type projects is their concurrent relevance to industry. The LDRD program is managed in overarching portfolios, each containing multiple projects each fiscal year. The LDRD Prime portfolio is further divided into strategic focus areas aligned with Argonne’s strategic plan. The largest component of Argonne’s program is LDRD Prime, which emphasizes R&D explicitly aligned with Laboratory major initiatives in support of Argonne’s strategic plan. The choice of Focus Areas under the LDRD Prime component reflects the major initiatives; the state of development of relevant technical fields; the potential value of advancing those fields to DOE/NNSA and the nation; and the compatibility of the fields with existing facilities, capabilities, and staff expertise at Argonne. Focus Areas with projects that ended in FY18 are: Advanced Computing, Biological and Environmental Science Capability Development, Energy Manufacturing Science and Engineering, Hard X-ray Sciences, Materials and Chemistry, Securing Energy and Critical Resources, and The Universe as Our Laboratory (ULab).
99 GENERAL AND MISCELLANEOUS↗
IST for Extended Deterrence
Department of Energy (DOE) scientists have long enjoyed technical collaboration with Japanese colleagues ₋ valuing their technical and scientific prowess in materials science and engineering, large-scale, scientific computing, and many other areas. In particular, NNSA’s laboratories are attractive partners for this effort because they have a heritage of performing high quality, basic research; they work with an awareness of the dual use nature of emerging science and technology; and, importantly, they work with deep national security sensibilities through engagement with NNSA and other national security mission responsibilities. Other DOE laboratories with significant national security bona fides are also significant collaborators.
99 GENERAL AND MISCELLANEOUS↗
Improving operational performance using machine learning analysis of Radiation Portal Monitor measurements
Radiation Portal Monitors (RPMs) have been installed worldwide to scan vehicles and cargo for the presence of radiological and nuclear materials. In field operations, the sensitivity of these systems is typically limited by the relatively high rates of nuisance alarms that usually must be followed up with secondary inspections. We have developed a machine-learning based alarm analysis system that has been deployed at numerous locations in the U.S. and internationally. Our Enhanced Radiological Nuclear Inspection and Evaluation (ERNIE) analysis software and its derivatives have demonstrated increased sensitivity to radiological and nuclear material of concern while reducing nuisance alarms by as much as an order of magnitude.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NA-22 Quarterly Report - Q3
Department of Energy (DOE) scientists have long enjoyed technical collaboration with Japanese colleagues ─ valuing their technical and scientific prowess in materials science and engineering, large-scale, scientific computing, and many other areas. In particular, NNSA’s laboratories are attractive partners for this effort because they have a heritage of performing high quality, basic research; they work with an awareness of the dual use nature of emerging science and technology; and, importantly, they work with deep national security sensibilities through engagement with NNSA and other national security mission responsibilities. Other DOE laboratories with significant national security bona fides are also significant collaborators.
36 MATERIALS SCIENCE↗
Hierarchical Eutectoid Nano-lamellar Decomposition in an Al 0.3 CoFeNi Complex Concentrated Alloy
This paper reports a novel eutectoid nano-lamellar (FCC + L1 2 )/(BCC + B2) microstructure that has been discovered in a relatively simple Al 0.3 CoFeNi high entropy alloy (HEA) or complex concentrated alloy (CCA). This novel eutectoid nano-lamellar microstructure presumably results from the complex interplay between Al-mediated lattice distortion (due to its larger atomic radius) in a face-centered cubic (FCC) CoFeNi solid solution, and a chemical ordering tendency leading to precipitation of ordered phases such as L1 2 and B2. This eutectoid microstructure is a result of solid-state decomposition of the FCC matrix and therefore distinct from the commonly reported eutectic microstructure in HEAs which results from solidification. This novel nano-lamellar microstructure exhibits a tensile yield strength of 1074 MPa with a reasonable ductility of 8%. The same alloy can be tuned to form a more damage-tolerant FCC + B2 microstructure, retaining high tensile yield stress (~900 MPa) with appreciable tensile ductility (>20%), via annealing at 700 °C. Such tunability of microstructures with dramatically different mechanical properties can be effectively engineered in the same CCA, by exploiting the complex interplay between ordering tendencies and lattice distortion.
36 MATERIALS SCIENCE↗
IST for Extended Deterrence: NA-22 Quarterly Report, Q3FY23
Department of Energy (DOE) scientists have long enjoyed technical collaboration with Japanese colleagues — valuing their technical and scientific prowess in materials science and engineering, large-scale, scientific computing, and many other areas. In particular, NNSA’s laboratories are attractive partners for this effort because they have a heritage of performing high quality, basic research; they work with an awareness of the rapid and dynamic growth of emerging science and technology; and, importantly, they work with deep national science sensibilities through engagement with NNSA and in concert with other national laboratories with significant technical bona fides, who act as significant collaborators.
36 MATERIALS SCIENCE↗
Examining polymer‐protein biophysical interactions with small‐angle x‐ray scattering and quartz crystal microbalance with dissipation
Abstract Polymer‐protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure–function behavior. Here, we show the value of tandem small‐angle x‐ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer‐protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer‐protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration ( R g ) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer‐protein hybrids.
60 APPLIED LIFE SCIENCES↗
Self-mixing in microtubule-kinesin active fluid from nonuniform to uniform distribution of activity
Active fluids have applications in micromixing, but little is known about the mixing kinematics of systems with spatiotemporally-varying activity. To investigate, UV-activated caged ATP is used to activate controlled regions of microtubule-kinesin active fluid and the mixing process is observed with fluorescent tracers and molecular dyes. At low Péclet numbers (diffusive transport), the active-inactive interface progresses toward the inactive area in a diffusion-like manner that is described by a simple model combining diffusion with Michaelis-Menten kinetics. At high Péclet numbers (convective transport), the active-inactive interface progresses in a superdiffusion-like manner that is qualitatively captured by an active-fluid hydrodynamic model coupled to ATP transport. Results show that active fluid mixing involves complex coupling between distribution of active stress and active transport of ATP and reduces mixing time for suspended components with decreased impact of initial component distribution. This work will inform application of active fluids to promote micromixing in microfluidic devices.
59 BASIC BIOLOGICAL SCIENCES↗
An finite element analysis surrogate model with boundary oriented graph embedding approach for rapid design
Abstract In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of 0.011 706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas.
42 ENGINEERING↗