Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “etc)”

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.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Online real-time learning of dynamical systems from noisy streaming data

Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. Inspired by the concept of Edge Artificial Intelligence (Edge AI), we propose a robust approach that enables fast FLI with no degradation of accuracy. This approach couples a recurrent neural network (RNN), which is trained to estimate the fluorescence lifetime directly from raw timestamps without building histograms, to SPAD TCSPC systems, thereby drastically reducing transfer data volumes and hardware resource utilization, and enabling real-time FLI acquisition. We train two variants of the RNN on a synthetic dataset and compare the results to those obtained using center-of-mass method (CMM) and least squares fitting (LS fitting). Results demonstrate that two RNN variants, gated recurrent unit (GRU) and long short-term memory (LSTM), are comparable to CMM and LS fitting in terms of accuracy, while outperforming them in the presence of background noise by a large margin. To explore the ultimate limits of the approach, we derive the Cramer-Rao lower bound of the measurement, showing that RNN yields lifetime estimations with near-optimal precision. To demonstrate real-time operation, we build a FLI microscope based on an existing SPAD TCSPC system comprising a 32 x 32 SPAD sensor named Piccolo. Four quantized GRU cores, capable of processing up to 4 million photons per second, are deployed on the Xilinx Kintex-7 FPGA that controls the Piccolo. Powered by the GRU, the FLI setup can retrieve real-time fluorescence lifetime images at up to 10 frames per second. The proposed FLI system is promising and ideally suited for biomedical applications, including biological imaging, biomedical diagnostics, and fluorescence-assisted surgery, etc.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Characterization of spent nuclear fuel canister surface roughness using surface replicating molds

In this study we present a replication method to determine surface roughness and to identify surface features when a sample cannot be directly analyzed by conventional techniques. As a demonstration, this method was applied to an unused spent nuclear fuel dry storage canister to determine variation across different surface features. In this study, an initial material down-selection was performed to determine the best molding agent and determined that non-modified Polytek PlatSil23-75 provided the most accurate representation of the surface while providing good usability. Other materials that were considered include Polygel Brush-On 35 polyurethane rubber (with and without Pol-ease 2300 release agent), Polytek PlatSil73-25 silicone rubber (with and without PlatThix thickening agent and Pol-ease 2300 release agent), and Express STD vinylpolysiloxane impression putty. The ability of PlatSil73-25 to create an accurate surface replica was evaluated by creating surface molds of several locations on surface roughness standards representing ISO grade surfaces N 3 , N 5 , N 7 , and N 8 . Overall, the molds were able to accurately reproduce the expected roughness average (R a ) values, but systematically over-estimated the peak-valley maximum roughness (R z ) values. Using a 3D printed sample cell, several locations across the stainless steel spent nuclear fuel canister were sampled to determine the surface roughness. These measurements provided information regarding variability in normal surface roughness across the canister as well as a detailed evaluation on specific surface features (e.g., welds, grind marks, etc.). The results of these measurements can support development of dry storage canister ageing management programs, as surface roughness is an important factor for surface dust deposition and accumulation. This method can be applied more broadly to different surfaces beyond stainless steel to provide rapid, accurate surface replications for analytical evaluation by profilometry.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Intercalation as a versatile tool for fabrication, property tuning, and phase transitions in 2D materials

Recent advances in two-dimensional (2D) materials have led to the renewed interest in intercalation as a powerful fabrication and processing tool. Intercalation is an effective method of modifying the interlayer interactions, doping 2D materials, modifying their electronic structure or even converting them into starkly different new structures or phases. Herein, we discuss different methods of intercalation and provide a comprehensive review of various roles and applications of intercalation in next-generation energy storage, optoelectronics, thermoelectrics, catalysis, etc. The recent progress in intercalation effects on crystal structure and structural phase transitions, including the emergence of quantum phases are also reviewed.

2D materials↗
DOE OSTI2021

Photoluminescence as a probe of phosphorene properties

Abstract Here, we provide a detailed evaluation of photoluminescence (PL) as a comprehensive tool for phosphorene characterization with the emphasis on a prominent quantitative role of PL in providing fingerprint-like features due to its extreme sensitivity to the band structure details, anisotropy, disorder, external fields, etc. Factors such as number of layers, dimensionality, structural and chemical disorder, and environmental factors and their effect on phosphorene’s PL signal are reviewed and discussed. Applications of PL in monitoring phosphorene and its modifications, as well as potential impacts on the fields of chemical and biosensing, nanomedicine, and solar energy harvesting, are also elaborated.

2D materials↗
DOE OSTI2025

Large bandgap observed on the surfaces of EuZn 2 As 2 single crystals

EuM 2 As 2 (M = Zn, Cd, In, Sn etc.) is an excellent material system for studying magnetism-tuned topological properties. However, discrepancies exist between experimental data and theoretical calculations regarding the bulk and surface bandgaps. In this work, cleaved EuZn 2 As 2 crystals are studied using scanning tunneling microscopy/spectroscopy and density functional theory calculations. Triangular-shaped defect-induced modifications in the local density of states help distinguish between Eu-terminated and AsZn-terminated surfaces. While large bandgaps (~1.5 eV at 77 K) are observed on both pristine surfaces, the bandgap size is found to be highly sensitive to local heterogeneity, tending to decrease. By combining experimental observations with theoretical simulations, we conclude that the reduced bandgap in heterogeneous regions arises from Zn vacancies and/or substitution by As atoms, both impacting greater in the Eu surface electronic properties than those in the AsZn surface. This demonstrates the intimate relationship between the electronic structure and magnetism in EuZn 2 As 2 .

interfaces↗
DOE OSTI2020

FeS colloids – formation and mobilization pathways in natural waters

For this study, we have used synchrotron-based X-ray absorption spectroscopy (structure of Fe–S clusters), transmission electron microscopy (solid-phase crystallinity), Fourier-transform ion-cyclotron-resonance mass spectrometry (identity and composition of natural organic carbon compounds), inductively coupled plasma optical emission spectrometry (total aqueous Fe), and the revised ferrozine method (aqueous Fe( II ) and Fe( III ) concentrations) to determine the stability and nature of colloids generated by sulfidation of ferrihydrite nanoparticles in the absence and presence of organic compounds. We observed that reductive dissolution of ferrihydrite by aqueous sulfide generates nm-scale FeS clusters. Their subsequent aggregation, which promotes settling of FeS aggregates into the solid fraction, was directly correlated with sulfide/Fe ratio. At sulfide/Fe ratios ≤0.5, FeS clusters and larger colloids remained in suspension for at least 14 days (and up to several months). At sulfide/Fe ratios >0.5, sulfidation reaction rates were rapid and FeS cluster aggregation was accelerated. Moreover, the presence of organic compounds increased the time of suspension of FeS colloids, whereas increased ionic strength inhibited the generation of FeS colloids. We present a general conceptual model to predict when and where FeS colloids can form and enhance or inhibit the mobility of contaminants and nutrients associated with them. Our study indicates that in lowsalinity fresh groundwater systems poor in sulfate (i.e. low sulfidation; lakes, floodplains, peatlands etc.), the ferrihydrite sulfidation reaction generates aqueous FeS clusters and larger colloids that remain suspended over long time periods, thus mobilizing a substantial fraction of the total aqueous Fe and S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Hierarchical Phenomena in Multicomponent Liquids: Simulation Methods, Analysis, Chemistry

Complex, multicomponent, solutions have often been studied solely through the lens of specific applications of interest. Yet advances to both simulation methodologies (enhanced sampling, etc.) and analysis techniques (network analysis algorithms and others), are creating a trove of data that reveal transcending characteristics across vast compositional phase space. This perspective discusses technical considerations of the reliable and accurate simulations of complex solutions, followed by the advances to analysis algorithms that elucidate coupling of different length and timescale behavior (hierarchical phenomena). The different manifestations of hierarchical phenomena are presented across an array of solution environments, emphasizing fundamental and ongoing science questions. With a more advanced molecular understanding in hand, a quintessential application (solvent extraction) is discussed, where significant opportunities exist to re-imagine the technical scope of an established technology.

Servis, Michael J.↗
DOE OSTI2020

Sustainable energy and fuels from biomass: a review focusing on hydrothermal biomass processing

Fossil fuels are being replaced with renewable energy resources (biomass and biomass waste, solar, geothermal, wind, etc.) to ensure sustainable development, reduce the dependence on fossil fuels, address environmental challenges including climate change. Today, biomass produces 5 × 10 19 kJ of energy/year, which corresponds to 10% of the annual global energy consumption. Considering the variety of biomass resources, this value is predicted to reach 150 × 10 19 kJ by 2050. Biomass may become even more important for use as an energy resource and chemical raw material in the 21st century. Hydrothermal biomass conversion stands out as a promising and alternative technology. Methods such as traditional gasification and pyrolysis require dry biomass. Hydrothermal techniques have been developed to eliminate the cost and time required for drying biomass. The purpose of this process is to decompose biomass with a high moisture content into small molecules and reduce its oxygen content to obtain liquid fuels or valuable chemicals. Finally, this review presents the current and future state of energy, energy sources, biomass properties and biomass conversion technologies with a focus on hydrothermal technologies.

09 BIOMASS FUELS↗
DOE OSTI2020

All-atom molecular dynamics simulations of weak polyionic brushes: influence of charge density on the properties of polyelectrolyte chains, brush-supported counterions, and water molecules

All atom molecular dynamics (MD) simulations of planar Na+-counterion-neutralized polyacrylic acid (PAA) brushes are performed for varying degrees of ionization (and thereby varying charge density) and varying grafting density. Variation in the PE charge density (or degree of ionization) and grafting density leads to massive changes of the properties of the PE molecules (quantified by the changes in the height and the mobility of the PE brushes) as well as the local arrangement and distribution of the brush-supported counterions and water molecules within the brushes. The effect on the counterions is manifested by the corresponding variation of the counterion mobility, counterion concentration, extent of counterion binding to the charged site of the PE brushes, water-in-salt-like structure formation, and counterion-water–oxygen radial distribution function within the PE brushes. On the other hand, the effect on water molecules is manifested by the corresponding variation of water–oxygen–water–oxygen RDF, local water density, water–water and water–PE functional group hydrogen bond networks, static dielectric constant of water molecules, orientational tetrahedral order parameter, and water mobility. Enforcing such varying degree of ionization of weak polyelectrolytes is possible by changing the pH of the surrounding medium. Thus, our results provide insights into the changes in microstructure (at the atomistic level) of weak polyionic brushes at varying pH. Finally, we anticipate that this knowledge will prove to be vital for the efficient design of several nano-scale systems employing PE brushes such as nanomechanical gates, current rectifiers, etc.

42 ENGINEERING↗
DOE OSTI2021

A near-infrared optical nanosensor for measuring aerobic respiration in microbial systems

We developed a ratiometric oxygen-sensitive nanosensor and demonstrated application in monitoring metabolic oxygen consumption in microbial samples over time. Based on a near-infrared (NIR) emitting oxygen-quenched luminophore, platinum(II) octaethylporphine ketone (PtOEPK), along with a stable dioctadecyl dicarbocyanine reference dye (DiD), this nanosensor system provides an advantageous approach for overcoming imaging issues in biological systems, such as autofluorescence and optical scattering in the visible wavelength region. The dyes are encapsulated within a polymer-based nanoparticle matrix to maintain them at a constant ratio in biological samples, precluding the need for complex synthetic approaches. With this constant ratio of the two dyes, the nanosensor response can be measured as a ratio of their two signals, accounting for nanosensor concentration artifacts in measurements. The nanosensors are reversible, which enabled us to temporally monitor systems in which dissolved oxygen concentrations both increase and decrease. These sensors were applied for the monitoring of oxygen in samples of Saccharomyces cerevisiae (brewing yeast) in a 96-well optical fluorescence plate reader format over 60 h. By mixing the nanosensors directly into the sample well with the yeast, we were able to dynamically track metabolic activity changes over time due to varying cell concentration and exposure to an antimicrobial agent. As a result, this system could be a potential platform for high-throughput screening of various species or variants of microbes with unknown metabolic rates in response to external stimuli (antimicrobials, metabolites, etc.).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Expanding and quantifying the crystal chemistry of the flexible ligand 15aneN5

Tetraazamacrocycles have been very extensively exploited as transition metal ligands for a variety of purposes, including catalysis, medical imaging, pharmaceuticals, etc. However, the pentaazamacrocycles are much less commonly used for similar purposes because of poor availability, difficulties in their synthesis, and less well-known metal coordination properties. 1,4,7,10,13-pentaazacyclopentadane (15aneN5) was initially synthesized by a published synthetic route, which we simplified and shortened with minimal drop in yield. Eight different transition metal complexes were made using typical complexation methods. X-ray crystallography of multiple novel complexes yielded insight into the flexibility in coordination geometry of this interesting macrocycle as well as the first crystal structures of 15aneN5 with Cr 3+ , Mn 3+ , Fe 3+ , Co 3+ , Cu 2+ , and Ru 2+ . A parameter to quantify the coordination geometry adopted by the ligand was devised and applied to all known crystal structures of its metal complexes. Lastly, oxidation of 15aneN5 to a novel diimine macrocycle was observed during complexation with ruthenium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

From bulk to interface: electrochemical phenomena and mechanism studies in batteries via electrochemical quartz crystal microbalance

Understanding the bulk and interfacial behaviors during the operation of batteries (e.g., Li-ion, Na-ion, Li–O 2 batteries, etc.) is of great significance for the continuing improvement of the performance. Electrochemical quartz crystal microbalance (EQCM) is a powerful tool to this end, as it enables in situ investigation into various phenomena, including ion insertion/deinsertion within electrodes, solid nucleation from the electrolyte, interphasial formation/evolution and solid–liquid coordination. As such, EQCM analysis helps to decipher the underlying mechanisms both in the bulk and at the interface. This tutorial review will present the recent progress in mechanistic studies of batteries achieved by the EQCM technology. Here, the fundamentals and unique capability of EQCM are first discussed and compared with other techniques, and then the combination of EQCM with other in situ techniques is also covered. In addition, the recent studies utilizing EQCM technologies in revealing phenomena and mechanisms of various batteries are reviewed. Perspectives regarding the future application of EQCM in battery studies are given at the end.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational design of thermoelectric alloys through optimization of transport and dopability

Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.

36 MATERIALS SCIENCE↗