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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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Accuracy is vital for many radiation transport applications. Monte Carlo (MC) codes are the most accurate, but they are expensive for fine meshes. Functional Expansion Tallies (FETs) yield smoother, lower-variance results with far fewer degrees of freedom.
The goals of this project are to design and develop tailored dual-functional materials (DFMs) and the accompanying pressure-swing process for the reactive capture and conversion (RCC) of CO 2 to methanol, a vital product in the chemical market and a versatile precursor to fuels. Throughout this project, NREL researcher team has successfully developed 2 groups of DFM and associated cyclic RCC procedure to selectivity produce either methanol or CO, which is a major intermediate for methanol synthesis with commercially available technologies.
This document outlines a method for processing functional output (i.e., curves) for the ultimate purpose of sampling curves under specified input conditions for use in modeling and simulation uncertainty quantification (UQ) studies. A set of benchmark curves sufficiently representative of the relevant scenario(s) being simulated are provided to the process and formatted as described in Section 1. Principal Component Analysis (PCA) is utilized to discover the components of uncertainty in the benchmark curves and is outlined in Section 2. Section 3 describes the application of uncertainty quantification to the PCA results for the purpose of sampling curves to be used in UQ analysis. Section 4 applies these techniques to an example benchmark dataset. Concluding remarks are provided in the final section.
This article describes a scalable test-bed for automation of measurements of the direct current current transformers (DCCT). Automation of measurement involves functional measurements, fault event based measurements and data validation. The test-bed is designed to measure up to 20 DCCTs at a time with software based measurement repetition checks and validation of the measurements.
Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.
Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.
This study aimed to determine the structure–function relationship (SFR) for ChCl–glycerol mixtures, a deep eutectic solvent (DES), by investigating their microscopic solvation dynamics and how it relates to their macroscopic properties across varying concentrations of ChCl. Femtosecond transient absorption (fs-TA) spectroscopy revealed two distinct solvation dynamics time constants: τ 1 , governed by glycerol–glycerol interactions, and τ 2 , dominated by the choline response. The τ 2 minimum at 25–30 mol % ChCl closely aligned with the eutectic composition (~33.33 mol % ChCl), where the glycerol network was the most organized and the choline ions exhibited the fastest relaxation. The viscosity decreased sharply up to ~25 mol % ChCl and then plateaued, while the conductivity increased monotonically with ChCl concentration, reflecting enhanced ionic mobility. The density decreased with both increasing ChCl concentration and temperature, indicating disrupted hydrogen bonding and reduced molecular packing. The polarity, measured using betaine-30 (B30) and the E T (30) polarity scale, increased steeply up to approximately 25 mol % ChCl before reaching a plateau. These findings identified the eutectic composition as the optimal concentration range for balancing stability, fluidity, conductivity, and enhanced dynamics within the glycerol system.
Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard’s relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.
We present a study of the evolution of type 1 quasars at 1.0 < z < 3.5, covering the peak epoch of quasar activity. The quasar evolution has been extensively explored by a variety of previous works and the derived quasar luminosity functions (QLFs) are not very consistent with each other, presumably due to the complexities introduced by different quasar selection techniques and associated completeness corrections. We use a new strategy to construct QLFs based on a library of all known quasars. We focus on a wide region of ∼1700 deg 2 and a deep field of ∼265 deg 2 that have rich spectroscopic data primarily from the Sloan Digital Sky Survey and DESI. We then apply traditional color cuts in the rest-frame UV–optical to select quasar candidates and use the quasar library to identify them. Our final sample consists of 62,426 quasars at 1.0 < z < 3.5, with a high completeness (∼96%) and a high purity (∼93%) in the color selection. Simple color cuts can potentially minimize selection biases for the study of quasar evolution. We derive binned QLFs and characterize them using a double power-law model. Sample incompleteness and contamination are considered as part of the uncertainties in the calculation. Compared to previous results, our QLFs are slightly higher at the faint end, and also higher at the bright end at 2.5 < z < 3.5. The QLFs suggest that the quasar evolution at 1.0 < z < 2.5 can be well described by the pure luminosity evolution model, while at 2.5 < z < 3.5, it can be described by either the pure luminosity evolution or the pure density evolution model.
This paper presents an innovative wave energy conversion system employing an interleaved Cuk converter and advanced nonlinear control for seamless integration with the grid. The proposed system utilizes the interleaved Cuk converter for efficient power extraction on the DC side, while an inverter facilitates power transfer to the grid or load on the AC side. A sophisticated nonlinear control architecture based on Lyapunov energy functions ensures stable and optimal operation under varying wave conditions. Case study results demonstrate the effectiveness of the proposed system, highlighting its ability to efficiently harness wave energy and seamlessly integrate with the grid, thus paving the way for sustainable and reliable renewable energy generation. The overall system is verified via computer simulations based on MATLAB/Simulink and various case study results are presented.
Functional expansion tallies (FETs) are a powerful tool for getting more information per history from Monte Carlo simulations, but in the past they have been constrained to orthogonal bases. Bezier curves, from computer aided design (CAD), could be well suited for FET due to their ability to assume many arbitrary shapes, but are non-orthogonal. Recent development has made non-orthongal FET possible. The convergence of B ´ezier curve FETs in both polynomial order and number of samples is explored. These bases are well suited for representing normal distributions, and opens the door to possible other CAD derived FET bases.
Functional expansion tallies (FETs) are powerful tools for getting more information per history from Monte Carlo simulations, but in the past they have been constrained to orthogonal bases. Bézier curves are used widely in computer aided design (CAD) geometry kernels and could be well-suited for FETs due to their ability to assume many arbitrary shapes, but they use nonorthogonal bases. Recent developments in 2021 have made nonorthogonal FETs possible. The convergence of Bézier curve FETs in both polynomial order and number of samples is explored in this work. It is shown that these bases are well-suited for representing normal distributions and this opens the door to the possibility of other CAD-derived FET bases.
Cyber threats to operational technology demand more than traditional IT defenses—they require full-spectrum mission assurance. Cyber-Informed Engineering (CIE) is an approach that embeds engineered controls into system design to ensure critical functions remain safe and reliable, even under attack. Unlike conventional cybersecurity tools, engineered controls act directly on physical processes to prevent unacceptable outcomes such as equipment damage or mission failure. This session will outline the CIE framework and share examples of consequence-based design that deliver true resilience, not just fail-safe behaviors. Attendees will learn how to integrate these principles into the engineering lifecycle to support resilient-by-design architectures and inform emerging standards. This talk sets the stage for the panel discussion on advancing CIE across sectors as digital and physical systems converge.
Methanogenic archaea are particularly rich in iron–sulfur proteins, yet their roles remain largely enigmatic. Here, we characterized aMethanococcus voltae(Mvo) protein from the domain of unknown function (DUF) 2193 family, a group of proteins present primarily in archaea and characterized by a conserved cysteine‐rich C‐terminal motif.MvoDUF2193 was heterologously expressed and characterized by a range of spectroscopic and analytical methods. The results demonstrate thatMvoDUF2193 binds a single [4Fe–4S] cluster per subunit and that cluster occupancy regulates the transition from an apo tetramer to a [4Fe–4S] monomeric form. We hypothesize thatMvoDUF2193 serves a regulatory role in the cell, mediated by [Fe–S] cluster binding and changes in oligomeric state.
Optical metasurfaces, consisting of subwavelength-scale meta-atom arrays, hold great promise of overcoming the fundamental limitations of conventional optics. Due to their structural complexity, metasurfaces usually require high-resolution yet slow and expensive fabrication processes. Here, using a metasurface polarimetric imaging device as an example, the photonic structures and the Nanoimprint lithography (NIL) processes are designed, creating two separate NIL molds over a patterning area of > 20 mm2 with designed Moiré alignment markers by electron-beam writing, and further subsequently integrate silicon and aluminum metasurface structures on a chip. Uniquely, the silicon and aluminum metasurfaces are fabricated by using the nanolithography and 3D pattern-transfer capabilities of NIL, respectively, achieving nanometer-scale linewidth uniformity, sub-200 nm translational overlay accuracy, and <0.017 rotational alignment error while significantly reducing fabrication complexity and surface roughness. Here, the micro-sized multilayer metasurfaces have high circular polarization extinction ratios as large as ≈20 and ≈80 in blue and red wavelengths. Further, the metasurface chip-integrated CMOS imager demonstrates high accuracy in broad-band, full Stokes parameter analysis in the visible wavelength ranges and single-shot polarimetric imaging. This novel, NIL-based, multilayered nanomanufacturing approach is applicable to the scalable production of large-area functional structures for ultra-compact optic, electronic, and quantum devices.
Abstract Stable and efficient deep‐blue organic light‐emitting diodes (OLEDs) are in high demand for display and lighting applications but are rarely reported due to their poor operational lifetimes. Herein, the study designs and synthesizes two novel N ‐heterocyclic carbene (NHC)‐based tetradentate Pt(II) complexes PtON5‐dtb and PtON5N‐dtb, and thoroughly investigate their electrochemical and photophysical properties. Functionalization of the NHC moieties can increase the metal‐to‐ligand charge transfer ( 1/3 MLCT) characters in their lowest triplet excited‐states, resulting in significantly shortened photoluminescent lifetimes and remarkably improved device performance. A deep blue OLED employing PtON5N‐dtb as an emitter exhibits a narrow spectral bandwidth with a full‐width at half maximum (FWHM) of 30 nm and a CIE y value of 0.17 and demonstrates a maximum external quantum efficiency (EQE) of 20.4% with a small efficiency roll‐off, which maintains a high EQE of 18.5% at 1000 cd m −2 . Moreover, the deep blue OLED also realizes a long‐measured operational lifetime LT 90 (time to 90% of the initial luminance) of 71 hours with an initial brightness of 1134 cd m −2 , corresponding to an estimated device lifetime LT 90 of 85 h at 1000 cd m −2 . This represented an eightfold lifetime improvement for PtON5N‐dtb‐based deep blue OLED compared to PtON7‐dtb in the same device setting.
Most virus infection assays have indirect readout such as virus number following entry (e.g., PCR, cell lysis). While effective, these technologies are labor‐intensive, require specialized environments (e.g., sterile or RNA‐free), and detect later‐stage viral events like lysis or cell death, lacking sensitivity to early fusion events. To address these limitations, we present biologically relevant 2D membrane materials, host‐cell‐derived supported lipid bilayers (hcd‐SLBs), integrated with organic microelectrode arrays (OMEAs) for detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) fusion. By overexpressing angiotensin‐converting enzyme 2 (ACE2) receptors on the native membranes, the platform functions as a viral sensor capable of detecting virus pseudo particles (VPPs) through the late pathway. Additionally, hcd‐SLBs extracted from human lung epithelium expressing native ACE2 detect fusion events through the early pathway. The platform's utility as a drug‐screening tool is demonstrated by testing antibodies targeting either the ACE2 on the host membrane or the viral spike (S) proteins. To enhance the throughput, microfluidics are integrated for automation and OMEAs are incorporated within each channel, miniaturizing the testing units. This system supports high‐throughput data generation, automation, and scalability, providing an efficient platform for viral fusion detection that advances the study of pathogen‐host interactions and accelerates antiviral drug discovery.
The human brain performs complex memory and computational tasks with high energy efficiency by regulating ion transport through membrane channels. These signaling mechanisms have been inspiring the development of nanofluidic memristors that emulate synaptic behavior. Here, in this study, we describe a membrane ion channel synapse (MICS), constructed from aqueous droplets linked by gramicidin A channels, that achieves neuromorphic functionality. MICS exhibits memristive ion transport with hysteretic current–voltage behavior arising from voltage-dependent channel formation and ion transport dynamics. MICS emulates a range of synaptic behaviors including associative learning. We further demonstrate its application in reservoir computing by performing handwritten digit classification and tic-tac-toe game and explore the system parameters that improve the computational performance. This droplet-based biomimetic synapse offers a potentially scalable and energy-efficient platform for next-generation neuromorphic computing systems.