SEARCH · Engineering Papers

Results for “Application Abstraction”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Tailoring the Thickness‐Dependent Optical Properties of Conducting Nitrides and Oxides for Epsilon‐Near‐Zero‐Enhanced Photonic Applications

Abstract The unique properties of the emerging photonic materials, conducting nitrides and oxides, especially their tailorability, large damage thresholds, and, importantly, the so‐called epsilon‐near‐zero (ENZ) behavior, have enabled novel photonic phenomena spanning optical circuitry, tunable metasurfaces, and nonlinear optical devices. This work explores direct control of the optical properties of polycrystalline titanium nitride (TiN) and aluminum‐doped zinc oxide (AZO) by tailoring the film thickness, and their potential for ENZ‐enhanced photonic applications. This study demonstrates that TiN–AZO bilayers support Ferrell–Berreman modes using the thickness‐dependent ENZ resonances in the AZO films operating in the telecom wavelengths spanning from 1470 to 1750 nm. The bilayer stacks also act as strong light absorbers in the ultraviolet regime using the radiative ENZ modes and the Fabry–Perot modes in the constituent TiN films. The studied Berreman resonators exhibit optically induced reflectance modulation of 15% with picosecond response time. Together with the optical response tailorability of conducting oxides and nitrides, using the field enhancement near the tunable ENZ regime can enable a wide range of nonlinear optical phenomena, including all‐optical switching, time refraction, and high‐harmonic generation.

Saha, Soham↗

DOE OSTI2021

Asc-Seurat: analytical single-cell Seurat-based web application

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Printed graphene and its composite with copper for electromagnetic interference shielding applications

Abstract Advances in mobile electronics and telecommunication systems along with 5G technologies have been escalating the electromagnetic interference (EMI) problem in recent years. Graphene-based material systems such as pristine graphene, graphene-polymer composites and other graphene-containing candidates have been shown to provide adequate EMI shielding performance. Besides achieving the needed shielding effectiveness (SE), the method of applying the candidate shielding material onto the object in need of protection is of enormous importance due to considerations of ease of application, reduced logistics and infrastructure, rapid prototyping and throughput, versatility to handle both rigid and flexible substrates and cost. Printing readily meets all these criteria and here we demonstrate plasma jet printing of thin films of graphene and its composite with copper to meet the EMI shielding needs. SE over 30 dB is achieved, which represents blocking over 99.9% of the incoming radiation. Graphene and its composite with copper yield higher green index compared to pure copper shields, implying reduced reflection of incoming electromagnetic waves to help reduce secondary pollution.

Materials Science↗

DOE OSTI2022

A review on the application of machine learning for combustion in power generation applications

Abstract Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

Engineering↗

DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗

DOE OSTI2024

Review of solar-enabled desalination and implications for zero-liquid-discharge applications

Abstract The production of freshwater from desalinating abundant saline water on the planet is increasingly considered a climate change adaptation measure. Yet, there are challenges associated with the high cost, intensive energy demand, and environmental implications of desalination. Effective integration of solar energy generation and freshwater production can address both issues. This review article highlights recent key advances in such integration achieved in a joint-research university-national laboratory partnership under the auspices of the United States Department of Energy and parallel efforts worldwide. First, an overview of current and emerging desalination technologies and associated pretreatment, brine treatment, and valorization technologies that together can result in zero-liquid-discharge systems is presented, and their technological readiness levels are evaluated. Then, advanced modeling techniques and new software platforms that enable optimization of solar-desalination applications with the dual objective of cost and environmental impact minimization are discussed.

14 SOLAR ENERGY↗

DOE OSTI2024

Purple non-sulfur bacteria for biotechnological applications

Abstract In this review, we focus on how purple non-sulfur bacteria can be leveraged for sustainable bioproduction to support the circular economy. We discuss the state of the field with respect to the use of purple bacteria for energy production, their role in wastewater treatment, as a fertilizer, and as a chassis for bioplastic production. We explore their ability to serve as single-cell protein and production platforms for fine chemicals from waste materials. We also introduce more Avant-Garde technologies that leverage the unique metabolisms of purple bacteria, including microbial electrosynthesis and co-culture. These technologies will be pivotal in our efforts to mitigate climate change and circularize the economy in the next two decades. One-sentence summary Purple non-sulfur bacteria are utilized for a range of biotechnological applications, including the production of bio-energy, single cell protein, fertilizer, bioplastics, fine chemicals, in wastewater treatment and in novel applications like co-cultures and microbial electrosynthesis.

Morrison, Hailee M. (ORCID:0000000225121824)↗

DOE OSTI2024

Large Area Near‐Field Thermophotovoltaics for Low Temperature Applications

Abstract Thermophotovoltaics, devices that convert thermal infrared photons to electricity, offer a key pathway for a variety of critical renewable energy technologies including thermal energy storage, waste heat recovery, and direct solar‐thermal power generation. However, conventional far‐field devices struggle to generate reasonable powers at lower temperatures. Near‐field thermophotovoltaics provide a pathway to substantially higher powers by leveraging photon tunneling effects. Here a large area near‐field thermophotovoltaic device is presented, created with an epitaxial co‐fabrication approach, that consists of a self‐supported 0.28 cm 2 emitter‐cell pair with a 150 nm gap. The device generates 1.22 mW at 460 °C, a 25‐fold increase over the same cell measured in a far‐field configuration. Furthermore, the near‐field device demonstrates short circuit current densities greater than the far‐field photocurrent limit at all the temperatures tested, confirming the role of photon tunneling effects in the performance enhancement. Modeling suggests several practical directions for cell improvements and further increases in power density. These results highlight the promise of near‐field thermophotovoltaics, especially for low temperature applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Harnessing Particle Size‐Control and DNA‐Oligo Functionalization in ZIF‐76 for Biological Applications

Abstract Advanced therapeutics require novel nanocarriers to ensure their functionality is preserved during transit. Zeolitic imidazolate frameworks (ZIFs) have emerged as promising materials in this field owing to their combined biocompatibility, high porosity, and tunable chemistry. While a diverse family of ZIFs has been reported, few have been explored beyond the prototypical ZIF‐8. Herein, the size‐controlled synthesis of three distinct ZIF‐76 analogs is demonstrated, overcoming the unique synthetic challenges intrinsic to the lta topology and complex crystallization kinetics associated with the mixed linker approach. This assesses the materials’ platform effectiveness for intracellular delivery first by exploring the structural and colloidal stability in biologically relevant media. To circumvent particle aggregation, fluorescently labeled DNA oligonucleotides are post‐synthetically attached to the ZIF surface. This modification significantly improves the colloidal stability in media and facilitates particle internalization tracking. Finally, the particle‐cell interactions are assessed, revealing rapid cell membrane association with macrophages, but not lung epithelial cells, and ZIF accumulation within macrophages which increased over time. Importantly, this study outlines a generalized approach toward expanding the available library of ZIFs for biological applications, enabling the potential for targeted therapeutic delivery for intracellular infections treatment.

Deneff, Jacob I.↗

DOE OSTI2023

Halide Perovskites and Their Derivatives for Efficient, High‐Resolution Direct Radiation Detection: Design Strategies and Applications

Abstract The past decade has witnessed a rapid rise in the performance of optoelectronic devices based on lead‐halide perovskites (LHPs). The large mobility‐lifetime products and defect tolerance of these materials, essential for optoelectronics, also make them well‐suited for radiation detectors, especially given the heavy elements present, which is essential for strong X‐ray and γ‐ray attenuation. Over the past decade, LHP thick films, wafers, and single crystals have given rise to direct radiation detectors that have outperformed incumbent technologies in terms of sensitivity (reported values up to 3.5 × 10 6 µC Gy air −1 cm −2 ), limit of detection (directly measured values down to 1.5 nGy air s −1 ), along with competitive energy and imaging resolution at room temperature. At the same time, lead‐free perovskite‐inspired materials (e.g., methylammonium bismuth iodide), which have underperformed in solar cells, have recently matched and, in some areas (e.g., in polarization stability), surpassed the performance of LHP detectors. These advances open up opportunities to achieve devices for safer medical imaging, as well as more effective non‐invasive analysis for security, nuclear safety, or product inspection applications. Herein, the principles behind the rapid rises in performance of LHP and perovskite‐inspired material detectors, and how their properties and performance link with critical applications in non‐invasive diagnostics are discussed. The key strategies to engineer the performance of these materials, and the important challenges to overcome to commercialize these new technologies are also discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Microwave-assisted synthesis of iron sulfide motifs for electrochemical applications

Abstract The syntheses of FeS 2 and Fe 3 S 4 nanomaterials were optimized using a novel facile, surfactant-free, and microwave-assisted, one-pot synthesis method, run under ambient and reasonably mild reaction conditions. Synthetic parameters, such as metal precursor salt identity, reaction time, reaction temperature, metal:sulfur molar ratios, and solvent combinations, were all systematically investigated and optimized. A series of FeS 2 (pyrite) samples was initially fabricated using thioacetamide (TAA) as the sulfur precursor to generate a distinctive, uniform octahedra-based morphology. Switching the sulfur precursor from TAA to L-cysteine resulted in a corresponding transformation in not only chemical composition from FeS 2 to an iron thiospinel structure, Fe 3 S 4 (otherwise known as greigite), but also an associated morphological evolution from octahedra to nanosheet aggregates. The study of these materials has enabled crucial insights into the formation mechanisms of these materials under a relatively non-conventional microwave-assisted setting. Furthermore, in separate experiments, multi-walled carbon nanotubes (MWNTs) and graphene were added in with underlying metal sulfide species to create conductive Fe–S/MWNT composites and Fe–S/graphene composites, respectively. The method of addition of either MWNTs or graphene was also explored, wherein an ‘ ex-situ ’ synthetic procedure was found to be the least disruptive means of attachment and immobilization onto iron sulfide co-reagents as a means of preserving the latter’s inherent composition and morphology. The redox acidity for the parent material and associated composites demonstrates the utility of our as-developed synthetic methods for creating motifs relevant for electrochemical applications, such as energy storage.

25 ENERGY STORAGE↗

DOE OSTI2024

Technical note: Optimizing the in situ cosmogenic 36 Cl extraction and measurement workflow for geologic applications

Abstract. In situ cosmogenic 36Cl analysis by accelerator mass spectrometry (AMS) is routinely employed to date Quaternary surfaces and assess rates of landscape evolution. However, standard laboratory preparation procedures for 36Cl dating require the addition of large amounts of isotopically enriched chlorine spike solution; these solutions are expensive and increasingly difficult to acquire from commercial sources. In addition, the typical workflow for 36Cl dating involves measuring both 35Cl/37Cl and 36Cl/Cl concurrently on the high-energy (post-accelerator) end of the AMS system, but 35Cl/37Cl determinations using this technique can be complicated by isotope fractionation and system memory during measurement. The traditional workflow also does not provide 36Cl extraction laboratories with the data needed to calculate native Cl concentrations in advance of 36Cl/Cl measurements. In light of these concerns, we present an improved workflow for extracting and measuring chlorine in geologic materials. Our initial step is to characterize 35Cl/37Cl on sample aliquots of up to ∼1 g prepared in Ag(Cl, Br) matrices, which greatly reduces the amount of isotopically enriched spike solution required to measure native Cl content in each sample. To avoid potential issues with isotope fractionation through the accelerator, 35Cl/37Cl is measured on the low-energy, pre-accelerator end of the AMS line. Then, for 36Cl/Cl measurements, we extract Cl as AgCl or Ag(Cl, Br) in analytical batches with a consistent total Cl load across all samples; this step is intended to minimize source memory effects during 36Cl/Cl measurements and allows the preparation of AMS standards that are customized to match known Cl contents in the samples. To assess the efficacy of this extraction and measurement workflow, we compare chlorine isotope ratio measurements on seven geologic samples prepared using standard procedures and the updated workflow. Measurements of 35Cl/37Cl and 36Cl/Cl are consistent between the two workflows, and 35Cl/37Cl values measured using our methods have considerably higher precision than those measured following standard protocols. The chemical preparation and measurement workflow presented here (1) reduces the amount of isotopically enriched chlorine spike used per rock sample by up to 95 %; (2) identifies rocks with high native Cl concentrations, which may be lower priority for 36Cl surface exposure dating, at an early stage of analysis; and (3) allows laboratory users to maintain control over the total chlorine content within and across analytical batches. These methods can be incorporated into existing laboratory and AMS protocols for 36Cl analyses and will increase the accessibility of 36Cl dating for geologic applications.

58 GEOSCIENCES↗

DOE OSTI2023

Suppressing Structural Relaxation in Nanoscale Antimony to Enable Ultralow‐Drift Phase‐Change Memory Applications

Abstract Phase‐change random‐access memory (PCRAM) devices suffer from pronounced resistance drift originating from considerable structural relaxation of phase‐change materials (PCMs), which hinders current developments of high‐capacity memory and high‐parallelism computing that both need reliable multibit programming. This work realizes that compositional simplification and geometrical miniaturization of traditional GeSbTe‐like PCMs are feasible routes to suppress relaxation. While to date, the aging mechanisms of the simplest PCM, Sb, at nanoscale, have not yet been unveiled. Here, this work demonstrates that in an optimal thickness of only 4 nm, the thin Sb film can enable a precise multilevel programming with ultralow resistance drift coefficients, in a regime of ≈10 −4 –10 −3 . This advancement is mainly owed to the slightly changed Peierls distortion in Sb and the less‐distorted octahedral‐like atomic configurations across the Sb/SiO 2 interfaces. This work highlights a new indispensable approach, interfacial regulation of nanoscale PCMs, for pursuing ultimately reliable resistance control in aggressively‐miniaturized PCRAM devices, to boost the storage and computing efficiencies substantially.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Birefringent Glass‐Engraved Tilted Pillar Metasurfaces for High Power Laser Applications

Abstract Birefringent materials—which are highly needed in high power laser systems—may be limited in usage due to the laser‐induced damage threshold of traditional birefringent materials. This work reports here on all‐glass metasurfaces, fabricated by angled etching through sacrificial metal nanoparticle (NP) etching masks, for generation of effective birefringence in the formed layer. As a result, a fused silica metasurface, monolithic to the underlying substrate, is demonstrated to exhibit a birefringence of 6.57° under 375 nm illumination. Full‐wave analysis shows a good agreement with the measurement and presents potential paths forward to increasing the effective metasurface birefringence. This is the first demonstration, to the best of knowledge, of an etching technique to obtain the resulting tilted pillar‐like nanofeatures. The anisotropy of the metasurface nanoelements along the two window in‐plane major axes presents different effective paths for the two polarizations and thus generates birefringence in a nonbirefringent material. Additionally, the imparted anisotropy lends itself to manipulation of physical properties of the surface as well, with metasurface feature orientation suppressing water flow along one principal axis and giving rise to water flow steering capabilities.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗

DOE OSTI2023

Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications

Abstract Metal‐organic frameworks (MOFs) are a class of porous materials with high surface areas, which are acquiring rapid attention on an exponential basis. A significant characteristic of MOFs is their ability to act as adsorbents to selectively separate component mixtures of similar size, thereby addressing the technological need for an alternative approach to conventional distillation methods. Recently, MOFs comprising a 3‐Dimensional (3D) linker have shown outstanding capabilities for difficult separations compared to the parent 2‐Dimensional (2D) analogue. 3D‐linkers with a polycyclic core are underrepresented in the MOF database due to the widespread preferred use of 2D‐linkers and the misconceived high‐cost of 3D linkers. We summarize the recent research of 3D‐linker MOFs and highlight their beneficial employment for selective gas and hydrocarbon adsorption and separation. Furthermore, we outline forecasts in this area to create a platform for widespread adoption of 3D‐linkers in MOF synthesis.

Macreadie, Lauren K.↗

DOE OSTI2023

Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications

Macreadie, Lauren K.↗

DOE OSTI2023

Are Phosphatidic Acids Ubiquitous in Mammalian Tissues or Overemphasized in Mass Spectrometry Imaging Applications?

Abstract Mass spectrometry imaging (MSI) is an invaluable tool for the spatial visualization of molecules in vivo. However, the question of whether observed annotations are endogenous or artificial (i. e., from in‐source fragmentation) is critical and has been largely unexplored in multimodal MSI. In matrix‐assisted laser desorption/ionization (MALDI)‐MSI datasets from researchers worldwide, PAs were found to represent up to 18 % of annotations in rat brain. Rat brain was additionally imaged here using nanospray desorption electrospray ionization (nano‐DESI), a softer ionization strategy. No PAs observed with MALDI were present in the nano‐DESI dataset. Further investigation strongly indicated lipid fragmentation to PAs for MALDI‐MSI, but not with nano‐DESI‐MSI. We finally extend this observation to the MALDI‐MSI analyses of human tissues, showing that PA annotations comprised up to 16 % of annotations. Therefore, this study shows that MSI annotations should be carefully interrogated, as in‐source fragmentation or modification of lipids may contribute substantially to false annotations and incorrect biological interpretations.

Vandergrift, Gregory W.↗