Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “STEP requirements”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Current Status on the Manufacturing of Nanomaterials for Proton Exchange Membrane Energy Systems by Vapor-Based Processes

Development of novel technologies for catalyst synthesis and membrane electrode assembly (MEA) fabrication is of primary importance for further improvement of the performance and economics of proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolyzers (PEMWEs). While the traditional manufacturing methods are time-consuming, energy intensive, and require many processing steps, the new vapor-based methods provide many benefits including the development of improved catalysts and catalyst supports, deposition of uniform thin films, reduction of catalyst loading, and minimizing the number of manufacturing steps. Recent publications in the field identified spray pyrolysis, reactive spray deposition technology, chemical vapor deposition, and atomic layer deposition as advanced vapor-based catalyst synthesis and deposition methods used for fabrication of MEAs for PEMFCs and PEMWEs. Here, the MEAs fabricated via vapor-based processes have shown significant performance improvements in comparison to the state-of-the-art MEAs, which are attributed to better catalyst distribution, improved catalyst supports, and controlled, uniform catalyst layer microstructures. This review provides an overview of the vapor-based synthesis and deposition methods currently being used for the development of PEM-based devices. The advantages and disadvantages of these methods are critically compared and discussed while the outlook for future development is provided.

25 ENERGY STORAGE↗
DOE OSTI2025

Sorbent Regeneration via Radiofrequency-Assisted Dielectric Heating for Direct Air Capture of CO 2

Direct air capture (DAC) of CO 2 is a negative emission technology that utilizes sorbents requiring a regeneration step for repeated ab(/d)sorption–desorption cycles. Here we report an unconventional approach to desorb captured CO 2 from a porous sorbent using radiofrequency (RF) irradiation for dielectric heating and targeted energy transfer, thus enabling modularity and promoting renewable energy input. Regeneration of composites of functional ionic liquid and metal–organic framework with high CO 2 capacity and enhanced transport is demonstrated under conditions relevant to DAC. RF-assisted dielectric heating at the megahertz frequency range shows effective absorption of electromagnetic energy and consequently rapid release of the captured CO 2 .

Ionic liquids↗
DOE OSTI2022

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual seismic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. Here, we present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation-Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid-search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.

58 GEOSCIENCES↗
DOE OSTI2021

A chemical switch enabled autonomous two-stage crosslinking polymeric binder for high performance silicon anodes

Silicon (Si) is a promising high-capacity anode material for high-energy-density lithium-ion batteries. However, the drastic volumetric changes of Si upon lithiation/delithiation hinder the practical use of Si anodes. Although adhesive polymeric binders, such as poly(acrylic acid) (PAA), mitigate this issue, the cycling performance of the fabricated Si anodes is still far from meeting the criteria of practical applications. In this study, we present a novel polymeric binder system for Si anodes consisting of PAA, a chemical switch (ammonia, NH 3 ), and a crosslinker (branched polyethylenimine, PEI). The crosslinking between PAA and PEI is switched off in the slurry, which can then be turned on during electrode drying. Interestingly, the crosslinking reaction consists of two stages: ionic cross-linking (PAA-PEI-i) and covalent crosslinking (PAA-PEI-c) at a higher temperature (e.g., 130 °C). In half-cells, Si anodes fabricated using the PAA-PEI-c binder show a 67% increase in capacity retention compared to PAA anodes over 150 cycles at C/3 rate. The PAA-PEI-c binder also outperforms PAA in full cells. In addition, the chemical switch controlled crosslinking binder system also facilitates the slurry making process by avoiding early crosslinking. This system requires no additional steps compared to the conventional electrode lamination process, showing enormous potential for direct adoption in large-scale manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2025

Electrochemistry-induced deposition for controlled formation of metal–organic framework films on insulator and conductor substrates

A number of technological applications of metal–organic frameworks (MOFs) require the formation of their thin films on insulator and/or conductor substrates at selected areas with desired thicknesses. However, fabrication of such MOF films often requires multi-step processes and/or sophisticated instruments. Herein, we discuss electrochemistry-induced MOF deposition, which permits the direct formation of a thin MOF film with controlled thickness at a desired area on various substrates. So far, we have reported the applicability of this deposition method for the formation of zeolitic imidazolate framework-8 (ZIF-8) films. In this method, a ZIF-8 film is formed on an insulator or a conductor substrate upon applying a cathodic potential to a working electrode that is placed above the substrate. Importantly, the film is formed just below the cathodic working electrode, indicating that the position and lateral dimensions (on the mm- to μm-scale) of the film can be controlled by those of the working electrode. In addition, film thickness is controllable in the range of tens to hundreds of nanometers by adjusting potential application conditions at the cathodic working electrode. These results show that the electrochemistry-induced deposition method will provide a simple means for the fabrication of a patterned MOF film on various substrates without additional lithographic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

WPEC SG50: Developing an Automatically Readable, Comprehensive and Curated Experimental Nuclear Reaction Database

The Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) Working Party on International Nuclear Data Evaluation Co-operation (WPEC) subgroup (SG) 50 was formed in 2020 to develop an automatically readable, comprehensive and curated experimental nuclear reaction database. This database is called MEDUSAL (Machine-readable Experimental Data User Application & Library), and will draw from EXFOR. The EXFOR database preserves experimental nuclear reaction data true to its original documentation and information from the authors of the data. MEDUSAL will deviate from EXFOR by storing additional information from users of the data for their fields of work (evaluation, model development, validation, etc.). This includes expert judgment on the data sets, identification of data points as outliers, renormalization of the data to the newest monitor reactions, and estimations of missing uncertainty sources. The format for MEDUSAL is being developed to enable easy automatic parsing of large amounts of data. Here, we will summarize the use cases, high-level requirements and first steps towards developing the database MEDUSAL and the API to access it.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Oxygenates production in a microfluidic dielectric barrier discharge device sustained in Ar/CH 4 /O 2

Reforming of methane (CH 4 ) is a process to produce syngas (CO/H 2 ) and other value-added chemicals including oxygenates such as methanol (CH 3 OH). Atmospheric pressure plasmas have the potential to be more energy efficient than traditional reforming methods as value-added chemicals can be synthesized directly in the plasma without requiring an additional step. In this paper, we discuss the results from a computational investigation of the formation of oxygenates by CH 4 oxidation in the presence of Ar, including CH 3 OH and CH 2 O, in a nanosecond pulsed dielectric barrier discharge. The plasma is formed in a microfluidic channel whose small dimensions are ideal for plasma formation at atmospheric pressure. The production and consumption mechanisms of dominant radicals and long-lived species are discussed in detail for the base case conditions of Ar/CH 4 /O 2 = 50/25/25. CH 3 OH is produced primarily by CH 3 O reacting with CH 3 O and CH 3 O 2 reacting with OH, while CH 2 O formation relies on reactions involving CH 3 O and CH 3 . The most abundant oxygenate formed is CO (produced by H abstraction from CHO). However, the greenhouse gas CO 2 is also formed as a by-product. The effects of gas mixture are examined to maximize the CH 3 OH and CH 2 O densities while decreasing the CO 2 density. Increasing the Ar percentage from 0% to 95% decreased the CH 3 OH and CH 2 O densities. At low Ar percentages, this is due to an increase in consumption of CH 3 OH and CH 2 O, while at high Ar percentages (>40% Ar), the production of CH 3 OH and CH 2 O is decreased. However, both CO and CO 2 reached peak densities at 70%–90% Ar. Changing the CH 4 /O 2 ratio while keeping 50% Ar in the discharge led to increased CH 3 OH and CH 2 O production, reaching peak densities at 35%–40% CH 4 . The CO and CO 2 densities decreased beyond 20% CH 4 , indicating that a CH 4 rich discharge is ideal for forming the desired oxygenates.

03 NATURAL GAS↗
DOE OSTI2021

Ubiquity of amplitude-modulated magnetic ordering in the H - T phase diagram of the frustrated non-Fermi-liquid YbAgGe

YbAgGe contains a magnetic geometrically frustrated kagome-like lattice that also features significant local single-ion anisotropy. The electronic state is established by hybridization of 4f and conduction electrons leading to heavy electronic masses. The competition between these various interactions leads to nontrivial behavior under external magnetic field, including a sequence of magnetic phase transitions, non-Fermi-liquid states, and possibly a quantum critical point. We present a series of neutron diffraction experiments performed in the mK temperature range and under magnetic fields up to 8 T in the hexagonal plane, revealing the microscopic nature of the first four subsequent magnetic states of this phase diagram. The magnetic phases are associated with the propagation vectors K 1 =($\frac{1}{3}$ 0 $\frac{1}{3}$) for H < 2 T, K 2 = (0 0 0.32) for 2 T < H < 3 T, K 1 = ($\frac{1}{3}$ 0 $\frac{1}{3}$) for 3 T < H < 4.5 T and k 3 = (0.195 0.195 0.38) for 4.5 T < H < 7 T. Our structural refinements reveal a strong modulation of the magnetic moment amplitude in all phases. We observe that the ordered moments of the three magnetically different Yb sites become increasingly different in field, which complies with the principle local anisotropy directions relative to the field direction. While the ordered moments are aligned predominantly in the hexagonal plane, we also find a significant out-of-plane component and a ferromagnetic contribution above 2 T. Here we discuss possible scenarios that may evolve around the phase boundary at 4.5 T, which is associated with putative quantum criticality as identified by various bulk probes. We propose further steps that are required to better understand the microscopic interactions in this material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Direct detection of dark matter substructure in strong lens images with convolutional neural networks

Strong gravitational lensing is a potential way of uncovering the nature of dark matter, by finding perturbations to images that cannot be accounted for well by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos. Here, we introduce results attempting to infer the presence of substructure from images without requiring an intermediate step in which a smooth model has to be subtracted, using a simple convolutional neural network (CNN). We find that the network is only able to infer the presence of subhalos with greater than 75% accuracy when they have masses of greater than or equal to 5 × 10 9 M ⊙ if they lie within the main lens galaxy. Since less massive foreground LOS halos can have the same effect as higher-mass subhalos, the CNN can probe lower masses in the halo mass function. The accuracy does not improve significantly if we add a population of less massive subhalos. With the expectation of experiments such as Hubble Space Telescope and Euclid yielding thousands of high-quality strong lensing images in the next years, having a way of analyzing images quickly to identify candidates that merit further analysis to determine individual subhalo properties while preventing extensive resources being used for images that would yield null detections could be very useful. By understanding the sensitivity as a function of substructure mass, nondetections could be combined with the information from images with substructure to constrain the cold dark matter scenario, in particular if the sensitivity can be pushed to lower masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exponential Improvements in the Simulation of Lattice Gauge Theories Using Near-Optimal Techniques

We report a first-of-its-kind analysis on post-Trotter simulation of U(1), SU(2), and SU(3) lattice gauge theories including fermions in arbitrary spatial dimension. We provide explicit circuit constructions as well as T-gate counts and logical qubit counts for Hamiltonian simulation. We find a reduction of up to 25 orders of magnitude in space-time volume over Trotter methods for simulations of non-Abelian lattice gauge theories relevant to the standard model. This improvement results from our algorithm having polynomial scaling with the number of colors in the gauge theory, achieved by utilizing oracle constructions relying on the sparsity of physical operators, in contrast to the exponential scaling seen in state-of-the-art Trotter methods, which employ explicit mappings onto Pauli operators. Our work demonstrates that the use of advanced algorithmic techniques leads to dramatic reductions in the cost of simulating fundamental interactions, bringing it in step with resources required for first-principles quantum simulation of chemistry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Analysis and Characterization of Four-quadrant Switches based Commutation Cell

A four-quadrant switch (FQS) blocks either polarity voltage and controls current flow in both directions. Unlike voltage-source converters, in which two-quadrant switches operate over a narrow voltage range, four-quadrant switches are required to operate over a wide range of both voltage and current in applications such as matrix converters and current-source converters. Furthermore, matrix converters require multi-step commutation schemes compared to two-step schemes for current-bidirectional switch based voltage-source converters and voltage-bidirectional switch based current-source converters. This paper provides a generalized overview of commutation schemes used for two and four quadrant switches based two-level commutation cells, identifies comparison indices for FQS commutation schemes, and discusses the need for adaptive commutation-step times for wide voltage and current variation applications. Also, the static and dynamic characteristics of 1.2 kV rated FQS implementations utilizing commercial SiC MOSFETs from four different manufacturers and novel monolithic SiC BiDirectional Field Effect Transistor (BiDFET) have been reported.

Commutation cell, four quadrant switch, SiC Bidire↗
DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗
DOE OSTI2024

Gradient Coding With Iterative Block Leverage Score Sampling

Gradient coding is a method for mitigating straggling servers in a centralized computing network that uses erasure-coding techniques to distributively carry out first-order optimization methods. Randomized numerical linear algebra uses randomization to develop improved algorithms for large-scale linear algebra computations. In this study, we propose a method for distributed optimization that combines gradient coding and randomized numerical linear algebra. The proposed method uses a randomized ℓ 2 -subspace embedding and a gradient coding technique to distribute blocks of data to the computational nodes of a centralized network, and at each iteration the central server only requires a small number of computations to obtain the steepest descent update. The novelty of our approach is that the data is replicated according to importance scores, called block leverage scores, in contrast to most gradient coding approaches that uniformly replicate the data blocks. Furthermore, we do not require a decoding step at each iteration, avoiding a bottleneck in previous gradient coding schemes. We show that our approach results in a valid ℓ 2 -subspace embedding, and that our resulting approximation converges to the optimal solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Black-box optimization of CT acquisition and reconstruction parameters: a reinforcement learning approach

Protocol optimization is critical in Computed Tomography (CT) for achieving desired diagnostic image quality while minimizing radiation dose. Due to the inter-effect of influencing CT parameters, traditional optimization methods rely on the testing of exhaustive combinations of these parameters. This poses a notable limitation due to the impracticality of exhaustive parameter testing. This study introduces a novel methodology leveraging Virtual Imaging Trials (VITs) and reinforcement learning to more efficiently optimize CT protocols. Computational phantoms with liver lesions were imaged using a validated CT simulator and reconstructed with a novel CT reconstruction Toolkit. The optimization parameter space included tube voltage, tube current, reconstruction kernel, slice thickness, and pixel size. The optimization process was done using a Proximal Policy Optimization (PPO) agent which was trained to maximize the Detectability Index (d’) of the liver lesion for each reconstructed image. Results showed that our reinforcement learning approach found the absolute maximum d’ across the test cases while requiring 79.7% fewer steps compared to an exhaustive search, demonstrating both accuracy and computational efficiency, offering a efficient and robust framework for CT protocol optimization. The flexibility of the proposed technique allows for use of varying image quality metrics as the objective metric to maximize for. Our findings highlight the advantages of combining VIT and reinforcement learning for CT protocol management.

Fenwick, David [Duke University Medical Center]↗
DOE OSTI2022

Capturing an Early Gene Induction Event during Wood Decay by the Brown Rot Fungus Rhodonia placenta

Brown rot fungi dominate wood decomposition in coniferous forests, and their carbohydrate-selective mechanisms are of commercial interest. Brown rot was recently described as a two-step, sequential mechanism orchestrated by fungi using differentially expressed genes (DEGs) and consisting of oxidation via reactive oxygen species (ROS) followed by enzymatic saccharification. There have been indications, however, that the initial oxidation step itself might require induction. To capture this early gene regulation event, here, we integrated fine-scale cryosectioning with whole-transcriptome sequencing to dissect gene expression at the single-hyphal-cell scale (tens of micrometers). This improved the spatial resolution 50-fold, relative to previous work, and we were able to capture the activity of the first 100 μm of hyphal front growth by Rhodonia placenta in aspen wood. This early decay period was dominated by delayed gene expression patterns as the fungus ramped up its mechanism. These delayed DEGs included many genes implicated in ROS pathways (lignocellulose oxidation [LOX]) that were previously and incorrectly assumed to be constitutively expressed. These delayed DEGs, which include those with and without predicted functions, also create a focused subset of target genes for functional genomics. However, this delayed pattern was not universal, with a few genes being upregulated immediately at the hyphal front. Most notably, this included a gene commonly implicated in hydroquinone and iron redox cycling: benzoquinone reductase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data for Reshaping the 2-Pyrone Synthase Active Site for Chemoselective Biosynthesis of Polyketides

Engineering enzymes with novel reactivity and applying them in metabolic pathways to produce valuable products are quite challenging due to the intrinsic complexity of metabolic networks and the need for high in vivo catalytic efficiency. Triacetic acid lactone (TAL), naturally generated by 2-pyrone synthase (2PS), is a platform molecule that can be produced via microbial fermentation and further converted into value-added products. However, these conversions require extra synthetic steps under harsh conditions. We herein report a biocatalytic system for direct generation of TAL derivatives under mild conditions with controlled chemoselectivity by rationally engineering the 2PS active site and then rewiring the biocatalytic pathway in the metabolic network of E. coli to produce high-value products, such as kavalactone precursors, with yields up to 17 mg/L culture. Computer modeling indicates sterics and hydrogen-bond interactions play key roles in tuning the selectivity, efficiency, and yield.

Conversion↗
DOE OSTI2020

ESS-DIVE reporting format for leaf-level gas exchange data and metadata

Here we present documentation of the ESS-DIVE reporting format for leaf-level gas exchange data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-leaf-gas-exchange v1.0. The zip contains the reporting format description, guidelines, variable tables and instructions in GitHub markdown language (*.md) and 2 metadata templates as spreadsheets with drop down options (*.xlsx files, also function in GoogleSheets).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗