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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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Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks
As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.
Light‐driven catalysis with engineered enzymes and biomimetic systems
Abstract Efforts to drive catalytic reactions with light, inspired by natural processes like photosynthesis, have a long history and have seen significant recent growth. Successfully engineering systems using biomolecular and bioinspired catalysts to carry out light‐driven chemical reactions capitalizes on advantages offered from the fields of biocatalysis and photocatalysis. In particular, driving reactions under mild conditions and in water, in which enzymes are operative, using sunlight as a renewable energy source yield environmentally friendly systems. Furthermore, using enzymes and bioinspired systems can take advantage of the high efficiency and specificity of biocatalysts. There are many challenges to overcome to fully capitalize on the potential of light‐driven biocatalysis. In this mini‐review, we discuss examples of enzymes and engineered biomolecular catalysts that are activated via electron transfer from a photosensitizer in a photocatalytic system. We place an emphasis on selected forefront chemical reactions of high interest, including CH oxidation, proton reduction, water oxidation, CO 2 reduction, and N 2 reduction.
Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale
Many industrial chemicals that are produced from fossil resources could be manufactured more sustainably through fermentation. In this work, we describe the development of a carbon-negative fermentation route to producing the industrially important chemicals acetone and isopropanol from abundant, low-cost waste gas feedstocks, such as industrial emissions and syngas. Using a combinatorial pathway library approach, we first mined a historical industrial strain collection for superior enzymes that we used to engineer the autotrophic acetogen Clostridium autoethanogenum. Next, we used omics analysis, kinetic modeling and cell-free prototyping to optimize flux. Finally, we scaled-up our optimized strains for continuous production at rates of up to ~3 g/L/h and ~90% selectivity. Life cycle analysis confirmed a negative carbon footprint for the products. Unlike traditional production processes, which result in release of greenhouse gases, our process fixes carbon. These results show that engineered acetogens enable sustainable, high-efficiency, high-selectivity chemicals production. We expect that our approach can be readily adapted to a wide range of commodity chemicals.
Nonlinear dynamics and instability of aqueous dissolution of silicate glasses and minerals
Aqueous dissolution of silicate glasses and minerals plays a critical role in global biogeochemical cycles and climate evolution. The reactivity of these materials is also important to numerous engineering applications including nuclear waste disposal. The dissolution process has long been considered to be controlled by a leached surface layer in which cations in the silicate framework are gradually leached out and replaced by protons from the solution. This view has recently been challenged by observations of extremely sharp corrosion fronts and oscillatory zonings in altered rims of the materials, suggesting that corrosion of these materials may proceed directly through congruent dissolution followed by secondary mineral precipitation. Here we show that complex silicate material dissolution behaviors can emerge from a simple positive feedback between dissolution-induced cation release and cation-enhanced dissolution kinetics. This self-accelerating mechanism enables a systematic prediction of the occurrence of sharp dissolution fronts (vs. leached surface layers), oscillatory dissolution behaviors and multiple stages of glass dissolution (in particular the alteration resumption at a late stage of a corrosion process). In conclusion, our work provides a new perspective for predicting long-term silicate weathering rates in actual geochemical systems and developing durable silicate materials for various engineering applications.
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework is based on using first principles to model the known physics in conjunction with utilizing the data-driven machine learning tools to model the remaining residual that is hidden in data. This framework employs proper orthogonal decomposition as a compression tool to construct orthonormal bases and Galerkin projection (GP) as a model to build the dynamical core of the system. Our proposed methodology hence compensates structural or epistemic uncertainties in models and utilizes the observed data snapshots to compute true modal coefficients spanned by these bases. The GP model is then corrected at every time step with a data-driven rectification using a long short-term memory (LSTM) neural network architecture to incorporate hidden physics. A Grassmannian manifold approach is also adopted for interpolating basis functions to unseen parametric conditions. The control parameter governing the system's behavior is thus implicitly considered through true modal coefficients as input features to the LSTM network. The effectiveness of the HAM approach is then discussed through illustrative examples that are generated synthetically to take hidden physics into account. Furthermore, our approach thus provides insights addressing a fundamental limitation of the physics-based models when the governing equations are incomplete to represent underlying physical processes.
Reshaping the Quantum Arrow of Time
While the microscopic laws of physics are often symmetric under time reversal, most natural processes that we observe are not. The emergent asymmetry between typical and time-reversed processes is referred to as the arrow of time. In quantum physics, an arrow of time emerges when a sequence of measurements is performed on a system. We introduce quantum control tools that can yield dynamics more consistent with time flowing backward than forward. The control tools are based on the explicit construction of a Hamiltonian that can replicate the stochastic trajectories of a monitored quantum system. Such a Hamiltonian can reverse the effect of monitoring and, via a feedback process, generate trajectories consistent with a reversed arrow of time. It can also be used to simulate the backward-in-time dynamics of an open quantum system. Finally, we design a feedback-driven continuous measurement engine powered by the energy pumped into the system by the monitoring process. We show that the engine can operate under experimentally realizable conditions with feedback delay and finite-efficiency measurements.
Lost Foam Thin Wall: Feasibility of Producing Lost Foam Castings in Aluminum and Magnesium Based Alloys
With the increased emphasis on vehicle weight reduction, production of near-net shape components by lost foam casting will make significant inroad into the next-generation of engineering component designs. The lost foam casting process is a cost effective method for producing complex castings using an expandable polystyrene pattern and un-bonded sand. The use of un-bonded molding media in the lost foam process will impose less constraint on the solidifying casting, making hot tearing less prevalent. This is especially true in Al-Mg and Al-Cu alloy systems that are prone to hot tearing when poured in rigid molds partially due to their long freezing range. Some of the unique advantages of using the lost foam casting process are closer dimensional tolerance, higher casting yield, and the elimination of sand cores and binders. Most of the aluminum alloys poured using the lost foam process are based on the Al-Si system. Very limited research work has been performed with Al-Mg and Al-Cu type alloys. With the increased emphasis on vehicle weight reduction, and given the high-strength-to-weight-ratio of magnesium, significant weight savings can be achieved by casting thin-wall (≤ 3 mm) engineering components from both aluminum- and magnesium-base alloys.
In-depth analysis on parallel processing patterns for high-performance Dataframes
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.
A Perspective on Thermochemical and Electrochemical Processes for Titanium Metal Production
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Roadmap for unconventional computing with nanotechnology
Abstract In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.
Impedance Measurement Box
The IMB 50V software provides functionality for design of impedance measurement tests or sequences of tests, execution of these tests or sequences, processing measured responses and displaying and saving of the results. The software consists of a Graphical User Interface that allows configuration of measurement parameters and test sequencing, a core engine that controls test sequencing, execution of measurements, processing and storage of results and a hardware/software data acquisition interface with the IMB hardware system.
Formation mechanism of chalcogenide nanocrystals confined inside genetically engineered virus-like particles
Engineered virus-like particles (VLP) are attractive for fabricating nanostructured materials for applications in diverse areas such as catalysis, drug delivery, biomedicine, composites, etc. Basic understanding of the interaction between the inorganic guest and biomolecular host is thus important for the controlled synthesis of inorganic nanoparticles inside VLP and rational assembly of ordered VLP-based hierarchical nanostructures. We have investigated in detail the formation mechanism and growth kinetics of semiconducting nanocrystals confined inside genetically engineered bacteriophage P22 VLP using semiconducting CdS as a prototypical example. The selective nucleation and growth of CdS at the engineered sites is found to be uniform during the early stage, followed by a more stochastic growth process. Furthermore, kinetic studies reveal that the presence of an engineered biotemplate helps in significantly retarding the reaction rate. These findings provide guidance for the controlled synthesis of a wide range of other inorganic materials confined inside VLP, and are of practical importance for the rational design of VLP-based hierarchical nanostuctures.
Bentonite buffer under high temperature: laboratory experiments and coupled process modeling
Abstract. Bentonite buffer in a geological repository will be simultaneously heated from decaying radioactive waste and hydrated from the surrounding host rock, triggering complex and coupled THMC (thermal–hydrological–mechanical–chemical) processes. Understanding the THMC behavior of bentonite-based engineered barrier system (EBS) is key to the evaluation and prediction of its long-term performance. Studies on the THMC process have been focused on conditions under 100 ∘C, as most design concepts impose a thermal limit of 100 ∘C in bentonite. Recently, studies under high-temperature conditions have been conducted to evaluate the possibility of raising the thermal limit and expanding the data/knowledge base to increase the confidence level. In this abstract, we present a series of bench-scale laboratory experiments at high temperatures (up to 200 ∘C) and the corresponding modeling work. Two sets of column tests were conducted, and each set consisted of two test columns: a control column undergoing only hydration (non-heated) and an experiment column experiencing both heating and hydration (heated). During the experiment, frequent X-ray computed tomography (CT) images were collected to provide a 3D visualization of the density distribution and present the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. The two sets of tests differ with respect to several experimental conditions, such as bentonite type, compacted density and water content, water chemistry, and hydration pressure, but the important difference is that the first set used bentonite powder with a dry density of 1.28 g cm−3, whereas the second set used granulated bentonite (mixture of pellets and powder) with a dry density of 1.45–1.5 g cm−3. In both sets of experiments, a comprehensive post-dismantling characterization of bentonite samples was carried out after the column tests had been running for 1.5 years. Comparing non-heated and heated columns, the temperature gradient led to lower degree of homogenization of bentonite after bentonite became fully saturated; comparing the first and second sets, granulated and powdered bentonite exhibited drastically different hydration behavior. A THM model with a 2D axisymmetric grid system was used to interpret the data from the first set of tests. The model considers the combined impact of saturation, fluid pressure, and porosity change due to swelling/compression on the spatiotemporal distribution of bulk density and movement of the thermocouple modules. Observations from the tests help us understand the early perturbation of bentonite buffer under high temperature, and data from these tests improve the calibration of key constitutive hydrological and mechanical models and, therefore, enhance the modeling capability with respect to calculating the long-term evolution of bentonite buffer.
ASSESSING THE EFFECTIVENESS OF ULTRASONIC IMPACT TREATMENT ON RESIDUAL STRESS PROFILES IN DISSIMILAR WELDED JOINTS
Residual stresses (RS) induced during welding processes are a critical concern in materials engineering, as they can significantly impair the mechanical performance of components by reducing fatigue strength and tensile load capacity. This challenge is especially pronounced in dissimilar metal welds (DMWs), where variations in thermal expansion properties between the joined alloys exacerbate the formation of tensile RS. Conventional post-weld heat treatments, though effective for homogeneous materials, often require substantial energy, specialized equipment, and extensive processing time, making them less practical for DMW applications. Thus, there is a clear need for innovative, energy-efficient techniques to mitigate these detrimental stresses. This study investigates ultrasonic impact treatment (UIT) as a possible alternative for mitigating tensile RS in both similar and dissimilar metal welds. To evaluate UIT’s effectiveness, neutron diffraction (ND) was employed as a nondestructive technique to quantify RS in three orthogonal directions—longitudinal, transverse, and normal. The results showed that UIT significantly reduced peak tensile RS, particularly in the longitudinal direction, by up to 180 MPa in similar welds and up to 150 MPa in dissimilar welds. Given the limited literature on UIT application in DMWs, this work contributes valuable data on stress redistribution mechanisms and highlights UIT’s potential as a practical stress-relief method. The findings lay the groundwork for further investigations aimed at optimizing process parameters and understanding long-term performance in welded joints.
CyTRICS Impact-Based Prioritization Process
Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS prioritization process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS prioritization process was premised largely upon the impact which could result to an energy sector industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. CyTRICS has termed this process the “CyTRICS Impact-based Prioritization Process.” This paper describes the factors identified for use in the Impact-based Prioritization process and identifies the rationale for inclusion. During development, three National Laboratories piloted this prioritization process and generated prioritization scores for seven systems. Following the piloting of the process, laboratory subject matter experts (SME) validated that the numerical scores generated by the prioritization process were consistent with their knowledge of the impact that may occur should any of these systems be disrupted. The following document explains how to perform the prioritization process to generate prioritization scores for energy sector systems. After outlining assumptions required to conduct the process, it describes how to identify and elicit data which can be leveraged to evaluate a system and assign numerical values for each factor. The prioritization process uses different weights on different factors; rationale for each weight is included within the paper. Additionally, the paper includes some recommendations for future enhancements to prioritization, including lessons learned from developing and piloting the process. Finally, a comprehensive appendix includes example documents to be leveraged by those looking to execute the prioritization process.
A Markerless Method for Genome Engineering in Zymomonas mobilis ZM4
Metabolic engineering of the biofuel-producing Zymomonas mobilis is necessary if we are to unlock the metabolic potential present in this non-model microbe. Manipulation of such organisms can be challenging because of the limited genetic tools for iterative genome modification. Here, we have developed an efficient method for generating markerless genomic deletions or additions in Z. mobilis . This is a two-step process that involves homologous recombination of an engineered suicide plasmid bearing Z. mobilis targeting sequences and a subsequent recombination event that leads to loss of the suicide plasmid and a genome modification. A key feature of this strategy is that GFP expressed from the suicide plasmid allows easy identification of cells that have lost the plasmid by using a fluorescence activated cell sorter. Using this method, we demonstrated deletion of the gene encoding lactate dehydrogenase ( ldh ) and the operon for cellulose synthase ( bcsABC ). In addition, by modifying the plasmid design, we demonstrated targeted insertion of the crtIBE operon encoding a neurosporene biosynthetic pathway into the Z. mobilis genome without addition of any antibiotic resistance genes. We propose this approach will provide an efficient and flexible platform for improved genetic engineering of Z. mobilis .
Lightweight Materials - Carbon Fiber and Polymer Composites, Integrated Computational Materials Engineering (ICME) Predictive Tools Development for Low-Cost Carbon Fiber for Lightweight Vehicles (University of Virginia) (Final Report)
This project aimed to develop low-cost alternative precursors and processing techniques through the implementation of an integrated computational materials engineering (ICME) framework to evaluate precursor conversion kinetics. Once established, this framework was then used to down-select precursors for laboratory- and pilot-scale production to validate resulting carbon fiber (CF) material properties and process cost-savings. The two most promising approaches to reduce the cost of CFs were to (1) use new low-cost precursors and (2) reduce the energy requirements of CF processing. These approaches were investigated over the course of three phases of work, spanning 3.5 years. This report describes the overall project objectives, approach, and the key accomplishments in each phase.