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At least 973 records · Page 54

Resilient Observer Design for Cyber-Physical Systems with Data-Driven Measurement Pruning

Resilient observer design for Cyber-Physical Systems (CPS) in the presence of adversarial false data injection attacks (FDIA) is an active area of research. The existing state-of-the-art algorithms tend to break down as more and more knowledge of the system is built into the attack model; also as the percentage of attacked nodes increases. From the view of optimization theory, the problem is often cast as a classical error correction problem for which a theoretical limit of has been established as the maximum percentage attacked nodes for which state recovery is guaranteed. Beyond this limit, the performance of -minimization based schemes, for instance, deteriorates rapidly. Similar performance degradation occurs for other types of resilient observers beyond certain percentages of attacked nodes. In order to increase the corresponding percentage of attacked nodes for which state recoveries can be guaranteed, researchers have begun to incorporate prior information into the underlying resilient observer design framework. For the most pragmatic cases, this prior information is often obtained through a data-driven machine learning process. Existing results have shown a strong positive correlation between the maximum attacked percentages that can be tolerated and the accuracy of the data-driven model. Motivated by these results, this chapter examines the case for pruning algorithms designed to improve the Positive Prediction Value (PPV) of the resulting prior information, given stochastic uncertainty characteristics of the underlying machine learning model. Theoretical quantification of the achievable improvement is given. Simulation results show that the pruning algorithm significantly increases the maximum correctable percentage of attacked nodes, even for machine learning model whose prediction power is comparable to the random flip of a coin.

Resilient Observer, Cyber-physical Systems, Data-D↗

Catalytic DNA Polymerization Can Be Expedited by Active Product Release**

Abstract The sequence‐specific hybridization of DNA facilitates its use as a building block for designer nanoscale structures and reaction networks that perform computations. However, the strong binding energy of Watson–Crick base pairing that underlies this specificity also causes the DNA dehybridization rate to depend sensitively on sequence length and temperature. This strong dependency imposes stringent constraints on the design of multi‐step DNA reactions. Here we show how an ATP‐dependent helicase, Rep‐X, can drive specific dehybridization reactions at rates independent of sequence length, removing the constraints of equilibrium on DNA hybridization and dehybridization. To illustrate how this new capacity can speed up designed DNA reaction networks, we show that Rep‐X extends the range of conditions where the primer exchange reaction, which catalytically adds a domain provided by a hairpin template to a DNA substrate, proceeds rapidly.

59 BASIC BIOLOGICAL SCIENCES↗

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗

Deactivation of Mo/H-ZSM-5 in Microwave-Assisted and Thermal-Driven Methane Dehydroaromatization

A better understanding of catalyst deactivation is needed to improve catalyst design and performance in microwave-enhanced methane dehydroaromatization (MDA). Here, this study investigates the deactivation of a molybdenum supported H-ZSM-5 zeolite (Mo/H-ZSM-5) catalyst in MDA under microwave-heated conditions, comparing its performance to that of the same catalyst under conventional heating. While the microwave-assisted (MW) process achieved higher benzene yields, the catalyst experienced faster deactivation due to the selective and rapid deposition of coke within the pores of the zeolite, as confirmed through Brunauer–Emmett–Teller (BET), X-ray diffraction (XRD), ammonia-temperature programmed desorption (NH 3 -TPD), thermal gravimetric analysis (TGA), temperature programmed oxidation (TPO), and X-ray photoelectron spectroscopy (XPS) analyses. The quantification of total coke content via TGA/TPO and surface carbon (XPS) revealed that nearly twice as much coke was deposited on the catalyst under MW conditions compared to that on the conventionally heated material, and the coke exhibited a more conductive and graphitic nature. The accelerated deactivation rates were attributed to the formation of hot spots in the MW system, leading to enhanced coupling with coke formed in situ during the reaction and resulting in increased Mo reduction. Observations indicated that the CO activation used to carburize the catalyst prior to the reaction is not advantageous in the MW heating environment. The presence of large amounts of Mo oxides at elevated temperatures (through hot spots) exposed to methane leads to instability under the reaction conditions. Optimizing the activation environment and improvement of the Mo dispersion within the pores are potential strategies to improve catalyst stability.

Mo/H-ZSM-5 zeolite↗

Capturing carbon dioxide from air with charged-sorbents

Emissions reduction and greenhouse gas removal from the atmosphere are both necessary to achieve net-zero emissions and limit climate change. There is thus a need for improved sorbents for the capture of carbon dioxide from the atmosphere, a process known as direct air capture. In particular, low-cost materials that can be regenerated at low temperatures would overcome the limitations of current technologies. In this work, we introduce a new class of designer sorbent materials known as ‘charged-sorbents’. These materials are prepared through a battery-like charging process that accumulates ions in the pores of low-cost activated carbons, with the inserted ions then serving as sites for carbon dioxide adsorption. We use our charging process to accumulate reactive hydroxide ions in the pores of a carbon electrode, and find that the resulting sorbent material can rapidly capture carbon dioxide from ambient air by means of (bi)carbonate formation. Unlike traditional bulk carbonates, charged-sorbent regeneration can be achieved at low temperatures (90–100 °C) and the sorbent’s conductive nature permits direct Joule heating regeneration using renewable electricity. Given their highly tailorable pore environments and low cost, we anticipate that charged-sorbents will find numerous potential applications in chemical separations, catalysis and beyond.

54 ENVIRONMENTAL SCIENCES↗

An effective “three-in-one” screening assay for testing drug and nanoparticle toxicity in human endothelial cells

Evaluating nanoparticle (NP) toxicity in human cell systems is a fundamental requirement for future NP biomedical applications. In this study, we have designed a screening assay for assessing different types of cell death induced by NPs in human umbilical vein endothelial cell (HUVEC) culture. This assay consists of WST-8, LDH and Hoechst 33342 staining, all performed in one well, which enables an evaluation of cell viability, necrosis and apoptosis, respectively, in the same cell sample. The 96-well format and automated processing of fluorescent images enhances the assay rapidity and reproducibility. After testing the assay functionality with agents that induced different types of cell death, we investigated the endothelial toxicity of superparamagnetic iron oxide nanoparticles (SPIONs, 8 nm), silica nanoparticles (SiNPs, 7–14 nm) and carboxylated multiwall carbon nanotubes (CNTCOOHs, 60 nm). Our results indicated that all the tested NP types induced decreases in cell viability after 24 hours at a concentration of 100 μg/ml. SPIONs caused the lowest toxicity in HUVECs. By contrast, SiNPs induced pronounced necrosis and apoptosis. A time course experiment showed the gradual toxic effect of all the tested NPs. CNTCOOHs inhibited tetrazolium derivatives at 100 μg/ml, causing false negative results from the WST-8 and LDH assay. In summary, our data demonstrate that the presented “three-in-one” screening assay is capable of evaluating NP toxicity effectively and reliably. Due to its simultaneous utilization of two different methods to assess cell viability, this assay is also capable of revealing, if NPs interfere with tetrazolium salts.

59 BASIC BIOLOGICAL SCIENCES↗

Open Radiation Monitoring: Conceptual System Design

The Open Radiation Monitoring (ORM) Project seeks to develop and demonstrate a modular radiation detection architecture designed specifically for use in arms control treaty verification (ACTV) applications that will facilitate rapid development of trusted systems to meet the needs of potential future treaties. Development of trusted systems to support potential future treaties is a complex and costly endeavor that typically results in a purpose-built system designed to perform one specific task. The majority of prior trusted system development efforts have relied on the use of commercial embedded computers or microprocessors to control the system and process the acquired data. These processors are complex, making authentication and certification of measurement systems and collected data challenging and time consuming. We believe that a modular architecture can be used to reduce more complex systems to a series of single-purpose building blocks that could be used to implement a variety of detection modalities with shared functionalities. With proper design, the functionality of individual modules can be confirmed through simple input/output testing, thereby facilitating equipment inspection and in turn building trust in the equipment by all treaty parties. Furthermore, a modular architecture can be used to control data flow within the measurement system, reducing the risk of "hidden switches" and constraining the amount of sensitive information that could potentially be inadvertently leaked. This report documents a conceptual modular system architecture that is designed to facilitate inspection in an effort to reduce overall authentication and certification burden. As of publication, this architecture remains in a conceptual phase and additional funding is required to prove out the utility of a modular architecture and test the assumptions used to rationalize the design.

61 RADIATION PROTECTION AND DOSIMETRY↗

Development of a Techno-Economic Analysis Framework for a Solar Thermochemical Fuel Production Process

Synthetic liquid fuels can provide a drop-in substitute for fossil-based fuels in sectors such as aviation and maritime, where electrification is not a viable option due to the need for high specific energy density. However, for these alternative fuels to be adopted at a commercial scale, their price must be competitive compared to their fossil-based counterparts. The reverse water-gas shift (RWGS) reaction offers a promising pathway, using hydrogen (sourced from electrolysis) and carbon dioxide as the feed and reacting to produce syngas - a mixture of H2 and CO at a specific ratio. Syngas is a useful precursor that can be converted into fuels and chemicals via known downstream processes, such as liquid transportation fuels via Fischer-Tropsch (FT) synthesis. The RWGS reaction is currently not applied in commercial scale, unlike the rest of the components in the process chain (electrolyzers and syngas-to-fuel synthesis units). The RWGS reaction poses several challenges due to its restrictive thermodynamics. Being an equimolar reaction, high temperatures and a large excess of H2 are needed to achieve reasonable CO2 conversion at equilibrium. This has detrimental effects on practical process implementation and the quality of syngas that can be produced, with direct effect on the energy and capital requirements, as well as the need for expensive downstream separation. In this work, we are proposing to develop a new concentrating solar thermal (CST) compatible RWGS reactor, performing the reaction in a 2-step chemical looping process using metal oxide at a temperature range of 600-800 degrees Celsius. By decoupling the reactor from the solar receiver, the Generation 3 (Gen3) CST technology could be utilized, together with its proposed thermal energy storage (TES) technology, benefitting from a good match to the required temperatures. CST technology is a viable option for supplying the heat that could be rapidly deployed in scale, thus being a good match to the gas-to-liquid (GTL) process which requires a large minimal scale to be commercially viable. The integration of TES with CST also allows operating the plant at large annual capacity factors and avoids multiple shutdown/startup cycles, thus fitting into the steady-state operation mode that most GTL processes require. The main innovation in the proposed design hinges on a countercurrent reaction design using a packed bed reactor. In 2019 Metcalfe et al. showed the benefits of countercurrent species exchange could be realized in a redox chemical-looping processes, by storing the favorable countercurrent chemical potential profiles in a packed bed of non-stoichiometric oxide. Metcalfe et al. applied this breakthrough concept to the WGS reaction, which is conventionally a co-feed catalytic process, showing a dramatic improvement. Bulfin et al. (2023) performed a similar proof-of-concept demonstration for the RWGS reaction using CeO2, achieving cumulative and peak CO2 conversions of 88% and 95%, respectively, compared to a thermodynamic limit of 58% for the co-feed catalytic process at the same conditions. In our new REGENLOOP project, we are developing a reactor prototype from the heat-exchange packed bed reactor-type, a commonly used reactor in the chemical industry. The endothermic heat of reduction will be supplied to the reactor using CST, while the same heat transfer fluid (HTF) mechanism will be used to extract the exothermic heat of oxidation. An array of multiple reactors is used to supply constant high-purity CO stream, that is then mixed with H2 from electrolysis to produce a high-purity syngas at the required composition. By removing the CO-CO2 separation after the RWGS process, significant energy and cost reduction can be achieved. A physics-based TEA framework is currently being developed, covering all the major plant processes, from the solar collection through storage, chemical looping RWGS, GTL, and auxiliary unit operations, up to the liquid hydrocarbon product. This modeling framework will utilize reduced-order models for the chemical looping RWGS and TES, CST modeling using SolarPILOT, and Aspen Plus for the GTL. By using this combined physics-based approach, the effects of design/operating parameters on the performance and cost can be elucidated. In our presentation, the modeling framework will be presented in detail, including preliminary cost predictions of using this plant configuration under a few selected relevant case studies. This study will be used to identify the major cost drivers, informing further system design and optimization needed to chart the way for a commercially viable pathway.

14 SOLAR ENERGY↗

Silicon halide-alkali metal flames as a source of solar grade silicon

The feasibility of using alkali metal-silicon halide diffusion flames to produce solar-grade silicon in large quantities and at low cost is demonstrated. Prior work shows that these flames are stable and that relatively high purity silicon can be produced using Na + SiCl4 flames. Silicon of similar purity is obtained from Na + SiF4 flames although yields are lower and product separation and collection are less thermochemically favored. Continuous separation of silicon from the byproduct alkali salt was demonstrated in a heated graphite reactor. The process was scaled up to reduce heat losses and to produce larger samples of silicon. Reagent delivery systems, scaled by a factor of 25, were built and operated at a production rate of 0.5 kg Si/h. Very rapid reactor heating rates are observed with wall temperatures reaching greater than 2000 K. Heat release parameters were measured using a cooled stainless steel reactor tube. A new reactor was designed.

Olsen, D. B.↗

Inverse sequential procedures for the monitoring of time series

When one or more new values are added to a developing time series, they change its descriptive parameters (mean, variance, trend, coherence). A 'change index (CI)' is developed as a quantitative indicator that the changed parameters remain compatible with the existing 'base' data. CI formulate are derived, in terms of normalized likelihood ratios, for small samples from Poisson, Gaussian, and Chi-Square distributions, and for regression coefficients measuring linear or exponential trends. A substantial parameter change creates a rapid or abrupt CI decrease which persists when the length of the bases is changed. Except for a special Gaussian case, the CI has no simple explicit regions for tests of hypotheses. However, its design ensures that the series sampled need not conform strictly to the distribution form assumed for the parameter estimates. The use of the CI is illustrated with both constructed and observed data samples, processed with a Fortran code 'Sequitor'.

Radok, Uwe↗

TPSAS-NF1676L-12284-DND

A much desired capability for conceptual design of aircraft is the ability to rapidly and accurately evaluate new concepts to avoid adverse trade decisions that can hinder the development in the later stages of design. For the conceptual design of supersonic aircraft, it is important to evaluate the robustness of a new low-boom concept. Here, robustness means that the aircraft configuration has low-boom ground signatures at both under-track and off-track locations. An integration process for off-track boom analysis is developed to facilitate robust design of low-boom supersonic aircraft. The integrated off-track analysis can also be used to study sonic boom impact on flight corridors where flight conditions and ground elevation might have a significant effect on ground signatures. The key for off-track sonic boom analysis is accurate solutions for off-body pressure distribution along the lower region of a cylinder cut around the aircraft. To ensure the numerical accuracy of the off-body pressure distribution, a mesh study is performed to determine the mesh requirements for off-track analysis as well as comparisons between Cart3D and USM3D off-track sonic boom results for various low-boom concepts. The integrated and automated process makes it possible to study the environmental impact of future supersonic concepts in the early design phases and support the planning of future flight corridors to mitigate sonic boom effects.

Irian Ordaz↗

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Additive Manufacturing (AM) and Integrated Computational Materials Engineering (ICME) are complementary enabling technologies for design and manufacturing of “fit-for-purpose” materials. Both technologies will impact rapid material design, reduction in cost- and time-to-market for new applications, and discovery and implementation of new materials. An ICME approach to design, however, requires experimentally validated material models at multiple length and time scales, an integrated framework that can connect analysis tools with one another to ensure the digital thread of an application is maintained, and the manufacturing (e.g., AM) capability to leverage processing-structure-property-performance (PSPP) relationships to achieve spatially varying material properties where desired. AM enables the implementation of the design of an optimized, spatially varying microstructure through careful selection of the processing parameters used during an additively manufactured build. In order to establish these PSPP relations, a large amount of data is necessary, and that data must be properly captured, analyzed and maintained in an information management system that can establish the required traceability between various aspects of the design process to ensure an application’s digital thread is maintained (from design to end of life). Such an information management system must be able to capture feedstock material pedigree, resulting microstructure from various build parameters, subsequent mechanical properties derived from testing, developed material models, and enable spatial variations in material assignment in an engineering application. Furthermore, the information management system should be easily integrated with traditionally engineered materials in a single, centralized platform to enable an ICME optimization tool to explore both types of manufacturing processes. At NASA GRC, a robust, 21st century materials information management system has been previously developed with a focus towards enabling ICME. In this work, GRC’s ICME schema is extended to accommodate additively manufactured materials, enabling storage of both traditionally and additively manufactured materials in the same construct. The methodology for properly capturing additively manufactured materials across the entire material lifecycle is presented, following the previously established database best practices, as a potential framework for establishing PSPP relationships for additively manufactured materials and applying them to engineering applications.

Data management↗

Application of Machine Learning to Improve Biobased Glucaric Acid Production

Kalion, Inc. is an industrial biotechnology manufacturer of high-purity chemicals from biomass including glucaric acid to be used in the industrial, material, and pharmaceutical markets. The project goal is application of machine learning in conjunction with high-throughput cultivations and metabolomics to understand and overcome two different challenges: (i) improve glucaric acid productivity (the rate of production slows considerably after 48 hours, which limits the overall productivity that can be achieved in the process), and (ii) decrease its production costs (components from complex media are necessary to achieve robust production). Through a series of experiments, a set of metabolites contained in rich media that improved performance were identified using metabolomics, high throughput cultivations in bioreactors, and machine learning-friendly experimental designs. It was found that the substrate feeding strategy was the potential major driver to enhance bacterial performance of glucaric acid production. The major industrial impacts by this project are the improvement of the production process technology, as well as advancing infrastructures and workflows (where machine learning approaches in conjunction with high-throughput cultivation and metabolomics are used) to improve the analyses by making them more rapid and comprehensive.

BIOMASS FUELS↗

Used nuclear fuel separations process simulation and testing

Recent efforts in separations process simulation at Argonne have expanded from the traditional focus on solvent extraction flowsheet design in order to capture process dynamics and to simulate other components, processing and systems of a used nuclear fuel reprocessing plant. For example, the Argonne Model for Universal Solvent Extraction (AMUSE) code has been enhanced to make it both more portable and more readily extensible. Moving away from a spreadsheet environment makes the addition of new species and processes simpler for the expert user, which should enable more rapid implementation of chemical models that simulate evolving processes. The dyAMUSE (dynamic AMUSE) version allows the simulation of transient behavior across an extractor. Electrochemical separations have now been modeled using spreadsheet codes that simulate the electrochemical recycle of fast reactor fuel. The user can follow the evolution of the salt, products, and waste compositions in the electro-refiner, cathode processors, and drawdown as a function of fuel batches treated. To further expand capabilities in integrating multiple unit operations, a platform for linking mathematical models representing the different operations that comprise a reprocessing facility was adapted to enable systems-level analysis and optimization of facility functions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

An instrument for in situ time-resolved X-ray imaging and diffraction of laser powder bed fusion additive manufacturing processes

In situ X-ray-based measurements of the laser powder bed fusion (LPBF) additive manufacturing process produce unique data for model validation and improved process understanding. Synchrotron X-ray imaging and diffraction provide high resolution, bulk sensitive information with sufficient sampling rates to probe melt pool dynamics as well as phase and microstructure evolution. Here, we describe a laboratory-scale LPBF test bed designed to accommodate diffraction and imaging experiments at a synchrotron X-ray source during LPBF operation. We also present experimental results using Ti-6Al-4V, a widely used aerospace alloy, as a model system. Both imaging and diffraction experiments were carried out at the Stanford Synchrotron Radiation Lightsource. Melt pool dynamics were imaged at frame rates up to 4 kHz with a ~1.1 μm effective pixel size and revealed the formation of keyhole pores along the melt track due to vapor recoil forces. Diffraction experiments at sampling rates of 1 kHz captured phase evolution and lattice contraction during the rapid cooling present in LPBF within a ~50 × 100 μm area. In conclusion, we also discuss the utility of these measurements for model validation and process improvement.

36 MATERIALS SCIENCE↗

Design and performance analysis of gas sorption compressors

Compressor kinetics based on gas adsorption and desorption processes by charcoal and for gas absorption and desorption processes by LaNi5 were analyzed using a two-phase model and a three-component model, respectively. The assumption of the modeling involved thermal and mechanical equilibria between phases or among the components. The analyses predicted performance well for compressors which have heaters located outside the adsorbent or the absorbent bed. For the rapidly-cycled compressor, where the heater was centrally located, only the transient pressure compared well with the experimental data.

Chan, C. K.↗

Effects of gas–wall partitioning in Teflon tubing and instrumentation on time-resolved measurements of gas-phase organic compounds

Recent studies have demonstrated that organic compounds can partition from the gas phase to the walls in Teflon environmental chambers and that the process can be modeled as absorptive partitioning. Here these studies were extended to investigate gas–wall partitioning of organic compounds in Teflon tubing and inside a proton-transfer-reaction mass spectrometer (PTR-MS) used to monitor compound concentrations. Rapid partitioning of C 8 –C 14 2-ketones and C 11 –C 16 1-alkenes was observed for compounds with saturation concentrations ( c * ) in the range of 3 × 10 4 to 1 × 10 7 µg m -3 , causing delays in instrument response to step-function changes in the concentration of compounds being measured. These delays vary proportionally with tubing length and diameter and inversely with flow rate and c * . The gas–wall partitioning process that occurs in tubing is similar to what occurs in a gas chromatography column, and the measured delay times (analogous to retention times) were accurately described using a linear chromatography model where the walls were treated as an equivalent absorbing mass that is consistent with values determined for Teflon environmental chambers. The effect of PTR-MS surfaces on delay times was also quantified and incorporated into the model. The model predicts delays of an hour or more for semivolatile compounds measured under commonly employed conditions. These results and the model can enable better quantitative design of sampling systems, in particular when fast response is needed, such as for rapid transients, aircraft, or eddy covariance measurements. They may also allow estimation of c * values for unidentified organic compounds detected by mass spectrometry and could be employed to introduce differences in time series of compounds for use with factor analysis methods. Best practices are suggested for sampling organic compounds through Teflon tubing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗