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NASA NTRS2012

Polyolefin-Based Aerogels

An organic polybutadiene (PB) rubberbased aerogel insulation material was developed that will provide superior thermal insulation and inherent radiation protection, exhibiting the flexibility, resiliency, toughness, and durability typical of the parent polymer, yet with the low density and superior insulation properties associated with the aerogels. The rubbery behaviors of the PB rubber-based aerogels are able to overcome the weak and brittle nature of conventional inorganic and organic aerogel insulation materials. Additionally, with higher content of hydrogen in their structure, the PB rubber aerogels will also provide inherently better radiation protection than those of inorganic and carbon aerogels. Since PB rubber aerogels also exhibit good hydrophobicity due to their hydrocarbon molecular structure, they will provide better performance reliability and durability as well as simpler, more economic, and environmentally friendly production over the conventional silica or other inorganic-based aerogels, which require chemical treatment to make them hydrophobic. Inorganic aerogels such as silica aerogels demonstrate many unusual and useful properties. There are several strategies to overcoming the drawbacks associated with the weakness and brittleness of silica aerogels. Development of the flexible fiber-reinforced silica aerogel composite blanket has proven one promising approach, providing a conveniently fielded form factor that is relatively robust toward handling in industrial environments compared to silica aerogel monoliths. However, the flexible silica aerogel composites still have a brittle, dusty character that may be undesirable, or even intolerable, in certain applications. Although the cross-linked organic aerogels such as resorcinol-formaldehyde (RF), polyisocyanurate, and cellulose aerogels show very high impact strength, they are also very brittle with little elongation (i.e., less rubbery). Also, silica and carbon aerogels are less efficient radiation shielding materials due to their lower content of hydrogen element. The present invention relates to maleinized polybutadiene (or polybutadiene adducted with maleic anhydride)- based aerogel monoliths and composites, and the methods for preparation. Hereafter, they are collectively referred to as polybutadiene aerogels. Specifically, the polybutadiene aerogels of the present invention are prepared by mixing a maleinized polybutadiene resin, a hardener containing a maleic anhydride reactive group, and a catalyst in a suitable solvent, and maintaining the mixture in a quiescent state for a sufficient period of time to form a polymeric gel. After aging at elevated temperatures for a period of time to provide uniformly stronger wet gels, the micro porous maleinized polybutadiene- based aerogel is then obtained by removing interstitial solvent by supercritical drying. The mesoporous maleinized polybutadiene-based aerogels contain an open-pore structure, which provides inherently hydrophobic, flexible, nearly unbreakable, less dusty aerogels with excellent thermal and physical properties. The materials can be used as thermal and acoustic insulation, radiation shielding, and vibration-damping materials. The organic PB-based rubber aerogels are very flexible, no-dust, and hydrophobic organics that demonstrated the following ranges of typical properties: densities of 0.08 to 0.255 grams per cubic centimeters, shrinkage factor (raerogel/rtarget) = 1.2 to 2.84, and thermal conductivity values of 20.0 to 35.0 mW/m-K.

Lee, Je Kyun↗

NASA NTRSDate not supplied

Shock Capturing via Limiting for High-Order Methods including Discontinuous Galerkin

High-order methods, such as discontinuous Galerkin (DG), spectral, and flux reconstruction (FR), are prone to generating unwanted oscillations near shocks and discontinuities. Conventional limiting techniques, while effective in suppressing oscillations near shocks, often compromise accuracy near extrema, where the solution is only first-order accurate. This paper introduces a novel limiting technique for these high-order schemes, aimed at effectively managing shocks while preserving accuracy. The key idea is to expand the standard monotonicity limits to provide “room” near smooth extrema, ensuring that limiting has no effect and thus preserving accuracy. Near a discontinuity, these expanded limits effectively reduce to the original monotonicity limits, suppressing oscillations. Additional motivation is drawn from a formula for the derivative of Radau polynomials, which depicts the behavior of oscillations resulting from discontinuities. This behavior leads to a simplification by applying the limits to the sum of magnitudes of all modes, linear and higher degree. Unlike typical approaches, which rely on successful detection to activate limiting, our limiter depends continuously on the data, there by avoiding potential issues if detection fails. To reduce computing time, efficient criteria for detecting smooth regions where limiting is unnecessary are presented. Combined with detection, the continuous dependence on the data is lost, but the method is more economical. A notable characteristic of the entire process is its simplicity in both concept and implementation. Numerical tests for advection and Euler equations are conducted to demonstrate the effectiveness of the proposed method.

numerical methods↗

NASA NTRSDate not supplied

Shock Capturing via Limiting for High-Order Methods including Discontinuous Galerkin

numerical methods↗

DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

EMSL Community Science Campaign Meeting: Critical Minerals and Materials - Rhizo Critical Campaign Breakout Session Report Summary

The “Critical Minerals Biogeochemistry in the Rhizosphere – Ultramafic Soils (Rhizo Critical)” campaign breakout (BO) session was organized to identify major knowledge gaps and fundamental research needs in rhizosphere microbiology and geochemistry that, if addressed, could transform our ability to recover critical minerals from ultramafic soil systems. We sought to identify significant challenges that must be surmounted in the pursuit of deeper science knowledge. Our ultimate goal is to understand this landscape well enough to identify and prioritize opportunities for EMSL to make the greatest impact with Environmental Transformations and Interactions (ETI) science area research campaigns focused on the biogeochemical processes controlling the behavior of critical minerals and materials in the rhizosphere. The increasing demand for critical materials and minerals (CMM) in the U.S. has heightened interest in low-grade ores with much attention on ultramafic soils, which contain valuable metals such as nickel (Ni), chromium (Cr), manganese, cobalt (Co), and copper (Lee et al., 2025; DOE CMM Report, 2023) used in advanced battery, magnet, wiring and wind turbines, and stainless steel technologies. Metal hyperaccumulating plants grown in ultramafic soils can extract economically valuable concentrations of CMMs through the process of phytomining. This technology has evolved from phytoremediation, which involves using plants to cleanse contaminated environments by removing, detoxifying, or stabilizing pollutants like metals and organic compounds. Hyperaccumulator plants are capable of storing metals in their living tissues at concentrations hundreds to thousands of times higher than those found in 'normal' plants. For instance, while the average concentration of Ni in the dry matter of plants growing in typical soils is usually less than 5 µg g?¹, Ni hyperaccumulation is defined by concentrations exceeding 1,000 µg g?¹ (Corzo Remigio et al., 2020; Reeves et al., 2018). Phytomining research has primarily focused on Ni (Rylott and van der Ent, 2025), for which the U.S. has very limited conventional mines in operation. Most soils typically contain Ni concentrations ranging from 7 to 50 mg kg-1, whereas serpentine soils exhibit significantly higher levels, with Ni content often ranging between 700 and 8,000 mg kg-1 (Sobczyk et al., 2017). While more than 500 plant species in over 50 different families have been identified as Ni hyperaccumulators (Kidd et al., 2018), Ni phytomining (and phytominng in general) remains largely untested because most studies are short-term, small-scale, and conducted under simplified or artificially enriched conditions, so they fail to capture the low metal concentrations, environmental variability, and management constraints that would be needed for a field-scale demonstration. Few hyperaccumulator species have been validated as true “metal crops,” and their biomass production, stress tolerance, and rooting characteristics are usually too poor to yield economically meaningful metal outputs. Critically, the basic mechanisms of metal uptake, transport, and sequestration, especially as shaped by belowground processes such as root exudation, rhizosphere chemistry, and root–microbe interactions that control metal mobility and bioavailability (Montreemuk et al., 2023; Kidd et al., 2018; Durand et al., 2023; Alford et al., 2010), are still only partially understood, and downstream metal recovery from biomass is rarely optimized. Because these limitations stem from gaps in fundamental knowledge rather than from a failure of the concept itself (Rylott and van der Ent, 2025; van der Ent et al., 2015), there is a strong need for basic science that dissects plant metal homeostasis, rhizosphere and microbial processes, and their integration with soil chemistry and process engineering to design more robust, scalable phytomining systems.

Ahkami, Amirhossein↗

DOE OSTI2022

New Insights into Physical Aging‐Induced Structure Evolution in Carbon Molecular Sieve Membranes

Abstract Carbon molecular sieve (CMS) membranes offer the best available combination of scalable economical processability with excellent separation performance. Physical aging of CMS membranes causes pore structure changes over time that affect CMS membrane performance. To provide fundamental insights into the structure evolution in CMS membranes during physical aging, a combined dual‐mode sorption and transport model is used in this study to characterize the diffusion coefficients of gas molecules in fresh and 7‐day vacuum aged CMS membranes. The results show physical aging of CMS membrane is primarily “diffusion related” and such aging behavior simultaneously causes ultramicropore changes in the continuous phase and Langmuir phase of CMS membrane. The new insights offered in this study suggest strategies to control the physical aging of CMS membranes and even use it as a valuable tool to tune the separation performance of CMS membranes for demanding gas separations.

Liu, Zhongyun↗

DOE OSTI2022

New Insights into Physical Aging-Induced Structure Evolution in Carbon Molecular Sieve Membranes

Carbon molecular sieve (CMS) membranes offer the best available combination of scalable economical processability with excellent separation performance. Physical aging of CMS membranes causes pore structure changes over time that affect CMS membrane performance. To provide fundamental insights into the structure evolution in CMS membranes during physical aging, a combined dual-mode sorption and transport model is used in this study to characterize the diffusion coefficients of gas molecules in fresh and 7-day vacuum aged CMS membranes. Further, the results show physical aging of CMS membrane is primarily “diffusion related” and such aging behavior simultaneously causes ultramicropore changes in the continuous phase and Langmuir phase of CMS membrane. The new insights offered in this study suggest strategies to control the physical aging of CMS membranes and even use it as a valuable tool to tune the separation performance of CMS membranes for demanding gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A Taxonomic Classification Approach for Global Spatio-temporal Data

The World Bank, World Health Organization, and other major vendors collectively provide thousands of global time series datasets that focus on issues of the environment, public health, economics, violence, education, and national security. Sorting these data into meaningful information requires the use of data mining techniques to cluster trends into an orderly and manageable number of cases. The World SpatioTemporal Analytics and Mapping (WSTAMP) project database (wstamp.ornl.gov) was developed to spatiotemporally harmonize global vendor data (23,300+ attributes, 200+ locations, 50+ years). Within the WSTAMP analytical environment, Dynamic Time Warping (DTW) has been a highly effective data-driven approach for clustering and mapping these time series into national spatiotemporal behavior maps. Two significant properties have surfaced from this work. First, several recognizable cluster patterns have emerged and persist across a range of locations, attributes, and time frames (e.g., increasing, decreasing, rebounding, peak, oscillating). Secondly, practitioners engaging WSTAMP have noted the explanatory and anticipatory value of these patterns and articulated particular interest in detecting them within the spatiotemporal cube. This need was addressed by shifting DTW-based clustering from an open ended, data-driven implementation to a taxonomic pattern matching approach. This paper presents the method including implementation strategies for visualization and human computer interaction and applies the approach to a sample data set and concludes with next steps.

Stewart, Robert↗

DOE OSTI2021

Additive Manufacturing of Lightweight Structures: Microfibrillated Cellulose – PLA Biofoams

Extrusion-based polymer additive manufacturing (AM) technology is growing rapidly. The introduction of fiber reinforced feedstock materials and recent developments in the manufacturing systems have promulgated large scale AM of composites to create new industries and applications. Synthetic fibers such as carbon and glass fibers are commonly used to reinforce polymer composites. However, increasing environmental and long-term sustainability concerns are leading to new materials using cellulose fiber reinforcement in bio-derived polymers. These materials offer new property sets, new supply chains and have the potential to provide economical solutions leading to new applications. Large scale AM can be attractive for many different applications because of its ability to freeform manufacture complex geometries; each application may require different material properties. One of the novel application areas for large scale AM is 3D printing of lightweight materials via foaming. Although achieving low density is the key in light weighting via foaming, mechanical performance is also important for many applications. In this study the impact of micro-cellulose fibers (MFC) on foaming behavior and the mechanical properties of additively manufactured parts is investigated. MFC-polylactic acid (PLA) feedstock pellets were prepared at varying MFC content (5, 10, 15 and 20 wt.%) to understand the impact of cellulose fiber content on density and mechanical properties of the AM biocomposites. Also, the impact of extrusion speed and foaming agent content on the AM biocomposites is investigated. Although achieving uniform printed foam structure is challenging with the presence of cellulose fibers, promising results were accomplished with density values below 0.5g/cm3.

Tekinalp, Halil↗

DOE OSTI2024

Understanding poromechanical response of a biogenic coalbed methane reservoir

Abstract Biogenic coalbed methane (BCBM) reservoirs aim to produce methane from in situ coal deposits following microbial conversion of coal. Success of BCBM reservoirs requires economic methane production within an acceptable timeframe. The work reported here quantifies the findings of previously published qualitative work, where it was found that bioconversion induces strains in the pore, matrix and bulk scales. Using imaging and dynamic strain monitoring techniques, the bioconversion induced strain is quantified here. To understand the effect of these strains from a reservoir geomechanics perspective, a corresponding poromechanical model is developed. Furthermore, findings of imaging experiments are validated using core-flooding flow experiments. Finally, expected field-scale behavior of the permeability response of a BCBM operation is modeled and analyzed. The results of the study indicated that, for Illinois coals, bioconversion induced strains result in a decrease in fracture porosity, resulting in a detrimental permeability drop in excess of 60% during bioconversion, which festers itself exponentially throughout its producing life. Results indicate that reservoirs with high initial permeability that will support higher Darcian flowrates, would be better suited for coal bioconversion, thereby providing a site-selection criteria for BCBM operations.

Pandey, Rohit (ORCID:0009000415483436)↗

DOE OSTI2022

Nanoconfinement matters in humidified CO 2 interaction with metal silicates

With enigmatic observations of enhanced reactivity of wet CO 2 -rich fluids with metal silicates, the mechanistic understanding of molecular processes governing carbonation proves critical in designing secure geological carbon sequestration and economical carbonated concrete technologies. Here, we use the first principle and classical molecular simulations to probe the impact of nanoconfinement on physicochemical processes at the rock–water–CO 2 interface. We choose nanoporous calcium–silicate–hydrate (C–S–H) and forsterite (Mg 2 SiO 4 ) as model metal silicate surfaces that are of significance in cement chemistry and geochemistry communities, respectively. We show that while a nanometer-thick interfacial water film persists at unsaturated conditions consistent with in situ infrared spectroscopy, the phase behavior of the water–CO 2 mixture changes from its bulk counterpart depending on the surface chemistry and nanoconfinement. We also observe enhanced solubility at the interface of water and CO 2 phases, which could amplify the CO 2 speciation rate. Additionally, through free energy calculations, we show that CO 2 could be found in a metastable state near the C–S–H surface, which can potentially react with surface water and hydroxyl groups to form carbonic acid and bicarbonate. These findings support the explicit consideration of nanoconfinement effects in reactive and non-reactive pore-scale processes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

Structural Dynamic Behavior of Wind Turbines

The structural dynamicist s areas of responsibility require interaction with most other members of the wind turbine project team. These responsibilities are to predict structural loads and deflections that will occur over the lifetime of the machine, ensure favorable dynamic responses through appropriate design and operational procedures, evaluate potential design improvements for their impact on dynamic loads and stability, and correlate load and control test data with design predictions. Load prediction has been a major concern in wind turbine designs to date, and it is perhaps the single most important task faced by the structural dynamics engineer. However, even if we were able to predict all loads perfectly, this in itself would not lead to an economic system. Reduction of dynamic loads, not merely a "design to loads" policy, is required to achieve a cost-effective design. The two processes of load prediction and structural design are highly interactive: loads and deflections must be known before designers and stress analysts can perform structural sizing, which in turn influences the loads through changes in stiffness and mass. Structural design identifies "hot spots" (local areas of high stress) that would benefit most from dynamic load alleviation. Convergence of this cycle leads to a turbine structure that is neither under-designed (which may result in structural failure), nor over-designed (which will lead to excessive weight and cost).

Thresher, Robert W.↗

DOE OSTI2020

Characterization of Connected Lighting System Potential for Grid Services Under Real-Time Pricing

Increasingly over the last decade, the management of end-use electricity demand has been examined as a potential source of services for the electric power grid beyond contracts with large commercial and industrial customers. Lighting represents about 17\% of U.S. commercial building annual electricity consumption; however, its potential rapid speed of response may position it to uniquely contribute to services that improve the reliability and resilience of the grid. Connected lighting systems (CLS), which build upon solid-state lamp technology, can change state by adjusting power demand more quickly than most other building electricity end uses and could be used as a platform to monitor lighting electricity use and space conditions. But the potential of CLS to provide grid services has not been extensively studied. In this paper, we describe initial research to evaluate the potential of CLS for grid services. We develop a model for CLS using a set of parameters to represent operation behaviors and constraints: maximal power, minimal power, nominal power, ramp rate, and time delay. Parameter values are generated for representative building types. CLS demand curves are constructed, indirectly capturing building occupant preferences for lighting as functions of electricity price. The CLS model and the demand curves are incorporated into a Transactive Control and Coordination (TCC) Platform to simulate CLS providing grid services. Previous research has shown TCC to be a powerful tool to enable end uses to provide grid services through a hybrid economic-control approach. Initial quantitative results will be provided for CLS grid services potential.

Wang, Peng↗

DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗

DOE OSTI2021

Investigating feasible light configurations for fish restoration: an ethological insight

Light environment significantly impacts the effectiveness of fish population restoration in developed watersheds, subserving fish passage reconstruction in hydraulic complex and habitat rehabilitation. To develop an interior connection between light-related fish response from the laboratory and field fish conservation practice, we employed a new indexing system, consisting of phototaxis rate, relative swimming speed (RSS), and optic evasion coefficient (OEC), to quantify the light-induced behaviors in Ptychobarbus kaznakovi, a representative rare cyprinid in Tibet, China, at four experimental illuminance levels (15 lx, 30 lx, 60 lx, 120 lx) and four wavelengths (red, yellow, green, blue). The fish showed negative phototaxis at all given illuminances and wavelengths. Under red light, the OEC increased significantly from 0.642 at 15 lx to 0.782 at 30 lx (P = 0.029), while the RSS decreased significantly from 3.752 to 2.383 (P < 0.001). Behaviorally, the fish shifted from sprint to wandering around, indicating the alteration of dominant physiological activity from behavioral stress response to negative phototaxis. Under the green treatment, P. kaznakovi swam around quickly and presented slight negative phototaxis, and OEC values were uniform among all illuminances, probably because of the similarity of green light to the ambient color of the habitat. Accordingly, ecologists and fisheries practitioners can utilize red light to exclude fish from dangerous waters and green light to guide them to ideal habitats, laying the necessary groundwork for the light-driven fish recovery effort. Furthermore, the appropriate light configuration can yield economic benefits through the tradeoff among fish protection, power generation, and investment costs.

Lin, Chenyu↗

DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗

DOE OSTI2020

Controlled evolution of rare earth phosphate in coal ash slag

Coal ash slag as a waste by-product, is generated from commercial processes that include gasification facilities, power plants, and steelmaking industries. Some is recycled into construction materials, insulation fibers, and used as a soil neutralizer, but the majority is still discarded. Coal ash slag exhibit trace amounts of critical materials including rare earth elements (REEs). An economically viable extraction of REEs from the coal ash would lower the production costs, addressing the availability and environmental concerns. In nature, most REEs exist as phosphate such as monazite but its synthetic evolution in coal ash slag has never been reported. In industrial processes, P may originate from coal, additives, and/or refractory liners. REEs may be extracted by first concentrating REEs into phosphates, sulphide, or fluorides [1], or carbonates [2]. In this work, P-enriched coal ash slag bearing REEs was first melted above 1600 °C, then crystallization behaviors in the melt during controlled cooling was studied. REE phosphate coprecipitated with mullite and hematite with mullite being the first crystal to form on cooling. The REEs were rejected from the molten slag, mullite, and hematite, and tended to segregate only to the phosphate phase with REE concentration as high as 58 wt.% in the structure.

Nakano, Jinichiro↗

DOE OSTI2021

Controlled evolution of rare earth phosphate in coal ash slag

Nakano, Jinichiro↗