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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Porous Liquids: Versatile Fusion of Porosity and Fluidity

Porous liquids and fluids with permanent porosity have been explored over the past decade as transformative functional materials for several sustainable chemical processes. This perspective tracks the evolution of porous liquids and summarizes recent advances in the development of porous liquids for several applications, such as sorptive separations, catalysis, etc. Finally, the latter section discusses the prospects of porous liquids in several novel applications (bio applications, gas storage, heat insulation) and machine learning guided pathways for task-specific design and synthesis of porous liquids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Empowering Lineworkers: The Case for Active Exoskeletons in Utility Work

Exoskeletons have evolved from early medical prototypes to advanced systems capable of addressing physical demands in various industries. This report explores the potential of active exoskeleton technology within the utility sector, focusing on its application for linemen who face significant risks of work-related musculoskeletal disorders (WMSDs). By analyzing existing literature on exoskeletons across industries such as construction, manufacturing, and military, the study identifies a gap in utility-specific applications. Task-specific design features like gravity compensation, limb support, and advanced safety measures, improve exoskeletons’ potential to alleviate physical strain, reduce workplace injuries, and enhance productivity. This review emphasizes the need for targeted research and development to optimize exoskeleton designs for the utility sector to provide benefits for workers, companies, and the broader community.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Multi-agent AI collaboration for digital twin development and assessment

Developing a digital twin (DT) model involves different steps that encompass formulating requirements, model development, implementation, and assessment with respect to real applications. Human expertise is required to coordinate and implement different steps in the DT development and assessment process. However, certain parts of this process can be automated using artificial intelligence (AI) agents for efficient workflow development. In this work, we test and analyze a multiagent AI collaboration with humans in the loop to automate different elements of the DT development and assessment process. To implement the workflow for multiagent AI DT development and assessment, we use Autogen, a multiagent framework developed by Microsoft. Autogen offers a modular and flexible framework for configuring and designing task-specific multiagent workflows. In this framework, large language models (LLMs) form the core intelligence of the AI agents where the quality and performance of the automated element is governed by the inherent capabilities and knowledge base of the LLM. We use retrieval augmented generation to supplement the LLM with relevant domain-specific information for DT requirement formulation. We illustrate this multiagent workflow using a case study on a thermal energy storage system, focusing on how AI agents can collaborate with humans to expedite and optimize different elements of DT development and assessment process.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Task-specific sensor optical designs

A method and system architecture for designing a compressive sensing matrix for machine learning includes receiving an image associated with a classification task and; generating a sensing matrix. The sensing matrix includes an array of nonzero elements of the image. A prism array of prism elements is in communication with the sensing matrix. A row of values corresponding with an input angle of the prism array is mapped to a respective column corresponding with a detector. Then the detector detects light refracted at an output angle dictated by the physical shape of the prism element. A physical model of the detector is fabricated and generates a compressed representation of the image. A machine learning classification algorithm is applied to the compressed representation of the image and generates an optimized non-invertible final determination of the image.

Birch, Gabriel Carlisle↗
DOE OSTI2022

Porous Materials for Water Purification

Water pollution is a growing threat to humanity due to the pervasiveness of contaminants in water bodies. Significant efforts have been made to separate these hazardous components to purify polluted water through various methods. However, conventional remediation methods suffer from limitations such as low uptake capacity or selectivity, and current water quality standards cannot be met. Recently, advanced porous materials (APMs) have shown promise in improved segregation of contaminants compared to traditional porous materials in uptake capacity and selectivity. These materials feature merits of high surface area and versatile functionality, rendering them ideal platforms for the design of novel adsorbents. Here, this Review summarizes the development and employment of APMs in a variety of water treatments accompanied by assessments of task-specific adsorption performance. Finally, we discuss our perspectives on future opportunities for APMs in water purification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Integrated Direct Air Capture and H₂-Free CO₂ Valorization

This project advances fundamental understanding of a novel integrated direct air capture (DAC) and CO₂ conversion process that valorizes atmospheric CO₂ without external H₂. The research encompasses four critical components: (1) design of task-specific ionic liquids for efficient CO₂ capture under ambient conditions, (2) development of H₂-free tandem catalytic systems using ethane as a reductant, (3) advanced operando characterization to elucidate capture and conversion mechanisms, and (4) data science-driven predictive computation to accelerate material discovery. Over the project period, we developed five high-performance DAC sorbent systems—including CaO/superbase ionic liquid composites, Ni-MOF/Ionic Liquid (IL) hybrids, fluorinated covalent organic frameworks with ion-pair functional groups, defect-engineered UiO-66, and a validated kinetic model for humid-condition operation, achieving CO₂ capacities up to 1.86 mmol/g at 400 ppm with excellent cycling stability. For H₂-free conversion, we constructed atomically synergistic Zn–O–Cr binuclear catalytic sites that achieve 100% ethylene selectivity, ~9.6% ethane conversion, and 99% CO₂ utilization in equimolar co-conversion of ethane and CO₂. We further demonstrated downstream valorization pathways converting CO and C₂H₄ into polyketones and C₃ chemicals. These advances strengthen the scientific foundation for producing value-added materials from ambient CO₂.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2024

Transformer Neural Networks with Spatiotemporal Attention for Predictive Control and Optimization of Industrial Processes

In the context of real-time optimization and model predictive control of industrial systems, machine learning, and neural networks represent cutting-edge tools that hold promise for enhancing dynamic modeling. This work presents a novel transformer neural network architecture for real-time optimization and model predictive control. This network design includes a modified attention mechanism inspired by positional embedding attention from vision transformers and task-specific modifications to the input-output structure of the transformer’s decoder stack. Experiments were conducted using data from a 450 MW coal-fired power plant to evaluate this approach's effectiveness. The transformer neural network was compared with conventional recurrent models, including GRU and LSTM. The transformer exhibited a 6% increase in the R-squared (R2) value of predictions and an 83% reduction in mean squared error (MSE). Computation time was also reduced by 84% compared to conventional recurrent models.

Gallup, Ethan R.↗
DOE OSTI2022

To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects

Abstract Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view. After their fabrication, the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes, while instantaneously erasing objects of the other data classes at the output field-of-view. Using the same framework, we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras, where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption, while the other classes of objects are irreversibly erased from the output image. The success of class-specific diffractive cameras was experimentally demonstrated using terahertz (THz) waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset, all-optically erasing the other handwritten digits. This diffractive camera design can be scaled to different parts of the electromagnetic spectrum, including, e.g., the visible and infrared wavelengths, to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗
DOE OSTI2026

Reining in an Agentic Harness for High Energy Physics

Agentic systems now address tasks across theoretical, phenomenological, and experimental high energy physics (HEP), but their scientific capabilities remain difficult to reuse across different large language models, providers, and harnesses. We argue that stable parts of these workflows should be promoted into versioned scientific operations and exposed through common protocols. Existing general-purpose harnesses can then be specialized for HEP through task-specific sets of tools and skills, while community-maintained registries would make these capabilities discoverable and citable. We identify mismatches in conventions, assumptions, and domains of validity among independently developed operations as a potential obstacle to their composition, and discuss machine-readable scientific contracts as one possible solution. These design principles and evaluation guidelines provide a near-term path toward a portable and community-maintained agentic harness for HEP.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
DOE OSTI2025

Field-Driven Out-of-Equilibrium Collective Patterns for Swarm Micro-Robotics

Soft robotics has been rapidly advancing, offering significant improvements over traditional rigid robotic systems through the use of compliant materials that enhance adaptability and interaction with the environment. However, current approaches face critical challenges, including the reliance on complex “top-down” fabrication techniques and the difficulty of wireless powering and control at the microscale. Swarm robotics introduces a paradigm shift, leveraging collective dynamics to achieve cooperative and adaptable behaviors among multiple robotic units. Inspired by nature, this “bottom-up” approach enables swarm robots to execute task-specific reconfigurations, enhancing flexibility and robustness. Field-driven active colloids emerge as a promising platform for swarm microrobotics, capable of self-propulsion and self-organization into dynamic collective patterns under external field excitation and manipulation. These systems mimic biologically inspired swarm behaviors, such as flocking and vortex formation, providing a versatile foundation for designing innovative swarm microrobots. Here, this review discusses the principles of electric and magnetic field-driven collective self-organization, focusing on the particle dynamics, the emergence of collective swarm patterns, and illustrative examples of functional swarm microrobots. It concludes with future perspectives on harnessing these systems for adaptive, scalable, and multifunctional microrobotic applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Molecular dynamics simulations of uranyl and plutonyl cations in a task-specific ionic liquid

Ionic liquids (ILs) are a unique class of solvents with potential applications in advanced separation technologies relevant to the nuclear industry. ILs are salts with low melting points and a wide range of tunable physical properties, such as viscosity, hydrophobiciy, conductivity, and liquidus range. ILs have negligible vapor pressure, are often non-flammable, and can have high thermal stability and a wide electrochemical window, making them attractive for use in separations processes relevant to the nuclear industry. Metal salts generally have a low solubility in ILs; however, by incorporating new functional groups into the IL cation or anion that promote complexation with the metal, the solubility can be greatly increased. One such task-specific ionic liquid (TSIL) is 1-carboxy-N, N, N-trimethylglycine bis(trifluoromethylsulfonyl)imide ([Hbet][Tf 2 N]). Water, which is detrimental for electrochemical separations, is a common impurity in ILs and can coordinate with actinyl cations, particularly in ILs containing only weakly coordinating components. Understanding the behavior of actinides in TSIL/water mixtures on a molecular level is vital for designing improved separations processes. Classical molecular dynamics simulations of uranyl(VI) and plutonyl(VI) in 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([EMIM][Tf 2 N]) with deprotonated Hbet (betaine) and water have been performed to understand the coordination and dynamics of the actinyl cations. We find that betaine is a much stronger ligand than water and prefers to coordinate the metal in a bidentate manner. Potential of mean force simulations yield a relative free energy for betaine coordination of approximately -120 to -90 kJ/mol in mixtures with water. As the amount of betaine coordinated to the actinide increases, the diffusion coefficient of the actinyl cation decreases. Moreover, the betaine ligand is able to bridge between two metal centers, resulting in dimeric complexes with actinide–actinide distances of ~5 Å. Potential of mean force simulations show that these structures are stable, with relative free energies of up to -40 kJ/mol. The crystal structure for [(UO 2 ) 2 (bet) 6 (H 2 O) 2 ][Tf 2 N] 4 shows that the betaine bridges between two uranium atoms to form dimeric complexes similar to those found in our simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2024

Intermolecular Proton Transfer Enabled Reactive CO 2 Capture by the Malononitrile Anion

Task-specific ionic liquids (ILs) employing carbanions represent a new class of ILs for carbon capture. The deprotonated malononitrile carbanion, [CH(CN) 2 ] - , has shown close to equimolar capacity for reactive CO 2 capture. Although the formation of the [C(CN) 2 COOH] - carboxylic acid was found to be the final product, how the hydrogen atom on the [CH(CN) 2 ] - carbanion transfers to the carboxylate group as a proton has not been fully understood. In this work, we employ density functional theory calculations with an implicit solvation model to investigate the proton transfer mechanisms in forming carboxylic acid from the reaction of the [CH(CN) 2 ] - carbanion with CO 2 . We find that the intramolecular proton-transfer pathway in [CH(CN) 2 COO] - to form [C(CN) 2 COOH] - is unlikely due to the high energy barrier of 152 kJ/mol. Instead, the intermolecular proton transfer pathway between two [CH(CN) 2 COO] - anions is more feasible to form two molecules of [C(CN) 2 COOH] - , with a significantly lower activation energy of 50 kJ/mol. Moreover, the [C(CN) 2 COOH] - dimer is further stabilized by the intermolecular hydrogen bonds of the two –COOH groups in the Z-configuration of the π-conjugated planar geometry. This insight of reactive CO 2 capture enabled by intermolecular proton transfer will be useful in designing novel carbanions and ILs for carbon capture and conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

CO 2 Chemisorption Behavior in Conjugated Carbanion-Derived Ionic Liquids via Carboxylic Acid Formation

Superbase-derived task-specific ionic liquids (STSILs) represent one of the most attractive and extensively studied systems in carbon capture via chemisorption, in which the obtained CO 2 uptake capacity has a strong relationship with the basicity of the anions. High energy input in desorption and side reactions caused by the strong basicity of the anions are still unsolved issues. The development of other customized STSILs leveraging an alternative driving force to achieve efficient CO 2 chemisorption/desorption is highly desirable yet challenging. Here, in this work, carbanion-derived STSILs were developed for efficient CO 2 chemisorption via a carboxylic acid formation pathway. The STSIL with the deprotonated malononitrile molecule ([MN]) as the anion exhibited much higher CO 2 uptake capacity than the one derived from 2-methylmalononitrile ([MMN]). Notably, this trend was opposite to their basicity ([MN] < [MMN]). Detailed characterization of the products, supported by density functional theory simulations of spectra and calculations of the reaction energetics, demonstrated that carboxylic acid was formed upon reacting with CO 2 via proton transfer in [MN]-derived STSILs but not in the case of [MMN] due to lack of an α-H. The preference of the carboxylic acid product over carboxylate formation was driven by the extended conjugation among the central sp 2 carbon, the as-formed carboxylic acid, and the two nitrile groups. The achievements made in this work provide an alternative design principle of STSILs by leveraging the extended conjugation in the CO 2 -integrated product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗
DOE OSTI2025

Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders

In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.

97 MATHEMATICS AND COMPUTING↗