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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

WRPS Hanford Update [Slides]

The purpose of this report is to coordinate with the staff from WRPS, the current contract holder for operations at the Hanford site to better inform design decisions for the Autonomous systems for Hanford waste tank handling project. The project goals are to support risk reduction associated with monitoring, inspection and mapping of Hanford tank underground pits and includes: 1. A pit mock-up demonstration of Idaho National Laboratory’s (INL) Autonomous Pit Exploration System (APES) technology, which has two configurations: A. The Autonomous Robotic Arm, which is a proposed collaboration with Florida International University (FIU) and has capabilities to visually inspect, monitor, map and conduct simple tool manipulation tasks in the tank pits, and B. The robotic crawler configuration, which has capabilities to deploy to the bottom of the pits and conduct closer and bottom-up visual inspections. 2. An Implementation Plan that identifies Hanford site-wide application of the INL remote robotics technologies to enhance the performance of the tank farm systems and the characterization of the waste they contain to mitigate risk and optimize the overall waste mission. 3. A first-of-a-kind environmental digital twin will be produced. Digital twins to date require permanently installed sensors in order to produce asset specific predictions. This project will use the intermittent signals during inspections from deployed sensors on robotic systems to produce the data sets used by AI/ML to enable predictions of issues on tanks.

42 - ENGINEERING↗
DOE OSTI2024

Embedded Fluidic Sensing and Control with Soft Open‐Cell Foams

Abstract The synthesis of soft matter intelligence with circuit‐driven logic has enabled a new class of robots that perform complex tasks or conform to specialized form factors in unique ways that cannot be realized through conventional designs. Translating this hybrid approach to fluidic systems, the present work addresses the need for sheet‐based circuit materials by leveraging the innate porosity of foam—a soft material—to develop pneumatic components that support digital logic, mixed‐signal control, and analog force sensing in wearables and soft robots. Analytical tools and experimental techniques developed in this work serve to elucidate compressible gas flow through porous sheets, and to inform the design of centimeter‐sized foam resistors with fluidic resistances on the order of 10 9 Pa s m −3 . When embedded inside soft robots and wearables, these resistors facilitate diverse functionalities spanning both sensing and control domains, including digital logic using textile logic gates, digital‐to‐analog signal conversion using ladder networks, and analog sensing of forces up to 40 N via compression‐induced changes in resistance. By combining features of both circuit‐based and materials‐based approaches, foam‐enabled fluidic circuits serve as a useful paradigm for future hybrid robotic architectures that fully embody the sensing and computing capabilities of soft fluidic materials.

Rajappan, Anoop↗
DOE OSTI2026

CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees

This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.

42 ENGINEERING↗
DOE OSTI2024

Low‐Volume Cores for Fabrication of Compact, Versatile, and Intelligent Soft Systems

Abstract This study introduces the low‐volume core (LVC) fabrication method, which enables the monolithic molding of compact, complex, versatile, and intelligent soft robotic systems. This method uses thin and flexible thermoplastic sheets to mold internal chambers in soft fluidic actuators, valves, and circuits. The LVC fabrication method creates low‐volume networks in soft actuators (LV‐net actuators) that can be made with compact and complex geometries, enabling both low actuation volume input and multi‐degree‐of‐freedom actuators. LVC fabrication can also be used for compact, completely soft, and monolithic logic components (valves with low‐volume core, also called as LV valves) to provide directional resistance as well as a switching mechanism that enables fluidic logic in soft systems. The compatibility of the fabrication methods for both soft actuators and valves facilitates the creation of compact, integrated, and versatile soft robotic systems with embodied intelligence. This study introduces two examples of such intelligent soft robotic systems that integrate both LV‐net actuators and LV valves to demonstrate capability for complex system fabrication.

Yu, Qifan↗
DOE OSTI2026

Surface Energy Modulated Self‐Assembly of Percolative Gallium Nanodroplet Films for Stretchable Electronics

Advancements in soft robotics and wearable technologies have led to an increased need for flexible and stretchable electronic systems. Liquid metals like Gallium (Ga) and its alloys offer high potential in the field of stretchable electronics and photonics due to their inherent high conductivity and malleability. However, their high surface tension and native oxide formation hinder uniform wetting and continuous film formation to get conducting films. Here, we report a fabrication strategy to realize electrically conductive Ga thin films with effective thicknesses around 100 nm on polymeric and elastomeric substrates. By enhancing the surface energy of the elastomer, we create uniform Ga nanodroplets capable of sustaining stretchability up to 200% strain. Our devices exhibit electronic conduction primarily through tunneling and display strain tunable piezoresistive characteristics, which are explained using Kirchhoff's resistive network and Simmon's analytical tunneling current model. To showcase the device's capability in fields of stretchable electronics, we demonstrate several functional prototypes, including flexible devices integrated with Light Emitting Diodes (LEDs), curvature and pressure sensors, and soft robotic grippers capable of sensing object curvature. The demonstrated devices highlight the potential of this approach for scalable integration of stretchable electronics in soft robotic and wearable platforms.

42 ENGINEERING↗
DOE OSTI2025

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications

The Modular Autonomous Research System (MARS) was created to address a key challenge in scientific discovery: experiments are often slow, require significant manual labor, and generate data that is not easily integrated across different tools. This limits how quickly scientists can explore new materials, processes, and chemical reactions. Our motivation was to design a system that makes research faster, more reliable, and adaptable by combining automation with artificial intelligence. By doing so, we aimed to reduce human error, accelerate discovery, and allow researchers to quickly test many possibilities that would otherwise take months or years. Our approach was to build a flexible platform that connects laboratory robots, measurement instruments, and a central data system, all guided by artificial intelligence. MARS integrates liquid handling robots, robotic arms, and plate readers with an intelligent decision-making system that chooses the most informative experiments to run next. This creates a closed loop where experiments are performed automatically, the data is analyzed in real time, and new conditions are immediately tested. Through this work, we demonstrated that MARS can carry out multiple experiments with little or no human intervention, adapt to different scientific problems, and handle uncertain or noisy measurements in a robust way. The results show that modular and intelligent automation can significantly accelerate the pace of discovery, providing a model for future self-driving laboratories. This approach addresses the growing scientific need for adaptable, data-driven research platforms that can keep up with the complexity and scale of modern science.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Automating Testing of DUNE Electronics via a Finite State Machine

The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six total institutions performing testing. The outcomes of this internship contribute to Fermilab s mission to advance high-energy physics and support the DOE s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.

Kang, Caleb [William Rainey Harper Coll.]↗
DOE OSTI2025

Automating Testing of DUNE Electronics via a Finite State Machine

The Deep Underground Neutrino Experiment (DUNE) is a flagship international collaboration designed to study neutrinos—tiny, nearly massless particles that may hold answers to fundamental questions about the Universe. Fermilab’s Robotic Test Stand (RTS) plays a critical role in ensuring the quality of approximately 50,000 Application-Specific Integrated Circuit (ASIC) chips that will be used in DUNE’s massive liquid argon detectors. These electronics will be inside the cryostat; therefore, they will need to have a high yield of working chips and low noise. To improve the automation and reliability of the RTS, this project focused on designing and implementing a Python-based finite state machine (FSM) to manage chip handling workflows. The FSM was developed as a modular software framework to coordinate robotic arm movements, manage chip tray positions, and monitor system states during testing. Key features include robust error handling routines, a pause/resume system for safe mid-cycle interruptions, and a simulation mode for iterative testing without hardware dependencies. The system was designed to prepare for seamless integration with RTS hardware components such as the robotic arm and vision system. This integration will streamline collaboration and enable efficient deployment of updates across the six institutions performing testing. The outcomes of this internship contribute to Fermilab’s mission to advance high-energy physics and support the DOE’s national goals by directly improving the testing of equipment to be used in DUNE. The project also provided valuable experience in software design and contributing to the success of DUNE.

Kang, Caleb [Fermilab]↗
DOE OSTI2024

Responsive‐Hydrogel Aquabots

Abstract It remains a challenge to produce soft robots that can mimic the responsive adaptability of living organisms. Rather than fabricating soft robots from bulk hydrogels,hydrogels are integrated into the interfacial assembly of aqueous two‐phase systems to generate ultra‐soft and elastic all‐aqueous aquabots that exhibit responsive adaptability, that can shrink on demand and have electrically conductive functions. The adaptive functions of the aquabots provide a new platform to develop minimally invasive surgical devices, targeted drug delivery systems, and flexible electronic sensors and actuators.

42 ENGINEERING↗
DOE OSTI2025

Automated Strain Construction for Biosynthetic Pathway Screening in Yeast

Automation accelerates the Design-Build-Test-Learn (DBTL) cycle for synthetic biology; however, most strain construction pipelines lack robotic integration. Here, in this study, we present the workflow design and source code for a modular, integrated protocol that automates the Build step in Saccharomyces cerevisiae. We programmed the Hamilton Microlab VANTAGE to integrate off-deck hardware via its central robotic arm, enabling automated steps that increased throughput to 2,000 transformations per week. We developed a user interface with the Hamilton VENUS software to support on-demand parameter customization. As a proof of concept, we screened a gene library in an engineered yeast strain producing verazine, a key intermediate in the biosynthesis of steroidal alkaloids. Our pipeline rapidly identified pathway bottlenecks and genes that enhanced verazine production by 2.0- to 5-fold. This technical note provides resources for synthetic biologists designing yeast workflows for biofoundries to screen libraries for pathway discovery/optimization, combinatorial biosynthesis, and protein engineering.

automation↗
DOE OSTI2024

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. Here, this work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A miniaturized feedstocks-to-fuels pipeline for screening the efficiency of deconstruction and microbial conversion of lignocellulosic biomass

Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a “one-pot” process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (~70%) and bisabolene titers (~1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.

09 BIOMASS FUELS↗
DOE OSTI2024

Fast Photoactuation Driven by Supramolecular Polymers Integrated into Covalent Networks

Abstract The design of robotic soft matter capable of emulating the complex movements of living organisms such as mechanical actuation, shape transformation, and autonomous translation remains a grand challenge in soft materials science. Functionalized hydrogels are excellent candidates for such materials since they can operate in water and are highly responsive to their environment, but their response times can be slow. This work investigates fast photoactuation of hybrid bonding hydrogels composed of peptide amphiphile (PA) supramolecular nanofibers bonded covalently to merocyanine‐based (MCH + ) photoresponsive networks. By incorporating ionizable acrylic acid (AA) co‐monomers in these networks, photoactuation at nearly neutral pH is observed, which in turn enables a new mechanism to accelerate the response by triggering the bundling of supramolecular nanofibers by rapid proton exchange reactions. Furthermore, this rapid response and its consequent large shape transformations lead to hydrogels capable of spontaneously tracking external light sources inspired by pedicellariae, defensive organs present in echinoderms like the starfish and the sea urchin. This work suggests that hybrid bonding polymers (HBPs), which leverage the interplay between supramolecular assemblies and covalent networks, offer novel strategies to design rapidly actuating soft robotic materials.

Cezan, S. Doruk↗
DOE OSTI2026

Toward Fully Soft and Multifunctional Shape Sensing via Optical Waveguide Arrays

For soft bodies, surface deformation and pressure provide proprioceptive and exteroceptive information, including body configuration, body compliance, and external forces. We develop a sheet sensor with a fully soft sensing surface that provides surface shape reconstruction using optical waveguide arrays. The waveguides are fabricated to achieve a tunable linear response to bi‐directional bending curvature, and the waveguide arrays are configured to differentiate between ambiguous shapes. We characterize the waveguide performance, relating curvature sensitivity to the core's surface roughness. Synergy of waveguide responses reduces the number of sensing elements required and achieves damage resilience. Using waveguide sensitivity to pressure, we also demonstrate feasibility for exteroception. We demonstrate the multifunctional sensing capability by wrapping the sheet around an upper arm, showcasing joint motion and external force sensing. Integrated into or applied onto surfaces of robotic or living systems, this design can be implemented in applications such as virtual reality, teleoperation, physical therapy, and soft robotics.

Yu, Qifan [Department of Mechanical Engineering Ma↗