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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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176 records · Page 10
All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic–inorganic hybrid nanomaterials, organic–inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Contech to Accelerate Cleantech: Seeding Emerging Innovation Programs for Construction Productivity and Energy Efficiency Integration; Preprint

Investments in U.S.-based start-ups that focus on advanced building construction technologies to increase construction productivity (contech) surged to approximately $3.1 billion in 2018 (as per Crunchbase data). More recently, emerging programs by government funding agencies, philanthropic foundations, and venture capitalists have been instrumental in supporting contech start-ups for innovations that increase productivity of energy efficiency integration and accelerate clean energy technologies (cleantech) for the buildings sector. These programs include R&D support and funding mechanisms for contech and cleantech. Traditionally, contech and cleantech are considered as two different innovation ecosystems. To enhance and scale up energy efficiency in buildings, creative programs that bring together contech and cleantech are critical. This paper provides a landscape assessment of the "contech-for-cleantech" innovation ecosystem in the U.S. and its impact in accelerating technology readiness and the development pipeline of "contech-for-cleantech". Technologies highlighted are robotics for retrofits, prefabrication of energy-efficient products, and advanced manufacturing of low-carbon net-zero buildings construction. Programs discussed include those led by (1) government funding agencies: American-Made Challenges with prizes like E-ROBOT for retrofits with robotics, (2) philanthropic foundations: Wells Fargo Innovation Incubator (IN2) that includes focus on energy efficiency and prefabrication, and (3) venture capitalists: Shadow Ventures Green Building Accelerator program which provides funding support to start-ups with ambitious plans for decarbonizing the built environment. This paper will also expand upon robust processes and criteria involved in judging and down-selection of start-ups through vetting and feedback from national lab researchers and industry experts in both cleantech and contech.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Automated Manufacturing of Grid Stiffened Panels with Radically Reduced Tooling

Grid stiffened continuous fiber reinforced composite panels are an attractive option for creating lightweight structures due to the tailorability for various applications and the resulting high specific properties. However, the panel stiffeners and stiffener intersections result in high tooling complexity and correspondingly high cost of implementation. These factors have limited the impact of such structures in the composites industry. Previous research has demonstrated the ability to produce high quality, high aspect ratio beams, representative of individual grid stiffeners, using E-glass/PET comingled tow via direct digital manufacturing. Further, prior preliminary efforts have demonstrated the potential to use the same approach to manufacture grid intersections that have continuous fiber in both directions. To expand on the previous efforts in grid stiffeners produced by direct digital manufacture with radically reduced tooling requirements, this effort compares two methods of providing positioning and consolidation, nozzle vs. roller. Both processes are based on a commingled yarn feedstock. The extrusion through a nozzle has been shown to enable grid intersection control through local variations in applied consolidation and serves as the baseline process. However, this approach requires a continuous placement path to create the complete grid stiffened panel as no mechanism for cutting and restarting has been implemented. Alternatively, a newly developed placement head incorporating cut and refeed, mounted to a 6-axis robot, offers the potential of improved path placement efficiency. The two techniques are used to produce similar grid composite stiffeners to evaluate the effectiveness of producing the grid intersections. Rate of deposition of the two end effectors are compared and the quality of the associated grid stiffeners, and intersections, are determined through measurement of geometry, fiber volume fraction and void fraction.

Engineering↗
DOE OSTI2025

Self-Driving Microscopy for AI/ML-Enabled Physics Discovery and Materials Optimization

Materials are the bedrock of economy and foundation for all real-world technologies. The viability of space travel, grid energy storage, solar to fuels conversion, methane removal, and photovoltaic energy solutions hinge on the discovery and optimization of novel materials and rapid scaling toward manufacturing. The last 20 years have seen an exponential growth in the theoretical predictive capability for crystalline materials and small molecules. However, it is only in the last five years that we have seen the rapid expansion of high-throughput synthesis enabled by laboratory robotics and microfluidics, as well as a resurgence of combinatorial synthesis (Abolhasani and Kumacheva 2023; Epps and Abolhasani 2021; Jiang et al. 2022; Rajan 2008; Soldatov et al. 2021; Szymanski et al. 2023). Combinatorial synthesis, microfluidics, and ultimately dip-pen megalibraries have demonstrated the ability to “write” multicomponent nanomaterials at high throughput scale, generating millions of material examples in the 3D, 4D, and 5D composition spaces (Chen et al. 2016, 2019; Jibril et al. 2022).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Engineering microscale systems for fully autonomous intracellular neural interfaces

Conventional electrodes and associated positioning systems for intracellular recording from single neurons in vitro and in vivo are large and bulky, which has largely limited their scalability. Further, acquiring successful intracellular recordings is very tedious, requiring a high degree of skill not readily achieved in a typical laboratory. We report here a robotic, MEMS-based intracellular recording system to overcome the above limitations associated with form-factor, scalability and highly skilled and tedious manual operations required for intracellular recordings. This system combines three distinct technologies: 1) novel microscale, glass-polysilicon penetrating electrode for intracellular recording, 2) electrothermal microactuators for precise microscale movement of each electrode and 3) closed-loop control algorithm for autonomous positioning of electrode inside single neurons. Here, we demonstrate the novel, fully integrated system of glass-polysilicon microelectrode, microscale actuators and controller for autonomous intracellular recordings from single neurons in the abdominal ganglion of Aplysia Californica (n = 5 cells). Consistent resting potentials (< –35 mV) and action potentials (> 60 mV) were recorded after each successful penetration attempt with the controller and microactuated glass-polysilicon microelectrodes. The success rate of penetration and quality of intracellular recordings achieved using electrothermal microactuators were comparable to that of conventional positioning systems. The MEMS-based system offers significant advantages: 1) reduction in overall size for potential use in behaving animals, 2) scalable approach to potentially realize multi-channel recordings and 3) a viable method to fully automate measurement of intracellular recordings. Furthermore, this system will be evaluated in vivo in future rodent studies.

42 ENGINEERING↗
DOE OSTI2022

Development of an Energy Efficient and Cost Effective Autonomous Vehicle Research Platform

Commercialization of autonomous vehicle technology is a major goal of the automotive industry, thus research in this space is rapidly expanding across the world. However, despite this high level of research activity, literature detailing a straightforward and cost-effective approach to the development of an AV research platform is sparse. To address this need, we present the methodology and results regarding the AV instrumentation and controls of a 2019 Kia Niro which was developed for a local AV pilot program. This platform includes a drive-by-wire actuation kit, Aptiv electronically scanning radar, stereo camera, MobilEye computer vision system, LiDAR, inertial measurement unit, two global positioning system receivers to provide heading information, and an in-vehicle computer for driving environment perception and path planning. Robotic Operating System software is used as the system middleware between the instruments and the autonomous application algorithms. After selection, installation, and integration of these components, our results show successful utilization of all sensors, drive-by-wire functionality, a total additional power* consumption of 242.8 Watts (*Typical), and an overall cost of $118,189 USD, which is a significant saving compared to other commercially available systems with similar functionality. This vehicle continues to serve as our primary AV research and development platform.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Adapting a Low-Cost and Open-Source Commercial Pipetting Robot for Nanoliter Liquid Handling

Nanoliter liquid handling capabilities in bioanalytical workflows can reduce reagent costs and improve sample processing efficiency. Here we report the successful modification of a low-cost commercially available pipetting robot, the Opentrons 1 (OT1), using inexpensive 3D-printed parts to accommodate low-nanoliter volumes. The pipetting accuracy and precision of the modified OT1 for 50 nL volumes of water was 0.2% and 2%, respectively. The accuracy and precision at 20 nL was 1% and 3.8%, respectively. The platform was successfully applied to the preparation of low-nanogram proteomic samples for liquid chromatography-mass spectrometry analysis. The modified OT1 is thus an effective platform for nanoliter liquid handling and, at a total cost of less than $6000 including the commercial liquid handler and all modifications, is far less expensive and more versatile than other platforms with similar capabilities, placing this technology within reach of a far broader scientific community.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing

Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high-quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, "graphene recipes for synthesis of high quality material" (Gr-ResQ: pronounced graphene rescue), which includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures similar to 300 parameters ranging from synthesis conditions such as a catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated, and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility, which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control and open opportunities for advancing not only graphene but also many other 1D and 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Improving Non-Destructive Detection Technology Through SAVY Feature Detection

Surveillance of special nuclear material (SNM) storage containers is required by the DOE to assess their integrity across the Complex. This work aims to improve the task of container inspections by leveraging automation through machine learning (ML) tools to reduce the human-intensive effort and expert-level knowledge needed to assess container status. A field-deployable, non-destructive technology was designed using off-the-shelf components to collect multiple images from different perspectives of containers in storage to detect both spatial features of interest and anomalies of concern. Nine ML models were generated using unique training datasets and parameters. Learned features include SAVY surface regions including the body side wall, collar, lid, filter, and printed/etched information. Average Precision (AP) is used to calculate detection performance when both viewing previously seen environments and previously unseen environments. The application of image transformations and resolution scaling while training greatly improved the detection performance in unseen environments, and significantly increasing the number of computation iterations improved detection performance on previously seen environments. Additional capabilities were developed including the novel detection of procedural non-compliance and the ability to localize anomalies relative to SAVY surface features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗
DOE OSTI2020

Quantitative and three-dimensional assessment of holdup material

Nuclear material deposited in equipment, transfer lines, and ventilation systems of a processing facility is usually referred to as holdup. Operators need to know the location and amount of holdup in their facility for radiation safety and material accountability. In this work, we propose to use an array of detectors co-axial to the inspected pipe to measure the holdup material. This method is implementable into an automated system capable of crawling on surfaces and pipes of various curvatures, which would enable faster, easier, and more accurate holdup safeguards measurements. We first demonstrated that the current holdup assay procedure could lead to a non-negligible bias in the estimate of special nuclear material mass, due to the simplified assumption of deposited geometry introduced by the Generalized Geometry Holdup (GGH) model. The new approach consists of imaging the inner holdup material by characterizing the detector array’s response and unfolding it from the measured light output. Our experimental proof of principle consists of three NaI(Tl) detectors surrounding an aluminum pipe containing two cesium-137 ( 137 Cs) sources. We derived the source distribution inside the pipe by first calculating the detector response matrix using a method adaptive to the surface geometry of the object containing the measured holdup material. Creating a matrix of the detector array’s measured counts, we then proceed to solve an inverse problem, resulting in an accurately located source position and activity distribution within the response matrix’s spatial resolution. Furthermore, we then developed a simulated model of the envisioned experimental setup, which accurately described both the activity and position of the source in 2D. Finally, we extended our model onto a discretized three-dimensional model of the system, encompassing 36 detectors. The model was simulated to measure the source distribution and activity of multiple sources in pipes of varied geometries, which could then be conveniently translated to a real-time holdup measurement protocol. For the 3D simulation of four different source geometries, the model accurately localized the source position in 3D, while the activity retained a maximum relative error of ±5.32%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Design and Development of a Robotic Exoskeleton Device for use in Glove Boxes - 20374

Glovebox operators in nuclear facilities often operate under radioactive environment and typically perform monotonous, dull and repetitive work. This can result in muscle fatigue leading to increased levels of accidents. Current research work aims in assisting the glovebox operators by designing a novel upper limb exoskeleton device to reduce injuries and increase worker efficiency. Almost none of the commercially available hand and upper body exoskeletons are directly suitable for use in gloveboxes. This explains the novelty of the present work in developing a wearable powered robotic exoskeleton device. The device consists of electronics, motors and spring systems mounted on adjustable plates and is supported by a stationary frame behind the worker. The device is intended to be portable and adjustable according to the operator height. The elbow has a flexion movement assisted with motor control by way of a Bowden cable system. The system is floated on a spring and pulley combination that allows for extra safety and smoothness of operation. The end points are fixed near the pivot points to help minimize unwanted movement/torque. The extension movement of the upper and lower arm are assisted by gravity and a counter spring. The shoulder and upper arm movement are held in a neutral position with a spring system for counter balance allowing small movements within the glovebox. This initial concept considers only the vertical movement of the arm. The abduction and adduction movement will not be assisted or impeded by the over shoulder plate or at the forearm plate allowing for full range of motion. Several glovebox aspects such as the level of task assistance, ergonomic design, minimum hindrance in dexterity/operator movements and tear resistance for hazardous safety reasons will be considered in the design of the exoskeleton. Computer models will be simulated to test the load levels and stress conditions on the exoskeleton to enhance the designs. The exoskeleton focusses on major muscular involvement reduction through weight offset and active assistance with minimal parts and hence the device is derived with several joint links and contains both passive and active joints. The paper details the various stages in the design development and prototype testing of the exoskeleton device. Upon successful prototype device development, human testing will be conducted in a replica of the SRS-235F hot cell facility constructed at FIU-ARC. The final stage of the project will be potential testing/deployment of the exoskeleton device in one of the glovebox facilities in Savannah River Nuclear Waste site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗