DOE SBIR Phase I Final Technical Progress Report Automated 3D data fusion surveys on Spot robot for next generation plant monitoring
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61 RADIATION PROTECTION AND DOSIMETRY↗
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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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Automated 3D Data Fusion Surveys on Spot Robot for Next Generation Plant Monitoring
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3D mapping↗
Self-assembled reconfigurable pump architectures via magnetic colloidal swarms
Self-assembled swarms of interactive active units, which are adaptive and dynamically reconfigurable to accommodate different functionalities, represent a promising platform for the development of next-generation robotics. Here, we utilize the emergent collective behavior of active magnetic colloids confined in quasi-two-dimensional arrays of overlapping wells to demonstrate the self-organization of a colloidal swarm into a dynamic pump architecture capable of controlled transport of passive cargo particles. This dynamic architecture provides a global unidirectional looping flow pattern along the entire length of the system. We show that the flow direction of the dynamic swarm-based pump can be externally controlled by a phase shift of a driving magnetic field energizing the swarm. The experimental observations are supported by computational modeling based on phenomenological coarse-grained particle dynamics coupled to shallow-water Navier-Stokes hydrodynamics. In conclusion, our findings demonstrate how the emergent collective behavior of a swarm can be orchestrated into a desired functionality by exploiting the interplay between activity and confinement potentials.
36 MATERIALS SCIENCE↗
Power at Sea: Use of Wave Energy Converters for Powering Offshore Oil and Gas Operations
New innovative services and goods based on artificial intelligence, next-generation robotics, autonomy, digitization, and electrification are taking root in the ocean economy. More typical competitive forces, such as value enhancement and safety improvement, are also present, as suppliers constantly strive to improve their competitive positioning. Concurrently, the Oil and Gas sector is experiencing mounting pressure to decrease its operational carbon emissions, forcing efforts to reduce or eliminate liquid fuel consumption offshore. Companies in the ocean economy are having to react to this combination of macro- and micro-level drivers and innovate at an ever more rapid pace. However, before many of these new capabilities can be implemented, new, clean, reliable power sources are needed, especially as liquid fuel grows increasingly out of favor. For many of these loads—both old and new and ranging from watts to megawatts—wave energy systems provide the best solution for remote power generation. As such, research, development and demonstration activities are underway to prove wave energy's ability to provide reliable, consistent energy supplies for certain offshore Oil and Gas activities, paving the way for near term commercialization. Furthermore, this paper outlines these drivers, competitive reaction to them, and why and how wave energy can be a preferable choice for power at sea.
16 TIDAL AND WAVE POWER↗
A Perspective into Operando Methods for Probing Catalytic Interfaces
Rational design of electrocatalysts for (photo)electrochemical (PEC) processes like hydrogen and oxygen evolution and CO2 reduction reactions is aided by the recent improvements in capabilities of operando measurements, where morphology, composition, and/or function are probed during active catalysis. Through operando microscopy and spectroscopy, structure, catalytic microenvironment, oxidation state, adsorbates, and products can be measured to gain a better understanding of catalyst behavior and suggest possible improvements. Visualizing evolving catalyst morphologies, surface compositions, and electrochemical behavior also helps address many fundamental research questions for a better understanding of catalytic mechanisms. Correlating morphology with chemical identity or functional behavior using a variety of innovative microscopy methods is particularly promising for guiding development of next generation catalysts, and there are also many recent examples of using AI and robotics tools to innovate and speed development. In this Perspective, advances made over the past few years in operando imaging of catalysts relevant to solar fuels will be explored, followed by an outlook on technological developments in instrumentation, sample design, and computational power that may be applied to this field.
Catalysts↗
Switching speed limits in electrically driven VO 2 structural Mott–Peierls transition
Mott materials are archetypal quantum systems actively explored as next-generation electronic and photonic platforms, with potential applications spanning non-Von Neumann computing, robotics, energy storage, and microwave technologies. Among these, vanadium dioxide (VO 2 ) has emerged as one of the most intensively studied compounds, owing to its sharp, near-room-temperature insulator-to-metal phase transition. VO 2 also serves as a benchmark system for testing cutting-edge theories and experimental techniques. Here, we directly visualize the electrically driven transition dynamics in VO 2 using a microwave-driven, frequency-tunable pulsed transmission electron microscope that combines nanometer spatial and picosecond temporal resolution. Under high-frequency (MHz–GHz) excitation, we capture the ultrafast nucleation, propagation, and dissolution of metallic domains within an operating device over millions of reversible cycles. We observe the ultrafast formation of consistent metallic nuclei beneath the electrodes, followed by the propagation of a structural phase front at 4.54 nm/ns. Our experiments show that phonon-mediated structural recovery ultimately limits reversible switching of VO 2 at GHz frequencies, and that a tunable regime for reversible operation spans from kHz to GHz through device engineering. Beyond VO 2 , our approach provides a powerful framework for probing non-equilibrium structural transformations in correlated and functional materials under realistic electrical stimuli.
36 MATERIALS SCIENCE↗
Developing a GUI for the Robotic Test Stand
The introduction of this poster explains the technology behind DUNE’s far and near detectors and how passing neutrinos generate electrons that drift into a wire grid. I then explain how 3 ASICs manage signals received from electron interception. Next, the poster states how COLDATA chips are undergoing quality control by a Robotic Test Stand using a state machine. I further explained how earlier tests were done via a command line script and the necessity to implement a user-friendly Graphical User Interface with new features a command line can’t implement. For the implementation section, tools and methods for implementation are listed such as Python, tkinter, and GitHub as well as how multithreading and queue implementation was necessary for GUI functionality. Then, I elaborated on the GUIs new features. Finally, I explain how the GUI will be distributed across multiple institutions and future changes planned for the GUI. Photos of the RTS, far detector cave, diagram of anode assembly plane, COLDATA chips, set up tab, result tab, and legacy command line interface are shown.
Gutierrez Villanueva, Jaziel [DuPage Coll.]↗
Visualizing energy transfer between redox-active colloids
Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficient D CT of the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics.
Qu, Alan Subing [Univ. of Illinois at Urbana-Champ↗
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↗
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↗
A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems
The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.
Taufer, Michela [University of Tennessee, Knoxvill↗
High-Fidelity Accelerated Design of High-performance Electrochemical Systems
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. To accelerate this process, our team has developed the AutoMat platform. AutoMat can accelerate development of new electrochemical materials along two avenues: first, automated input generation and management of simulations at multiple lengthscales as well as “handoff” of outputs from one lengthscale as inputs to the next; and second, replacement of the most computationally intensive simulation processes with machine-learned surrogate models. The crux of our team’s effort was not “reinventing the wheel” by developing entirely new techniques, but rather building a “superhighway” that allows existing state-of-the-art techniques to run faster and more smoothly than before. AutoMat can utilize tools spanning from first-principles quantum chemistry computations to automated robotic experimentation, and is driven by design space search techniques to reduce the number of iterations through the full simulation loop by rapidly targeting promising regions of design spaces such as single-atom alloy catalysts or blends of liquid electrolytes.
25 ENERGY STORAGE↗
Automated Nanocrystal Synthesis: Lessons from 25 Years of Robots, Microfluidics, and Machine Learning
Here, this perspective highlights the evolution of techniques for automating the synthesis of colloidal nanocrystals. Over the past 25 years, microfluidic reactors and robotic workflows have been developed to enhance the reproducibility of nanocrystal synthesis, facilitate rapid screening of reaction conditions, optimize material properties, and perform multistep syntheses of high-quality nanoparticles with complex heterostructures. Modern automated systems are now valued for their ability to generate robust data sets for validating physical models, supporting chemical mechanisms, training machine learning models, and for directing autonomous experimentation. We discuss the early challenges and limitations of these technologies and present key lessons for effectively utilizing automated and ML-guided tools to accelerate nanocrystal discovery for the next 25 years.
Nanocrystals↗
Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)
This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Additive manufacturing and applications of nanomaterial-based sensors
Nanoscale materials possess distinct physical and chemical attributes including size-dependent properties, quantum confinement, high surface-to-volume ratio, and superior catalytic activity. These unique qualities enable sensors with high sensitivity, robustness, and fast time response. As the emergence of the Internet of Things (IoT) demands increased production of sensors, it also provides an impetus for concentrated nanomaterial-based sensor research. Meanwhile, additive manufacturing (AM) of nanomaterial-based sensors is critical to bridge the gap between one-off, lab-scale fabrication and cost-effective, industrial-scale production with high reproducibility. By applying the design flexibility and cost savings of AM techniques, a new generation of nanomaterial-based sensing platforms can be integrated with IoT devices in the consumer space. Furthermore, emergent research in human-machine interfaces, food safety, and point-of-care diagnostics will be expedited by the development of sensors that can be printed with irregular form factors. In this Review, the relative strengths and weaknesses of printed sensor systems based on zero-, one-, and two-dimensional nanomaterials are discussed. In addition, sensors enabled by printable soft nanomaterials, heterostructures, and nanocomposites are surveyed due to their synergistic advantages for wearable healthcare monitoring and soft robotics. Lastly, a roadmap for the next decade of research on this topic is provided.
36 MATERIALS SCIENCE↗
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.
36 MATERIALS SCIENCE↗
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↗