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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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Electrochemical reactors based on gas diffusion electrodes (GDE) enable high current densities for reactions such as CO 2 reduction. AutoGDE is a platform for automating GDE experiments to accelerate catalyst discovery and reaction engineering.
Automation of electrochemical measurements can accelerate the discovery of new electroactive materials. One of the hurdles to automated electrochemical measurement is the pretreatment of electrodes because mechanical polishing is usually conducted manually. Here we investigate the automation of electrochemical measurements using a robotic arm. We demonstrate automated mechanical polishing using a station with a moving polishing pad and evaluate the effect of different polishing patterns. Our automatic method improved the corroded electrodes, and we found the effect of pattern was not significant, which diverges from the current common belief amongst practitioners that a figure eight pattern is best for pretreatment. This research is a step toward automating electrochemistry experiments without human intervention.
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We propose a framework to develop cyber solutions to support the remote steering of science instruments and measurements collection over instrument-computing ecosystems. It is based on provisioning separate data and control connections at the network level, and developing software modules consisting of Python wrappers for instrument commands and Pyro server-client codes that make them available across the ecosystem network. We demonstrate automated measurement transfers and remote steering operations in a microscopy use case for materials research over an ecosystem of Nion microscopes and computing platforms connected over site networks. The proposed framework is currently under further refinement and being adopted to science workflows with automated remote experiments steering for autonomous chemistry laboratories and smart energy grid simulations.
This workshop aims to identify key research directions for transforming biology using artificial intelligence (AI), machine learning (ML) and computational methods to facilitate the discovery of new behaviors, mechanisms, and designs of biological processes relevant to DOE missions, underpinning a broader U.S. bioeconomy. By developing novel AI/ML technologies to analyze and interpret complex biological data, researchers can organize and simulate biological processes at various scales as well as advance predictive understanding and manipulation of biological systems. This integration of computation, experimentation, and next-generation experimental technologies can lead to discoveries in new biological behaviors and mechanisms relevant to DOE missions. The focus is on how advanced computational and mathematical methods can impact this mission by exploring digital twins, foundation models, automated laboratory experiments, modeling of complex living systems, and data-driven approaches for the biodesign of plants and microbial systems. While data management is important, it is not the primary focus of this workshop, which will assess the current state, trends, and AI/ML challenges at the interface between biology and computational science to identify opportunities for high-impact research at their intersection. The goal is to define research needs and opportunities that align with biological sciences, computational sciences, and applied mathematics research.
In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.
In the formulations of many composite materials, polymeric materials are often used as binder materials to enhance processability, functionality, and safety during manufacturing. For instance, poly(ester urethane), such as Estane® 5703, is used in the formulation of PBX 9501, and vinyl copolymer elastomer (VCE) is used in the formulation of VCE/filler compos ites. The stability of these binders directly impacts the overall stability and performance of their composites. Estane and VCE are prone to hydrolytic degradation, with water sorption behavior playing a significant role in their long-term stability and aging behavior. Hence, it is critical to study their water sorption behavior under various humidity and tempera ture conditions. To improve the accuracy and reduce labor-intensive work involved in water sorption experiments, an automated Dynamic Vapor Sorption (DVS) system was employed, which can generate large datasets. To efficiently process and analyze these datasets, Python scripts were developed. These scripts not only streamlined data processing, but also accu rately derived the Arrhenius parameters related to water diffusion and sorption properties for the tested materials. This approach offers a robust framework for evaluating water trans port and sorption processes in polymeric materials and adds valuable capabilities to future water sorption testing efforts.
Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Modifications in segmentation also show potential in improving the accuracy of our current pyCHIP classifier. Replacing previous encoders trained on ImageNet with those trained on microscopy images like MicroNet has initially shown better performance at classifying images of irradiated samples. Additionally, different normalization approaches were tested to show no discernable effect on classification. The Louvain method for community detection is analyzed on a set of irradiated samples with different parameters to determine which proved beneficial under what circumstances. We suggest that microscopy experiments be automated in the future using a combination of these techniques to enable high-throughput analyses.
AUTOTEST is a large scale real-time computer program designed to automatically test spacecraft and experiments. Resident portions of the program collect and store incoming real-time data and process test control instructions which control experiment turn-on, select tests to be performed, and select data to be displayed.
The purpose and format of a panel session that addressed the procedures by which the hardware components of geographic information systems are evaluated and selected are described. State agencies from Alaska, Colorado, Montana, and Washington were represented and the topic was discussed within the general context of information requirements in land management decision making.
The Atmospheric Lifetime Experiment is designed to determine accurately the atmospheric concentrations of the four halocarbons CFCl3, CF2Cl2, CCl4, and CH3CCl3, and also of N2O with emphasis on measurement of their long-term trends in the atmosphere. Comparison of these concentrations and trends for the four halocarbons with estimates of their industrial emission rates then enables calculations of their global circulation rates and globally averaged atmospheric lifetimes. The experiment utilizes automated dual-column electron-capture gas chromatographs which sample the background air about 4 times daily at the following globally distributed sites: Adrigole, Ireland, Cape Meares, Oregon; Ragged Point, Barbados; Point Matatula, American Samoa, and Cape Grim, Tasmania. The climatology of these 'clean air' sites and their ability to describe the global air mass are reviewed. The instrumentation and methods for data acquisition and processing are then described. An overview of the data obtained and the trends derived during the 3-year period from July 1978 through June 1981 for each of the five species being measured is presented.
For experimentation, space offers a unique environment which is unobtainable on Earth. One characteristic is a gravity force less than 1 g, where g is the mean Earth gravity acceleration of 9.8 m/sq s. The production of uniform gravity levels above zero g in space is discussed in relationship to experimental needs. For planetology experiments, providing gravity in space will make it possible to more nearly simulate conditions on natural bodies. The g-level is but one parameter involved in the design of a specific experiment. Other requirements may be: g-level range; g-level tolerance value; Coriolis tolerance value; volume requirement; g-level duration; power and materials for the experiment; and automated or man-tended operation. These requirements, and certainly others, will dictate the type of facility which should be considered. The use of the Space Station or the Tethered Satellite System configurations is discussed.
JPL has been leading a concentrated effort to develop improved thermoelectric materials for space applications. Thermoelectric generators are an attractive source of electrical energy for space power because of lack of moving parts and slow degradation of performance. Thermoelectric material is characterized by: Seebeck coefficient, electrical resistivity and thermal conductivity. To measure the high temperature thermal conductivity is experimentally very difficult. However, it can be calculated from the specific heat and thermal diffusivity which are easier to measure at high temperatures, especially using the flash method. Data acquisition and analysis for this experiment were automated at JPL using inexpensive microcomputer equipment. This approach is superior to tedious and less accurate manual analysis of data. It is also preferred to previously developed systems utilizing expensive minicomputers or mainframes.
For experimentation, space offers an environment which is unobtainable on earth. One characteristic is a gravity force less than 1 g, where g is the mean earth gravity acceleration of 9.8 m/sq s. The production of uniform gravity levels above zero g in space is discussed in relationship to experimental needs. For planetology experiments, providing gravity in space will make it possible to more nearly simulate conditions on natural bodies. The g-level is but one parameter involved in the design of a specific experiment. Other requirements may be: g-level range; g-level tolerance value; Coriolis tolerance value; volume requirement g-level duration; power and materials for the experiment; and automated or man-tended operations. These requirements, and certainly others, will dictate the type of facility which should be considered. The use of the Space Station of the Tethered Satellite System configurations is discussed.
To achieve an environment less dependent on the flow of paper, automated techniques of data storage and retrieval must be utilized. This software system, 'Automated Payload Experiment Tool,' seeks to provide a knowledge-based, hypertext environment for the development of NASA documentation. Once developed, the final system should be able to guide a Principal Investigator through the documentation process in a more timely and efficient manner, while supplying more accurate information to the NASA payload developer. The current system is designed for the development of the Science Requirements Document (SRD), the Experiment Requirements Document (ERD), the Project Plan, and the Safety Requirements Document.
There are two important reasons to explore the Moon. First, we would like to know more about the Moon itself: its history, its geology, its chemistry, and its diversity. Second, we would like to apply this knowledge to a useful purpose. namely finding and using lunar resources. As a result of the recent Clementine and Lunar Prospector missions, we now have global data on the regional surface mineralogy of the Moon, and we have good reason to believe that water exists in the lunar polar regions. However, there is still very little information about the subsurface. If we wish to go to the lunar polar regions to extract water, or if we wish to go anywhere else on the Moon and extract (or learn) anything at all, we need information in three dimensions an understanding of what lies below the surface, both shallow and deep. The terrestrial mining industry provides an example of the logical steps that lead to an understanding of where resources are located and their economic significance. Surface maps are examined to determine likely locations for detailed study. Geochemical soil sample surveys, using broad or narrow grid patterns, are then used to gather additional data. Next, a detailed surface map is developed for a selected area, along with an interpretation of the subsurface structure that would give rise to the observed features. After that, further sampling and geophysical exploration are used to validate and refine the original interpretation, as well as to make further exploration/ mining decisions. Integrating remotely sensed, geophysical, and sample datasets gives the maximum likelihood of a correct interpretation of the subsurface geology and surface morphology. Apollo-era geophysical and automated sampling experiments sought to look beyond the upper few microns of the lunar surface. These experiments, including ground-penetrating radar and spectrometry, proved the usefulness of these methods for determining the best sites for lunar bases and lunar mining operations.
In the Fall of 1997 the Atmospheric Radiation Measurement (ARM) program held an intensive observation period (IOP) to study atmospheric aerosols using in situ and remote sensing techniques at its Southern Great Plains (SGP) site near Lamont, Oklahoma. As part of this experiment five automated, tracking sunphotometers were present to measure total column aerosol optical depth over the three-week period. which included many clear days or parts of days that were clear. The World Meteorological Organization (WMO 1993) has recommended a comparison of tracking sunphotometers to assess the ability of different instruments to arrive at similar aerosol optical depths. It was further recommended that the comparison be staged at a clean mountain site. In fact, this comparison has not occurred, but the comparison that we describe in this paper is representative of what contemporary instruments may accomplish in an environment more typical of sites where aerosols measurements will be required. The measurements were made over the period 15 September to 5 October 1997. The aerosol loading varied from extremely clean to moderately turbid conditions. In the next section the instruments will be described along with a brief explanation of the calibration techniques. The third section contains the results compared graphically on moderately turbid and fairly clean days and in a table representing the whole period. The paper ends with a section of discussion and a summary of the results.
Nanosatellites offer frequent, low-cost space access as secondary payloads on launches of larger conventional satellites. We summarize the payload science and technology of the Microsatellite in-situ Space Technologies (MisST) nanosatellite for conducting automated biological experiments. The payload (two fused 10-cm cubes) includes 1) an integrated fluidics system that maintains organism viability and supports growth and 2) a fixed-focus imager with fluorescence and scattered-light imaging capabilities. The payload monitors temperature, pressure and relative humidity, and actively controls temperature. C. elegans (nematode, 50 m diameter x 1 mm long) was selected as a model organism due to previous space science experience, its completely sequenced genome, size, hardiness, and the variety of strains available. Three strains were chosen: two green GFP-tagged strains and one red tdTomato-tagged strain that label intestinal, nerve, and pharyngeal cells, respectively. The integrated fluidics system includes bioanalytical and reservoir modules. The former consists of four 150 L culture wells and a 4x5 mm imaging zone the latter includes two 8 mL fluid reservoirs for reagent and waste storage. The fluidic system is fabricated using multilayer polymer rapid prototyping: laser cutting, precision machining, die cutting, and pressure-sensitive adhesives it also includes eight solenoid-operated valves and one mini peristaltic pump. Young larval-state (L2) nematodes are loaded in C. elegans Maintenance Media (CeMM) in the bioanalytical module during pre-launch assembly. By the time orbit is established, the worms have grown to sufficient density to be imaged and are fed fresh CeMM. The strains are pumped sequentially into the imaging area, imaged, then pumped into waste. Reagent storage utilizes polymer bags under slight pressure to prevent bubble formation in wells or channels. The optical system images green and red fluorescence bands by excitation with blue (473 nm peak) and amber (587 nm peak) LEDs it achieves 8 m lateral resolution using a CMOS imaging chip (as configured for serial data speeds) or 4 m resolution using USB imaging chips. The imager consists of a modified commercial off-the-shelf CMOS chip camera, amber, blue and white LEDs, as well as a relay lens and dual-band filters to obviate moving parts while supporting both fluorescence wavelengths.