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NASA NTRSDate not supplied

TPSAS-NF1676L-13606-DND

For over two decades, researchers at NASA Langley Research Center have been combining principles of engineering and psychology to explore the cognitive experiences of system operators. Conceptualizing the application of this interdisciplinary combination in the context of operators? extracurricular activities has resulted in the creation of numerous mental training spin-off technologies. Methods derived from feedback control concepts have been employed to assess the brains responses when managing automation and principles of conditioning have been embodied in devices designed to reinforce effective modes of psychophysiological activity. These methods and devices will be discussed and how they are being applied to ameliorate attention and stress disorders in clinical practice and impediments to optimal performance in sports will be demonstrated.

Alan T Pope↗

NASA NTRS2000

Video Based Sensor for Tracking 3-Dimensional Targets

Video-Based Sensor for Tracking 3-Dimensional Targets The National Aeronautics and Space Administration's (NASAs) Marshall Space Flight Center (MSFC) has been developing and testing video-based sensors for automated spacecraft guidance for several years, and the next generation of video sensor will have tracking rates up to 100 Hz and will be able to track multiple reflectors and targets. The Video Guidance Sensor (VGS) developed over the past several years has performed well in testing and met the objective of being used as the terminal guidance sensor for an automated rendezvous and capture system. The first VGS was successfully tested in closed-loop 3-degree-of-freedom (3- DOF) tests in 1989 and then in 6-DOF open-loop tests in 1992 and closed-loop tests in 1993-4. Development and testing continued, and in 1995 approval was given to test the VGS in an experiment on the Space Shuttle. The VGS flew in 1997 and in 1998, performing well for both flights. During the development and testing before, during, and after the flight experiments, numerous areas for improvement were found. The VGS was developed with a sensor head and an electronics box, connected by cables. The VGS was used in conjunction with a target that had wavelength-filtered retro-reflectors in a specific pattern, The sensor head contained the laser diodes, video camera, and heaters and coolers. The electronics box contained a frame grabber, image processor, the electronics to control the components in the sensor head, the communications electronics, and the power supply. The system works by sequentially firing two different wavelengths of laser diodes at the target and processing the two images. Since the target only reflects one wavelength, it shows up well in one image and not at all in the other. Because the target's dimensions are known, the relative positions and attitudes of the target and the sensor can be computed from the spots reflected from the target. The system was designed to work from I meter out to I 10 meters. The VGS was mounted on the Space Shuttle while its target was mounted on the Spartan free-flyer carried on the same Shuttle flight. The VGS tracked the Spartan at ranges up to 170m, and the VGS range data very closely matched the range data from the Hand-Held Laser- Rangefinder used by the astronauts on board the Shuttle. While the VGS was designed primarily as a terminal guidance sensor for an automated spacecraft, it could be applied to other uses. It could be used as an alignment aid for an operator of a remote system (giving position and attitude feedback data, as well as a camera view of the target), as a feedback system for a robotic arm, or for automated vehicle guidance. The next generation VGS, with its higher tracking rates, smaller size, and lower power could be used in more places than the original VGS, and by using LED's instead of laser diodes, the system would be eye-safe at any range. Other possible uses include tracking 3-dimensional objects with retro-reflectors mounted at various locations or motion analysis by placing several retro-reflectors on the moving object and tracking them at high speeds. There are few sensors capable of performing tasks similar to those the VGS can perform, and the next generation VGS will be even more capable than the original. Some of this work is previously presented in the papers.

Howard, R. T.↗

DOE OSTI2022

AutodiDAQt v1.1.0

AutodiDAQt automates and simplifies writing data acquisition software for spectroscopy and microscopy. After defining only how to communicate with instruments and hardware, autodiDAQt generates user interfaces for long running acquisition applications, handlings data collation and retention, and provides remote communication to analysis computers. This reduces the time to get experiments running from months to hours and increases reliability for scientific experiments. AutodiDAQt metaprograms from instrument drivers directly, where possible.

Stansbury, Conrad↗

DOE OSTI2026

Rhodotorula toruloides Nitrogen Limitation PTM Profiling Multi-Omics (TZ-DP1).

The purpose of this experiment was to evaluate the regulatory stress response of Oleaginous yeast species Rhodotorula toruloides NBRC 0880 (JGI strain IFFO0880 v4.0) under nitrogen-rich and nitrogen-limited conditions over time. Time course experimental samples (24, 48, and 72 hours after inoculation) were prepared using a semi-automated multi-PTM proteomic approach, using tandem mass tag 18-plex (TMT18), and lipidome remodeling for downstream multi-omics analysis.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1972

The Automated Primate Research Laboratory (APRL)

A description is given of a self-contained automated primate research laboratory to study the effects of weightlessness on subhuman primates. Physiological parameters such as hemodynamics, respiration, blood constituents, waste, and diet and nutrition are analyzed for abnormalities in the simulated space environment. The Southeast Asian pig-tailed monkey (Macaca nemistrina) was selected for the experiments owing to its relative intelligence and learning capacity. The objective of the program is to demonstrate the feasibility of a man-tended primate space flight experiment.

Pace, N.↗

NASA NTRS1993

Advanced ballistic range technology

Optical images, such as experimental interferograms, schlieren, and shadowgraphs, are routinely used to identify and locate features in experimental flow fields and for validating computational fluid dynamics (CFD) codes. Interferograms can also be used for comparing experimental and computed integrated densities. By constructing these optical images from flow-field simulations, one-to-one comparisons of computation and experiment are possible. During the period from February 1, 1992, to November 30, 1992, work has continued on the development of CISS (Constructed Interferograms, Schlieren, and Shadowgraphs), a code that constructs images from ideal- and real-gas flow-field simulations. In addition, research connected with the automated film-reading system and the proposed reactivation of the radiation facility has continued.

Yates, Leslie A.↗

NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗

NASA NTRS2017

Tack Measurements of Prepreg Tape at Variable Temperature and Humidity

NASA’s Advanced Composites Project has established the goal of achieving a 30 percent reduction in the timeline for certification of primary composite structures for application on commercial aircraft. Prepreg tack is one of several critical parameters affecting composite manufacturing by automated fiber placement (AFP). Tack plays a central role in the prevention of wrinkles and puckers that can occur during AFP, thus knowledge of tack variation arising from a myriad of manufacturing and environmental conditions is imperative for the prediction of defects during AFP. A full design of experiments was performed to experimentally characterize tack on 0.25-inch slit-tape tow IM7/8552-1 prepreg using probe tack testing. Several process parameters (contact force, contact time, retraction speed, and probe diameter) as well as environmental parameters (temperature and humidity) were varied such that the entire parameter space could be efficiently evaluated. Mid-point experimental conditions (i.e., parameters not at either extrema) were included to enable prediction of curvature in relationships and repeat measurements were performed to characterize experimental error. Collectively, these experiments enable determination of primary dependencies as well as multi-parameter relationships. Slit-tape tow samples were mounted to the bottom plate of a rheometer parallel plate fixture using a jig to prevent modification of the active area to be interrogated with the top plate, a polished stainless steel probe, during tack testing. The probe surface was slowly brought into contact with the pre-preg surface until a pre-determined normal force was achieved (2-30 newtons). After a specified dwell time (0.02-10 seconds), during which the probe substrate interaction was maintained under displacement control, the probe was retracted from the surface (0.1-50 millimeters per second). Initial results indicated a clear dependence of tack strength on several parameters, with a particularly strong dependence on temperature and humidity. Although an increase in either of these parameters reduces tack strength, a maximum in tack was predicted to occur under conditions of low temperature and moderate humidity.

Wohl, Christopher↗

NASA NTRSDate not supplied

Developing and Testing Two Interfaces for Supplemental Data Service Provider (SDSP) Tools to Support UAS Traffic Management (UTM)

Researchers conducted a usability study using two graphical user interfaces (GUIs) to explore how individuals interpret and interact with different preflight information displays, and to inform the development of Uncrewed Aircraft System (UAS) preflight planning predictive support tools to assess and mitigate flight hazards and risks. A series of preflight risk-assessment tasks were developed to evaluate participant performance using the Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD) and the Human Automation Team Interface System (HATIS) GUIs. Participants were trained to use both interfaces and their performance was evaluated. These evaluations focused on participants’ preflight planning activities. Objective data on performance tasks across different scenarios involving multi-UASs, as well as self-reports of interactions and subjective experiences using the GUIs were collected. Scores on the system usability scale (SUS) and on a simple task set were examined, as well as user feedback on open-ended questions, to inform development and identify potential improvements to the interfaces.

sUAAV interfaces↗

NASA NTRSDate not supplied

Developing and Testing Two Interfaces for Supplemental Data Service Provider (SDSP) Tools to Support UAS Traffic Management (UTM)

sUAAV interfaces↗

NASA NTRSDate not supplied

Detection of CH 4 Hotspots from NASA’s GEOS Composition Analysis System

Recent research indicates that 8 to 12% of the global oil and gas production methane emissions could be attributed to ultra-emitters, which result in high concentration ‘hotspots’ near point sources. Identifying these emissions in near real time provides useful information to the policy makers and private industry, who are working to reduce their impact. To meet this need, scientists are increasingly analyzing satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) instrument aboard ESA’s Sentinel 5-Precursor mission. While direct analysis of TROPOMI level 2 swath data has been successful in identifying some large emission events, identifying hotpots is challenging because of the imaging noise due to a variety of artifacts and limits in daily coverage. Here we explore possible methodologies to detect methane hotspots using a new, gap-filled, and temporally continuous methane product from NASA’s Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), which assimilates column averaged methane mole fractions from the TROPOMI with capabilities to assimilate other remote sensing measurements. The CoDAS has been expanded from a heritage of stratospheric composition and carbon dioxide assimilation allowing for the support of regional modeling, validation with non-coincident operations, and merging variety of datasets. The current work mainly explores Observing System Simulation Experiments with methane GEOS simulations without assimilation to prepare the groundwork for further experiments with assimilated TROPOMI. First, known hotspots based on the known inventory are identified to demonstrate the capability of the system to point out emission hotspots. In the next step, a variety of machine learning techniques such as Self-Organizing Maps and Deep Learning are explored to automate detection of the plumes. Finally, a few approaches to quantify emissions from the identified hotspots are presented and are evaluated against the inventory. The effort is directed toward a future evaluation of the CoDAS based methane monitoring system’s ability to successfully detect and quantify hotspots.

Nikolay Balashov↗

DOE OSTI2026

Archi: Agentic Operations at the CMS Experiment

We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of heterogeneous data sources with the deployment of configurable, private, and extensible agents that retrieve and reason over them. An instance of Archi has been deployed for the Computing Operations team of the CMS experiment at CERN's LHC since February 2026 as a support agent for technical operators, offering retrieval and analysis capabilities by combining documentation, historical data, and live monitoring systems. We evaluate the system on operator feedback and a question set collected from production usage, graded by human and automated panels. The system proves effective at operational tasks, resolving real-world queries posed by CMS operators. We also observe that locally-hosted, open-weight models perform competitively, enabling fully private management of sensitive data.

Lugato, Pietro [MIT; CERN]↗

NASA NTRS2008

Automated Generation and Assessment of Autonomous Systems Test Cases

This slide presentation reviews some of the issues concerning verification and validation testing of autonomous spacecraft routinely culminates in the exploration of anomalous or faulted mission-like scenarios using the work involved during the Dawn mission's tests as examples. Prioritizing which scenarios to develop usually comes down to focusing on the most vulnerable areas and ensuring the best return on investment of test time. Rules-of-thumb strategies often come into play, such as injecting applicable anomalies prior to, during, and after system state changes; or, creating cases that ensure good safety-net algorithm coverage. Although experience and judgment in test selection can lead to high levels of confidence about the majority of a system's autonomy, it's likely that important test cases are overlooked. One method to fill in potential test coverage gaps is to automatically generate and execute test cases using algorithms that ensure desirable properties about the coverage. For example, generate cases for all possible fault monitors, and across all state change boundaries. Of course, the scope of coverage is determined by the test environment capabilities, where a faster-than-real-time, high-fidelity, software-only simulation would allow the broadest coverage. Even real-time systems that can be replicated and run in parallel, and that have reliable set-up and operations features provide an excellent resource for automated testing. Making detailed predictions for the outcome of such tests can be difficult, and when algorithmic means are employed to produce hundreds or even thousands of cases, generating predicts individually is impractical, and generating predicts with tools requires executable models of the design and environment that themselves require a complete test program. Therefore, evaluating the results of large number of mission scenario tests poses special challenges. A good approach to address this problem is to automatically score the results based on a range of metrics. Although the specific means of scoring depends highly on the application, the use of formal scoring - metrics has high value in identifying and prioritizing anomalies, and in presenting an overall picture of the state of the test program. In this paper we present a case study based on automatic generation and assessment of faulted test runs for the Dawn mission, and discuss its role in optimizing the allocation of resources for completing the test program.

Testing challenges↗

DOE OSTI2023

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Validating automated resonance evaluation with synthetic data

The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process and enhancing reproducibility. This research aims to provide a methodology, framework, and metrics for the validation of automated nuclear data evaluation software leveraging high-quality synthetic data that closely mimic real experimental observables. An introduced error metric provides a scale and intuitive measure of the evaluation quality by quantifying the estimate’s accuracy and performance across the specified energy range. Synthetic data provides access to experimental observables and underlying resonance parameters, enabling comparison of different evaluations. The methodology is demonstrated using Ta-181 isotope data in the resolved resonance region. The Automated Resonance Identification Subroutine (ARIS), which operates without prior resonance information, was used to test and showcase the framework’s capabilities utilizing the proposed error metrics. The results demonstrate the effectiveness of the proposed approach and framework for optimizing software parameters and testing hypotheses through “what-if” controlled experiments, such as modifying assumptions about experimental conditions or average resonance parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Ultraviolet digital holographic microscopy (DHM) of micron-scale particles from shocked Sn ejecta

A cloud of very fast, O (km/s), and very fine, O (µm), particles may be ejected when a strong shock impacts and possibly melts the free surface of a solid metal. To quantify these dynamics, this work develops an ultraviolet, long-working distance, two-pulse Digital Holographic Microscopy (DHM) configuration and is the first to replace film recording with digital sensors for this challenging application. A proposed multi-iteration DHM processing algorithm is demonstrated for automated measures of the sizes, velocities, and three-dimensional positions of non-spherical particles. Ejecta as small as 2 µm diameter are successfully tracked, while uncertainty simulations indicate that particle size distributions are accurately quantified for diameters ≥4 µm. These techniques are demonstrated on three explosively driven experiments. Measured ejecta size and velocity statistics are shown to be consistent with prior film-based recording, while also revealing spatial variations in velocities and 3D positions that have yet to be widely investigated. Having eliminated time-consuming analog film processing, the methodologies proposed here are expected to significantly accelerate future experimental investigation of ejecta physics.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Laboratory-Scale Coal-Derived Graphene Process (Final Report)

The Energy & Environmental Research Center (EERC) conducted a laboratory-scale coal-derived graphene (CDG) project focused on developing a technological process for making graphene from four U.S. domestic coal or coal wastes, including lignite from North Dakota, subbituminous coal from Wyoming, bituminous coal from Utah, and anthracite from Pennsylvania. The project was divided into two performance or budget periods (BPs), with BP1 comprising the up-front laboratory experiments to make graphene materials from coal beginning on May 1, 2020, to April 30, 2022. BP2 was conducted from May 1, 2022, to April 30, 2023, and was focused on analyzing the CDG process economic feasibility and the technical gaps for technological scale-up and commercialization. During this project, a few different coal-derived high-value products have been demonstrated, including graphite, graphene oxide (GO), reduced graphene oxide (rGO), and graphene quantum dots (GQDs). A new graphite microstructure was discovered and named “croissant graphite” because of the exterior morphological and textural resemblance to croissant food items sold in commercial groceries stores. The new graphite structure and the associated preparation from coal or coal waste feedstocks has been the subject of a U.S. patent application. The systematic experimental processes involving coal cleaning, upgrading, and conversion to high-value carbon products culminated into a developed upgraded coal-to-products (UCP) technology that is being pursued for potential fast-track commercialization, if funding is available. It is envisioned that commercialization of the UCP technology would increase consumption of U.S. domestic coals or coal wastes to make environmentally sustainable high-value products for the electronics industry, high-energy-storage applications, and clean energy technologies such as electric vehicle (EV) lithium-ion batteries (LIBs), for which graphite has become a critical mineral commodity. Croissant graphite microstructures, when observed by field emission scanning electron microscopy (FESEM), display wavy surface morphology and often grow from a base that is made of graphitized particles with honeycomb-like layers, which are believed to be graphene layers. While more studies are needed to fully ascertain the mechanisms of the croissant graphite microstructure formation, it is postulated that their growth may begin from curling of the graphene sheets into ribbon-like structures, and continuous growth and densification of the ribbon-like structures forms croissant microstructures. Additional studies are ongoing to evaluate the electrochemical performance of croissant graphite for LIB applications and to determine the experimental conditions necessary to tune on/off croissant formation so that it can be either optimized or suppressed depending on performance evaluation results. In addition to the discovery of croissant graphite, the graphitization process from the four coal ranks in general was successful. X-ray diffraction (XRD) analysis showed that the degree of graphitization (DoG) ranged from 12% to 80% in an early sample set, and further optimization on lignite coal produces a DoG of about 92%, which was spectacular to see as lignite is the lowest-rank coal. Thus, it is expected that the graphitization performance for higher-rank coals will be similar or better when optimized as well. The coal-derived graphite was used to make GO and rGO. Analytical characterization, e.g., by methods such as Raman spectroscopy, XRD, Fourier transform infrared (FTIR) spectroscopy and FESEM, showed that the sequence of converting the coal to graphite, exfoliating it to GO, and then chemically reducing the GO to rGO was successful. Although coal naturally contains aromatic compounds and some relatively small-sized condensed aromatic units, it does not contain graphene sheets. In the UCP process, the aromatic domains in the coals, particularly low-rank coals, are concentrated and condensed further into graphene sheets, which are ordered into a 3D stack during graphitization. The synthesized graphite is then unpacked by methods such as exfoliation to various graphene products. GQDs were synthesized from all four coal types, and their optical properties were demonstrated to be tunable by the coal precursor preprocessing treatments. In all four coal types, enhanced optical properties were observed for the produced GDQs with incremental improvements made to the coal precursors. GQDs produced from raw coal samples displayed lower ultraviolet–visible (UV–Vis) spectroscopy absorbance intensity compared to those obtained from cleaned and upgraded coal residues. The photoluminescence (PL) intensities also varied with pretreatment conditions and with the concentration of GQDs in aqueous solutions. GQDs obtained from anthracite show longer emission wavelengths and can be excited by visible light as opposed to GQDs derived from the other coal ranks. UV fluorescence 3D maps and spectra revealed that the emission wavelength at which the GQDs solutions display the highest intensity was slightly redshifted based on the coal precursor pretreatments. In low-rank coal (lignite and subbituminous) samples, two clusters were observed in the maps for GQDs, which may suggest that there are potentially two types of fluorophores in solution or two main size populations. The ability to tune the properties of GQDs based on processing methods can be exploited to make GQDs for various optical display or optoelectronics applications. The results also highlight the importance of removing coal-borne impurities to improve the quality of the coal precursor for preparation of graphene products. Coal and/or coal wastes preprocessing methods were developed and applied to clean and upgrade the coal precursors prior to graphitization and subsequent conversion to graphene products. The preprocessing methods involve high specific-gravity separations, mineral acid cleaning (no hydrofluoric acid), and subsequent upgrading by reducing the coal-borne heteroatom (nitrogen, sulfur, and oxygen) content using proprietary chemical agents. Analytical characterization revealed that the preprocessing steps were successful, with ash reductions that range from 38% to 80% and residual ash content that was below the 5 wt% initial target. Based on proximate and ultimate analysis, the heteroatom reduction reactions produced upgraded coal residues with the oxygen content reduced by 8% to 24%, with additional reductions in the nitrogen and sulfur contents. An initial assessment of the waste streams from the UCP process shows very small to negligible environmental impact due to CO 2 , NO x , and SO x because most process steps are performed under inert atmosphere with argon. Consequently, reactive oxygen environments that tend to create these species are avoided. The inorganic and potentially hazardous species are released into aqueous waste streams that are easy to handle for proper disposal. The liquid waste streams were found to contain low-level concentrations of rare-earth elements (REEs), which could be concentrated and recovered as value-added by-products. Additionally, the volatile and gaseous fractions from carbonization and heat treatment contain useful organic compounds that can also be recovered as potential valuable by-products. Thus, the UCP technology is considered an environmentally sustainable and promising emerging technology for making high-value products from coal and coal wastes, with potential additional value-added by-products. Analysis of potential markets for the coal-derived carbon products shows a strong demand in both niche market sectors and across a wide variety of other industrial sectors. Graphite is currently considered a critical mineral commodity that has a large and growing demand in the LIB industry for EV applications. Based on data from Fortune Business Insights (2022) and Marketwatch (2023) reports, the average global graphite market is projected to reach about 33 billion by 2028, growing at a compound annual growth rate (CAGR) of about 7%, with much of this growth expected to be in the LIB industry. GO and rGO have strong market potentials in various application areas, such as coatings for anticorrosion, anti-icing, and antimicrobial protection, thermal barriers, wear resistance, sensors, additive manufacturing such as 3D inks, and others. GQDs are the emerging key player in the bioimaging, photovoltaics, and light-emitting diodes (LEDs) applications, with the potential to replace traditional semiconductor quantum dots (SQDs), which are based on metallic systems that are more toxic and more expensive. Biomedical applications of GQDs are becoming more attractive because of low to no toxicity and extremely low cost compared to SQDs. The major challenges for scale-up and commercialization of coal-derived carbon products such as graphene vary from the inherent attributes of graphene itself to reluctance to accept graphene in new manufacturing processes because of the uncertainty of the unknown. The 2D nature of graphene materials with a thickness of one atom presents significant challenges to proper handling/processing, and process scale-up becomes difficult because it requires high-end, expensive equipment, even for routine handling and analysis for quality assurance and control. Pristine graphene can also be extremely difficult to work into other matrices, thus hindering downstream processibility, especially at large scale. Currently, the cost of graphene and graphene products is still high and presents an economic risk that tends to slow down investment in scaling up emerging technologies. The lack of a standard for graphene materials for quality assurance and quality control poses a great challenge not only for the markets but also for commercialization efforts. A first-look economic feasibility analysis of the UCP technology provided valuable information that suggests the UCP process would be feasible, especially when it is scaled to a pilot scale and could be more competitive at the full scale. Graphitization was found to be the most energy-consuming and most capital-intensive step in the overall process. In small laboratory- and bench-scale experiments, labor is a significant contributor to the total process costs. Although these energy, capital, and labor constraints contribute to a higher selling price for the product, a preliminary economic model suggests that the process would be feasible at large scale when the process is fully integrated, optimized, and automated.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Rhodotorula toruloides Nitrogen Limitation PTM Profiling Multi-Omics (TZ-DP1)

The purpose of this experiment was to evaluate the regulatory stress response of Oleaginous yeast species Rhodotorula toruloides NBRC 0880 (JGI strain IFO0880 v4.0) under nitrogen-rich and nitrogen-limited conditions over time. Time course experimental samples (0, 24, 48, and 72 hours after inoculation) were prepared using a semi-automated multi-PTM proteomic approach, using tandem mass tag 18-plex (TMT18), and lipidome remodeling for downstream multi-omics analysis. Processed datasets are openly accessible from PNNL DataHub and contain secondary processed proteomic (redox, phospho, and global TMT) and lipidomic (positive and negative ion mode) results files and experimental design metadata.

59 BASIC BIOLOGICAL SCIENCES↗