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NASA NTRS2018

Nature of and Lessons Learned from Lunar Ice Cube and the First Deep Space Cubesat 'Cluster'

Cubesats operating in deep space face challenges Earth-orbiting cubesats do not. 15 deep space cubesat 'prototypes' will be launched over the next two years including the two MarCO cubesats, the 2018 demonstration of dual communication system at Mars, and the 13 diverse cubesats being deployed from the SLS EM1 mission within the next two years. Three of the EM1 cubesat missions, including the first deep space cubesat 'cluster', will be lunar orbiters with remote sensing instruments for lunar surface/regolith measurements. These include: Lunar Ice Cube, with its 1-4 micron broadband IR spectrometer, BIRCHES, to determine volatile distribution as a function of time of day; Lunar Flashlight, to confirm the presence of surface ice at the lunar poles, utilizing an active source (laser), and looking for absorption features in the returning signal; and LunaH-Map to characterize ice at or below the surface at the poles with a compact neutron spectrometer. In addition, the BIRCHES instrument on Lunar Ice Cube will provide the first demonstration of a microcryocooler (AIM/IRIS) in deep space. Although not originally required to do so, all will be delivering science data to the Planetary Data System, the first formal archiving effort for cubesats. 4 of the 20 recently NASA-sponsored (PSDS3) study groups for deep space cubesat/smallsat mission concepts were lunar mission concepts, most involving 12U cubesats. NASA SIMPLEX 2/SALMON 3 AO will create ongoing opportunities for low-cost missions as 'rides' on government space program or private sector vehicles as these become available.

Ice Cube↗

NASA NTRS2016

Enabling Affordable Communications for the Burgeoning Deep Space Cubesat Fleet

The low costs of development and launch, coupled with new propulsive technologies, have made CubeSats increasingly popular for use in science investigations beyond geosynchronous orbit. As this deep space CubeSat fleet grows in size, the challenge of trying to provide affordable communications for it grows commensurately. The mass, power, and volume constraints inherent to CubeSats limit the antenna size and transmit power that they can use to close the deep space link. As a consequence, CubeSats need to rely more heavily on ground antennas that are characterized by large aperture, low noise temperatures, and relatively high-power transmitters. Such antennas are not in great abundance, nor are they inexpensive to build. For this reason, NASA’s Deep Space Network has been advocating a three-pronged approach to meeting anticipated CubeSat demand: development of simultaneous, shared-beam multi-spacecraft communications capabilities, development of large-antenna cross-support arrangements with other agencies and universities, and development of less uplink-intensive navigation techniques. This paper focuses on the pursuit of simultaneous, shared-beam multi-spacecraft communications capabilities. While the Multiple Spacecraft per Antenna (MSPA) technique has existed for over a decade, it has generally been limited to supporting downlink for just two in-beam spacecraft at a time. This limitation has largely been a function of the number and cost of available receivers. A relatively new technique that potentially overcomes this limitation is Opportunistic MSPA (OMSPA). Instead of relying on additional receivers, OMSPA makes use of a digital recorder at each ground station that is capable of capturing the intermediate frequency (IF) signals from every spacecraft in the antenna beam within the frequency bands of interest. When CubeSat projects see one or more opportunities for their CubeSat(s) to intercept the traditionally scheduled antenna beam of a “host” spacecraft, they can arrange for the CubeSat(s) to transmit open loop during those opportunities. Via a secure Internet site, the CubeSat mission operators can then retrieve the time- and frequency-relevant portions of the digital recording for subsequent demodulation and decoding, or subscribe to a service that does it for them. This “opportunistic” use of a host spacecraft’s ground antenna beam potentially enables CubeSat projects to make use of large ground antennas for downlink without having to compete with bigger, better-funded missions for antenna time in the formal scheduling process. In so doing, it also potentially enables CubeSat projects to avoid the aperture fees associated with formally scheduled downlink time - fees that factor into the “bottom-line” of competitively-bid NASA missions and that actually get charged to non-NASA missions. Taking advantage of these potential OMSPA benefits, however, will require CubeSat projects to pursue mission designs that ensure at least periodic in-beam operations relative to a “host” spacecraft. In the case of a constellation of CubeSats with inter-spacecraft distances that do not extend outside of the beam-width of the desired ground antenna at the given range, one CubeSat can serve as the “host” and have a formally scheduled downlink while the rest of the CubeSats can downlink essentially for “free” via OMSPA. Deep space CubeSats, of course, will need uplink in addition to downlink. Beyond commanding, this need is driven by the use of two-way ranging and Doppler for navigation. While OMSPA may not directly facilitate uplink, it does have the potential to free up antennas for those spacecraft that periodically require formally scheduled links for commanding and two-way radio metrics. NASA is also exploring the physical feasibility of an in-beam, simultaneous multi-spacecraft uplink technique. As with OMSPA, if successful, it will require little new equipment, further enabling affordable deep space CubeSat communications.

Abraham, Douglas S.↗

NASA NTRS2016

Deep Space Network: the Next 50 Years

In 2014, NASA’s Deep Space Network (DSN) celebrated its 50th year of enabling exploration of the Moon, Solar System planets, and beyond. During those 50 years, the DSN has grown along with the associated spacecraft flight systems, providing some 13 orders of magnitude communications systems improvement. It has also contributed to many of the world’s most important scientific discoveries and provided substantial technological spinoffs that have become part of everyday life on Earth. Studies of the next 25 years indicate that deep space missions will need an order of magnitude increased communications performance per decade - and this is likely to continue beyond that time. As exhibited by this meeting, another major change happening right now is the emergence of a standardized international community for tracking deep space missions. The future definitely involves increased international cooperation. This paper explains the near term plans already underway for the DSN. We also consider the plans and possibilities for deep space optical communications. Finally, we will discuss opportunities for international participation in the next 50 years of deep space exploration.

Deutsch, Leslie J.↗

NASA NTRS2016

Deep Space Network: The Next 50 Years

In 2014, NASA’s Deep Space Network (DSN) celebrated its 50th year of enabling exploration of the Moon, Solar System planets, and beyond. During those 50 years, the DSN has grown along with the associated spacecraft flight systems, providing some 13 orders of magnitude communications systems improvement. It has also contributed to many of the world’s most important scientific discoveries and provided substantial technological spinoffs that have become part of everyday life on Earth. Studies of the next 25 years indicate that deep space missions will need an order of magnitude increased communications performance per decade – and this is likely to continue beyond that time. As exhibited by this meeting, another major change happening right now is the emergence of a standardized international community for tracking deep space missions. The future definitely involves increased inter-national cooperation. This paper explains the near term plans already underway for the DSN. We also consider the plans and possibilities for deep space optical communications. Finally, we will discuss opportunities for international participation in the next 50 years of deep space exploration.

Deutsch, Leslie J.↗

NASA NTRS2020

Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability

This study analyses the observed monthly deseasonalized and detrended variability of the tropical radiation budget, and suggests that variations of the lower-tropospheric stability and of the spatial organization of deep convection both strongly contribute to this variability. Satellite observations show that on average over the tropical belt, when deep convection is more aggregated, the free troposphere is drier, the deep-convective cloud coverage is less extensive, and the emission of heat to space is increased; an enhanced aggregation of deep convection is thus associated with a radiative cooling of the tropics. An increase of the tropical-mean lower-tropospheric stability is also coincident with a radiative cooling of the tropics, primarily because it is associated with more marine low clouds and an enhanced reflection of solar radiation, although the free-tropospheric drying also contributes to the cooling. The contributions of convective aggregation and lower-tropospheric stability to the modulation of the radiation budget are complementary, largely independent of each other, and equally strong. Together, they account for more than sixty percent of the variance of the tropical radiation budget. Satellite observations are thus consistent with the suggestion from modeling studies that the spatial organization of deep convection substantially influences the radiative balance of the Earth. This emphasizes the importance of understanding the factors that control convective organization and lower-tropospheric stability variations, and the need to monitor their changes as the climate warms.

convective aggregation↗

NASA NTRS2017

Deep-Space Navigation Using Optical Communications Systems

Optical communication links using lasers can potentially deliver data rates much higher than those possible using radio frequencies. If optical communications equipment is going to be carried by future deep-space missions, this equipment, with some adaptations, could also be used to perform tracking for trajectory determination. A number of experiments have been performed in Earth orbit and in lunar orbit using optical data links, while other missions have demonstrated optical links over interplanetary distances. Laser ranging using corner cube retroreflectors is a well-established technique that has been used for orbit determination of Earth orbiting spacecraft, for geodesy, and for lunar research, achieving centimeter-level precisions, but it is not a practical method for deep-space distances. There are two main optical tracking types that are being considered for deep-space navigation. The first is optical astrometry of spacecraft: a telescope on the ground images the laser beam coming from a spacecraft against the star background, determining its plane-of-sky position as seen from the observatory. This type will greatly benefit from the release of the high-accuracy star catalog produced by ESA’s Gaia mission, allowing for the generation of plane-of-sky measurements with an accuracy similar to that obtained today using VLBI tracking techniques. The second is optical ranging using active optical systems at both ends of the link, requiring a more careful design of the spacecraft optical communications system. One of the advantages of using optical frequencies is that they are not affected by charged particles in the signal path the way that radio frequencies are, eliminating solar plasma and ionospheric effects from the light-time calculation and the corresponding noise. On the other hand, clouds would preclude any type of optical communication, and daytime light scattering precludes astrometric measurements. This paper presents our analysis so far of the performance that could be achieved using optical data types in a number of deep-space scenarios. One of the questions that we are trying to answer is whether spacecraft equipped with optical communications terminals would also need to carry radio-frequency equipment for navigational purposes. We also want to understand how accurately we will be able to navigate spacecraft in different mission types and phases, and what would be the constraints, advantages, and disadvantages of using optical communications systems for deep-space navigation.

Karimi, Reza↗

NASA NTRS2018

Nature of and Lessons Learned from Lunar Ice Cube and the First Deep Space Cubesat 'Cluster'

Bujold, Emily↗

NASA NTRS2022

GNSS-RO Deep Refraction Signals from Moist Marine Atmospheric Boundary Layer (MABL)

The marine atmospheric boundary layer (MABL) has a profound impact on sensible heat and moisture exchanges between the surface and the free troposphere. The goal of this study is to develop an alternative technique for retrieving MABL-specific humidity (q) using GNSS-RO data in deep-refracted signals. The GNSS-RO signal amplitude (i.e., signal-to-noise ratio or SNR) at the deep straight-line height (H(SL)) was been found to be strongly impacted by water vapor within the MABL. This study presents a statistical analysis to empirically relate the normalized SNR (S(RO)) at deep HSL to the MABL q at 950 hPa (~400 m). When compared to the ERA5 reanalysis data, a good linear q–S(RO) relationship is found with the deep H(SL) S(RO) data, but careful treatments of receiver noise, SNR normalization, and receiver orbital altitude are required. We attribute the good q–S(RO) correlation to the strong refraction from a uniform, horizontally stratiform and dynamically quiet MABL water vapor layer. Ducting and diffraction/interference by this layer help to enhance the S(RO) amplitude at deep H(SL). Potential MABL water vapor retrieval can be further developed to take advantage of a higher number of S(RO) measurements in the MABL compared to the Level-2 products. A better sampled diurnal variation of the MABL q is demonstrated with the S(RO) data over the Southeast Pacific (SEP) and the Northeast Pacific (NEP) regions, which appear to be consistent with the low cloud amount variations reported in previous studies.

diurnal variation↗

NASA NTRSDate not supplied

Neurovascular Responses to Simulated Deep Space Radiation in a Human Organ-on-a-Chip Model

A major health risk for human deep space exploration is central nervous system (CNS) damage by galactic cosmic ray radiation. Simulated galactic cosmic rays or their components, especially the high-linear energy transfer (LET) particles such as 56Fe ions, have been shown to cause CNS damage, neuroinflammation and cognitive dysfunction in rodent models, but their effects on human CNS remain to be investigated. CNS damage from any insult, including ionizing radiation, is partially mediated by the blood-brain barrier (BBB), which regulates interactions between CNS and the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuroinflammation. However, there have been few studies on BBB and astrocyte functions in regulating CNS responses, especially in human tissue analogs. Therefore, we utilized a high-throughput 3D organ-on-a-chip system, seeded with human induced pluripotent stem cell-derived astrocytes and brain endothelial cells, or brain endothelial cells alone, to study human neurovascular responses to simulated deep space radiation. We investigated the permeability and morphology of vascular structures formed by endothelial cells, as well as oxidative stress and secreted cytokines and chemokine levels over 1-7 days after irradiation with 0.25 – 0.5 Gy 5-ion simplified simulated galactic cosmic rays or 0.3 – 0.8 Gy high-LET 600 MeV/n 56Fe particles, and compared the outcomes to low-LET X-ray irradiation. We observed that simulated deep space radiation caused delayed astrocyte activation in a pattern resembling CNS responses to brain injury, caused oxidative stress and the production of inflammatory cytokines, and compromised BBB integrity by damaging tight junctions, thus increasing vascular permeability. Furthermore, our results indicate that astrocytes have a dual role in regulating radiation responses: they exacerbate blood-brain barrier permeability early after irradiation, followed by switching to a more protective scar-like phenotype by reducing oxidative stress and pro-inflammatory cytokine and chemokine secretion. In a follow-up study using the same platform, we investigated the dose-rate effects of ionizing radiation, by exposing our model to chronic, low dose-rate, gamma radiation. Our model was significantly improved by adding additional cell types composing the BBB, modelling immune cell infiltration into the brain, and studying the effect of an antioxidant, to measure more complex outcomes and model more closely the effect of deep space radiation on the human BBB. In summary, our results present a human neurovascular model for space radiation studies and potential future automated payload adaptation, and suggest astrocyte regulatory mechanisms as targets for countermeasures to mitigate human neurovascular impairments during deep space exploration.

Ionizing radiation↗

NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗

NASA NTRS2022

Deep Space Atomic Clock Technology Demonstration Mission Results

The Deep Space Atomic Clock (DSAC), a NASA Technology Demonstration Mission, was launched into low-Earth orbit on June 25, 2019 as a hosted payload aboard General Atomics’ Orbital Test Bed (OTB) spacecraft. The DSAC mission has been conducting a two-year demonstration of a mercury ion atomic clock to characterize its space-based performance and to validate its utility for deep space navigation and radio science. Analysis of the collected data using JPL’s GIPSY-OASIS software has shown DSAC’s AD at one-day to be near 310-15; much better than required AD of 210-14. Such low spacecraft clock errors will enable one-way radiometric tracking data with precision equivalent to or better than current-day two way tracking data, allowing a shift to a more efficient and flexible one-way deep space navigation architecture. To verify this, an analog deep space navigation experiment was performed using JPL’s operational navigation software (Monte). The experiment recovered orbit solutions with reduced data sets and geometric variations that are more representative of deep space missions, and showed that orbit determination using DSAC derived data is on par with more traditional two-way datatypes. As a technology demonstrator, DSAC’s development focus has been on maturing the mercury ion trap clock technology rather than achieving the smallest size, weight, and power (SWaP). Over the course of DSAC’s development the project has identified numerous improvements that could be made to significantly reduce SWaP for DSAC’s next version. Indeed, DSAC-2 was recently selected by NASA for further demonstration on the VERITAS mission to Venus. This work will review the DSAC technology, mission, and results from its two-year mission.

Wang, Rabi↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRSDate not supplied

Addressing the High-Rate Deep Space Communications Shortfall in NASA’s Space Technology Mission Directorate's Envisioned Future

NASA’s Space Technology Mission Directorate (STMD) has identified key technologies needed for future crewed and robotic exploration and science missions. STMD is helping to build the civilian technology base by working with other NASA Mission Directorates, other United States government agencies, commercial industry, and academia to identify technology shortfalls and to develop plans to address them. One critical area of shortfalls lies with deep space communications and navigation. While NASA had huge success to date with the Deep Space Network (DSN), recent studies have shown that without enhancements to current systems, the DSN will be unable to support the anticipated increases in the pace of space exploration or the expected higher data rates from deep space needed soon without severely impacting other missions. High-rate communications from the Moon and beyond is needed to enable future exploration and science missions currently being developed or under consideration. For example, a robust communications infrastructure will be needed to support a sustained human presence on the Moon and its eventual industrialization. High data rate trunk lines between the Earth and the Moon are needed to reduce the number of individual links. The human exploration of Mars will also require high-rate communications between Earth and Mars. Return data rates to Earth from Mars for a single link, for example, are anticipated to be greater than 100 Mb/s; forward data rates to Mars, based on experience from the International Space Station, are anticipated to be greater than 20 Mb/s. Future deep space science missions will also require higher data rates than possible with today’s technology and the current capabilities of the DSN. To support future exploration and science needs, it will be necessary to upgrade the DSN to enhance its radio frequency (RF) capabilities. In addition, it is envisioned that NASA will gradually introduce optical communications to augment its RF systems. Optical communications will enable new science and exploration missions by providing high data rates and better navigation over long distances. This paper will briefly describe STMD’s envisioned future for deep space communications in the 2030+ timeframe and the technology roadmaps being developed for both radio frequency and optical systems.

Bernard Edwards↗

NASA NTRS2012

Deep Space Communication

ITU defines deep space as the volume of Space at distances from the Earth equal to, or greater than, 2 106 km. Deep Space Spacecraft have to travel tens of millions of km from Earth to reach the nearest object in deep space. Spacecraft mass and power are precious. Large ground-based antennas and very high power transmitters are needed to overcome large space loss and spacecraft's small antennas and low power transmitters. Navigation is complex and highly dependent on measurements from the Earth. Every deep space mission is unique and therefore very costly to develop.

deep space communications↗

DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗

DOE OSTI2024

A deep learning approach to fast analysis of collective Thomson scattering spectra

Fast analysis of collective Thomson scattering ion acoustic wave features using a deep convolutional neural network model is presented. The network was trained from spectra to predict the plasma parameters, including ion velocities, population fractions, and ion and electron temperatures. A fully kinetic particle-in-cell simulation was used to model a laboratory astrophysics experiment and simulate a diagnostic image of the ion acoustic wave feature. Network predictions were compared with Bayesian inference of the plasma model parameters for both the simulated and experimentally measured images. Both approaches were fairly accurate predicting the simulated image and the network predictions matched a good portion of the Bayesian results for the experimentally measured image. The Bayesian approach is more robust to noise and motivates future work to train deep learning models with realistic noise. The advantage of the deep learning model is making thousands of predictions in a few hundred milliseconds, compared to a few seconds to minutes per prediction for the optimization and Bayesian approaches presented here. The results demonstrate promising capabilities of deep learning models to analyze Thomson data orders of magnitude faster than conventional methods when using the neural network for standalone analysis. If more rigorous analysis is needed, neural network predictions can be used to quickly initialize other optimization methods and increase chances of success. This is especially useful when the dataset becomes very large or highly dimensional and manually refining initial conditions for the entire dataset are no longer tractable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Novel candidate taxa contribute to key metabolic processes in Fennoscandian Shield deep groundwaters

The continental deep biosphere contains a vast reservoir of microorganisms, although a large proportion of its diversity remains both uncultured and undescribed. In this study, the metabolic potential (metagenomes) and activity (metatranscriptomes) of the microbial communities in Fennoscandian Shield deep subsurface groundwaters were characterized with a focus on novel taxa. DNA sequencing generated 1270 de-replicated metagenome-assembled genomes and single-amplified genomes, containing 7 novel classes, 34 orders, and 72 families. The majority of novel taxa were affiliated with Patescibacteria, whereas among novel archaea taxa, Thermoproteota and Nanoarchaeota representatives dominated. Metatranscriptomes revealed that 30 of the 112 novel taxa at the class, order, and family levels were active in at least one investigated groundwater sample, implying that novel taxa represent a partially active but hitherto uncharacterized deep biosphere component. The novel taxa genomes coded for carbon fixation predominantly via the Wood–Ljungdahl pathway, nitrogen fixation, sulfur plus hydrogen oxidation, and fermentative pathways, including acetogenesis. These metabolic processes contributed significantly to the total community’s capacity, with up to 9.9% of fermentation, 6.4% of the Wood–Ljungdahl pathway, 6.8% of sulfur plus 8.6% of hydrogen oxidation, and energy conservation via nitrate (4.4%) and sulfate (6.0%) reduction. Key novel taxa included the UBA9089 phylum, with representatives having a prominent role in carbon fixation, nitrate and sulfate reduction, and organic and inorganic electron donor oxidation. These data provided insights into deep biosphere microbial diversity and their contribution to nutrient and energy cycling in this ecosystem.

Candidatus↗

DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗