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NASA NTRS2000

Radiation Physics for Space and High Altitude Air Travel

Galactic cosmic rays (GCR) are of extra-solar origin consisting of high-energy hydrogen, helium, and heavy ions. The GCR are modified by physical processes as they traverse through the solar system, spacecraft shielding, atmospheres, and tissues producing copious amounts of secondary radiation including fragmentation products, neutrons, mesons, and muons. We discuss physical models and measurements relevant for estimating biological risks in space and high-altitude air travel. Ambient and internal spacecraft computational models for the International Space Station and a Mars mission are discussed. Risk assessment is traditionally based on linear addition of components. We discuss alternative models that include stochastic treatments of columnar damage by heavy ion tracks and multi-cellular damage following nuclear fragmentation in tissue.

Cucinotta, F. A.↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.

Life Sciences data↗

NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

Life Sciences data↗

NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗

DOE OSTI2023

A computational modeling framework for pre-clinical evaluation of cardiac mapping systems

There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant “gold standard”. Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system’s ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a “silver standard” comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1 ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2000

Modeling of Solid Waste Processing Options in BIO-Plex

BIO-Plex is a ground-based test bed currently under development by NASA for testing technologies and practices that may be utilized in future long-term life support missions. All aspects of such an Advanced Life Support (ALS) System must be considered to confidently construct a reliable system, which will not only allow the crew to survive in harsh environments, but allow the crew time to perform meaningful research. Effective handling of solid wastes is a critical aspect of the system, especially when recovery of resources contained in the waste is required. This is particularly important for ALS Systems configurations that include a Biomass Production Chamber. In these cases, significant amounts of inedible biomass waste may be produced, which can ultimately serve as a repository of necessary resources for sustaining life, notably carbon, water, and plant nutrients. Numerous biological and physicochemical solid waste processing options have been considered. Biological options include composting, aerobic digestion, and anaerobic digestion. Physicochemical options include pyrolysis, SCWO (supercritical water oxidation), various incineration configurations, microwave incineration, magnetically assisted gasification, and low temperature plasma reaction. Modeling of these options is a necessary step to assist in the design process. A previously developed top-level model of BIO-Plex implemented in MATLAB Simulink (r) for the use of systems analysis and design has been adopted for this analysis. Presently, this model only considered incineration for solid waste processing. Present work, reported here, includes the expansion of this model to include a wider array of solid waste processing options selected from the above options, bearing in mind potential, near term solid waste treatment systems. Furthermore, a trade study has also been performed among these solid waste processing technologies in an effort to determine the ideal technology for long-term life support missions.

Rodriguez, Luis F.↗

DOE OSTI2024

Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions

Methane (CH 4 ) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH 4 fluxes is examined at 14 FLUXNET- CH 4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH 4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH 4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Causes and consequences of experimental variation in Nicotiana benthamiana transient expression

Infiltration of Agrobacterium tumefaciens into Nicotiana benthamiana has become a foundational technique in plant biology, enabling efficient delivery of transgenes in planta with technical ease, robust signal, and relatively high throughput. Despite transient expression’s prevalence in disciplines such as synthetic biology, little work has been done to describe and address the variability inherent in this system, a concern for experiments that rely on highly quantitative readouts. In a comprehensive analysis of N. benthamiana agroinfiltration experiments, we model sources of variability that affect transient expression. Our findings emphasize the need to validate normalization methods under the specific conditions of each study, as distinct normalization schemes do not always reduce variation either within or between experiments. Using a dataset of 1915 plants collected over three years, we develop a model of variation in N. benthamiana transient expression, using power analysis to determine the number of individual plants required for a given effect size. Drawing on our longitudinal data, these findings inform practical guidelines for minimizing variability through strategic experimental design and power analysis, providing a foundation for more robust and reproducible use of N. benthamiana in quantitative plant biology and synthetic biology applications.

Tang, Sophia N. [Joint BioEnergy Institute (JBEI),↗

NASA NTRS1999

Effects of Space Flight-Associated Stimuli on Development of Murine and Medaka Sensory-Motor Systems

The major goal of these studies was to continue investigations into the influence of altered gravitational fields on the development and function of the vertebrate brain and nervous system. Of major focus during the 18-month finding period of this award was the maintenance of the animals used in the experimental mouse and medaka model paradigms. The experiments focused on characterization of stress-sensitive periods in neural development and immediate or delayed effects on gene expression, physiology and behavior. The hypothesis under investigation was that the environment of space will have biologically significant effects on the development and function of the vertebrate nervous system. We have postulated that these effects will be more significant on certain neural compartments, such as the vestibular-motor system, and that these effects will have greater impact at particular stages of embryonic and post-natal development of the animal. Development of the central nervous system is well known for its vulnerability and sensitivity to environmental stimuli, although the effects of gravitational influences are poorly understood. The long-term goals of this research effort, initiated previously and continued in limited capacity during this interim period, were to provide important new information on the effects of altered environments during these critical periods.

Wolgemuth, Debra J.↗

DOE OSTI2022

Sequence Modulates Polypeptoid Hydration Water Structure and Dynamics

We use molecular dynamics simulations to investigate the effect of polypeptoid sequence on the structure and dynamics of its hydration waters. Polypeptoids provide an excellent platform to study small-molecule hydration in disordered polymers, as they can be precisely synthesized with a variety of sidechain chemistries. We examine water behavior near a set of peptoid oligomers in which the number and placement of nonpolar versus polar sidechains are systematically varied. To do this, we leverage a new computational workflow enabling accurate sampling of polypeptoid conformations. We find that the hydration waters are less dense, are more tetrahedral, and have slower dynamics compared to bulk water. The magnitude of these shifts increases with the number of nonpolar groups. Here, we also find that shifts in the water structure and dynamics are strongly correlated, suggesting that experimental insight into the dynamics of hydration water obtained by Overhauser dynamic nuclear polarization (ODNP) also contains information about water structural properties. We then demonstrate the ability of ODNP to probe site-specific dynamics of hydration water near these model peptoid systems.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Warming increased bark beetle-induced tree mortality by 30% during an extreme drought in California

Quantifying the responses of forest disturbances to climate warming is critical to our understanding of carbon cycles and energy balances of the Earth system. The impact of warming on bark beetle outbreaks is complex as multiple drivers of these events may respond differently to warming. Using a novel model of bark beetle biology and host tree interactions, we assessed how contemporary warming affected western pine beetle (Dendroctonus brevicomis) populations and mortality of its host, ponderosa pine (Pinus ponderosa), during an extreme drought in the Sierra Nevada, California, United States. When compared with the field data, our model captured the western pine beetle flight timing and rates of ponderosa pine mortality observed during the drought. In assessing the influence of temperature on western pine beetles, we found that contemporary warming increased the development rate of the western pine beetle and decreased the overwinter mortality rate of western pine beetle larvae leading to increased population growth during periods of lowered tree defense. We attribute a 29.9% (95% CI: 29.4%–30.2%) increase in ponderosa pine mortality during drought directly to increases in western pine beetle voltinism (i.e., associated with increased development rates of western pine beetle) and, to a much lesser extent, reductions in overwintering mortality. These findings, along with other studies, suggest each degree (°C) increase in temperature may have increased the number of ponderosa pine killed by upwards of 35%–40% °C-1 if the effects of compromised tree defenses (15%–20%) and increased western pine beetle populations (20%) are additive. Due to the warming ability to considerably increase mortality through the mechanism of bark beetle populations, models need to consider climate's influence on both host tree stress and the bark beetle population dynamics when determining future levels of tree mortality.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

PAVC Gridded 20m Alaska NGEE Tier3 PFTs v1.0

These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

NASA/ASEE Summer Faculty Fellowship Program, 1990, Volume 1

The 1990 Johnson Space Center (JSC) NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston-University Park and JSC. A compilation of the final reports on the research projects are presented. The topics covered include: the Space Station; the Space Shuttle; exobiology; cell biology; culture techniques; control systems design; laser induced fluorescence; spacecraft reliability analysis; reduced gravity; biotechnology; microgravity applications; regenerative life support systems; imaging techniques; cardiovascular system; physiological effects; extravehicular mobility units; mathematical models; bioreactors; computerized simulation; microgravity simulation; and dynamic structural analysis.

Bannerot, Richard B.↗

NASA NTRS2002

Fundamental Studies on Two-Phase Gas-Liquid Flows Through Packed Beds in Microgravity

In the typical operation of a packed-bed reactor, gas and liquid flow simultaneously through a fixed bed of solid particles. Depending on the application, the particles can be of various shapes and sizes and provide for intimate contact and high rates of transport between the phases needed to sustain chemical or biological reactions. The packing may also serve as either a catalyst or as a support for growing biological material. NASA has flown two of these packed-bed systems in a microgravity environment with limited or no success. The goal of this research is to develop models (with scale-up capability) needed for the design of the physicochemical equipment to carry out these unit operations in microgravity. New insight will also lead to improvements in normal gravity operations. Our initial experiment was flown using an existing KC-135 two-phase flow rig with a modified test section. The test section is a clear polycarbonate rectangular column with a depth of 2.54 cm, a width of 5.08 cm, and 60 cm long. The column was randomly packed with spherical glass beads by slowly dropping the beads into the bed. Even though care was taken in handling the column after it was filled with packing, the alternating high and low gravity cycles with each parabola created a slightly tighter packed bed than is typically reported for this type. By the usual method of comparing the weight difference of a completely dry column versus a column filled with water, the void fraction was found to be .345 for both sizes of beads used. Five flush mounted differential pressure transducers are spaced at even intervals with the first location 4 cm from the inlet port and the subsequent pressure transducers spaced at 13 cm intervals along the column. Differential pressure data was acquired at 1000 Hz to adequately observe pulse formation and characteristics. Visual images of the flow were recorded using a high-speed SVHS system at 500 frames per second. Over 250 different test conditions were evaluated along with a companion set of tests in normal gravity. The flow rates, fluid properties and packing properties were selected to provide a range of several orders-of-magnitude for the important dimensionless parameters. Additional information is included in the original extended abstract.

Balakotaiah, Vemuri↗

NASA NTRS2018

Impacts of NASA's Earth Observations on Subseasonal and Seasonal Forecasts

NASA's suite of Earth-observing satellites provides a unique view of many processes on Earth, with relevance on timescales ranging from hours to weeks and even years. NASA's observations span all parts of the Earth system: atmospheric, ocean, land and cryosphere, and include physical, chemical and biological components. This presentation explores use of NASA observations in extended-range prediction, from many days to months, using the Goddard Earth Observing System (GEOS) assimilation and predictive modeling capabilities. The skill of a weather forecast is linked to the fidelity of the initialization process (data assimilation) and the realistic representation of "fast" physical processes in the model. At longer time horizons, the slower "feedback" processes in the Earth System begin to take a more prominent role in the accuracy of the forecast. Simultaneously, forecast-skill attribution transitions away from feature-based metrics that emphasize smaller scales (e.g., representations of fronts and vortices) to metrics that emphasize the statistical distributions of large-scale features (e.g., ENSO diagnostics and teleconnections). This presentation will summarize studies performed using the GEOS-S2S (subseasonal to seasonal) system that explore the impacts of NASA observations on the fidelity of the forecasts. The GEOS-S2S system is configured for the atmosphere-ocean-land-ice model and is initialized using in-situ and space-based observations, including atmospheric aerosols and ozone which are not typically analyzed in such systems. The GEOS-S2S model routinely includes aerosol feedbacks, which systematically impact the realism of the forecasts, providing a first example of how suitable NASA observations impact the performance of the GEOS-S2S system. Studies in which a stratospheric chemistry module is activated in the GEOS-S2S system allow the impacts of ozone radiative feedbacks to be isolated. Space-based observations of sea-surface temperature and altimetry are routinely analyzed for the initialization of the GEOS-S2S system; recent advances allow the use of NASA's sea-surface salinity data, which are shown to impact the long-range skill of the forecasts.

Pawson, Steven↗

NASA NTRSDate not supplied

Investigating Biological Responses to Space-like Radiation using the yeast Saccharomyces cerevisiae

As we plan crewed missions to the Moon, Mars, and beyond, it is essential to understand how persistent exposure to space radiation affects biology. Unlike on the International Space Station, where crew support and sample return are possible, experiments for long-duration missions require autonomous systems with no sample return. Human cells would be ideal biosensors, but limitations in culture methods, extended prelaunch storage, and long flight durations make it very difficult to keep human cells alive. Unlike other model systems, yeast can survive the constraints of long-duration spaceflight. Despite a billion years of evolution separating yeast from humans, we share homology in hundreds of genes important for basic cell function, including responses to DNA damage. Thus, yeast are excellent biosensors for detecting types/extent of damage induced by space radiation. BioSentinel is NASA’s latest biological CubeSat, and first interplanetary space bioscience mission. BioSentinel is manifested on Artemis 1, the first test flight of NASA’s Space Launch System, in the coming year. The BioSensor payload within BioSentinel contains two yeast strains. The wild type serves as a control for health and “normal” DNA damage repair (DDR). The rad51 deletion mutant is defective for DDR and will undergo alterations to growth and metabolism as it accumulates radiation damage. Changes in growth and metabolic activity will be measured using a 3-color LED detection system and the metabolic redox dye alamarBlue®. Preliminary tests indicate a significant change in alamarBlue response to space-like, low-dose ionizing radiation. We will discuss these findings in four parts – Introduction to biological CubeSats and the BioSentinel mission (presented by Sergio Santa Maria), preliminary responses to space-like ionizing radiation (presented here), a deeper dive into tracking metabolic changes after exposure to ionizing radiation (presented by Diana Gentry), and a look into methods for correcting flight optical data (presented by Abbey Kim). This work is funded by NASA’s Advanced Exploration Systems.

CubeSat↗

NASA NTRSDate not supplied

Investigating Molecular Responses to Space Radiation for Biological Missions Beyond Low Earth Orbit

As we plan crewed missions to the Moon, Mars, and beyond, it is essential to understand how persistent exposure to deep space radiation affects biology. Unlike on the International Space Station (ISS), where crew support and sample return are possible, experiments for long-duration missions require autonomous systems with no sample return. Human cells would be ideal biosensors, but limitations in culture methods, extended prelaunch storage, and long flight durations make it difficult to keep human cells alive. Unlike other model systems, yeast can survive the constraints of long-duration spaceflight. Despite a billion years of evolution separating yeast from humans, we share homology in hundreds of genes important for basic cell function, including responses to DNA damage. Thus, yeast are excellent biosensors for detecting types/extent of damage induced by space radiation. BioSentinel is NASA’s latest biological CubeSat, and first interplanetary space bioscience mission. BioSentinel is launching on Artemis 1, the first flight of NASA’s Space Launch System, in 2022. The BioSensor payload within BioSentinel contains two yeast strains. The wild type serves as a control for health and normal DNA damage repair (DDR). The rad51 deletion mutant is defective for DDR and will undergo alterations to growth and metabolism as it accumulates radiation damage. Changes in growth and metabolic activity will be measured using a 3-color LED detection system and the metabolic redox dye alamarBlue®. Preliminary tests indicate a significant change in alamarBlue responses to space-like, low-dose ionizing radiation. We will discuss these findings in five parts – Introduction to NASA’s biological CubeSats and BioSentinel (presented by Sergio Santa Maria), analysis of flight data from the ISS mission (presented by Kylie Akiyama), preliminary molecular responses to space radiation (presented here), a deeper dive into those pathways (presented by Kyra Keenan), and characterizing stress response through redox potential data (presented by Diana Gentry).

Lauren Courtney Liddell↗

DOE OSTI2023

Mapping the Morphology of DNA on Carbon Nanotubes in Solution Using X-ray Scattering Interferometry

Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with potential applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes such as (GT)n-SWCNTs recognizing the neuromodulator, dopamine. It remains unclear how the ssDNA conformation on the SWCNT surface contributes to functionality, as observations have been limited to computational models or experiments under dehydrated conditions that differ substantially from the aqueous biological environments in which the nanosensors are applied. We demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two (GT) n ssDNA oligomer lengths (n = 6, 15) that are used on SWCNTs in the context of dopamine sensing and measure the ssDNA conformational changes as a function of ionic strength and during dopamine interaction. Here, we show that the shorter oligomer, (GT) 6 , adopts a more periodically ordered ring structure along the SWCNT axis (inter-ssDNA distance of 8.6 ± 0.3 nm), compared to the longer (GT) 15 oligomer (most probable 5'-to-5' distance of 14.3 ± 1.1 nm). During molecular recognition, XSI reveals that dopamine elicits simultaneous axial elongation and radial constriction of adsorbed ssDNA on the SWCNT surface. Our approach using XSI to probe solution-phase morphologies of polymer-functionalized SWCNTs can be applied to yield insights into sensing mechanisms and inform future design strategies for nanoparticle-based sensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗