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At least 829 records · Page 46

Spectroscopic Characterization of Key Aromatic and Heterocyclic Molecules: A Route toward the Origin of Life

To gain information on the abiotic synthesis of the building blocks of life from simple molecules, and their subsequent chemical evolution to biological systems, the starting point is the identification of target species in Titan-like planets; i.e., planets that resemble the primitive Earth, as well as in Earth-like planets in the habitable zone of their star, namely planets where life can be already originated. In this scenario, molecular spectroscopy plays a crucial role because spectroscopic signatures are at the basis of an unequivocal proof for the presence of these target molecules. Thanks to advances in many different techniques and NASA’s successful Kepler exoplanet transit mission, thousands of diverse planets outside of our solar system have been discovered. The James Webb Space Telescope (JWST), scheduled to be launched in 2018, will be very helpful in the identification of biosignature gases in Earth-like planets’ atmospheres and prebiotic molecule signatures in Titan-like atmospheres, by observing their absorption during transits. Although the search for key-target molecules in exoplanet atmospheres can be carried out by the JWST Transit Spectroscopy in the infrared (IR) region (0.6–29 μm wavelength range), opportunities for their detection in protostellar cores, protoplanetary disks, and on Titan are also offered by interferometric high spectral and spatial resolution observations using the Atacama Large Millimeter/submillimeter Array. In the present work, target molecules have been selected, and their spectroscopic characterization presented in view of supporting their infrared and complementary millimeter/submillimeter-wave spectral observations. In detail, the selected target molecules include: (1) the three-membered oxygen-containing heterocycles, oxirane and protonated oxirane; (2) the cyclopropenyl cation and its methyl derivative; (3) two examples of ortho- and peri-fused tri-cyclic aromatic rings, i.e., the phenalenyl cation (C{sub 13}H{sub 9}{sup +}) and anion (C{sub 13}H{sub 9}{sup -}); and (4) uracil, a specific RNA base.

79 ASTRONOMY AND ASTROPHYSICS↗

Spatiotemporal characterization of cerium monoxide in laser ablation plasmas using spectrally-resolved fast-gated imaging

The impact of oxidation chemistry on the emission characteristics and spatial structure of laser-produced Ce metal plasmas was investigated using laser-induced breakdown spectroscopy (LIBS) and time-resolved fast-gated imaging employing narrowband optical filters. Images of the plasma emission show that CeO coexists with atomic species in the periphery and vortex ring of the plasma plume. Image processing was also applied to combine independent monochromatic images of the plasma emission into a single merged image, culminating in a timelapse on the spatiotemporal evolution of atomic and molecular species within the plasma plume. The formation of CeO species was observed to proceed faster for plasmas generated in atmospheres containing larger concentrations of oxygen based on ratios of CeO-to-atomic emission intensities. Furthermore, these same ratios were shown to plateau and decrease at later times (≥25 μs), potentially indicating the depletion of CeO number densities in the plasma as the monoxide undergoes reactions to form higher polyatomic oxides. Altogether, these results provide fundamental insights into the chemical dynamics and intermixing between plasma-gas species in laser ablation cerium plasmas, advancing our understanding on optical signatures of nuclear-relevant materials to enable in-field measurement capabilities.

47 OTHER INSTRUMENTATION↗

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗

A hybrid CoOOH-rGO/Fe 2 O 3 photoanode with spatial charge separation and charge transfer for efficient photoelectrochemical water oxidation

As a promising photoanode for photoelectrochemical (PEC) water oxidation, hematite (Fe 2 O 3 ) still suffers from poor charge mobility and serious charges recombination and sluggish surface oxygen evolution kinetics. Herein, a hybrid photoanode of cobalt (oxy)hydroxide coupled with reduced graphene oxide modified Fe 2 O 3 (CoOOH-rGO/Fe 2 O 3 ) is well crafted by a facile hydrothermal synthesis with a chelation-mediated in-situ growth method. Morphology characterizations indicate rGO forms the internal network among isolated Fe 2 O 3 and CoOOH nanosheets distribute on the terminal of Fe 2 O 3 , forming a spatial separated nanostructure. The resultant CoOOH-rGO/Fe 2 O 3 exhibits an obviously reduced onset potential of ca. 150 mV and a significantly enhanced photocurrent density of 2.56 mA cm -2 at 1.23 V, which is ca. 3.3 times higher than that of bare Fe 2 O 3 . Especially, the functions of rGO and CoOOH are studied by using electrochemical impedance spectroscopy, open circuit potentials and intensity modulated photocurrent spectroscopy. It is found rGO act as conductive network which facilitates the electron transfer from Fe 2 O 3 to the substrate, while CoOOH evidently passivate the surface states of Fe 2 O 3 , improve charge separation and provide catalytic active sites for water oxidation. Additionally, the spatial charge separation and charge transfer caused by CoOOH and rGO are responsible for the enhanced PEC performance of water oxidation. The rational design and the facile fabrication strategy exhibit great potential to be used for other PEC system with great efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain

Abstract. Scanning lidars enable the collection of spatially distributed measurements of turbine wakes and the estimation of wake properties such as magnitude, extent, and trajectory. Lidar-based characterizations, however, may be subject to distortions due to the observational system. Distortions can arise from the resolution of the measurement points across the wake, the projection of the winds onto the beam, averaging along the beam probe volume, and intervening evolution of the flow over the scan duration. Using a large-eddy simulation and simulated measurements with a virtual lidar model, we assess how scanning lidar systems may influence the properties of the retrieved wake using a case study from the Perdigão campaign. We consider three lidars performing range-height indicator sweeps in complex terrain, based on the deployments of lidars from the Danish Technical University (DTU) and German Aerospace Center (DLR) at the Perdigão site. The unwaked flow, measured by the DTU lidar, is well-captured by the lidar, even without combining data into a multi-lidar retrieval. The two DLR lidars measure a waked transect from different downwind vantage points. In the region of the wake, the observation system reacts to the smaller spatial and temporal variations of the winds, allowing more significant observation distortions to arise. While the measurements largely capture the wake structure and trajectory over its 4–5 D extent, limited spatial resolution of measurement points and volume averaging lead to a quicker loss of the two lobes in the near wake, smearing of the vertical bounds of the wake (< 30 m), wake center displacements up to 10 m, and dampening of the maximum velocity deficit by up to a third. The virtual lidar tool, coupled with simulations, provides a means for assessing measurement capabilities in advance of measurement campaigns.

17 WIND ENERGY↗

X-ray characterization of the micromechanical response ahead of a propagating small fatigue crack in a Ni-based superalloy

The small fatigue crack (SFC) growth regime in polycrystalline alloys is complex due to the heterogeneity in the local micromechanical fields, which result in high variability in crack propagation directions and growth rates. Here in this study, we employ a suite of techniques, based on high-energy synchrotron-based X-ray experiments that allow us to track a nucleated crack, propagating through the bulk of a Ni-based superalloy specimen during cyclic loading. Absorption contrast tomography is used to resolve the intricate 3D crack morphology and spatial position of the crack front. Initial near-field high-energy X-ray diffraction microscopy (HEDM) is used for high-resolution characterization of the grain structure, elucidating grain orientations, shapes, and boundaries. Cyclic loading is periodically interrupted to conduct far-field HEDM to determine the centroid position, average orientation, and average lattice strain tensor for each grain within the volume of interest. Reciprocal space analysis is used to further examine the deformation state of grains that plasticize in the vicinity of the crack. Analysis of the local micromechanical state in the grains ahead of the crack front is used to rationalize the advancing small crack path and growth rate. Specifically, the most active slip system in a grain, determined by the maximum resolved shear stress, aligns with the crack growth direction; and the degree of microplasticity ahead of the crack tip helps to identify directions for potential occurrences of crack arrest or propagation. The findings suggest that both the slip system level stresses and microplasticity events within grains are necessary to get a complete description of the SFC progression. Further, this detailed dataset, produced by a suite of X-ray characterization techniques, can provide the necessary validation, at the appropriate length-scale, for SFC models.

36 MATERIALS SCIENCE↗

Coarse-grained simulation of colloidal self-assembly, cation exchange, and rheology in Na/Ca smectite clay gels

Knowledge Gap: The aggregation of clay minerals—layered silicate nanoparticles—strongly impacts fluid flow, solute migration, and solid mechanics in soils, sediments, and sedimentary rocks. Experimental and computational characterization of clay aggregation is inhibited by the delicate water-mediated nature of clay colloidal interactions and by the range of spatial scales involved, from 1 nm thick platelets to flocs with dimensions up to micrometers or more. Simulations: Using a new coarse-grained molecular dynamics (CGMD) approach, we predicted the microstructure, dynamics, and rheology of hydrated smectite (more precisely, montmorillonite) clay gels containing up to 2,000 clay platelets on length scales up to 0.1 μm. Further, simulations investigated the impact of simulation time, platelet diameters (6 to 25nm), and the ratio of Na to Ca exchangeable cations on the assembly of tactoids (i.e., stacks of parallel clay platelets) and larger aggregates (i.e., assemblages of tactoids). We analyzed structural features including tactoid size and size distribution, basal spacing, counterion distribution in the electrical double layer, clay association modes, and the rheological properties of smectite gels. Findings: Our results demonstrate new potential to characterize and understand clay aggregation in dilute suspensions and gels on a scale of thousands of particles with explicit representation of counterion clouds and with accuracy approaching that of all-atom molecular dynamics (MD) simulations. For example, our simulations predict the strong impact of Na/Ca ratio on clay tactoid formation and the shear-thinning rheology of clay gels.

42 ENGINEERING↗

Underground hydrogen storage leakage detection and characterization based on machine learning of sparse seismic data

Underground hydrogen storage (UHS) is considered as a scalable approach for massive storage and seasonal extraction of hydrogen (H 2 ). Although conventional leakage detection and characterization methods based on time-lapse seismic imaging and inversion generally apply to H 2 leakage detection problem, a high-fidelity yet cost effective geophysics approach is still missing to reliably inform leakage location and properties based on very sparse data. In response, we develop a novel supervised machine learning method to detect and characterize H 2 leakage from UHS. The input to our neural network are sparse time-lapse seismic waveforms, while the output from the neural network includes the spatial location and physical properties of a H 2 leakage. Here, we generate high-quality time-lapse waveforms using the elastic-wave equations to train the neural network. We train and validate our machine learning model and find that it attains high accuracy in using extremely sparse time-lapse seismic data to detect and characterize H 2 leakage. Our investigation is the first systematic study that focuses on applying machine learning to subsurface H 2 leakage detection and characterization and could potentially serve as a cost-effective geophysical tool for underground hydrogen leakage detection and characterization with high fidelity.

08 HYDROGEN↗

WHONDRS Surface Water and Sediment Geochemistry and Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon (v2)

This dataset supports a broader study developing conceptual models for river corridor critical zone processes across spatial scales and was generated in collaboration with the HJ Andrews River Corridor Critical Zone Workshop in 2025. The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen) from 48 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Some of the sites have been impacted by the Holiday Farm Fire and the Lookout Fire in 2020 and 2023, respectively. Related data were collected as part of the workshop and will be published separately in collaboration with other workshop attendees and available at http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. Related genomic data can be found on the National Center for Biotechnology Information (NCBI) under BioProject PRJNA1503030 (see critical details section below for more information). Additional related data collected in 2016 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377027 and http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1-2019 (Ward et al., 2019). This data package was originally published in March 2026. It was updated in August 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) a folder of field photos, (2) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data, (3) a data checks report, (4) a folder of sample data, (5) file-level metadata, (6) data dictionary, (7) field metadata, (8) readme, (9) international generic sample number (IGSN) mapping file; and (10) field protocol. The sample data subfolder contains surface water and sediment (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages, (2) total dissolved nitrogen data and averages, (3) methods codes, (4) FTICR-MS methods; and (5) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the CoreMS processed data and seven subfolders, thee containing .xml files for each sample type (sediment, surface water and blank samples), three containing the sediment CoreMS output files for each sample type (sediment, surface water and blank samples), and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, .json, .jpg, or .jpeg.

Biogeochemistry↗

In Situ Chemical Monitoring and Imaging of Contents within Microfluidic Devices Having a Porous Membrane Wall Using Liquid Microjunction Surface Sampling Probe Mass Spectrometry

The ability to observe dynamic chemical processes (e.g., signaling, transport, etc.) in vivo or in situ using nondestructive chemical imaging opens a new door to understanding the complex dynamics of developing biological systems. With the advent of “biology-on-a-chip” devices has come the ability to monitor dynamic chemical processes in a controlled environment, using these engineered habitats to capture key features of natural systems while allowing visual observation of system development. Having the capability to spatially and temporally map the chemical signals within these devices may yield new insights into the forces that drive biosystem development. Here, a porous membrane sealed microfluidic device was designed to allow normal microfluidic operation while enabling continuous, location specific sampling and chemical characterization by liquid microjunction surface sampling probe mass spectrometry (LMJ-SSP MS). LMJ-SSP was used to extract fluids with nL-to-μL/min flow rates directly from selected areas of the microfluidic device without negatively impacting the device function. These extracts were subsequently characterized using MS. This technique was used to acquire MS images of the entirety of several multi-input microfluidic devices having different degrees of fluid mixing. LMJ-SSP MS imaging visualized the spatial distribution of chemical components within the microfluidic channels and could visualize chemical reactions occurring in the device. These microfluidic devices with a porous membrane wall are wholly compatible with the construction of biology-on-a-chip devices. This ultimately would enable correlation of biosystem physical structure with an evolving chemical environment

47 OTHER INSTRUMENTATION↗

Observation of signatures of subresolution defects in two-dimensional superconductors with a scanning SQUID

The diamagnetic susceptibility of a superconductor is directly related to its superfluid density. Mutual inductance is a highly sensitive method for characterizing thin films, however, in traditional mutual inductance measurements, the measured response is a nontrivial average over the area of the mutual inductance coils, which are typically of millimeter size. Here we measure localized, isolated features in the diamagnetic susceptibility of Nb superconducting thin films with lithographically defined through holes, δ-doped SrTiO 3 , and the two-dimensional electron system at the interface between LaAlO 3 and SrTiO 3 , using scanning superconducting quantum interference device susceptometry, with spatial resolution as fine as 0.7 μm. We show that these features can be modeled as locally suppressed superfluid density, with a single parameter that characterizes the strength of each feature. In conclusion, this method provides a systematic means of finding and quantifying submicron defects in two-dimensional superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Impact of Individual Structural Defects in GaAs Solar Cells: A Correlative and In Operando Investigation of Signatures, Structures, and Effects

Defects usually degrade device performance. Thus, many techniques and effort are devoted to studying semiconductor defects. However, it is rarely known: i) how individual defects affect device performance; ii) how the impact depends on the device operating conditions; iii) how the impact varies from one defect to another; and iv) how these variations are correlated to the microscopic-scale defect structure. To address these crucial questions, an array of correlative and spatially resolved techniques, including electroluminescence, photoluminescence, Raman, I–V characteristics, and high-resolution electron microscopy, are used to characterize dislocation defects in GaAs solar cells. Significantly, the study is carried out in a series mode and in operando. This approach provides quantitative and definitive correlation between the atomistic structure of defects and their explicit effects on device performance, thus giving unprecedented insight into defect physics.

dislocation defects↗

Combined Falling Drop/Open Port Sampling Interface System for Automated Flow Injection Mass Spectrometry

The aim of this work was to demonstrate and to evaluate the analytical performance of a combined falling drop/open port sampling interface (OPSI) system as a simple noncontact, no-carryover, automated system for flow injection analysis with mass spectrometry. The falling sample drops were introduced into the OPSI using a widely available autosampler platform utilizing low cost disposable pipet tips and conventional disposable microtiter well plates. The volume of the drops that fell onto the OPSI was in the 7–15 μL range with an injected sample volume of several hundred nanoliters. Sample drop height, positioning of the internal capillary on the sampling end of the probe, and carrier solvent flow rate were optimized for maximum signal. Sample throughput, signal reproducibility, matrix effects, and quantitative analysis capability of the system were established using the drug molecule propranolol and its isotope labeled internal standard in water, unprocessed river water and two commercially available buffer matrices. A sample-to-sample throughput of ~45 s with a ~4.5 s base-to-base flow injection peak profile was obtained in these experiments. In addition, quantitation with minimally processed rat plasma samples was demonstrated with three different statin drugs (atorvastatin, rosuvastatin, and fluvastatin). Direct characterization capability of unprocessed samples was demonstrated by the analysis of neat vegetable oils. Employing the autosampler system for spatially resolved liquid extraction surface sampling exemplified by the analysis of propranolol and its hydroxypropranolol glucuronide phase II metabolites from a rat thin tissue section was also illustrated.

47 OTHER INSTRUMENTATION↗

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗

Spin–phonon interactions in silicon carbide addressed by Gaussian acoustics [Probing spin-phonon interactions in silicon carbide with Gaussian acoustics]

Hybrid spin-mechanical systems provide a platform for integrating quantum registers and transducers. Efficient creation and control of such systems require a comprehensive understanding of the individual spin and mechanical components as well as their mutual interactions. Point defects in silicon carbide (SiC) offer long-lived, optically addressable spin registers in a wafer-scale material with low acoustic losses, making them natural candidates for integration with high quality factor mechanical resonators. Here, we show Gaussian focusing of a surface acoustic wave in SiC, characterized by a novel stroboscopic X-ray diffraction imaging technique, which delivers direct, strain amplitude information at nanoscale spatial resolution. Using ab initio calculations, we provide a more complete picture of spin-strain coupling for various defects in SiC with C 3v symmetry. This reveals the importance of shear for future device engineering and enhanced spin-mechanical coupling. We demonstrate all-optical detection of acoustic paramagnetic resonance without microwave magnetic fields, relevant to sensing applications. Lastly, we show mechanically driven Autler-Townes splittings and magnetically forbidden Rabi oscillations. These results offer a basis for full strain control of three-level spin systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

From lattice QCD to in-medium heavy-quark interactions via deep learning

Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions in high-energy nuclear collisions rely on the in-medium interactions between the bottom and antibottom quarks, which can be characterized by real (V R (T, r)) and imaginary (V I (T, r)) potentials, as functions of temperature and spatial separation. Recently, the masses and thermal widths of up to 3S and 2P bottomonium states in QGP were calculated using lattice quantum chromodynamics (LQCD). Starting from these LQCD results and through a novel application of deep neural network (DNN), here, we obtain model-independent results for V R (T, r) and V I (T, r). The temperature dependence of V R (T, r) was found to be very mild between T ≈ 0 - 330 MeV. Meanwhile, VI(T, r) shows rapid increase with T and r, which is much larger than the perturbation theory based expectations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Ocean Energy for Ocean Worlds

Energy systems generating power from ocean waves, currents, tides, and thermal and salinity gradients are being developed to power oceanographic instrumentation and autonomous vehicles in Earth’s oceans. Exploration missions enabled or extended with in-situ energy harvesting on Earth may be similar to those on extra-terrestrial ocean worlds including mapping, biogeochemical sampling, acoustic monitoring, search for building blocks of life, and physical oceanographic characterization. With the addition of ocean energy available at the point of observation, data can be collected on larger spatial and temporal scales, enabling more comprehensive missions on other worlds. Power resource characteristics (e.g. current speed, variability) will similarly need to be assessed. Earth and extra-terrestrial ocean missions share vast technical challenges including high expense, need for advanced autonomy, ability to survive in the harshest environments, and limited communications or opportunities for intervention. Marine energy systems may be the only option for in-situ generation in certain scenarios (e.g., under a thick layer of ice) or advantageous over onboard energy storage options that may interfere with the natural environment (e.g., radioisotope thermoelectric generators). This work provides an overview of existing marine energy technology pertaining to ocean observations, highlights emerging research and development and new funding and program initiatives, explores similarities in mission requirements between terrestrial and extra-terrestrial applications, and suggests innovation pathways towards enabling high-value ocean world exploration powered by ocean energy.

Cavagnaro, Robert J.↗

Sustainability, Scalability, and Resiliency of the Town of Innisfil Mobility-on-Demand Experiment: Preliminary Results, Analyses, and Lessons Learned

In 2017, the town of Innisfil, Ontario, launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report by Sweet, Mitra, and Benaroya, which shows greater cost effectiveness of the mobility provided over the proposed bus alternative. This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative first-order estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil.

ADVANCED PROPULSION SYSTEMS↗