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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

EJFAT Scientific Perspective

Presented new computing model to the test by deploying the EJFAT system alongside a data-stream processing framework running the production-level CLAS12 event reconstruction application. In this experiment, a continuous stream of CLAS12 Level-1 identified events was processed in real-time using the EJFAT load balancer, distributing the workload across 90 computing nodes located across the U.S. This marks the first-ever large-scale, real-time distributed data stream processing experiment, demonstrating that scientific data-streaming pipelines can efficiently scale across four dimensions, thanks to EJFAT’s advanced hardware and software capabilities.

Gyurjyan, Vardan [Thomas Jefferson National Accele↗
DOE OSTI2025

HydroBio: Hydropower Capacity and Freshwater Biodiversity in Conterminous United States Sub-basins

This dataset summarizes existing and potential hydropower capacity and freshwater biodiversity at the sub-basin level throughout the conterminous United States (CONUS). It contains descriptive information regarding each sub-basin (e.g., 8-digit hydrologic unit code identifier, name, states, and size) along with sub-basin-level summaries of: 1) existing hydropower capacity (MW), 2) potential nominal non-powered dam (NPD) capacity (MW), 3) potential capacity of new stream reach development (NSD) (MW), and 4) freshwater biodiversity, including the total richness of fish, crayfish, and mussels and metrics that account for how rare and threatened those species tend to be. Hydropower data were obtained from Oak Ridge National Laboratory data resources (Existing Hydropower Assets, Non-Powered Dam Technical Potential, and New Stream Reach Development). Freshwater biodiversity data were obtained from NatureServe. Sub-basin characteristic information was obtained from the United States Geological Survey. Additionally, long data that provide lists of unique elements within each sub-basin for each constituent data resource (e.g., NatureServe, Existing Hydropower Assets) are provided to enhance dataset utility for users. The dataset provides, for the first time, a national-level assessment of existing and potential hydropower capacity in the context of freshwater biodiversity and is a valuable resource for stakeholders tasked with providing affordable, reliable energy to the American public while maintaining or enhancing invaluable freshwater resources. The dataset contains six data files in comma separated (*.csv) format that are within a zipped file.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Upcycling of Mixed Aluminum Alloy Shredder Scrap using Shear Processing

Conservation of critical materials is an increasing area of focus in the Unites States. In 2023, aluminum was added to the US Department of Energy Final Critical Materials List which has spurred public and private research into sustainable management of these resources. Additionally, efficiency in manufacturing and conservation of natural resources are growing concerns with targets to lower global carbon emissions, as primary aluminum alloy production is energy intensive, requiring 14 MWh of electricity plus 0.4 tonnes of CO2 per tonne of Al. Due to these factors, is essential that more sustainable manufacturing methods for aluminum alloys are developed going forward. To this end, much research is ongoing on the topic of more efficient utilization and recovery. However, most of this research still requires primary aluminum in the production process. Here, it will be attempted to bypass the use of primary aluminum and produce useful material recycled from 100% post-consumer scrap. Even considering recent developments in recycling of Al scrap, there is still a large amount of post-consumer scrap that is underutilized due to high impurity content, and that amount will increase significantly as more and more aluminum alloys are utilized in vehicles. This “scrap wave” is expected to cover 80% of the demand for automotive aluminum alloys by 2050 . A challenge to be addressed before the coming scrap wave can be fully utilized is that the tolerance of manufacturing techniques to impurities or off-spec alloy compositions must be increased. Particularly, in 5000-and 6000-series alloys (the most common wrought alloys in durable products), excess iron, copper, and silicon create brittle intermetallics during casting that remain in the extruded microstructure which limit the formability, ductility, and corrosion resistance of the alloy. Concerningly, many of the highest-volume post-consumer aluminum scrap streams such as automotive shredder scrap contain a mix of alloys including both wrought and cast alloys. Their compositions can vary widely depending on geography and the time of year. Because they are mixed, they often contain high content of multiple alloying elements such as Si and Cu in higher concentrations than are found in typical wrought alloys. They may also be contaminated with non-Al alloys from fasteners that get mixed in and often have high content of unwanted elements such as Fe. As a method for utilizing these underused scrap streams that are high in tramp elements, an emerging extrusion technology is being developed at the Pacific Northwest National Laboratory (PNNL) that aims to upcycle 100% post-consumer aluminum scrap directly into extruded components without the addition of primary aluminum . This new technology, called Shear Assisted Processing and Extrusion (ShAPE), is enabling a shift away from today’s recycling paradigm by reaching deeper into lower-value scrap streams, using shredder scrap as the extrusion billet material. Sometimes referred to as Twitch or Tweak, these scrap streams result from shredding and sorting of automobiles, building materials, appliances, and consumer goods. ShAPE combines the linear axis of conventional extrusion with a rotating extrusion die. This rotating die applies large strain to the material during extrusion, which breaks up large impurity-containing intermetallic particles, reducing their deleterious effects. This has been demonstrated for 6063 machining scrap spiked with excess Fe, and Twitch scrap high in Fe, Si and Cu where strength and ductility were retained for both feedstock compositions. Additionally, the extreme plastic deformation during ShAPE enables extrusion of billets with high Si that are too brittle for processing by conventional extrusion. By using 100% post-consumer shedder scrap as feedstock, ShAPE has the potential to slash embodied energy and carbon in extruded components by >80% compared to conventional extrusion of primary aluminum alloys.

Milligan, Brian K.↗
DOE OSTI2025

$S^5$: Tidal Disruption in Crater 2 and Formation of Diffuse Dwarf Galaxies in the Local Group

We present results of a spectroscopic campaign around the diffuse dwarf galaxy Crater 2 (Cra2) and its tidal tails as part of the Southern Stellar Stream Spectroscopic Survey ($S^5$). Cra2 is a Milky Way dwarf spheroidal satellite with extremely cold kinematics, but a huge size similar to the Small Magellanic Cloud, which may be difficult to explain within collisionless cold dark matter. We identify 143 Cra2 members, of which 114 belong to the galaxy's main body and 29 are deemed part of its stellar stream. We confirm that Cra2 is dynamically cold (central velocity dispersion $2.51^{+0.33}_{-0.30}\,{\rm km\,s^{-1}}$) and also discover a $\approx$7$σ$ velocity gradient consistent with its tidal debris track. We separately estimate the stream velocity dispersion to be $5.74^{+0.98}_{-0.83}\,{\rm km\,s^{-1}}$. We develop a suite of $N$-body simulations with both cuspy and cored density profiles on a realistic Cra2 orbit to compare with $S^5$ observations. We find that the velocity dispersion ratio between Cra2 stream and galaxy ($2.30^{+0.41}_{-0.35}$) is difficult to reconcile with a cuspy halo with fiducial concentration and an initial mass predicted by standard stellar mass$-$halo mass relationships. Instead, either a cored halo with relatively small core radius or a low-concentration cuspy model can reproduce this ratio. Despite tidal mass loss, Cra2 is metal-poor ($\langle \rm[Fe/H]\rangle=-2.16\pm0.04$) compared to the stellar mass$-$metallicity relation for its luminosity. Other diffuse dwarf galaxies similar to Cra2 in the Local Group (Antlia 2 and Andromeda 19) also challenge galaxy formation models. Finally, we discuss possible formation scenarios for Cra2, including ram-pressure stripping of a gas-rich progenitor combined with tides.

Limberg, Guilherme [Chicago U., KICP; Chicago U.] ↗
DOE OSTI2024

Hydrokinetic tidal energy resource assessment following international electrotechnical commission guidelines

Marine renewable energy can be used as a viable energy source to alleviate the impact of the climate crisis and have a carbon-free electricity sector in the future. Especially the energetic tidal streams are an attractive source of clean energy due to the periodic occurrence of high tidal flows daily. However, before any deployment of tidal turbine farms, it is essential to perform a resource assessment depending on the scope and scale of the project. Here, the International Electrotechnical Commission has developed a technical standard for assessing the tidal stream resource "IEC 62600-201 TS" to aid in this effort: determine a particular site's feasibility and perform the project layout design. In this study, we implemented and validated a high-resolution three-dimensional numerical model and provided results following the IEC TS for a project layout design in a highly energetic tidal channel, Tacoma Narrows of Puget Sound, in the State of Washington, USA. Implementation of the guidelines has helped adequately identify the undisturbed theoretical and technical resources with less bias, where the latter included energy extraction from the flow field arranging a hypothetical tidal energy converter (TEC) array. Also, following the standard, we carefully assessed the changes to channel flow properties from TECs, such as the kinetic energy flux and annual energy production (AEP), to provide the detailed information required for a larger project layout design. Ultimately, this work has shown the important role of IEC TS in tidal stream resource assessment, which can simultaneously act as a benchmark for other studies worldwide.

13 HYDRO ENERGY↗
DOE OSTI2026

Synoptic NO3 in Slate River Watershed, Colorado (2022)

The synoptic nitrate (NO3) dataset in the Slate River Watershed, Colorado consists of NO3 data collected at 19 locations three times during the summer of 2022. Stream samples were collected in early summer (early July), mid summer (late August), and late summer (late September). The samples include mainstem, tributary, and point source input water samples. These data were collected to evaluate spatiotemporal variability in stream NO3 during the summer, and evaluate anthropogenic controls on stream NO3 dynamics. This data package contains: (1) a csv of all NO3 samples and (2) a csv of locations for each sampling site. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > FRESHWATE↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Controls From Above and Below: Snow, Soil, and Steepness Drive Diverging Trends of Subsurface Water and Streamflow Dynamics

ABSTRACT The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognised. Yet, our understanding of their drivers and links to streamflow generation has remained elusive, especially in small headwater streams that are often data‐limited but crucial for downstream water quantity and quality. Large‐scale analyses have focused on streamflow characteristics across rivers with varying drainage areas, often overlooking the subsurface water dynamics that shape streamflow behaviour. Here we ask the question: What are the climate and landscape characteristics that regulate subsurface dynamic storage, flow path partitioning, and dynamics of streamflow generation in headwater streams? To answer this question, we used streamflow data and a widely‐used hydrological model (HBV) for 15 headwater catchments across the contiguous United States. Results show that climate characteristics such as aridity and precipitation phase (snow or rain) and land attributes such as topography and soil texture are key drivers of streamflow generation dynamics. In particular, steeper slopes generally promoted more streamflow, regardless of aridity. Streams in flat, rainy sites (< 30% precipitation as snow) with finer soils exhibited flashier regimes than those in snowy sites (> 30% precipitation as snow) or sites with coarse soils and deeper flow paths. In snowy sites, less weathered, thinner soils promoted shallower flow paths such that discharge was more sensitive to changes in storage, but snow dampened streamflow flashiness overall. Results here indicate that land characteristics such as steepness and soil texture modify subsurface water storage and shallow and deep flow partitioning, ultimately regulating streamflow response to climate forcing. As climate change increases uncertainty in water availability, understanding the interacting climate and landscape features that regulate streamflow will be essential to predict hydrological shifts in headwater catchments and improve water resources management.

Kerins, Devon [Department of Civil and Environment↗
DOE OSTI2025

Optimization based process modeling of an anaerobic membrane bioreactor system: Application to swine wastewater

To maintain current levels of consumption in the economy with the dwindling supply of non-renewable material and energy, alternative resource streams more traditionally viewed as waste streams must be considered. Fermentation of high-strength wastewaters is one such pathway that allows for the recovery of energy, nitrogen, phosphorus, and carbon compounds. Anaerobic membrane bioreactors (AnMBRs) are an emerging technology that allow for the digestion of wastewater in a much smaller footprint than traditional anaerobic digesters. Adoption of this technology into industry has been limited by membrane capital and cleaning costs, but these costs may be offset through the recovery of valuable products. To evaluate the viability of AnMBR technology in the context of swine wastewater treatment, an optimization-based process model built upon Anaerobic Digestion Model No. 1 (ADM1) has been developed. Modeling results show that a swine wastewater stream provides potential for net positive energy generation from the AnMBR system in most cases. Sensitivity analyses around important variables were conducted to determine focus areas for future research into AnMBR technology and evaluate the robustness of the model to microbial variables that may change with different microbial communities.

09 BIOMASS FUELS↗
DOE OSTI2024

Co-treating flue gas desulfurized effluent and produced water enables novel waste management and recovery of critical minerals

Herein this study reports a novel approach of resource recovery from co-managing two geographically co-located and chemically complementary wastewaters using a pilot-scale treatment process. Designed to treat flue gas desulfurized (FGD) effluent from combustion powerplants and produced water (PW) from energy industries, the process consists of soda-ash softening, nanofiltration (NF), and reverse osmosis (RO). Recovered products are barite, calcite, and low-salinity water. Using field-collected waters, the results show that softening at pH 8.5 produces calcite (yield: 30 kg/m 3 treated water), a chemical used as SO ₂(g) scrubbers. NF treatment under an applied pressure of 3.5 MPa yields a permeate stream laden with monovalent ions (water recovery 60%) and a concentrate stream with a sulfate concentration 1.8 times of the feedwater concentration. Mixing the NF concentrate and PW at a volumetric ratio of 1.0 precipitates a high-density barite material (4.1 g/cm 3 , yield: ~7.5 kg/m 3 mixture) – a critical mineral commonly used as a weighting agent in drilling. The RO treatment recovers >64% water as the permeate, which can be readily used as cooling make-up water at the powerplants. The RO concentrate stream can be further processed in a thermal evaporative system for additional water recovery and brine production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Maximizing Marine Carbon Removal by Coupling Electrochemical and Biological Methods

Integrated development of emerging marine decarbonization strategies offers the possibility of lowering CO2 removal costs and enabling their widespread deployment. In this study we examine the feasibility and benefits of coupling electrochemical and biological marine carbon removal strategies. Bipolar membrane electrodialysis (BPMED) is used to generate acid and alkalinity from seawater and electricity, and the alkalinity is returned to the ocean for indirect CO2 removal from the atmosphere, but the acid stream is a waste product. Considering the large-scale of CO2 removal necessary, the acid storage, neutralization, and disposal have prohibitive costs and carbon footprint. Here we investigate the feasibility to valorize the acid stream to enhance the growth and CO2 uptake through photosynthesis in the fast-growing marine phytoplankter Picochlorum celeri. When added to active algae cultures, the BPMED-generated acidified seawater alters the carbonate-bicarbonate equilibrium thereby increasing the bioavailability of CO2 and the observed growth rates. Additions of up to 2 mM H+ from BPMED effluent streams increased algal productivity up to 3-fold. A high-level analysis conducted based on experimental data to estimate the potential of sequestered CO2 emissions when compared to conventional commercial means of acid utilization or disposal, is estimated to be ~30 kgCO2 / kgacid. Through further development and optimization in terms of choice of algal species, growth conditions, acid addition rates, etc. the combined electrochemical-biological approach has the potential to achieve higher net CO2 removal.

carbon dioxide, marine, marine algae↗
DOE OSTI2025

Structural Evolution and Stability of Rh/TiO 2 Catalysts under CO 2 Hydrogenation Conditions: Influence of the Initial Rh Structure

Characterizing catalyst stability by identifying the predominant mechanisms, timescales and driving forces of catalyst reconstruction under relevant reaction conditions is necessary for the design and commercialization of new catalysts. Here, in this paper, we study Rh/TiO 2 catalysts under CO 2 hydrogenation conditions (773 K, 75% H 2 , 25% CO 2 ) at high conversion and utilize reactivity studies along with ex-situ and in-situ spectroscopy and microscopy to characterize changes in catalyst activity and structure as a function of time on stream and the initial catalyst structure. This is a prototypical catalyst for CO 2 hydrogenation where Rh structure and Rh-TiO 2 interactions have been proposed to explain reactivity, selectivity (between CO and CH 4 formation) and catalyst stability. The influence of the initial Rh structure (varying from Rh single atoms to Rh nanoparticles), support stability, regeneration and pretreatment(s), and the chemical potential(s) of the reaction environment on reaction selectivity and catalyst stability were explored. The product selectivity between CO and CH 4 was determined to be dependent on the relative fraction of Rh single atoms and Rh nanoparticle-TiO 2 interfacial sites under reaction conditions, each exhibiting distinct stability under prolonged time on stream. Surprisingly, Rh single atoms exhibited stability for the duration of 90 h reactivity measurements, even at high Rh density (≥ 1.8 Rh atoms/nm 2 ) on the support, while Rh nanoparticles sintered under reaction conditions. As a result, all catalysts exhibited increasing selectivity to CO with increasing time on stream (> 10 h). We conclude the distribution of Rh structures evolved over time under reaction conditions through three distinct reconstruction mechanisms (Rh particle fragmentation, Ostwald ripening, and particle migration and coalescence) that occurred on varying timescales. Catalyst stability on the ~90 h time scale was ultimately controlled by the initial Rh structure.

25 ENERGY STORAGE↗
DOE OSTI2026

Near-Real-Time Material Tracking: Combining Vis–NIR Spectroscopy with Flow Sensing for Accurate Nd(III) Quantification

A fiber-optic visible–near-infrared (vis–NIR) absorption spectroscopy and flow sensor system has been developed for near-real-time tracking of Nd mass in the effluent stream from a column in a fume hood. The approach leverages two unique data streams and a partial least-squares regression (PLSR) model trained on vis–NIR absorption spectra of Nd(III) (0–1.5 M) in 1 M HNO 3 . In-line volumetric flow rate and vis–NIR spectra are measured in sequence after a chromatography column. The time stamps from each data stream are then synchronized, which allows integrated volumes to be combined with Nd(III) molarities predicted by a PLSR model to accurately calculate the Nd mass flowing through the column. This integrated measurement provides instantaneous mass flow and accumulates these data over time to obtain the total mass processed. The methodology developed in this study contributes critical technical infrastructure to improve monitoring capabilities to support chemical separations and the production of strategic materials and isotopes.

Irvine, Sawyer B. [Oak Ridge National Laboratory (↗
DOE OSTI2025

Developing Fluorescence-Based Sensors to Support Rare Earth Element Separation

Rare earth elements (REEs) are essential to most renewable energy technologies. Unfortunately, as we transition to sustainable energy production, the demand for REEs is rapidly growing well beyond current rates of production. As a result, novel means of efficient, scalable, and easily adaptable methods for processing primary and recycle feedstocks are needed. Development and integration of sensors for highly selective in-line monitoring can support more efficient design and testing of such novel separation processes, as well as more cost-effective deployment of those separation flowsheets. Work here will explore the application of fluorescence spectroscopy, a highly sensitive and selective technique, to quantify multiple lanthanides in complex mixtures including known interferents or quenching agents. Results include identification of the optimal excitation wavelength and the limit of detection of various rare earth elements as well as the performance of data-science-based quantification approaches in streams where “unknowns” are present. Overall, the data science tools in conjunction with optical sensor data were able to quantify analytes in the presence of other lanthanides which can be anticipated in the actual industrial stream. Here we include characterization of lanthanides in a microfluidic device similar to those used in new process development. This study demonstrates the capability of utilizing fluorescence spectroscopy to quantify analytes in a complicated solution matrix, suggesting this is a successful approach for in-line monitoring to optimize the separation efficiency in an industrial stream.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Sorbent Mediated Electrocatalytic Reduction of Dilute CO 2 to Methane

Efficient CO 2 utilization is a critical component of closing the anthropogenic carbon cycle. Most studies have focused on the use of pure streams of CO 2 . However, CO 2 is generally available only in dilute streams, which requires capture by sorbents followed by energy-intensive regeneration to release concentrated CO 2 . Direct utilization of sorbed-CO 2 avoids the costly regeneration step, and the sorbent-CO 2 interaction can kinetically activate CO 2 to tune its reactivity toward products that could otherwise be inaccessible with direct CO 2 reduction. We demonstrate that an N-heterocyclic carbene, 1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene (DPIy), quantitatively reacts with CO 2 from dilute streams (0.04 and 10%) to form the sorbent-CO 2 substrate 1,3-bis(2,6-diisopropylphenyl)imidazolium-2-carboxylate (DPICx). Electrocatalyst iron tetraphenylporphyrin chloride (Fe(TPP)Cl) typically reduces CO 2 to CO; however, with DPICx as the substrate, the eight-electron reduced product methane (CH 4 ) is produced with a high Faradaic efficiency (>85%) and regeneration of the sorbent DPIy. In addition to the overall energy and capital advantages of integrated CO 2 capture and conversion, this result illustrates how sorbents can serve a dual purpose for both CO 2 capture and chemical auxiliary purposes to access unique products. CO 2 has a spectrum of reactivity with different types of sorbents; thus, these studies demonstrate how sorbent-CO 2 interactions can be leveraged for integrated capture and utilization platforms to access a wider range of CO 2 -derived products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Integrated CO 2 Capture and Conversion to Formate with a Molecular Platinum Bis(diphosphine) Electrocatalyst

Carbon dioxide is a potentially valuable feedstock for carbon-based fuels or commodities but is only available in dilute streams. Many studies have focused on either the capture and concentration of CO 2 or the reduction of pure CO 2 streams. The direct reduction of sorbent-captured CO 2 in an integrated process would skip the energy-intensive CO 2 concentration and sorbent regeneration step. Herein, we report the electrocatalytic reduction of 1,3-bis(2,6-diisopropylphenyl)imidazolium-2-carboxylate (IPr·CO 2 ), which forms quantitatively from the reaction of sorbent 1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene (IPr) with 10% and 0.04% CO 2 streams, by catalyst [Pt(dmpe) 2 ](PF 6 ) 2 (dmpe = 1,2-bis(dimethylphosphino)ethane) to formate with >70% Faradaic efficiencies. Unexpectedly, experimental studies indicate that the proton source phenol facilitates rapid decarboxylation of IPr·CO 2 to release CO 2 , which is the substrate for reduction. Kinetic studies determined the rate of hydride transfer from a catalytic intermediate [HPt(dmpe) 2 ](PF 6 ) to form the C–H bond in formate to be 0.22 M –1 s –1 . Further details on the mechanism, transition state energy, and structure for hydride transfer to CO 2 , a common step in CO 2 reduction, were explored using computational methods.

Chemistry↗
DOE OSTI2024

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Abstract Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

58 GEOSCIENCES↗
DOE OSTI2025

Nutrient Limitation Induces a Productivity Decline From Light‐Controlled Maximum

Abstract Nutrient impacts on productivity in stream ecosystems can be obscured by light limitation imposed by canopy cover and water turbidity, thereby creating uncertainties in linking nutrient and productivity regimes. Evaluations of nutrient limitations are often based on a response ratio (RR) quantifying productivity stimulation above ambient levels given augmented nutrient supply. This metric neglects the primacy of light effects on productivity. We propose an alternative approach to quantify nutrient limitations using a “decline ratio” (DR), which quantifies the productivity decline from the maximum established by light availability. The DR treats light as the first‐order control and nutrient depletion as a disturbance causing productivity decline, allowing separation of nutrient and light influences. We used DR to assess nutrient diffusing substrate (NDS) experiments with three nutrients (nitrogen [N], phosphorus [P], iron [Fe]) from five Greenland streams during summer, where light is not limited due to the lack of canopy and low turbidity. We tested two hypotheses: (a) productivity maximum (i.e., highest chlorophyll‐ a among NDS treatments) is controlled by light and (b) DR depends on both light and nutrients. The productivity maximum was strongly predicted by light ( R 2 = 0.60). The productivity decline induced by N limitation (i.e., DR N ) was best explained by light availability when parameterized with either dissolved inorganic nitrogen concentration ( R 2 = 0.79) or N:Fe ratio ( R 2 = 0.87). These predictions outperformed predictions of RR for which light was not a significant factor. Reversing the perspective on nutrient limitation from “stimulation above ambient” to “decline below maximum” provides insights into both light and nutrient impacts on stream productivity.

Shin, Yuseung [School of Natural Resources and Env↗