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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Creep anisotropy modeling and uncertainty quantification of an additively manufactured Ni-based superalloy

The advantages offered by additive manufacturing over traditional processes has driven a great deal of industrial and academic interest in recent years. However, the process is relatively new and requires additional investigation to become sufficiently mature for wide scale industrial adoption. Electron beam melting powder bed fusion is one technology that has shown promise for fabricating high temperature resistant materials such as nickel based superalloys. The resulting microstructures typically exhibit a strong fiber texture in the build direction giving rise to anisotropic time-dependent deformation behavior. In order to accelerate the qualification of these materials for industrial adoption accurate numerical models are needed for simulating their behavior. In this work a crystal plasticity model including non-Schmid effects is presented for capturing creep anisotropy observed in additively manufactured IN738LC. The model is calibrated via a probabilistic framework where model parameters are treated as random variables. An iterative sequential design strategy is utilized to efficiently identify the probability density of the unknown model parameters. As a case study the model is utilized to investigate the behavior of randomly oriented equiaxed grain clusters sometimes observed embedded in the additively manufactured columnar structure. A synthetic realization is simulated and uncertainty is propagated through to the full-field response. Results indicate that these features are the source of significant creep relaxation and strain accumulation which partially explains observed grain boundary decohesion at these locations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Compression of tokamak boundary plasma simulation data using a maximum volume algorithm for matrix skeleton decomposition

This report demonstrates satisfactory data compression of SOLPS-ITER simulation output ranging from 2D fields, 1D profiles, and 0D scalar variables with a novel matrix decomposition approach. The singular value decomposition (SVD) scales poorly for large matrix sizes and is unsuited to the application on high dimensional data common to fusion plasma physics simulation. In this work, we employ the columns-submatrix-rows (CUR) matrix factorization technique in order to compute a low-rank approximation up to two orders of magnitude faster than the SVD, but within a nominal L2-norm relative error of ε = 10 –2 . In addition, the CUR approach maintains the original format of the data, in its extracted columns and rows, allowing for interpretable data storage at the original resolution of the simulation. We utilize an iterative algorithm to compute the CUR decomposition of simulation output by maximizing the volume, or linearly independent information content, of a low-rank submatrix contained within the data. Experiments over $\textit{n} × \textit{n}$ randomized test matrices with embedded rank-deficient features show that this maximum volume implementation of CUR matrix approximation has reduced asymptotic computational complexity on the order of n compared to the SVD, which scales approximately as $n^3$. These results show that the CUR technique can be used to effectively select time step snapshots (columns) of over 140 SOLPS-ITER output variables and the associated discretized coordinate timeseries (rows) allowing for reconstruction of the complete simulation dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Constraining Erosion Rates and Landscape Evolution With In Situ 10 Be and 26 Al Cosmogenic Nuclides at Table Mountain, Antarctica

Abstract This study investigates surface weathering and sediment preservation at Table Mountain, a high‐elevation, hyperarid, polar landscape in the Transantarctic Mountains. We report cosmogenic nuclide concentrations ( 10 Be and 26 Al) in quartz from bedrock surfaces, erratic boulder lag, and cobbles embedded within Sirius Group sediments to quantify erosion rates. In situ 10 Be and 26 Al depth profiles from a 2.95 m permafrost core in the Sirius Group further constrain surface erosion rates and elucidate landscape stability. Measured 10 Be and 26 Al concentrations from two sandstone bedrock surfaces adjacent to Sirius Group sediments give erosion rates of 0.18–0.28 m/Myr. An erratic sandstone boulder within the lag above the Sirius Group yields erosion rates of ∼0.42 ± 0.03 m/Myr, whereas two cobbles embedded within the Sirius Group yield higher rates of 0.81–1.12 m/Myr. Depth profiles of in situ 10 Be and 26 Al indicate no vertical mixing of Sirius Group permafrost since deposition. Depth profile models are best explained by erosion rates of 0.53 +0.13 / −0.12 m/Myr, and an exposure age of 0.78 +0.06 / −0.08 Ma. We view the model “age” to represent the ∼0.8‐million‐year time‐scale for surface lowering equivalent to one attenuation length of cosmic ray production to achieve steady‐state conditions. Continual exhumation of embedded clasts from within the Sirius Group results in an accumulation of clasts forming the observed erosional lag deposit covering the landscape. Our erosion rates of the Sirius Group surface based on in situ 10 Be and 26 Al depth profiles are an order‐of‐magnitude larger than those based on meteoric 10 Be infiltration and further clarification is required.

58 GEOSCIENCES↗
DOE OSTI2022

The level-1 trigger for the SuperCDMS experiment at SNOLAB

The SuperCDMS SNOLAB dark matter search experiment aims to be sensitive to energy depositions down to Script O(1 eV). This imposes requirements on the resolution, signal efficiency, and noise rejection of the trigger system. To accomplish this, the SuperCDMS level-1 trigger system is implemented in an FPGA on a custom PCB. A time-domain optimal filter algorithm realized as a finite impulse response filter provides a baseline resolution of 0.38 times the standard deviation of the noise, σn, and a 99.9% trigger efficiency for signal amplitudes of 1.1 σ n in typical noise conditions. Embedded in a modular architecture, flexible trigger logic enables reliable triggering and vetoing in a dead-time-free manner for a variety of purposes and run conditions. The trigger architecture and performance are detailed in this article.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Combining Eddy Covariance Towers, Field Measurements, and the MEMS 2 Ecosystem Model Improves Confidence in the Climate Impacts of Bioenergy With Carbon Capture and Storage

ABSTRACT Carbon dioxide removal technologies such as bioenergy with carbon capture and storage (BECCS) are required if the effects of climate change are to be reversed over the next century. However, BECCS demands extensive land use change that may create positive or negative radiative forcing impacts upstream of the BECCS facility through changes to in situ greenhouse gas fluxes and land surface albedo. When quantifying these upstream climate impacts, even at a single site, different methods can give different estimates. Here we show how three common methods for estimating the net ecosystem carbon balance of bioenergy crops established on former grassland or former cropland can differ in their central estimates and uncertainty. We place these net ecosystem carbon balance forcings in the context of associated radiative forcings from changes to soil N 2 O and CH 4 fluxes, land surface albedo, embedded fossil fuel use, and geologically stored carbon. Results from long term eddy covariance measurements, a soil and plant carbon inventory, and the MEMS 2 process‐based ecosystem model all agree that establishing perennials such as switchgrass or mixed prairie on former cropland resulted in net negative radiative forcing (i.e., global cooling) of −26.5 to −39.6 fW m −2 over 100 years. Establishing these perennials on former grassland sites had similar climate mitigation impacts of −19.3 to −42.5 fW m −2 . However, the largest climate mitigation came from establishing corn for BECCS on former cropland or grassland, with radiative forcings from −38.4 to −50.5 fW m −2 , due to its higher plant productivity and therefore more geologically stored carbon. Our results highlight the strengths and limitations of each method for quantifying the field scale climate impacts of BECCS and show that utilizing multiple methods can increase confidence in the final radiative forcing estimates.

Falvo, Grant [Department of Plant, Soil and Microb↗
DOE OSTI2020

Microgrid Energy Management System Integration with Advanced Distribution Management System

The Integrated Distribution Management System (IDMS) project was initiated to demonstrate the interactive operation of microgrid systems and the distribution systems with which they interconnect. The key technologies for this are the microgrid management system and the utility distribution management system. The IDMS project successfully demonstrated that a utility’s advanced distribution management system/distributed energy resources management system (ADMS/DERMS) could effectively manage microgrids to provide visibility and control functionalities as well as use the microgrid as a dispatchable resource to support the utility grid. The DERMS accomplishes this by determining active and reactive power needs at the point of common coupling (PCC) using advanced applications like volt/VAR watt optimization (VVWO) and load relief (LR) with the underlying core applications state estimation (SE) and load flow (LF). The ADMS/DERMS can use the microgrid as a resource to resolve and prevent violations in the grid and to optimize the operational working state of the grid. The IDMS project demonstrated that a utility-operated ADMS with embedded DERMS functionality can flexibly manage a variety of microgrids and other aggregated distributed energy resources (DER) in concert with the wider distribution grid. Microgrids can provide grid services in any number of different ways to meet the operational needs of the distribution utility. The manner of aggregation — microgrid or virtual power plant — is not necessarily relevant to the utility as long as the grid services from the aggregated DER are available and can be managed by its ADMS/DERMS for the stability and reliability of the grid. The IDMS project demonstrated this integrated ADMS concept by combining hardware/software-in-the-loop testing with commercial products from different vendors and a utility’s network model. The project integrated the Schneider Electric EcoStruxure™ ADMS with DERMS with the Schweitzer Engineering Laboratories (SEL) POWERMAX ® Microgrid Control System, and the simulated resources with energy company PECO’s model of a utility-owned microgrid, establishing an operational relationship in which the utility manages the operational functionality of a microgrid at the PCC and that provides the utility with the capability to control the comprehensive power system, inclusive of the macrogrid and microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2021

Identification of opportunities for integrating chemical processes for carbon (dioxide) utilization to nuclear power plants

The energy and industrial sectors are responsible for more than 75% of global carbon dioxide emissions, which are primary contributors to climate change. Decarbonization strategies to reduce overall carbon dioxide emissions are urgently needed. Among the numerous decarbonization strategies that are being defined and implemented, a particular strategy of interest for the present work is “the Re-X or zero waste” strategy, which involves recycling, reuse, repurposing, and remanufacturing of industrial emissions, by-products, and waste in general. A circular value chain or Circular Economy is embedded within this strategy. In a circular carbon economy, efficient reuse of emitted carbon dioxide is considered. However, the stability of carbon dioxide implies the requirement of significant amounts of energy for its transformation into value-added chemicals or products, which must be supplied by low carbon emitting energy sources. Nuclear power plants are low carbon energy sources that, additionally to electricity, could also supply heat and radiation for chemical transformations. This work identifies the opportunities and challenges for the development of integrated energy systems to upgrade and transform carbon dioxide, involving chemical and nuclear energy. The analysis encompasses possible use of the different forms of energy that can be obtain from nuclear reactors (i.e., radiation, electricity, and heat), by reviewing the published literature on potential routes for its conversion. Overall, our review indicates that an universal technology of the one-fits-all solution-type is an utopic dream. Instead, a suite of (contaminants tolerant) technologies for processing various concentrations CO 2 and producing a variety of products is currently needed.

08 HYDROGEN↗
DOE OSTI2024

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Geothermal Hydraulic Stimulation: Overview of Methods and Best Practices

This paper outlines important information related to the hydraulic stimulation of geothermal wells and considerations for regulators responsible for evaluating applications to conduct these operations. Hydraulic stimulation applied to geothermal wells is an evolving technology that is different from the hydraulic fracturing process widely used to complete unconventional oil and gas wells. Important differences relate to operating pressures, variations in rock and fracture processes, and characteristics and disposal of hydraulic fracture fluids. Hydraulic stimulation is not widely used in the geothermal industry; however, future development of enhanced geothermal systems (EGS) will require hydraulic stimulation in order to enhance and create fracture permeability to allow wells to deliver sufficient heat and fluid to power plants. Gigawatts of EGS potential have been identified (U.S. Department of Energy 2019), so it is expected that the use of hydraulic stimulation of geothermal wells will be more common in the future. At present, there are no formal regulations guiding drilling programs or sundry notices that propose hydraulic stimulation of geothermal wells. On federal lands, important constraints and oversight are embedded in the process for obtaining Geothermal Drilling Permits outlined in the Code of Federal Regulations (43 CFR Section 3262.11); in Geothermal Resource Order 2 (GRO 2) guidance for assessment and mitigation of impacts of geothermal operations as well as construction and testing of geothermal wells; and in a Bureau of Land Management (BLM) Induced Seismicity Instruction Memorandum (BLM 2018). Additionally, local knowledge from BLM field offices and expertise of BLM engineers, or that of state regulators for operations on private and state lands, will be applied to fully assess operator applications to conduct geothermal hydraulic stimulation activities. Significant research effort has been directed toward EGS, so geothermal hydraulic stimulation techniques, impacts, and results are evolving and becoming better understood, including adaptation of decades of oil and gas industry experience with hydraulic fracturing of unconventional reservoirs. Recent EGS activities in the United States provide details about hydraulic stimulation with respect to oversight, stimulation design, execution, and results. Based on experience to date, the important issues to address when hydraulically stimulating a geothermal well include the following: 1. Wellbore construction and integrity must be appropriate to protect groundwater and manage stimulation pressures. 2. Understanding of lithology, faults, fractures, and subsurface stress state is necessary to design stimulation plans and predict results. 3. Seismic monitoring allows for observation and mitigation of induced seismicity.

geothermal↗
DOE OSTI2025

Visualization of Noisy and Less Noisy Computational Basis States in Quantum Computing

Quantum computing technology holds substantial promise as a reliable computational paradigm. However, current noisy intermediate scale quantum (NISQ) systems, are significantly impacted by noise originating from hardware inconsistencies. This noise causes errors and lowers output fidelity. So we must find which basis states cause errors. However, there are two main challenges in analyzing noise corresponding to basis states. First, the noise distribution data is high dimensional in nature, thereby making its analysis challenging. Second, although functional box plots have been used in the state of the art research to understand such a high dimensional data, they suffer from clutter and occlusion issues because of overplotting. In this study, we introduce an innovative visualization pipeline to address the aforementioned challenges to provide a clear depiction of noisy and less-noisy basis states. Specifically, our proposed visualization pipeline comprises three stages namely, low dimensional embedding, clustering, and violin plot visualization, to reduce visual clutter and effectively analyze high-dimensional noise distribution data. Our analysis uses quantum machine learning (QML) circuits as case study for drawing a distinction between noisy and less noisy basis states.

Senapati, Priyabrata [Kent State University]↗
DOE OSTI2021

SURFplus model calibration for PBX 9012

PBX 9012 is a plastic bonded explosive composed of 90.2 wt % HMX (cyclo-tetramethylene-tetranitramine) and 9.8 % Viton A (vinylidene-hexafluoropropylene copolymer). It has nearly the same composition as LX-07 (90% HMX and 10% Viton A) but with a slightly lower density. Hence it has a higher porosity and is slightly more shock sensitive. Here we describe the SURFplus model calibration for PBX 9012 using data from five shock-to-detonation transition (SDT) gas gun experiments by Burns and Chiquete, detonation front shape data from rate stick experiments and cylinder test experiment reported in Anderson et al., Jackson et al. The SDT experiments provide Pop plot data points (run distance-to-detonation) which characterize shock initiation, Lagrangian velocity time histories from embedded magnetic velocity gauges which give information on the reaction behind the lead shock, and Hugoniot data points for the reactants EOS. The rate stick experiments provide data on the curvature effect which characterize propagating detonation waves. The cylinder test experiments provide data on the detonation release isentrope used to calibrate the products EOS. The HE model uses a Davis reactants EOS with parameters that Burns and Chiquete [2020] fit to Hugoniot data, and Davis products EOS with parameters that Anderson et al. [2020] fit to cylinder test data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Results of Speciation Determination Measurements of Insulating Sulfur Hexafluoride Gas

Under the direction of the NNSA NA-231 Mo-99 Program, SRNL has provided technical assistance to U.S. companies under cooperative agreements. One such area of technical assistance has been for the recommendation for contamination management methods in the eventuality that tritium contaminates the insulating gas of a high-voltage ion source used in the accelerator-based process developed by SHINE Medical Technologies to produce Mo-99. The SHINE accelerator process uses tritium as the target of a deuterium ion beam, where the ion beam is produced within a high-voltage ion source insulated with sulfur hexafluoride (SF 6 ), and is separated from the tritium target by an extensive pump train. Normal operating conditions preclude the conditions necessary for contamination of the insulating gas, however certain atypical conditions could result in a tritium contamination event. Accelerator based processes for medical isotope production represent an anomalous tritium contamination challenge due to the presence of SF 6 . SF 6 decomposes through normal use as an insulator gas, and the exact speciation of the decomposition byproducts depends on a variety of factors. This creates a scenario where the anticipation of species present in the insulating gas mixture is difficult. In turn, the fate of tritium within such a chemical system is unclear. This document discusses the results of measurements made in-situ of the SF 6 insulating gas in use within a SHINE Medical Technology’s high-voltage ion source. This was done in an effort to determine the chemical speciation of the gas, allowing for the recommendation of management strategies in the event that tritium contaminates the insulating gas. The results of the measurements reported in this work suggest that tritium contamination in the insulating gas may result in two chemical forms of tritium in the system, tritiated water and molecular tritium. These two forms of tritium would be captured by two methods. Tritiated water may be captured and removed from the SF 6 by an adsorbent canister already present within the pressure vessel that houses the insulating gas while in use. It may also be separated from the SF 6 during routine purification of the SF 6 , which is performed by SHINE personnel routinely. The purification cart will perform all non-embedded systems cleanup necessary to remediate tritiated species in the system. The purification cart passes the insulating gas through several filters, followed by the liquefication of the SF 6 . The filters will separate tritiated water from the SF 6 at this stage. It is expected that molecular tritium will accumulate in the gas-phase during the liquification step, which is separated and stored. Tritium may be recovered from this mixture through more conventional hydrogen purification methods such as a palladium diffuser.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

58 GEOSCIENCES↗
DOE OSTI2026

Nanoscale elemental mapping reveals the effect of light and temperature on Gephyrocapsa huxleyi coccoliths

Coccolithophores produce vast quantities of stable calcium carbonate coccoliths, occupying the distinctive position as one of the most productive calcifying organisms on Earth. Gephyrocapsa huxleyi, the most abundant species of coccolithophores across modern oceans, plays an important role in the biological carbon pump and may serve as inspiration for nature-based carbon capture and sequestration technologies. Despite intensive study into G. huxleyi, the biological mechanisms of coccolith synthesis remain unclear. Here, we employ synchrotron X-ray fluorescence to quantitatively map elemental incorporation (calcium, Ca; strontium, Sr; sulfur, S; iron, Fe; and silicon, Si) into G. huxleyi coccoliths at 40-nanometer resolution. Trace elements play a role in determining the thermodynamic stability of coccolith carbonate sequestration and may provide insight into unknown mechanisms of coccolithogenesis. Although the coccolith Sr/Ca ratio has been widely applied as a paleoceanographic proxy, understanding of the Sr/Ca phenomena is limited. Our results confirm that Sr/Ca is temperature dependent but challenge the hypothesis that growth rate is a primary determinant. We find that increased light irradiance led to an ~30% decrease in calcium incorporated into the coccoliths. Surprisingly, X-ray fluorescence spectra and mapping revealed evidence of sulfur, at approximately three times the concentration of Sr, embedded in and around the calcite matrix, suggesting that G. huxleyi coccoliths may play an expanded role in the global sulfur cycle. Here, based on our elemental maps, we postulate that sulfur is active in the intracellular coccolith vesicle and plays a direct role in formation of G. huxleyi coccoliths.

36 MATERIALS SCIENCE↗