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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FHOD formin and SRF promote post-embryonic striated muscle growth through separate pathways in C. elegans

Highlights: • Reduced SRF or formin FHOD-1 activity similarly inhibit muscle growth in worms. • Formin does not promote worm SRF-dependent muscle gene transcription. • Proteasome-driven myosin proteolysis is increased in worm muscle lacking formin. Previous work with cultured cells has shown transcription of muscle genes by serum response factor (SRF) can be stimulated by actin polymerization driven by proteins of the formin family. However, it is not clear if endogenous formins similarly promote SRF-dependent transcription during muscle development in vivo. We tested whether formin activity promotes SRF-dependent transcription in striated muscle in the simple animal model, Caenorhabditis elegans. Our lab has shown FHOD-1 is the only formin that directly promotes sarcomere formation in the worm's striated muscle. We show here FHOD-1 and SRF homolog UNC-120 both support muscle growth and also muscle myosin II heavy chain A expression. However, while a hypomorphic unc-120 allele blunts expression of a set of striated muscle genes, these genes are largely upregulated or unchanged by absence of FHOD-1. Instead, pharmacological inhibition of the proteasome restores myosin protein levels in worms lacking FHOD-1, suggesting elevated proteolysis accounts for their myosin deficit. Interestingly, proteasome inhibition does not restore normal muscle growth to fhod-1(Δ) mutants, suggesting formin contributes to muscle growth by some alternative mechanism. Overall, we find SRF does not depend on formin to promote muscle gene transcription in a simple in vivo system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling

Global Sensitivity Analysis (GSA) aims to understand the relative importance of uncertain input variables to model response. Conventional GSA involves calculating sensitivity (Sobol’) indices for a model with known model parameter distributions. However, model parameters are affected by aleatory and epistemic uncertainty, with the latter often caused by lack of data. In this paper, we propose a new framework to quantify uncertainty in probability model-form and model parameters resulting from small datasets and integrate these uncertainties into Sobol’ index estimates. First, the process establishes, through Bayesian multimodel inference, a set of candidate probability models and their associated probabilities. Imprecise Sobol’ indices are calculated from these probability models using an importance sampling reweighting approach. This results in probabilistic Sobol’ indices, whose distribution characterizes uncertainty in the sensitivity resulting from small dataset size. The imprecise Sobol’ indices thus provide a measure of confidence in the sensitivity estimate and, moreover, can be used to inform data collection efforts targeted to minimize the impact of uncertainties. Through an example studying the parameters of a Timoshenko beam, we show that these probabilistic Sobol’ indices converge to the true/deterministic Sobol’ indices as the dataset size increases and hence, distribution-form uncertainty reduces. The approach is then applied to assess the sensitivity of the out-of-plane properties of an E-glass fiber composite material to its constituent properties. This second example illustrates the approach for an important class of materials with wide-ranging applications when data may be lacking for some input parameters.

42 ENGINEERING↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

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

Understanding the Effect of Sample Geometry on Temperature Distribution during Optical Floating Zone Crystal Growth in Vacuum Environment through Heat Transfer Modeling

Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Carbon Dioxide Conversion to Nanomaterials: Methods, Applications, and Challenges

Anthropogenic carbon dioxide (CO 2 ) emission is one of the biggest global threats that we are confronted with in the 21st century. A lack of prompt action to mitigate the atmospheric CO 2 level could lead to global catastrophic events. As a result of the significance of the aforementioned threat, multiple nations have agreed to enact carbon capture, storage, utilization, and sequestration (CCUS) technologies at academic and industrial scales with the aim of lowering the atmospheric CO 2 level. Although there are many reviews on CCUS technologies, there is a lack of comprehensive review on CO 2 conversion to more economically valuable products, such as nanomaterials. Carbon dioxide capture and conversion into nanomaterials is an interesting research topic in many ways because it contributes to our fundamental knowledge of material conversion, provides alternative ways to reduce atmospheric pollution, and yields lucrative products, which has not been explored broadly. Aside from the positive outcomes, we have a moral obligation to rectify a global threat caused by anthropogenic emission. Herein, we present current methods of converting CO 2 into organic and inorganic nanomaterials with a focus on operating conditions and challenges as well as the potential of nanomaterials in specifically biomedical applications as opposed to extensively reviewed energy-related uses. Furthermore, our review emphasizes the toxicity issues related to using such nanomaterials in biological settings based on literature and other comparable studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

CASSCF with Extremely Large Active Spaces Using the Adaptive Sampling Configuration Interaction Method

The complete active space self-consistent field (CASSCF) method is the principal approach employed for studying strongly correlated systems. However, exact CASSCF can only be performed on small active spaces of ~20 electrons in ~20 orbitals due to exponential growth in the computational cost. Here, we show that employing the Adaptive Sampling Configuration Interaction (ASCI) method as an approximate Full CI solver in the active space allows CASSCF-like calculations within chemical accuracy (<1 kcal/mol for relative energies) in active spaces with more than ~50 active electrons in ~50 active orbitals, significantly increasing the sizes of systems amenable to accurate multiconfigurational treatment. The main challenge with using any selected CI-based approximate CASSCF is the orbital optimization problem; they tend to exhibit large numbers of local minima in orbital space due to their lack of invariance to active–active rotations (in addition to the local minima that exist in exact CASSCF). We highlight methods that can avoid spurious local extrema as a practical solution to the orbital optimization problem. We employ ASCI-SCF to demonstrate a lack of polyradical character in moderately sized periacenes with up to 52 correlated electrons and compare against heat-bath CI on an iron porphyrin system with more than 40 correlated electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Amphiphile Organization in Organic Solutions: An Alternative Explanation for Small-Angle X-ray Scattering Features in Malonamide/Alkane Mixtures

In this work, the role of different intermolecular interactions in the aggregation of amphiphiles in an organic solvent is studied for systems of relevance to liquid-liquid extraction (LLE), a chemical process used to selectively recover metals from complex mixtures. Of specific interest is the role, or lack thereof, of hydrogen bonding, which is often assumed to be a main driver of the organic phase structural organization that has been linked to separation efficacy. Toward that end, a series of malonamide extractants in n-dodecane have been studied in the absence of any extracted aqueous solutes, including water. The series of extractants includes N,N'-dimethyl-N,N'-dibutyltetradecylmalonamide (DMDBTDMA), two of its homologs, and N,N'-dimethyl-N,N'-dioctylhexylethoxymalonamide (DMDOHEMA). This simplified model LLE system enables systematic investigation of the role of dipole-dipole and alkyl tail steric interactions in amphiphile aggregation. Small-angle X-ray scattering (SAXS) profiles computed from molecular dynamics trajectories are in good agreement with experimental SAXS data. Molecular dynamics simulations show that malonamide aggregation results from dipole-driven self-association and lacks characteristic aggregate sizes. Mid-q correlation peaks in the SAXS profiles emerge at high concentration for each malonamide. In those densely packed solutions, the correlation peaks are observed to result from alkyl tail-induced spacing between electron-rich polar head groups, with peak positions determined by the different alkyl tail lengths present in the malonamide molecule. This explanation of the SAXS correlation peaks contrasts with the prevailing literature, which attributes mesoscale features observed in small-angle scattering to the formation of microemulsions. Instead, this work finds that these features are present in the absence of water or any reverse micellar organization of the malonamides. As such, molecular-scale malonamide self-association and packing, rather than microemulsion-based colloidal-scale descriptions, is a more appropriate framework for these LLE systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

New Insights into Secondary Organic Aerosol Formation at the Air–Liquid Interface

Air-liquid interfacial processing of volatile organic compound oxidation has been suggested as an important source of secondary organic aerosols. However, owing to the lack of techniques for in situ air-liquid interface analysis, the detailed interfacial mechanism remains speculative. To obviate this, the analysis of air-liquid interfacial reactions and the dynamics of glyoxal oxidation as a model volatile organic compound using in situ liquid synchrotron-based vacuum ultraviolet single photon ionization mass spectrometry enabled by the system for analysis at the liquid vacuum interface microreactor is reported. This approach allows for determination of reaction intermediates and oxidation products including polymers and oligomers at the air-liquid interface by mass spectral analysis and appearance energy measurements. Furthermore, an expanded reaction mechanism of photooxidation free radical induced reactions as a source of aqueous secondary organic aerosol formation is proposed based on these new results, suggesting that single photon ionization could be applied to provide unique insights into interfacial chemistry changing the earth atmospheric composition.

aqueous secondary organic aerosol, glyoxal, photoo↗
DOE OSTI2021

In Situ Probing Potassium-ion Intercalation-induced Amorphization in Crystalline Iron Phosphate Cathode Materials

Na-ion and K-ion batteries are promising alternatives for large-scale energy storage applications due to their abundance and low cost. Intercalation of these large ions could cause irreversible structural deformation and partial to complete amorphization in the crystalline electrodes. The designing of new amorphous electrodes is another route to develop electrodes to store these ions reversibly. Lack of understanding of dynamic changes in the amorphous nanostructures during battery operation is the bottleneck for further developments. Here, we report the utilization of in situ digital image correlation and in-operando X-ray diffraction (XRD) techniques to probe dynamic changes in the amorphous phase of iron phosphate during potassium intercalation. In-operando XRD demonstrates amorphization in the electrode’s nanostructure during the first charge/discharge cycle. Additionally, the ex-situ high-resolution transmission electron microscopy further confirms the amorphization after potassium insertion. In situ strain analysis detects the reversible deformation associated with redox reactions in the amorphous phases. Our approach offers new insights on the mechanisms of ion intercalation in the amorphous nanostructures which are highly potent for development of next-generation batteries.

Ozdogru, Bertan↗
DOE OSTI2023

Metal–Carbodithioate-Based 3D Semiconducting Metal–Organic Framework: Porous Optoelectronic Material for Energy Conversion

Solar energy conversion requires the working compositions to generate photoinduced charges with high potential and the ability to deliver charges to the catalytic sites and/or external electrode. These two properties are typically at odds with each other and call for new molecular materials with sufficient conjugation to improve charge conductivity but not as much conjugation as to overly compromise the optical band gap. In this work, we developed a semiconducting metal-organic framework (MOF) prepared explicitly through metal-carbodithioate"(-CS 2 ) n M" linkage chemistry, entailing augmented metal-linker electronic communication. The stronger ligand field and higher covalent character of metal-carbodithioate linkages-when combined with spirofluorene-derived organic struts and nickel-(II) ion-based nodes-provided a stable, semiconducting 3D-porous MOF, Spiro-CS 2 Ni. This MOF lacks long-range ordering and is defined by a flexible structure with non-aggregated building units, as suggested by reverse Monte Carlo simulations of the pair distribution function obtained from total scattering experiments. The solvent-removed "closed pore"material recorded a Brunauer-Emmett-Teller area of similar to 400m 2 /g, where the "open pore" form possesses90 wt % solvent-accessible porosity. Electrochemical measurements suggest that Spiro-CS 2 Ni possesses a band gap of 1.57 eV(sigma = 10 -7 S/cm at -1.3 V bias potential),which can be further improved by manipulating the d-electron configuration through an axial coordination (ligand/substrate), the latter of which indicates usefulness as an electrocatalyst and/or a photoelectrocatalyst(upon substrate binding). Transient-absorption spectroscopy reveals a long-lived photo-generated charge-transfer state (τ CR = 6.5 mu s) capable of chemical transformation under a biased voltage. Spiro-CS 2 Ni can endure a compelling range of pH(1-12 for weeks) and hours of electrochemical and photoelectrochemical conditions in the presence of water and organic acids. We believe this work provides crucial design principles for low-density, porous,light-energy-conversion materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Phosphonium-Based Metal Halide Semiconductors: When a Single Atom Dictates Water and Thermal Stability

Phosphonium-based (R-P(R’) 3 + ) metal halide semiconductors (MHS) emerged recently as a promising family of materials offering enhanced water and thermal stability over conventional ammonium-based (R-NH 3 + ) MHS. Despite this performance, there is a lack of systematic studies that elucidate the origin of these features as well as the underlying optoelectronic properties. Here, we report here the synthesis of nine new 1D materials (L)PbX 3 (L= C4-P, C6-P, C6-N, P3-P, P4-P, X= I, Br), using custom made organic ligands, namely butyl(triethyl)phosphonium bromide (C4-P), hexyl(triethyl)phosphonium bromide (C6-P), benzyl(triethyl)phosphonium bromide (P3-P), benzyl(trimethyl)phosphonium bromide (P4-P), and hexyl(triethyl)ammonium bromide (C6-N). Most of the materials are water stable for over 21 months, while the assembly of the isostructural (C6-N)PbBr 3 and (C6-P)PbBr 3 allowed us to directly identify the impact of a single atom (phosphorus versus nitrogen), on water stability. Notably, the C6-P analog remains water stable for 21 months, whereas the C6-N analog dissolves in water. It was found that neither crystal packing nor the lack of hydrogen bond interactions with water is responsible for this record stability performance, challenging currently established claims on the 1 origin of water stability in MHS. All materials feature broad light emission at RT, with (P3-P)PbBr 3 exhibiting a PLQY of 22.0% ascribed to permanent traps, while electrochemical studies uncovered for the first time the redox properties and their strong potential for the sensing of per- and polyfluoroalkyl substances (PFAS) in water samples. Our work showcases the potential of these compounds for applications where thermal and water stability are interwoven, potentially rendering their (R-NH 3 + ) based counterparts obsolete.

PFAS sensing↗
DOE OSTI2021

U.S. Manufacturing Water Use Data and Estimates: Current State, Limitations, and Future Needs for Supporting Manufacturing Research and Development

Water is essential to manufacturing operations; without it, many facilities could not operate or meet production demands. Physical, reputational, and regulatory risks to water supplies compounded by climate change-induced impacts on hydrological conditions threaten the adequacy of water supplies for manufacturing. Manufacturing water use has not been a major focus of either water or manufacturing-related research. Research and development (R&D) aimed at helping manufacturers use water more sustainably and adapt to changing water conditions is needed to ensure a thriving sector and economy. However, the ability to identify R&D needs is severely limited due to a lack of current, statistically representative data on manufacturing water use and its environmental implications. In this Perspective, we outline four key questions to inform R&D on manufacturing use and highlight how the current state of water data in the United States does not support the adequate investigation of these questions. We make recommendations for the water data characteristics needed to explore the research questions and knowledgeably inform R&D on manufacturing water use.

McCall, James↗
DOE OSTI2021

Mechanistic Duality of Bacterial Efflux Substrates and Inhibitors: Example of Simple Substituted Cinnamoyl and Naphthyl Amides

Antibiotic resistance poses an immediate and growing threat to human health. Multidrug efflux pumps are promising targets for overcoming antibiotic resistance with small-molecule therapeutics. Previously, we identified a diaminoquinoline acrylamide, NSC-33353, as a potent inhibitor of the AcrAB–TolC efflux pump in Escherichia coli. This inhibitor potentiates the antibacterial activities of novobiocin and erythromycin upon binding to the membrane fusion protein AcrA. It is also a substrate for efflux and lacks appreciable intrinsic antibacterial activity of its own in wild-type cells. Here, we have modified the substituents of the cinnamoyl group of NSC-33353, giving rise to analogs that retain the ability to inhibit efflux, lost the features of the efflux substrates, and gained antibacterial activity in wild-type cells. The replacement of the cinnamoyl group with naphthyl isosteres generated compounds that lack antibacterial activity but are both excellent efflux pump inhibitors and substrates. Surprisingly, these inhibitors potentiate the antibacterial activity of novobiocin but not erythromycin. Surface plasmon resonance experiments and molecular docking suggest that the replacement of the cinnamoyl group with naphthyl shifts the affinity of the compounds away from AcrA to the AcrB transporter, making them better efflux substrates and changing their mechanism of inhibition. These results provide new insights into the duality of efflux substrate/inhibitor features in chemical scaffolds that will facilitate the development of new efflux pump inhibitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2020

LixNiO/Ni Heterostructure with Strong Basic Lattice Oxygen Enables Electrocatalytic Hydrogen Evolution with Pt-like Activity

The low-cost hydrogen production from water electrolysis is crucial for practical deployment of sustainable hydrogen economy, but is often constrained by the lack of active and robust electrocatalysts from Earth-abundant materials. We describe here an unconventional heterostructure composed of strongly coupled Ni-deficient LixNiO nanoclusters and polycrystalline Ni nanocrystals, and its exceptional activities toward hydrogen evolution reaction (HER) in aqueous electrolytes. The presence of unique lattice oxygen species with strong Brønsted basicity is a significant feature, which spontaneously split water molecules and effectively accelerate the sluggish Volmer H-OH dissociation step in neutral and alkaline HER. In combination with the unpreceded level of intimate LixNiO and Ni interfacial junctions that produces abundant “hotspots” for promoted hydride coupling, the catalyst exhibited kinetic activities almost identical as Pt/C and decent long term stability in universal pH.

Lu, Ke↗
DOE OSTI2025

Telecom-Luminescent and Room Temperature Coherent Tetrathiafulvalene-Based Qubits in Spin-Rich Solids

One of the main challenges facing quantum information science (QIS) is the development of robust qubits that can be op-erated under ambient conditions. Current state-of-the-art anionic nitrogen vacancy center (NV–) defect qubits are robust enough to be operated at room temperature but lack scalability and tunability. These are areas where molecular qubits excel, although they typically suffer from poor air stability and fast decoherence at elevated temperatures and in magneti-cally noisy environments. Organic-based systems offer advantages to this end, although examples retaining room tem-perature coherence are still rare. Furthermore, most organic-based systems lack optical transitions similar to NV– centers that could allow for optical initialization and readout. Here we report two new organic-based qubit candidates that demonstrate room temperature coherence in nuclear and electron spin-rich environments. These qubits luminesce far into the near-infrared (NIR, 700-1700 nm) and telecom (~1260-1625 nm) regions, ideal for biological sensing and com-munications applications, respectively.

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

The [4Fe-4S] Cluster of HydF Is Essential for [FeFe]-Hydrogenase Maturation

The organometallic H-cluster of the [FeFe]-hydrogenase is assembled in vivo through a complex process requiring the action of three dedicated maturation enzymes, HydG, HydE, and HydF, as well as the aminomethyl-lipoyl-H-protein (H met ) of the glycine cleavage system (GCS). Here we probe the role of HydF and its [4Fe-4S] cluster in [FeFe]-hydrogenase maturation by using a defined semisynthetic approach in which [Fe I 2 (μ-SH) 2 (CO) 4 (CN) 2 ] 2– ([2Fe] E ) is used to bypass HydE and HydG, and GCS components are used in place of cell lysate. We show that inclusion of the iron–sulfur carrier protein NfuA and the high-CO-affinity myoglobin variant Mb H64L provides dramatically improved hydrogenase activities up to 828 μmol/min/mg, equivalent to the best reported activities for Chlamydomonas reinhardtii [FeFe]-hydrogenase isolated from the native organism. Apo-HydF lacking a [4Fe-4S] cluster provides very little hydrogenase activity; however, full maturation is restored with the addition of NfuA, which we demonstrate reconstitutes the [4Fe-4S] cluster of HydF. In addition, a HydF variant lacking a [4Fe-4S] cluster by changing two cysteine ligands to alanine is completely unable to support either semisynthetic maturation using [2Fe] E , or full maturation using HydG and HydE, even in the presence of NfuA, demonstrating that the HydF [4Fe-4S] cluster is absolutely essential for [FeFe]-hydrogenase maturation. The possibility that the HydF [4Fe-4S] cluster plays a role in direct binding of [2Fe] E is negated by our results with the HydF D311C variant, which demonstrate that the labile Asp311 cluster ligand is not essential for [2Fe] E binding and HydA maturation. We therefore conclude that [2Fe]E binds HydF adjacent to, but not directly coordinated to, the [4Fe-4S] cluster. The HydF [4Fe-4S] cluster is proposed to be essential due to its impact on the [2Fe] E binding orientation and the ability of the HydF/[2Fe] E complex to form productive interactions with H met or the H met /T-protein complex during DTMA ligand biosynthesis.

cluster chemistry↗
DOE OSTI2021

Testing the Recycled Gabbro Hypothesis for the Origin of “Ghost Plagioclase” Melt Signatures Using 87 Sr/ 86 Sr of Individual Olivine-Hosted Melt Inclusions From Hawai'i

Melt inclusions with large, positive Sr anomalies have been described in multiple tectonic settings, and the origins of this unusual geochemical feature are debated. Three origins have been proposed, all involving plagioclase as the source of the elevated Sr: (i) direct assimilation of plagioclase-rich lithologies, (ii) recycled lower oceanic gabbro in the mantle source, and (iii) shallow-level diffusive interaction between present day lower oceanic crust (i.e., plagioclase-bearing lithologies) and the percolating melt. A “ghost plagioclase” signature (i.e., a large, positive Sr anomaly without associated high Al 2 O 3 ) is present in melt inclusions from Mauna Loa. Here, we present new 87 Sr/ 86 Sr measurements of individual olivine-hosted melt inclusions from three Hawaiian volcanoes, Mauna Loa, Loihi, and Koolau. The data set includes a Mauna Loa melt inclusion with the highest reported Sr anomaly (or highest (Sr/Ce) N , which is 7.2) for Hawai'i. All melt inclusions have 87 Sr/ 86 Sr values within the range reported previously for the lavas from each volcano. Critically, the 87 Sr/ 86 Sr of the high (Sr/Ce) N melt inclusion lies within the narrow range of 87 Sr/ 86 Sr for Mauna Loa melts that lack high (Sr/Ce) N signatures. Therefore, to explain the high (Sr/Ce) N ratio of the ghost plagioclase signature using an ancient recycled gabbro, the gabbro-infused mantle source would have had to evolve, by chance, to have the same 87 Sr/ 86 Sr as the source of the Mauna Loa melts that lack a recycled gabbro (ghost plagioclase) signature. Alternatively, shallow-level diffusive interactions between Mauna Loa plagioclase-rich cumulates and a percolating mantle-derived melt provides a simpler explanation for the presence of the high (Sr/Ce) N Mauna Loa melts.

58 GEOSCIENCES↗