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At least 919 records · Page 51

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Fiber motion in highly confined flows of carbon fiber and non-Newtonian polymer [Advanced modeling of non-Newtonian polymer and carbon fibers]

Inks compounded of short carbon fibers suspended in polymer resin can be extruded to produce composite materials during additive manufacturing or 3D printing processes. The flow process induces anisotropic orientation of the fibers which is set into the matrix and significantly affects the mechanical and physical properties of the final product. Therefore, the flow of fiber suspensions needs to be understood in order to predict the orientation distribution of the fibers during such manufacturing processes. There is still a lack of knowledge for extrusion of the complex mixture of a non-Newtonian polymer containing a high-volume fraction of fibers with high fiber aspect ratio, where both inter-particle, fluid-particle and fiber-wall interactions are computationally evaluated. This paper presents predictive numerical simulations and experimental results of such confined flow in a concentrated regime. The code is based on Lagrange multiplier technique and resolves each particle and interaction between the fibers and surrounding fluid and nozzle walls. We investigate numerically how the fiber length impacts the fiber alignment during extrusion. We found that a fiber length above 67% of the nozzle's diameter induces dramatic change in the fiber flow, causing fibers to concentrate at the nozzle boundaries with very low concentration of fibers in the center of the nozzle. It was found that the best fiber alignment is reached for fiber lengths equal to 40–50% of the nozzle diameters. Numerical findings are supported by experimental results. In conclusion, this work improves understanding of fiber orientation during 3D printing and is an important milestone for the prediction of the complex mechanical properties of additively manufactured fiber composites.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Advancements in Life Cycle Human Exposure and Toxicity Characterization

The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA). The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose–response modeling. The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose–response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose–response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential. Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Data-driven design of metal–organic frameworks for wet flue gas CO 2 capture

Limiting the increase of CO 2 in the atmosphere is one of the largest challenges of our generation. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO 2 emissions, much effort is focused on developing solid adsorbents that can efficiently capture CO 2 from flue gases emitted from anthropogenic sources. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO 2 from nitrogen do not perform well when using realistic flue gas that contains water, because water competes with CO 2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO 2 -binding sites-which we term 'adsorbaphores'-that endow MOFs with CO 2 /N 2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO 2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Chromium (VI) reduction in acetate- and molasses-amended natural media: empirical model development

Stimulating indigenous microbes to reduce heavy metals from highly toxic oxidized species to more benign reduced species is a promising groundwater remediation technique that has already seen successful field applications. Designing such a bio-remediation scheme requires a model incorporating the kinetics of nonlinear bio-geochemical interactions between multiple species. With this motivation, we performed a set of microcosm experiments in natural sediments and their indigenous pore water and microbes, generating simultaneous time series for concentrations of Cr(VI), an electron donor (both molasses and acetate were considered), and biomass. Molasses was found to undergo a rapid direct abiotic reaction which eliminated all Cr(VI) before any biomass had time to grow. This was not found in the acetate microcosms, and a distinct zero-order bio-reduction process was observed. Existing models were found inappropriate and a new set of three coupled governing equations representing these process dynamics were developed and their parameters calibrated against the time series from the acetate-amended microcosms. Cell suspension batch experiments were also performed to calibrate bio-reduction rates in the absence of electron donor and sediment. The donor used to initially grow the cells (molasses or acetate) was found not to impact the reduction rate constants in suspension, which were orders of magnitude larger than those explaining the natural media microcosm experiments. This suggests the limited utility of kinetics determined in suspension for remedial design. Scoping studies on the natural media microcosms were also performed, suggesting limited impact of foreign abiotic material and minimal effect of diffusion limitation in the vertical dimension. These analyses may be of independent value to future researchers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Evaluation and correction of timing inconsistencies from the Thor 1 and Thor 2 experiments at the Nevada National Security Site.

Active source seismic data was collected at the Nevada National Security Site using the Seismic Hammer(TM) (SH), under contract from HK Exploration. The SH generates a seismic pulse by dropping a 13 metric ton mass from a height of 1.5 m. Post-survey evaluation of collected data revealed inconsistencies in shot trigger time that required additional analysis and correction using cross-correlation and/or time shifts derived from manual picks of trigger times. While the primary analysis for which this data set was collected is independent of the knowledge of shot trigger time, other processing methods require highly precise knowledge of the trigger time. In order to make the Thor data set more usable to the larger community, additional work was undertaken. Results using the preferred method of cross-correlation were found to be satisfactory. An improved timing fiducial approach is required to reduce timing errors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Light source and method for making a light source

A light source fabrication process to fabricate a customized light source includes attaching a plurality of light emitting diodes to a circuit board, where each light emitting diode is configured to emit light within a same wavelength range. Further, the light source fabrication process includes applying a first phosphor layer to a first set of light emitting diodes of the plurality of light emitting diodes and applying a second phosphor layer to a second set of light emitting diodes of the plurality of light emitting diodes. The phosphor layers are applied to the light emitting diodes using one of a screen printing process and an inkjet printing process. The first phosphor layer is different from and has different optical properties from the second phosphor layer.

42 ENGINEERING↗

DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Causally‐Informed Deep Learning to Improve Climate Models and Projections

Abstract Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines.

Meteorology & Atmospheric Sciences↗

DOE OSTI2017

Development of utility generic functional requirements for electronic work packages and computer-based procedures

The Nuclear Electronic Work Packages - Enterprise Requirements (NEWPER) initiative is a step toward a vision of implementing an eWP framework that includes many types of eWPs. This will enable immediate paper-related cost savings in work management and provide a path to future labor efficiency gains through enhanced integration and process improvement in support of the Nuclear Promise (Nuclear Energy Institute 2016). The NEWPER initiative was organized by the Nuclear Information Technology Strategic Leadership (NITSL) group, which is an organization that brings together leaders from the nuclear utility industry and regulatory agencies to address issues involved with information technology used in nuclear-power utilities. NITSL strives to maintain awareness of industry information technology-related initiatives and events and communicates those events to its membership. NITSL and LWRS Program researchers have been coordinating activities, including joint organization of NEWPER-related meetings and report development. The main goal of the NEWPER initiative was to develop a set of utility generic functional requirements for eWP systems. This set of requirements will support each utility in their process of identifying plant-specific functional and non-functional requirements. The NEWPER initiative has 140 members where the largest group of members consists of 19 commercial U.S. nuclear utilities and eleven of the most prominent vendors of eWP solutions. Through the NEWPER initiative two sets of functional requirements were developed; functional requirements for electronic work packages and functional requirements for computer-based procedures. This paper will describe the development process as well as a summary of the requirements.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Development of utility generic functional requirements for electronic work packages and computer-based procedures - 273

The Nuclear Electronic Work Packages - Enterprise Requirements (NEWPER) initiative is a step toward a vision of implementing an eWP framework that includes many types of eWPs. This will enable immediate paper-related cost savings in work management and provide a path to future labor efficiency gains through enhanced integration and process improvement in support of the Nuclear Promise (Nuclear Energy Institute 2016). The NEWPER initiative was organized by the Nuclear Information Technology Strategic Leadership (NITSL) group, which is an organization that brings together leaders from the nuclear utility industry and regulatory agencies to address issues involved with information technology used in nuclear-power utilities. NITSL strives to maintain awareness of industry information technology-related initiatives and events and communicates those events to its membership. NITSL and Light Water Reactor Sustainability Program researchers have been coordinating activities, including joint organization of NEWPER-related meetings and report development. The main goal of the NEWPER initiative was to develop a set of utility generic functional requirements for eWP systems. This set of requirements will support each utility in their process of identifying plant-specific functional and non-functional requirements. The NEWPER initiative has 140 members where the largest group of members consists of 19 commercial U.S. nuclear utilities and eleven of the most prominent vendors of eWP solutions. Through the NEWPER initiative two sets of functional requirements were developed; functional requirements for electronic work packages and functional requirements for computer-based procedures. This paper will describe the development process as well as a summary of the requirements. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Application of Genetic Algorithm (GA) Assisted Partial Least Square (PLS) Analysis on Trilinear and Non-trilinear Fluorescence Data Sets to Quantify the Fluorophores in Multifluorophoric Mixtures: Improving Quantification Accuracy of Fluorimetric Estimations of Dilute Aqueous Mixtures

Excitation-emission matrix fluorescence (EEMF) and total synchronous fluorescence spectroscopy (TSFS) are the 2 fluorescence techniques that are commonly used for the analysis of multifluorophoric mixtures. These 2 fluorescence techniques are conceptually different and provide certain advantages over each other. The manual analysis of such highly correlated large volume of EEMF and TSFS towards developing a calibration model is difficult. Partial least square (PLS) analysis can analyze the large volume of EEMF and TSFS data sets by finding important factors that maximize the correlation between the spectral and concentration information for each fluorophore. However, often the application of PLS analysis on entire data sets does not provide a robust calibration model and requires application of suitable pre-processing step. The present work evaluates the application of genetic algorithm (GA) analysis prior to PLS analysis on EEMF and TSFS data sets towards improving the precision and accuracy of the calibration model. The GA algorithm essentially combines the advantages provided by stochastic methods with those provided by deterministic approaches and can find the set of EEMF and TSFS variables that perfectly correlate well with the concentration of each of the fluorophores present in the multifluorophoric mixtures. The utility of the GA assisted PLS analysis is successfully validated using (i) EEMF data sets acquired for dilute aqueous mixture of four biomolecules and (ii) TSFS data sets acquired for dilute aqueous mixtures of four carcinogenic polycyclic aromatic hydrocarbons (PAHs) mixtures. In the present work, it is shown that by using the GA it is possible to significantly improve the accuracy and precision of the PLS calibration model developed for both EEMF and TSFS data set. Hence, GA must be considered as a useful pre-processing technique while developing an EEMF and TSFS calibration model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

All-optical phase conjugation using diffractive wavefront processing

Abstract Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

42 ENGINEERING↗

DOE OSTI2019

Manufacturing Thermal Energy Use in 2014

The first data set estimates thermal energy use (i.e., fuels combusted for process heating, boilers, and combined heat and power/cogeneration) by end use, temperature, county, and facility employment size class for all U.S. manufacturing industries in 2014. The estimation methodology builds off of prior estimates of industrial energy use (https://dx.doi.org/10.7799/1481899, https://dx.doi.org/10.7799/1461488). The second data set estimates hourly heat load (as a fraction of annual energy used for heat) using hourly observations of industrial boiler and combined heat and power/cogeneration heat load from EPA's Air Markets Program Data (https://ampd.epa.gov/ampd/), weekly operating hours in 2014 from Census Quarterly Survey of Plant Capacity Utilization (https://www.census.gov/programs-surveys/qpc/data.html), and annual production hours reported by assessments conducted by Industrial Assessment Centers (https://iac.university/).

14 SOLAR ENERGY↗

DOE OSTI2019

Manufacturing Thermal Energy Use in 2014

14 SOLAR ENERGY↗

DOE OSTI2024

Core-Shell Oxidative Aromatization Catalysts for Single Step Liquefaction of Distributed Shale Gas (Final Technical Report)

The objective of this project was to design and demonstrate a core-shell structured multifunctional catalyst to convert the light (dry) components of shale gas into liquid aromatic compounds (primarily benzene and toluene) in a single step. Operated in a modular oxidative aromatization system (OAS) under a cyclic redox scheme, the novel catalyst and process can significantly improve the value and transportability of distributed shale gas. Since the project started, each quarter addressed a different set of tasks related to the completion of the milestone detailed in the project award. The yearly summaries of these tasks are summarized below: Q1-Q4: • Conducted project planning and literature search. • Investigated a number of SHC redox catalysts using thermogravimetric analysis and fixed-bed reactor experiments. • Initiated process modeling towards generating two process models for the methane DHA base case and OAS process. • Developed DHA catalysts capable of producing >500 g/kg-cat-hr aromatics at 80% or greater aromatics selectivity at 700°C. Q5-Q8: • Developed alternative approaches with sequential bed configurations to enhance the aromatic yields based on OCM+DHA • Improved the zeolite synthesis efficiency by using the microwave-assisted technique and investigated the synthesis conditions on the zeolite yield, crystalline structure and morphology • Constructed a set of Aspen Plus process models with significant energy savings for OAS as compared to the base case non-oxidative DHA. • Adapted conventional hydrothermal method to be applicable to the microwave synthesizer unit for more efficient catalyst synthesis. • Studied the structure of the OCM catalyst and the dispersion of the carbonate in the redox reactions and in methane flow with Raman Spectroscopy. Q9-Q12: • Scaled up the catalyst synthesis with the microwave synthesis method. Based on its performance, procedural characterizations and catalytic performance testing were further conducted for the new microwave synthesized catalysts with the newly-developed product analysis procedure. • Developed the reaction system setup for the C2-DHA or OCM+DHA reaction product and achieved a better product collection-analysis method for the aromatic products with an improved carbon balance. The product from the OCM reaction exhibited complicated effects on the DHA catalyst. • Conducted additional OCM catalyst characterization using Near Ambient Pressure X-ray Photoelectron Spectroscopy and in situ Raman characterization • Validated the significant energy savings for OAS as compared to the base case non-oxidative DHA. Successfully set up the simulation model for the OCM+DHA+SHC reaction system based on the updated experimental results from NCSU. Q13-End of project: • Synthesized new zeolite catalysts by the microwave method, conducted characterizations (XRD, SEM, and TEM) and catalytic behavior testing. • Explored the “wet” C 2 H 6 and C 2 H 4 DHA reactions with using steam co-feed. A subsequent reduction as the regeneration step can regenerate the DHA catalyst and recover 99% activity of the fresh performance. • Achieved a 15.3% single-pass aromatic yield from methane by rationally combining the OCM and DHA at different temperatures. • Conducted a 105-hour stability test with an improved regeneration procedure, with an average aromatic yield of 13.8%. • Developed new catalyst and achieved a record-high 23.2% yield.

03 NATURAL GAS↗

DOE OSTI2019

The Power of Environmental Observatories for Advancing Multidisciplinary Research, Outreach, and Decision Support: The Case of the Minnesota River Basin

Observatory-scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation-funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes-Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south-central Minnesota, USA, a 43,400-km 2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change, allowing for transdisciplinary research into the complex cascade of environmental changes that occur due to hydrology and land use alterations from intensive agricultural management and climate change. Data sets collected include water chemistry and biogeochemical data, geochemical fingerprinting of major sediment sources, high-resolution monitoring of river bluff erosion, and repeat channel cross-sectional and bathymetry data following major floods. The data collection efforts led to development of a series of integrative reduced complexity models that provide deeper insight into how water, sediment, and nutrients route and transform through a large channel network and respond to change. These models represent the culmination of efforts to integrate interdisciplinary data sets and science to gain new insights into watershed-scale processes in order to advance management and decision making. Finally, the purpose of this paper is to present a synthesis of the data sets and models, disseminate them to the community for further research, and identify mechanisms used to expand the temporal and spatial extent of short-term observatory-scale data collection efforts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

U redox state tracked in mineralized hydrothermal carbonate with implications for U-Pb geochronology

U-Pb carbonate geochronology can directly constrain the timing and rates of important geological processes. However, the mechanisms and controls on U incorporation, distribution, and retention in carbonate minerals remain unclear, limiting geological interpretations. Here X-ray absorption spectroscopy (µXAS) and in-situ U-Pb carbonate geochronology are combined to temporally track U distribution and redox state in a porphyry-epithermal system. In this setting, multiple generations of carbonate minerals record fluid conditions and processes which control the solubility and deposition of metals, including U. This novel approach provides the first evidence of both oxidized UO 2 2+ and reduced U 4+ species in temporally distinct generations of carbonate within a single sample. Preservation of two different U oxidation states during discrete precipitation events requires U retentivity within older domains, demonstrating that the U-Pb carbonate geochronometer is robust under hydrothermal conditions. Furthermore, crystal zones with abundant fluid/vapour inclusions linked to boiling processes coincide with relatively high levels of U and favourable U/Pb. Targeting carbonate domains with these textures may therefore increase success in U-Pb geochronology. U-Pb carbonate dating combined with µXAS can track the temporal evolution of processes critical for metal deposition in long-lived and multistage hydrothermal-magmatic ore deposit settings.

58 GEOSCIENCES↗