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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Interface stability of ultrasonic additively manufactured Zircaloy-4 during hydrothermal corrosion

Simulated pressurized water reactor conditions (330 °C, 15.6 MPa, ~20 ppb oxygen) without irradiation were used to investigate the hydrothermal corrosion behavior of ultrasonic additively manufactured Zircaloy-4 up to 1000 h. X-ray computed tomography allowed for visualization of defects from processing and their progression after corrosion experiments. The specimens were found to have clear variability in the mass change data, compared to typical wrought Zircaloy-4 specimens. The variation in the mass change after exposure was attributed to weld defects connected to the specimen surface which allowed ingress of oxidant into the samples. Defects visualized by computed tomography were found via metallography and characterized. In conclusion, ultrasonic additively manufactured Zircaloy-4 was found to have comparable corrosion behavior as wrought Zircaloy-4 for specimens which did not have clear surface defects along weld interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impacts of photovoltaic solar energy on soil carbon: A global systematic review and framework

Globally, solar energy is anticipated to be the primary source of electricity as early as 2050, and the greatest additions in capacity are currently in the form of large, ground-mounted photovoltaic solar energy facilities (GPVs). Growing interest lies in understanding and anticipating opportunities to increase soil carbon sequestration across the footprint and perimeter of both conventional and multi-use GPVs (e.g., ecovoltaics, agrivoltaics, and rangevolatics), especially as operators increasingly deputize as land managers. To date, studies on the relationship between soils and PV solar energy are limited to unique, localized sites. This study employed a systematic review to (i) identify a global corpus of 18 studies on interactions between GPVs and soils, (ii) collect and characterize 113 soil and soil-related experimental variables interacting with GPVs from this corpus, and (iii) synthesize trends among these experimental variables. Next, this study combined data from the systematic review with an iterative, knowledge co-production approach to produce a conceptual model for the study of soil and GPV interactions that applies to multiple installation types, scales, and contexts where GPVs are deployed, and identified research opportunities, threats, and priorities. This study's baseline understanding, conceptual model, and co-produced knowledge confer unique insight into the feasibility of combining soil carbon sequestration with the climate change mitigation potential of PV solar energy.

agrivoltaics↗
DOE OSTI2021

Assessing the Relative Climate Impact of Carbon Utilization for Concrete, Chemical, and Mineral Production

Estimates show that 6.2 gigatons of carbon dioxide (CO2) can be captured and utilized across three pathways, concrete, chemical, and minerals, by 2050. However, it is difficult to compare the climate benefit across these three carbon capture and utilization (CCU) pathways to determine the most effective use of captured CO2. The life cycle assessment methods to evaluate the climate benefit of CCU chemicals should additionally account for the change in material properties of concrete due to CO2 utilization. Furthermore, with most CO2 utilization technologies being in the early stages of research and development, the uncertainty and variability in process and inventory data present a significant challenge in evaluating the climate benefit. We present a stochastically determined climate return on investment (ROI) metric to rank and prioritize CO2 utilization across 20 concrete, chemical and mineral pathways based on the realized climate benefit. We show that two concrete pathways, which use CO2 during concrete mixing, and two chemical pathways, which produce formic acid through hydrogenation of CO2 and carbon monoxide through dry reforming of methane, generate the greatest climate ROI and are the only CCU pathways with a higher likelihood of generating a climate benefit than a climate burden.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Insensitivity of Magnetic Coupling to Ligand Substitution in a Series of Tetraoxolene Radical-Bridged Fe 2 Complexes

The elucidation of magnetostructural correlations between bridging ligand substitution and strength of magnetic coupling is essential to the development of high-temperature molecule-based magnetic materials. Toward this end, we report the series of tetraoxolene-bridged Fe II 2 complexes [(Me 3 TPyA) 2 Fe 2 ( R L)] n + (Me 3 TPyA = tris(6-methyl-2-pyridylmethyl)amine; n = 2: OMe LH 2 = 3,6-dimethoxy-2,5-dihydroxo-1,4-benzoquinone, Cl LH 2 = 3,6-dichloro-2,5-dihydroxo-1,4-benzoquinone, Na 2 [ NO 2 L] = sodium 3,6-dinitro-2,5-dihydroxo-1,4-benzoquinone; n = 4: SMe 2 L = 3,6-bis(dimethylsulfonium)-2,5-dihydroxo-1,4-benzoquinone diylide) and their one-electron-reduced analogues. Furthermore, variable-temperature dc magnetic susceptibility data reveal the presence of weak ferromagnetic superexchange between Fe II centers in the oxidized species, with exchange constants of J = +1.2(2) (R = OMe, Cl) and +0.3(1) (R = NO 2 , SMe 2 ) cm –1 . In contrast, X-ray diffraction, cyclic voltammetry, and Mössbauer spectroscopy establish a ligand-centered radical in the reduced complexes. Magnetic measurements for the radical-bridged species reveal the presence of strong antiferromagnetic metal–radical coupling, with J = -57(10), -60(7), -58(6), and -65(8) cm –1 for R = OMe, Cl, NO 2 , and SMe 2 , respectively. The minimal effects of substituents in the 3- and 6-positions of R L x –• on the magnetic coupling strength is understood through electronic structure calculations, which show negligible spin density on the substituents and associated C atoms of the ring. Finally, the radical-bridged complexes are single-molecule magnets, with relaxation barriers of U eff = 50(1), 41(1), 38(1), and 33(1) cm –1 for R = OMe, Cl, NO 2 , and SMe 2 , respectively. Taken together, these results provide the first examination of how bridging ligand substitution influences magnetic coupling in semiquinoid-bridged compounds, and they establish design criteria for the synthesis of semiquinoid-based molecules and materials.

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

Direct Measure of Electrode Spatial Heterogeneity: Influence of Processing Conditions on Anode Architecture and Performance

The spatial (in)homogeneity of aqueous processed silicon electrodes using standard poly(acrylic acid)-based binders and slurry preparation conditions is demonstrated in this work. X-ray nanotomography shows segregation of materials into submicron-thick layers depending on the mixing method and starting binder molecular weights. Using a dispersant, or in situ production of dispersant from the cleavage of the binder into smaller molecular weight species, increases the resulting lateral homogeneity while drastically decreasing the vertical homogeneity as a result of sedimentation and separation due to gravitational forces. This data explains some of the variability in the literature with respect to silicon electrode performance and demonstrates two potential ways to improve slurry-based electrode fabrications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System

Abstract The International Ocean Model Benchmarking (IOMB) software package is a new community resource that we use here to evaluate surface and upper ocean biogeochemical variables and integrated anthropogenic carbon uptake from earth system models (ESMs) contributing to the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). IOMB generates graphics and tables for systematically comparing model predictions against multiple datasets. Our analysis reveals some improvement in the multi‐model mean from CMIP5 to CMIP6 for most of the variables we examined. Compared to data‐constrained estimates of ocean anthropogenic carbon uptake for the 1994–2007 period, negative biases exist for many models between 30 and 50°S. Global model estimates of anthropogenic carbon uptake for the same period do not change significantly from CMIP5 to CMIP6, with the combined ensemble mean estimate of 27.8 ± 0.5 Pg C lower than a data‐constrained estimate of 33.0 ± 4.0 Pg C. At the same time, the change in the natural carbon inventory from CMIP is estimated to be a source of 0.7 ± 0.3 Pg C, which is considerably smaller in magnitude than a data‐constrained estimate of 5.0 ± 3.0 Pg C. With chlorofluorocarbon (CFC) predictions available for several models, we demonstrate that negative anthropogenic dissolved inorganic carbon biases coincide with negative biases in CFC concentration, highlighting the importance of weak exchange between the surface and interior ocean in regulating rates of anthropogenic carbon uptake. To examine the robustness of this attribution across the CMIP models, we calculate the global vertical temperature gradient between 200 and 1,000 m as a metric for global stratification and exchange between the surface and deeper waters. We find a linear relationship between the bias of the vertical temperature gradients and the bias in global anthropogenic carbon uptake, consistent with the hypothesis that model biases in anthropogenic carbon uptake are related to biases in surface‐to‐interior exchange by physical processes.

58 GEOSCIENCES↗
DOE OSTI2025

Probing the influence of ion-pairing on ligand-field excited-state dynamics

Exploration of the photophysical and photochemical properties of transition metal complexes has driven ground-breaking advancements in solar energy conversion technologies, including photoredox catalysis. While significant research has been devoted to understanding excited state properties of second- and third-row transition metal complexes, earth-abundant first-row metal complexes have received comparatively little attention in this context until very recently. In particular, the role of ion-pairing – which has been identified as a potentially significant factor for Ir(III)-based photosensitizers – has not been examined with regard to its influence on the ligand-field excited states that dominate much of first-row photophysics. A key challenge in studying ion-pair interactions lies in quantifying the extent and nature of ion-pairing, particularly in non-aqueous media where the vast majority of photophysical studies are performed. Cobalt(III) polypyridyl complexes provide an attractive platform to address such questions due to their demonstrated potential for applications in photoredox catalysis involving ligand-field excited states. In the present study, we prepared a cobalt(III) polypyridyl complex, [Co(4,4′-OMebpy) 3 ](BAr F 4 ) 3 (where 4,4′-OMebpy is 4,4′-dimethoxy-2,2′-bipyridine and BAr F 4 is tetrakis(3,5-bis(trifluoromethyl)-phenyl)borate) to probe ion-pairing in non-aqueous solutions. Specifically, analysis of data acquired from both variable-temperature diffusion ordered spectroscopy (DOSY) NMR and 1-D rotating-frame nuclear Overhauser effect (ROE) experiments allowed us to identify and differentiate between solvent-separated ion pairs in high-dielectric media and contact ion pairs in a low-dielectric solvent. Time-resolved absorption spectroscopy was then used to measure ground-state recovery dynamics under these varying conditions of ion-pairing, the results of which revealed an increase in excited-state lifetime for contact ion-pairs that we suggest arises from a reduction in outer-sphere reorganization energy relative to conditions which favored solvent-separated ion pairs. We believe this study demonstrates that one can leverage broadly available NMR-based methods to understand ion-pairing in non-aqueous solutions, which in turn can provide a microscopic picture of intermolecular interactions that can impact the photophysical properties of transition metal-based chromophores.

Ghosh, Atanu [Michigan State University, East Lans↗
DOE OSTI2026

Towards fully predictive gyrokinetic full- f simulations: validation and triangularity studies in TCV

Designing economical magnetic confinement fusion power plants motivates computational tools that can estimate plasma behavior from engineering parameters without direct reliance on experimental measurement of the plasma profiles. In this work, we present full-f global long-wavelength gyrokinetic simulations of edge and scrape-off layer turbulence in tokamaks that use only magnetic geometry, heating power, and particle inventory as inputs. Unlike many modeling approaches that employ free parameters fitted to experimental data, raising uncertainties when extrapolating to reactor scales. This approach directly simulates turbulence and resulting profiles through gyrokinetics without such empirical adjustments. This is achieved via an adaptive sourcing algorithm in Gkeyll that strictly controls energy injection and emulates particle sourcing due to neutral recycling. We show that the simulated kinetic profiles compare reasonably well with Thomson scattering and Langmuir probe data for Tokamak á Configuration Variable (TCV) discharge #65125, and that the simulations reproduce characteristic features such as blob transport and self-organized electric fields. Applying the same framework to study triangularity effects suggests mechanisms contributing to the improved confinement reported for negative triangularity (NT). Simulations of TCV discharges #65125 and #65130 indicate that NT increases the E x B flow shear (by about 20% in these cases), which correlates with reduced turbulent losses and a modest change in the distribution of power exhaust to the vessel wall. While the physical models contain approximations that can be refined in future work, the predictive capability demonstrated here, evolving multiple profile relaxation times with kinetic electron and ion models in hundreds of GPU hours, indicates the feasibility of using Gkeyll to support design studies of fusion devices.

Hoffmann, Antoine Cyril David [Princeton Plasma Ph↗
DOE OSTI2025

Lack of clear standards and usable comparisons of downscaled climate projections pose a roadblock for US climate discovery and adaptation

Abstract The release of global climate projections coupled with the demand for local-resolution climate-forced meteorology has prompted many research groups to downscale these projections using various statistical, dynamical, and current machine learning techniques. Such downscaled datasets are being used to plan infrastructure and other community needs over the coming decades. Faced with roughly a dozen available US downscaled datasets, many practitioners ask, ‘What are the relevant differences between datasets?’ This work highlights the difficulty of comparing downscaled datasets and illustrates ways in which datasets differ even when using identical climate model input data. We show that substantial variability in precipitation projections arises from downscaling alone and that the downscaled dataset agreement varies depending on global climate projection. This analysis emphasizes the need for greater coordination and movement toward rigorous benchmarking of downscaling strategies within the downscaling research community, à la the land-modeling community, to better quantify downscaling dataset differences, strengths, and weaknesses for practitioners.

Hartke, Samantha H. (ORCID:0000000202394723)↗
DOE OSTI2025

Measurement of event shapes in minimum-bias events from proton-proton collisions at $\sqrt{s}$ = 13

A measurement of event-shape variables is presented, using a data sample produced in a special run with approximately one inelastic proton-proton collision per bunch crossing. The data were collected with the CMS detector at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 64 μ⁢b −1 . A number of observables related to the overall distribution of charged particles in the collisions are corrected for detector effects and compared with simulations. Inclusive event-shape distributions, as well as differential distributions of event shapes as functions of charged-particle multiplicity, are studied. None of the models investigated are able to satisfactorily describe the data. Moreover, there are significant features common amongst all generator setups studied, particularly showing data being more isotropic than any of the simulations. Multidimensional unfolded distributions are provided, along with their correlations.

Chekhovsky, V. [Yerevan Physics Institute]↗
DOE OSTI2021

Validation of Subhourly Clipping Loss Error Corrections

Under-performance of solar PV systems is an important issue that increases risks for stakeholders, including developers, investors and operators. Recently some attention has focused on underestimation of inverter clipping losses as a possible source of over-prediction where sub-hourly solar variability is high. Several models and data sets have been analyzed over the past few years, with the aim of quantifying, predicting, and correcting underestimated clipping loss errors for systems with high DC/AC ratio and solar variability. In this research, we apply a machine learning model developed at NREL to two physical PV systems, to correct for subhourly clipping losses. For each system, we compare overall AC power output for the model taken at 1-minute intervals to AC power output taken at 1-hour intervals with the addition of the subhourly clipping correction. Our findings consistently show that the addition of the clipping loss correction lead to a reduction in mean bias error of 0.8% and 1.2% for systems A and B, respectively, with no additional filtering applied. When examining high solar variability periods where clipping is more pronounced, system A and B experienced a 1.8% and 2.7% reduction in mean bias error, respectively, when the clipping correction was applied.

machine learning↗
DOE OSTI2022

JGCRI/stitches

Amalgamate existing climate data to create monthly climate variable fields. The STITCHES python library provides a user-friendly interface to top-down climate emulation.

Snyder, Abigail↗
DOE OSTI2021

Impact of Non-Steady State Operation on Cooling Water Consumption at Coal- and Natural Gas-Fired Power Plants

Increased renewable energy penetration in the electricity grid in coming decades will result in more frequent cycling at thermal power plants. Simultaneously, thermal power plants may face water scarcity with declining availability of cooling water. Therefore, to enhance thermal power plants’ resiliency to water shortages it is important to understand how non-steady conditions due to cycling will impact cooling water consumption and withdrawal intensity. Non-steady operation at power plants has been previously shown to decrease power plant thermal efficiency. Energy balance models have also demonstrated that a decrease in thermal efficiency is expected to increase cooling water consumption intensity. Furthermore, past work has used operating hours data to show idling and cycling gaps where cooling systems operate more than corresponding generators. As a result, an increase in cycling behavior may impact cooling water consumption and withdrawal intensity. This study uses data from the Energy Information Administration (EIA) and Environmental Protection Agency (EPA) to quantify the impact of cycling cooling water consumption intensity for recirculating cooling systems and withdrawal intensity for once-through cooling systems using energy balance and statistical approaches. In a novel application of a fixed effects model to study the effect of temperature on cooling water consumption and withdrawal intensity, this study finds temperature was consistently expected to increase consumption intensity and withdrawal intensity. Non-steady state conditions do not increase cooling water consumption intensity with statistical significance across unit types. However, additional validation of cooling water data is required to confirm these observed trends due to the sensitivity of these findings to model form and concerns with data quality of the dependent variables, cooling water consumption and withdrawal.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

A Guide for Improved Resource Adequacy Assessments in Evolving Power Systems: Institutional and Technical Dimensions

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when coordinated with detailed operational strategies; (ii) multi-year data is critical to capture a wide variety of system conditions; (iii) not incorporating transmission limits into RA assessment could lead to substantial underestimation of traditional “expected value” RA metrics; and (iv) new RA metrics that capture event-specific shortfall characteristics should be used as supplements to traditional metrics. Finally, we examine RA assessments and use this information to propose a guide of evolving industry standards for resource adequacy assessments in resource planning and transmission planning. We report minimum, best, and frontier practices for temporal resolution of assessments, metrics and targets, weather data, load forecasting, characterization of variable renewable resources, characterization of transmission and market transactions, RA modeling and integration with planning processes, and capacity accreditation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Methodology for Digital Image Correlation and Infrared Measurement of Melting Aluminum Bars

Ultimately, our experiment measures two quantities on an aluminum bar: motion (which modeling must predict) and temperature (which sets thermal boundary conditions). For motion, stereo DIC is a technique to use imaging data to provide displacements relative to a reference image down to 1/100th of a pixel. We use a calibrated infrared imaging method for accurate temperature measurements. We will be capturing simultaneous data and then registering temperature data in space to the same coordinate system as the displacement data. While we will later show that our experiments are repeatable, indicating that separate experiments for motion and temperature would provide similar data, the simultaneous and registered data removes test to test variability as a source of uncertainty for model calibration and reduces the number of time-consuming tests that must be performed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pilot-Scale Pelleting Tests on High-Moisture Pine, Switchgrass, and Their Blends: Impact on Pellet Physical Properties, Chemical Composition, and Heating Values

In this study, we evaluated the pelleting characteristics of southern yellow pine (SYP), switchgrass (SG), and their blends for thermochemical conversion processes, such as pyrolysis and gasification. Using a pilot-scale ring-die pellet mill, we specifically assessed the impact of blend moisture, length-to-diameter (L/D) ratio in the pellet die, and ratio of pine to SG on the physico-chemical properties of the resulting pellets. We found that an increase in pine content by 25–50% marginally affected the bulk density; however, it also led to an increase in calorific value by 7% and a decrease in ash content by 72%. A moisture content of 25% (wet basis) and an L/D ratio of 5 resulted in poor pellet durability at <90% and bulk density values of <500 kg/m 3 , but increasing the L/D ratio to 9 and lowering the moisture content to 20% (w.b.) improved the pellet durability to >90% and the bulk density to >500 kg/m 3 . Blends with ≥50% pine content resulted in lower energy consumption, while a lower L/D ratio resulted in higher pelleting energy. Based on these findings, we successfully demonstrated the high-moisture pelleting of 2.5 ton of pine top residues blended with SG at 60:40 and 50:50 ratios. The quality of the pellets was monitored off-line and at-line by near infrared (NIR) spectroscopy. Multivariate models constructed by combining the NIR data and the pelleting process variables could successfully predict the pine content (R 2 = 0.99), higher heating value (R 2 = 0.98), ash (R 2 = 0.95), durability (R 2 = 0.94), and bulk density (R 2 = 0.86) of the pellets. Thus, we established how blending and densification of SYP and SG biomass could improve feedstock specifications and that NIR spectroscopy can effectively monitor the pellet properties during the high-moisture pelleting process.

09 BIOMASS FUELS↗
DOE OSTI2023

Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

Abstract Transit spectroscopy is a powerful tool for decoding the chemical compositions of the atmospheres of extrasolar planets. In this paper, we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. After cleaning and validating the data, we demonstrate methods for: (i) initial exploratory data analysis, based on summary statistics (estimates of location and variability); (ii) exploring and quantifying the existing correlations in the data; (iii) preprocessing and linearly transforming the data to its principal components; (iv) dimensionality reduction and manifold learning; (v) clustering and anomaly detection; and (vi) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in a fully unsupervised fashion. We advocate for lower-dimensional representations of the spectroscopic data in terms of the main principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.

Matchev, Konstantin T. (ORCID:0000000341829096)↗