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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Crystallization and Phase Transformations of Aluminum (Oxy)hydroxide Polymorphs in Caustic Aqueous Solution

Gibbsite, bayerite, and boehmite are important aluminum (oxy)hydroxide minerals in nature and have been widely deployed in various industrial applications. They are also major components in caustic nuclear wastes stored at various U.S. locations. Knowledge of their crystallization and phase transformation processes contributes to understanding their occurrence and could help optimize waste treatment processes. While it has been reported that partial conversion of bayerite and gibbsite to boehmite occurs in basic solutions at elevated temperatures, systematic studies of factors affecting the phase transformation as well as the underlying reaction mechanisms are non-existent, particularly in highly alkaline solutions. We explored the effects of sodium hydroxide concentrations (0.1~3 M), reaction temperature (60~100 ?) and aluminum concentrations (0.1~1 M) on the crystallization and transformation of these aluminum (oxy)hydroxides. Detailed structural and morphological characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), and nuclear magnetic resonance (NMR) spectrometry revealed that these processes depend largely on the reaction temperature and the Al/OH- ratio. When 1 = Al/OH- = 2.5, the reactions favor formation of high crystallinity precipitates, whereas at Al/OH- ratio ? 2.5 precipitation ceases unless the Al concentration is higher than 1 M. We identified pseudoboehmite, bayerite and gibbsite as intermediate phases to bayerite, gibbsite and boehmite, respectively, all of which transform via dissolution-reprecipitation. Gibbsite transforms to boehmite in both acidic and weak caustic environments at temperatures above 80 oC. However, a ‘bar-shaped’ gibbsite morphology dominates in highly caustic environments (3 M NaOH). The findings enable a robust basis for selection of various solid phases by tuning the reaction conditions.

aluminum (oxy)hydroxide polymorphs, crystallizatio↗
DOE OSTI2022

Folding Coarse-Grained Oligomer Models with PyRosetta

Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Although these models are simplified, they can help explore overarching physical hypotheses about how oligomers can form. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses at the CG scale to inform the design process of new foldamer chemistries. In this study, we demonstrate CG PyRosetta’s ability to identify minimum energy structures with a diverse structural search over a range of simple models, as well as two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structures. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. Here, we show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side chains, while specific backbone bond angles influence the specific helix type found in folded structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multicriteria Measures to Assess the Sustainability of Diets: A Systematic Review

Abstract Context Assessing the overall sustainability of a diet is a challenging undertaking requiring a holistic approach capable of addressing the multicriteria nature of this concept. Objective The aim was to identify and summarize the multicriteria measures used to assess the sustainability characteristics of diets reported at the individual level by healthy adults. Data Sources Articles were identified via PubMed, Scopus, and Web of Science. The search strategy consisted of key words and MeSH terms, and was concluded in September 2022, covering references in English, Spanish, and Portuguese. Data Extraction This systematic review followed the PRISMA guidelines. The search identified 5663 references, from which 1794 were duplicates. Two reviewers independently screened the titles and abstracts of each of the 3869 records and the full-text of the 144 references selected. Of these, 7 studies met the inclusion criteria. Data Analysis A total of 6 multicriteria measures were identified: 3 different Sustainable Diet Indices, the Quality Environmental Costs of Diet, the Quality Financial Costs of Diet, and the Environmental Impact of Diet. All of these incorporated a health/nutrition dimension, while the environmental and economic dimensions were the second and the third most integrated, respectively. A sociocultural sustainability dimension was included in only 1 of the measures. Conclusion Despite some methodological concerns in the development and validation process of the identified measures, their inclusion is considered indispensable in assessing the transition towards sustainable diets in future studies. Systematic Review Registration PROSPERO registration no. CRD42022358824.

Rei, Mariana (ORCID:0000000189453708)↗
DOE OSTI2026

Texture development in magnetostrictive Fe-Ga alloys processed by laser powder bed fusion

Iron-gallium (Fe-Ga, Galfenol) alloys are promising magnetostrictive materials for actuators, sensors, and energy harvesting, but their performance is highly sensitive to microstructure and texture. Additive manufacturing by laser powder bed fusion (LPBF) offers a pathway to engineer texture and integrate functional materials into complex geometries. Here, we fabricate Fe-Ga alloys (Fe 82.2 Ga 17.8 ) by LPBF of gas-atomized powders and systematically optimize laser power and scan speed to maximize density and control texture. Nearly full-density parts (up to 99.6 %) are achieved within a narrow processing window. Electron backscatter diffraction (EBSD) reveals a strong <100> fiber texture aligned with the build direction and columnar grains up to 1 mm long. Magnetostriction measurements show saturation magnetostriction of 190 ppm in the build direction. Correlating texture data with macroscopic magnetostriction, we estimate intrinsic magnetostriction constants (λ 100 = 228 ppm, λ 111 = 12 ppm), closely matching single crystal-derived values. These results demonstrate the critical interplay between processing, texture, and functional performance in additively manufactured Fe-Ga alloys and establish LPBF as a viable route for high-performance magnetostrictive materials.

Additive manufacturing↗
DOE OSTI2026

Impact of in situ nuclear networks and atomic opacities on neutron star merger ejecta dynamics, nucleosynthesis, and kilonovae

Context. Binary neutron star merger (BNSM) ejecta are key sites of rapid neutron capture (r-process) nucleosynthesis and they produce kilonovae powered by the radioactive decay of freshly synthesized nuclei. Modeling their evolution requires multi-physics simulations involving hydrodynamics, nuclear reactions, and radiative processes. The impact of nuclear burning and atomic opacity is poorly understood and often treated with simplified prescriptions. Aims. We systematically investigate different treatments of nuclear heating, particle thermalization, and atomic opacities in radiation-hydrodynamics simulations of BNSM ejecta and kilonova light curves. Methods. Ejecta profiles from long-term numerical-relativity simulations of asymmetric neutron star binaries with a massive neutron star remnant were evolved to ∼30 days using a 2D ray-by-ray approach. We compared simplified heating-rate and thermalization prescriptions with in situ Nuclear reaction Network (NN) calculations that track nuclear energy deposition and include a composition-dependent thermalization scheme. We also contrasted various gray opacity models with a frequency-dependent treatment based on atomic calculations. Results. Coupling NN and hydrodynamics significantly affects nucleosynthesis and kilonova emission. Assuming homologous expansion alters abundance evolution and produces a narrower, less populated second r-process peak and a third peak shifted to higher mass numbers. The back-reaction of nuclear heating affects the temperature evolution enough to delay and redden the early (t∼ hours) kilonova peaks. A constant thermalization efficiency underestimates and reddens the early emission while overestimating the late-time luminosity compared to the composition-dependent treatment. Analytical opacity prescriptions yield a more extended, colder photosphere, resulting in dimmer, redder kilonovae at early times (t≲ hour), while the delayed recession of the photosphere prolongs the red emission at t ≳ 5 days. Conclusions. Coupling hydrodynamics to an in situ NN is crucial for reliable nucleosynthesis and kilonova predictions. Resolving the first several hundred milliseconds of the hydrodynamics is essential for robust nucleosynthesis calculations. Composition-dependent thermalization and frequency-dependent, atomic-physics-based opacities are needed to accurately capture the temperature evolution of the ejecta and the brightness and color evolution of the kilonova. Calibrated analytic nuclear-power fits with simplified thermalization and opacity prescriptions can still reproduce the density and temperature evolution of the ejecta.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Testing the Froggatt-Nielsen mechanism with lepton flavor and number violating processes

The Froggatt-Nielsen (FN) mechanism offers an elegant explanation for the observed masses and mixings of Standard Model fermions. In this work, we systematically study FN models in the lepton sector, identifying a broad range of charge assignments (“textures”) that naturally yield viable masses and mixings for various neutrino mass generation mechanisms. Using these textures, we consider higher-dimensional operators consistent with a FN origin and find that natural realizations predict distinct patterns in lepton flavor- and number-violating observables. For Dirac and Majorana neutrinos, FN-related correlations can lead to detectable rates of charged lepton flavor violation at next-generation low-energy experiments. Majorana and type-I seesaw models predict measurable rates of neutrinoless double beta decay. Determination of inverted neutrino mass ordering would exclude the Dirac neutrino FN scenario. Only a small minority of purely leptonic FN models predict detectable flavor violation at future muon colliders, though it is possible that a combined analysis with the quark sector will reveal motivated signals. These findings highlight the power of the FN mechanism to link neutrino mass generation to testable leptonic observables, offering new pathways for the experimental exploration of lepton number and underscoring the importance of next-generation low-energy probes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Developing Open-Source Tools for Increasing the Efficiency of Synthetic Aviation Turbine Fuel Certification Process

FuelLib is an open-source Python-based fuel library, developed by NREL, that leverages the group contribution method (GCM) of [1] to systematically estimate the thermodynamic and transport properties of hydrocarbon fuels. FuelLib predicts these properties based on the molecular structure of individual compounds or compound families, using weight percentages of a fuel's composition, typically measured using techniques such as gas chromatography (GC). FuelLib enables property estimation over a wide range of temperatures and pressures of multi-component fuels in the absence of detailed molecular composition data, making it particularly valuable for complex fuel mixtures where detailed experimental characterization of fuel composition is unavailable. These capabilities contribute directly to synthetic aviation turbine fuels (SATF) development, supporting the short-term American Society for Testing and Materials (ASTM) qualification of drop-in fuels while potentially expanding ASTM boundaries to certify a broader range of fuels.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Electronic structure calculations of twisted multi-layer graphene superlattices

Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and studied, a systematic investigation of the properties of arbitrarily layered graphene assemblies is still lacking. We introduce theoretical concepts and methods for the processing of materials information, and as a case study, apply them to investigate the electronic structure of multi-layer graphene-based assemblies in a high-throughput fashion. We provide a critical discussion of patterns and trends in tight binding band structures and we identify specific layered assemblies using low-dispersion electronic bands as indicators of potentially interesting physics like strongly correlated behavior. A combination of data-driven models for visualization and prediction is used to intelligently explore the materials space. This work more generally aims to increase confidence in the combined use of physics-based and data-driven modeling for the systematic refinement of knowledge about 2D layered materials, with implications for the development of novel quantum devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Adsorptive behavior of poly (vinylidene fluoride) membranes for the recovery of lignin-derived hydrophobic deep eutectic solvents

Abstract Recently, membrane technology has gained significant traction as an energy-efficient alternative to traditional thermal processes for solvent recovery. Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional organic solvents, yet a systematic methodology for selecting compatible membrane materials for their recovery remains underdeveloped. This study established a predictive framework for membrane material selection in hydrophobic DES applications using Hansen Solubility Parameters (HSP) with inverted criteria targeting materials with relative energy difference (RED) values greater than 1.0. Flat sheet membranes were fabricated via the non-solvent induced phase separation (NIPS) technique. Four NIPS fabricated polymer membranes were evaluated: polysulfone, cellulose acetate, polyvinylidene fluoride (PVDF) fabricated with polyethylene glycol (PEG) as a pore-forming agent, and polybenzimidazole (PBI). The HSP approach successfully predicted membrane-solvent compatibility, with polysulfone (RED = 0.6) and cellulose acetate (RED = 0.9) dissolving completely within 24 h, while PVDF (RED = 1.9) and PBI (RED = 1.1) maintained structural integrity throughout a 7-day exposure period. Furthermore, PVDF demonstrated superior performance with minimal weight gain (3.0%), hydrophobic surface characteristics (122° water contact angle), and enhanced mechanical properties following DES exposure. Comprehensive chemical and morphological characterization confirmed PVDF’s chemical stability and revealed a surface-selective interaction mechanism involving simultaneous PEG (pore-forming agent) extraction and DES component adsorption. Adsorption kinetics followed pseudo-first-order behavior with reversible characteristics, best described by the Temkin isotherm model (R² = 0.9987). PVDF membranes-maintained separation functionality with average lignin rejection (75.2 ± 7.69%) and demonstrated filtration permeability of 2.0 ± 0.34 LMH/bar. This methodology provides a rational approach for membrane selection in emerging solvent systems, contributing to the advancement of sustainable separation technologies for DES-based biomass processing applications.

Science & Technology - Other Topics↗
DOE OSTI2025

Effect of 3D-printed surface textures on wear mechanism in 3-body abrasion of soil

This study systematically investigates the enhancement of wear resistance in 3D printed surface textures through both experimental and theoretical approaches. Three distinct surface morphologies (Smooth Surface, Surface with uniformly distributed Pits, and Surface with uniformly distributed Bumps) were fabricated using High-Impact Polystyrene, where the meso-scale textures were precisely controlled through the 3D printing process. Wear behavior was evaluated using a 3-body wear tester in an abrasive particle environment, analyzing the influence of surface textures under various operating conditions. Systematic wear tests revealed that optimally designed surface textures achieved a remarkable 77 % reduction in wear compared to the worst-performing sample. The wear mechanisms were comprehensively characterized through weight loss measurements, Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS) analyses, elucidating the surface morphology changes and their interaction with wear particles. Notably, the study identified how the geometric characteristics of surface textures influence the movement of wear particles and the distribution of contact stresses. Discrete element method simulations corroborated the experimental findings, providing theoretical validation for the enhanced wear resistance of the optimal structure. The high correlation between simulated wear patterns and experimental results validates the reliability of the proposed design methodology. In conclusion, these results demonstrate that 3D printed surface texturing offers a cost-effective and scalable approach to significantly improve wear resistance in engineering applications, presenting a practical alternative to conventional, high-cost surface engineering methods.

3-body abrasion↗
DOE OSTI2023

Generalizing the Gurson model using symbolic regression and transfer learning to relax inherent assumptions

Abstract To generate material models with fewer limiting assumptions while maintaining closed-form, interpretable solutions, we propose using genetic programming based symbolic regression (GPSR), a machine learning (ML) approach that describes data using free-form symbolic expressions. To maximize interpretability, we start from an analytical, derived material model, the Gurson model for porous ductile metals, and systematically relax inherent assumptions made in its derivation to understand each assumption’s contribution to the GPSR model forms. We incorporate transfer learning methods into the GPSR training process to increase GPSR efficiency and generate models that abide by known mechanics of the system. The results show that regularizing the GPSR fitness function is critical for generating physically valid models and illustrate how GPSR allows a high level of interpretability compared with other ML approaches. The method of systematic assumption relaxation allows the generation of models that address limiting assumptions found in the Gurson model, and the symbolic forms allow conjecture of decreased material strength due to void interaction and non-symmetric void shapes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

ADC Nonlinearity Correction for the Majorana Demonstrator

Imperfections in analog-to-digital conversion cannot be ignored when signal digitization requirements demand both wide dynamic range and high resolution, as is the case for the MAJORANA DEMONSTRATOR 76 Ge neutrinoless double beta decay search. Enabling the experiment's high-resolution spectral analysis and efficient pulse shape discrimination required careful measurement and correction of ADC nonlinearites. Here, a simple measurement protocol was developed that did not require sophisticated equipment or lengthy data taking campaigns. A slope-dependent hysteresis was observed and characterized. A correction applied to digitized waveforms prior to signal processing reduced the differential and integral nonlinearites by an order of magnitude, eliminating these as dominant contributions to the systematic energy uncertainty at the double-beta decay Q value.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

On the cogent formulation of an elastomeric silicone ink material for direct ink write ( DIW ) 3D printing

Abstract Adhesives and sealants show fine rheology with good physical and mechanical properties as viscous pastes, a possible starting point for developing direct ink writing (DIW) 3D printing ink. However, many commercial adhesives and sealants take days or weeks to cure fully. DIW 3D‐printed parts made directly from these sealants are not designed for a scalable manufacturing process and high‐volume production. Moreover, most of these adhesives and sealants have volume shrinkage during cure. A systematic understanding of formulation methods and design principles for an elastomeric silicone DIW ink can overcome these issues. This study presents the cogent formulation development of a 3D printable thermoset elastomer silicone that gels and cures isotropically in minutes, reducing cycle time for rapid ink development with no shrinkage during cure. More specifically, we outline the principles of raw material selection of a formulation to achieve excellent rheology, printability, synchronized working, and gel time fitting requirements closer to scalable manufacturing. The reaction kinetics and their corresponding 3D‐printed structural properties are also described. Interest in future work is toward a rational DIW 3D printing ink material development protocol and use of machine learning (ML). Highlights Formulation method flexibility and design principle of DIW ink. Raw material selection principle to achieve optimal rheology for DIW printing. Ink gel kinetics for large‐scale DIW manufacturing. Hydrosilylation conversion over time at different ambient temperatures. Structural properties of DIW 3D printed parts.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-efficiency perovskite photovoltaic modules achieved via cesium doping

Perovskite solar modules have been attracting increasing attention due to their market potential, yet publications concerned with theintrinsic scale-up potential of different perovskite compositions remain relatively scarce. On the other hand, while great success is being made towards improving the power conversion efficiency (PCE) of perovskite solar cells (PSCs) by cesium cation (Cs + ) doping of the perovskite, more attention is being paid to the perovskite phase stabilization effect of Cs + doping, and less to other properties that are critical to understand and futher improve the PSC's. In this work, moderately-Cs-doped MAPbI 3 was employed as a model perovskite material in order to exclude the phase stabilization effect. Our systematic study revealed the influence of Cs + in organic-inorganic hybrid perovskites on the crystal structure, crystallization process, trap state density, band structure and charge (i.e., ions or photo-carriers) transport. Markedly, it has been observed that Cs + doping can greatly increase the carrier diffusion length in the perovskite films, thus improving the potential to scale-up PSC's.The PCE of small area devices (0.09 cm 2 ) was increased to 21.72% from 19.73%, with decreased hysteresis behavior and increased operational stability (T85 = 1000 h) after Cs + doped, where T85 refers to the retention of 85% of the initial PCE. Moreover, a PCE of 21.08% was obtained for a Cs + -containing perovskite module with an active area > 30 cm 2 , which demonstrates a better "reproducibility" than the reference sample (MAPbI3-based perovskite modules, PCE = 18.26%).

14 SOLAR ENERGY↗
DOE OSTI2022

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.

09 BIOMASS FUELS↗
DOE OSTI2022

Effect of powder layer thickness on the microstructural development of additively manufactured SS316

By Increasing the powder layer thickness we could increase material deposition rates and reduce build times during laser powder bed fusion (LPBF). However, increasing the powder layer thickness could also negatively affect the local melting and subsequent microstructure of the printed material. This study systematically investigated, for the first time, the defect evolution and microstructural changes of LPBF stainless steel 316 (SS316) processed by using various powder layer thicknesses of 40, 80, and 120 μm while also varying the laser dwell time for the pulsed laser. Through image analysis, the pore density was found to increase with increasing powder layer thickness, particularly when the dwell time was too high or too low. The maximum achievable density (>99%) was found over a wide processing window when using the smallest powder layer thickness of 40 μm between dwell times of 60 and 100 μs. Increasing the powder layer thicknesses to 80 and 120 μm resulted in maximum densities of 98.7 and 96.8%, respectively, but the window for acceptable laser dwell times that could achieve these densities narrowed considerably. A microstructural analysis of the melt pools was performed to measure the melt pool depths and widths, both of which increased with increasing dwell time. However, increasing the powder layer thickness did not affect the melt pool depth and only minorly affected the width. A sub-grain cellular structure distinguished the melt pool boundaries. The cell size increased with increasing dwell time and decreasing powder layer thickness. Moreover, the cell size was used to calculate a cooling rate that had a magnitude of 10 7 K/s and increased with increasing powder layer thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Structural Design of Bismuth Telluride Nanoplates through Process Variables

Binary pnictogen chalcogen compounds, primarily bismuth tellurides and selenides, are of great interest due to their applications in emerging quantum devices, as well as thermoelectric generators. The performance of bismuth telluride in these roles depends on its structure at the nanoscale, particularly the size, shape, and crystallinity of its nanocrystalline forms. However, current methods for controlling these features are often slow, inconsistent, or difficult to scale. Here, we demonstrate that through a solvothermal synthesis and hot injection process, precise control over the morphology of bismuth telluride nanoplates is possible with independent tuning of process variables, such as temperature and reaction time. We find that the nanoplate shape and internal porosity vary systematically with synthesis temperature and that the same morphological outcomes can be rapidly achieved at a fixed temperature by adjusting reaction duration. These results reveal that both the temperature and time can independently direct bismuth telluride morphological features, allowing for rapid, tunable synthesis strategies. Our approach offers a scalable framework, not only for bismuth telluride but also for related layered chalcogenides used in energy harvesting and quantum technologies.

Ackley, Jordan [Boise State Univ., ID (United Stat↗
DOE OSTI2025

Carbonation and Sulfidation of Mg- and Ni-Containing Solutions: Implications for Carbon Mineralization and Critical Element Recovery

Carbonation of alkaline earth metals ( e.g. , magnesium (Mg)) and sulfidation of nickel (Ni) are promising methods to achieve concurrent carbon mineralization and selective Ni recovery. However, the coexistence of alkaline earth metals and Ni from silicate ores or mining wastewater complicates the carbonation and sulfidation owing to cation coprecipitation. To better understand simultaneous metal carbonation and Ni-sulfide formation, we used Mg- and Ni-containing solutions and systematically investigated the Mg and Ni coprecipitates’ phase transformation during sequential/concurrent carbonation and sulfidation. During a single carbonation process, hydromagnesite dehydrated and formed magnesite over time. Nickel bicarbonate formed and became a Mg–Ni carbonate solid solution because of their similar ionic radii. During a single sulfidation process, the pH did not affect Ni-sulfide formation, but it controlled Mg behavior. Specifically, at pH 9.6, brucite formed, while at pH 7.8, Mg 2+ remained in the solution. For the sequential carbonation–sulfidation process, Ni-carbonate formed during carbonation converted to Ni-sulfide because of the low Ni-sulfide K sp . For the sulfidation–carbonation process, Ni-sulfide remained the same even after carbonation and Mg-carbonate precipitates. For the concurrent carbonation and sulfidation process, Mg-carbonate and Ni-sulfide formed simultaneously. Finally, this study develops a scientific foundation of carbonation and sulfidation processes, benefiting coupled CO 2 storage and sulfide-enabled resource recovery.

54 ENVIRONMENTAL SCIENCES↗