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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Robust Solar Receivers Using MAX Phase Materials

This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy under the Solar Energy Technologies Office Award Number 35928. The objective of the proposed effort was to develop and optimize additive manufacturing technologies for low-cost fabrication of high-temperature receivers using MAX phase-based materials (Ti 3 SiC 2 and Ti 3 AlC 2 ). MAX phase materials are a group of ternary metal carbides and nitrides where M stands for an early transition metal element, A is a group 13–16 element, and X is C and/or N. In Phase 1, the binder jetting additive manufacturing process was used to synthesize and characterize the Ti 3 SiC 2 MAX phase material. The typical process involved first producing a TiC preform using binder jetting followed by infiltration of the preform with silicon melt to form Ti 3 SiC 2 in situ. The reaction-infiltrated samples showed formation of MAX phase in the sample core; however, the surface showed cracking. Various process conditions—cooling rates, hold times, Si proportion, etc.—were varied to minimize the surface cracking. The fabricated MAX phase core was characterized by microstructure analysis and evaluations of mechanical properties such as hardness and thermal shock. In Phase 2, the focus included fabrication of Ti 3 SiC 2 MAX phase materials by spark plasma sintering (SPS) and synthesis of Ti 3 AlC 2 MAX phase materials by the Al melt infiltration process. It is expected that Al infiltration will not cause sample cracking, since Al does not expand during solidification. In addition, other processing approaches were investigated to fabricate the MAX phase materials, such as SPS with a graphite bedding approach for producing short-length Ti 3 AlC 2 MAX phase tubes for demonstration of prototypical Concentrating Solar Power receiver tubes. Fabricated samples underwent thermo-mechanical testing to validate the materials for the solar receiver application at temperatures >1000°C. In Phase 3, the effort focused on the development and optimization of the Ti-Al-C MAX phase composite material using the Al melt infiltration approach. We started with optimization of precursor powders and making preform structures by either pressing them in a die or using the binder jetting additive manufacturing process followed by Al melt infiltration. In addition, we investigated the formation of preform structures by cold isostatic pressing followed by Al melt infiltration for making Ti-Al-C MAX phase composite. Thermo-mechanical characterizations, such as creep, strength, and thermal shock, were conducted to establish the structures’ performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

14 SOLAR ENERGY↗
DOE OSTI2024

A Review of Nanocarbon-Based Anode Materials for Lithium-Ion Batteries

Renewable and non-renewable energy harvesting and its storage are important components of our everyday economic processes. Lithium-ion batteries (LIBs), with their rechargeable features, high open-circuit voltage, and potential large energy capacities, are one of the ideal alternatives for addressing that endeavor. Despite their widespread use, improving LIBs’ performance, such as increasing energy density demand, stability, and safety, remains a significant problem. The anode is an important component in LIBs and determines battery performance. To achieve high-performance batteries, anode subsystems must have a high capacity for ion intercalation/adsorption, high efficiency during charging and discharging operations, minimal reactivity to the electrolyte, excellent cyclability, and non-toxic operation. Group IV elements (Si, Ge, and Sn), transition-metal oxides, nitrides, sulfides, and transition-metal carbonates have all been tested as LIB anode materials. However, these materials have low rate capability due to weak conductivity, dismal cyclability, and fast capacity fading owing to large volume expansion and severe electrode collapse during the cycle operations. Contrarily, carbon nanostructures (1D, 2D, and 3D) have the potential to be employed as anode materials for LIBs due to their large buffer space and Li-ion conductivity. However, their capacity is limited. Blending these two material types to create a conductive and flexible carbon supporting nanocomposite framework as an anode material for LIBs is regarded as one of the most beneficial techniques for improving stability, conductivity, and capacity. This review begins with a quick overview of LIB operations and performance measurement indexes. It then examines the recently reported synthesis methods of carbon-based nanostructured materials and the effects of their properties on high-performance anode materials for LIBs. These include composites made of 1D, 2D, and 3D nanocarbon structures and much higher Li storage-capacity nanostructured compounds (metals, transitional metal oxides, transition-metal sulfides, and other inorganic materials). The strategies employed to improve anode performance by leveraging the intrinsic features of individual constituents and their structural designs are examined. The review concludes with a summary and an outlook for future advancements in this research field.

25 ENERGY STORAGE↗
DOE OSTI2025

Material Needs and Measurement Challenges for Advanced Semiconductor Packaging: Understanding the Soft Side of Science

This Perspective builds upon insights from the National Institute of Standards and Technology (NIST)-organized workshop, “Materials and Metrology Needs for Advanced Semiconductor Packaging Strategies,” held at the 35th annual Electronics Packaging Symposium in Binghamton, NY, on September 5, 2024. It outlines critical challenges and opportunities related to polymer-based “soft” materials in advanced semiconductor packaging, with emphasis on polymer science, measurement science (metrology), and the strategic development of Research-Grade Test Materials (RGTMs). These efforts, led by the NIST CHIPS team, aim to advance the fundamental understanding of structure-property-processing relationships, promote standardized guidelines and innovative methods for material characterization, and accelerate the development, qualification, and adoption of next-generation packaging materials. The Perspective also distills key insights from the panel discussion with industry experts, emphasizing the need for close collaboration among materials scientists, process engineers, and metrology experts to enable a holistic strategy, further highlighting the importance of cross-sector partnerships among industry, academia, and government to address pressing challenges in packaging materials and processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Accelerated Discovery of Cost-Effective Photoabsorber Materials for Near-Infrared (λ = 1600 nm) Photodetector Applications

Current infrared sensing devices are based on costly materials with relatively few viable alternatives known. To identify promising candidate materials for infrared photodetection, we have developed a high-throughput screening methodology based on high-accuracy r 2 SCAN and HSE calculations in density functional theory. Using this method, we identify ten already synthesized materials between the inverse perovskite family, the barium silver pnictide family, the alkaline pnictide family, and ZnSnAs 2 as top candidates. Among these, ZnSnAs 2 emerges as the most promising candidate due to its experimentally verified band gap of 0.74 eV at 0 K and its cost-effective synthesis through Bridgman growth. BaAgP also shows potential with an HSE-calculated band gap of 0.64 eV, although further experimental validation is required. Lastly, we discover an additional material, Ca 3 BiP, which has not been previously synthesized, but exhibits a promising optical spectra and a band gap of 0.56 eV. The method applied in this work is sufficiently general to screen wider bandgap materials in high-throughput and now extended to narrow-band gap materials.

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

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗
DOE OSTI2026

New Perspectives on Vibrational Energy Transfer in Energetic Materials: Insights from Pressure-Tuned Ultrafast Spectroscopy

Understanding the manner in which vibrational energy flows between molecular and lattice vibrations is of great interest in physical chemistry due to its central role in reactivity and energy dissipation in molecular materials. Here, in this feature article, we highlight our recent efforts employing ultrafast broadband infrared spectroscopy toward understanding the interplay between molecular and lattice vibrations in energetic materials, motivated by the open questions surrounding the role of vibrational energy transfer (VET) in reaction initiation in these materials. Our work addresses the ongoing debate on the participation of doorway modes in VET. We further present new results from high-pressure ultrafast experiments on RDX, a hydrogen-bonded material, and BNFF, a hydrogen-free material, to explore how intermolecular interaction strength governs VET pathways and time scales. Collectively, our findings reveal that vibrational dynamics in these systems occurs across three distinct time regimes, with VET being incomplete out to hundreds of picoseconds, suggesting the importance of considering nonstatistical reactions in the modeling of these materials. These time scales vary as intermolecular interaction strength is indirectly modified by application of static pressure, indicating dramatic changes to the vibrational structure of these materials under shock-relevant conditions. Our results thus shed light on how intermolecular interactions shape vibrational energy redistribution in molecular materials, and highlight the need for further theoretical and experimental investigation.

Carlson, Daniel Ryan [Sandia National Laboratories↗
DOE OSTI2025

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials

Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.

Sayed, Shehrin↗
DOE OSTI2025

Al–W gradient density materials—Processing and dynamic ramp compression

Materials with high-density gradients are desired for controlling loading paths in dynamic compression, important for studying material properties in extreme conditions and inertial confinement fusion. The large density difference between Al and W makes them ideal choices for producing gradient density materials, but their extremely different melting temperatures make them challenging to fabricate simultaneously. We report a method for producing Al–W porosity-free materials with a fourfold increase in density (2.7–11 g/cm 3 ) across the composition range, from Al-rich to W-rich, without intermetallic phase formation. This was achieved by understanding the aluminum-dominated densification behavior and examining the influence of pressure and temperature on the densification of Al–W composites. Dynamic compression experiments conducted with the Al–W gradient density material produced shock ramp compressions as expected based on the designed composition, and the performed hydrodynamics simulations showed excellent agreement with experimental results. The results demonstrate that current activated pressure-assisted densification allows for the easy and rapid fabrication of gradient density materials with significant density gradients and tailored compositions, facilitating precise control of the loading paths. These materials have the potential to create customized pressure drives for advancing the fields of material science in extreme environments and dynamic compression.

Alloys↗
DOE OSTI2025

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabytescale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.

Li, Chaojian [ORNL] (ORCID:0000000340309777)↗
DOE OSTI2025

Multiplexing core & sheath extrusion system development for additive manufacturing for inner-bead multi-material capability

Single-feed polymer extruders are widely used in large-format additive manufacturing (AM) systems; however, the increasing demand for multi-material functionality within a single part has driven significant innovation in this field. One approach involves robotic pick-and-place operations, while another explores mechanical switching of feed lines during extrusion. Although robotic pick-and-drop systems offer flexibility, they introduce longer layer times during material changes, which can negatively affect the structural integrity of the part. On the other hand, mechanical feed switching causes delays in material transitions, as the existing material must be flushed before the new material emerges from the nozzle. This poses particular challenges for smaller or more intricate parts. In this study we are developing a unique multiplexing extrusion system with core & sheath nozzle that combines two extruders via co-extrusion. This allows for a unique inside and outside inner-bead (i.e., within the same bead) multi-material capability. We believe that this technology will allow for combining neat and filled materials, ductile and stronger materials, and many other combinations to address the problems aforementioned above and disrupt the AM technology creating new opportunities and opening application areas.

Tekinalp, Halil [ORNL]↗
DOE OSTI2024

A Bulk versus Nanoscale Hydrogen Storage Paradox Revealed by Material-System Co-Design

Metal hydrides are serious contenders for materials-based hydrogen storage to overcome constraints associated with compressed or liquefied H 2 . Their ultimate performance is usually evaluated using intrinsic material properties without considering a systems design perspective. An illustrative case with startling implications is (LiNH 2 +2LiH). Using models that simulate the storage system and associated fuel cell of a light-duty vehicle (LDV), the performance of the bulk hydrides is compared with a nanoscaled version in porous carbon (PC), (LiNH 2 +2LiH)@(6-nm PC). Using experimental material properties, the simulations show that (LiNH 2 +2LiH)@(6-nm PC) counterintuitively has higher usable gravimetric and volumetric capacities than the bulk counterpart on a system basis despite having lower capacities on a materials-only basis. Nanoscaling increases the thermal conductivity and lowers the desorption enthalpy, which consequently increases heat management efficiency. In a simulated drive cycle for fuel cell-powered LDV, the fuel cell is inoperable using bulk (LiNH 2 +2LiH) as the storage material but completes the drive cycle using the nanoscale material. Further, these results challenge the notion that nanoscaling incurs mass and volume penalties. Instead, the synergistic nanoporous host-hydride interaction can favorably modulate chemical and heat transfer properties. Moreover, a co-design approach considering application-specific tradeoffs is essential to accurately assess a material's potential for real-world hydrogen storage.

08 HYDROGEN↗
DOE OSTI2025

Mono‐Materials Created by Engineering a Continuum of P3HB Stereomicrostructures in a One‐Step Catalytic Process

Multi-material products that combine multiple complementary polymers can create products with desired performance but present challenges to end-of-life (EoL) management. The emerging mono-material product design based on a single polymer type addresses the fundamental EoL issue, but challenges of delivering vastly tunable material properties by the single polymer still remain. Here, we introduce a simple strategy to produce biodegradable poly(3-hydroxybutyrate) (P3HB) materials with a wide range of material properties by engineering a stereomicrostructure continuum, achieved through polymerizing diastereomeric mixtures of racemic and meso-dimethyl diolides at various feed ratios with a single catalyst. This one-step, one-pot process produces biodegradable P3HB mono-materials ranging from rigid to flexible thermoplastics, to tough thermoplastic elastomers, to a pressure-sensitive adhesive (PSA), which have been then combined to fabricate prototype all-P3HB PSA tapes, demonstrating the feasibility of designing mono-material products through engineering polymer stereomicrostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Rapid particle generation from an STL file and related issues in the application of material point methods to complex objects

Abstract In this paper, we focus on three issues related to applications of material point methods (MPMs) to objects with complex geometries. They are material point generation, compatibility of material points with a mesh, and sensitivity to mesh orientation. An efficient method of generating material points from a stereolithography (STL) file is introduced. This material point generation method is independent of the mesh used in MPM calculations. The compatibility between the material points and the mesh is then studied. We also show that the original MPM and the dual domain material point (DDMP) method are sensitive to mesh orientation. These issues are related to the calculation of the internal force and are concerns of the MPMs. They become more prominent when MPMs are applied to complex geometries. Our numerical results show that the recently developed local stress difference (LSD) algorithm (Perez et al. in J Comp Phys 498:112681, 2024) can be used to effectively address them.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Are quantum materials economically and environmentally sustainable?

Quantum materials have revolutionized energy, information, and healthcare technologies, yet their development has largely prioritized performance over economic and environmental impacts—key factors for industrial adoption. Using topological materials as a case study, we present a data-driven framework that evaluates over 16,000 materials based on cost, supply chain resilience, energy demand, toxicity, and environmental footprint. By integrating the recently proposed quantum weight – a metric quantifying quantum behavior – we reveal a striking trend: materials with stronger quantum effects often exhibit higher environmental impact, posing challenges for scalability and industrial adoption. To address this, we identify a small set of materials that achieve a balance between quantum functionality and sustainability. Furthermore, our approach enables high-throughput, AI-driven materials discovery that incorporates economic and environmental influences from the outset, guiding the development of quantum materials for next-generation microelectronics and energy harvesting technologies.

AI↗
DOE OSTI2024

Computational Discovery of Intermolecular Singlet Fission Materials Using Many-Body Perturbation Theory

Intermolecular singlet fission (SF) is the conversion of a photogenerated singlet exciton into two triplet excitons residing on different molecules. SF has the potential to enhance the conversion efficiency of solar cells by harvesting two charge carriers from one high-energy photon, whose surplus energy would otherwise be lost to heat. The development of commercial SF-augmented modules is hindered by the limited selection of molecular crystals that exhibit intermolecular SF in the solid state. Computational exploration may accelerate the discovery of new SF materials. The GW approximation and Bethe–Salpeter equation (GW+BSE) within the framework of many-body perturbation theory is the current state-of-the-art method for calculating the excited-state properties of molecular crystals with periodic boundary conditions. In this Review, we discuss the usage of GW+BSE to assess candidate SF materials as well as its combination with low-cost physical or machine learned models in materials discovery workflows. We demonstrate three successful strategies for the discovery of new SF materials: (i) functionalization of known materials to tune their properties, (ii) finding potential polymorphs with improved crystal packing, and (iii) exploring new classes of materials. In addition, three new candidate SF materials are proposed here, which have not been published previously.

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

Single Crystalline Na 0.67 Ni 0.33 Mn 0.67 O 2 Positive Electrode Material via Molten Salt Synthesis for Sodium Ion Batteries

P2-layered Na 0.67 Ni 0.33 Mn 0.67 O 2 (NNMO) has emerged as a promising positive electrode material for sodium ion batteries due to its appealing electrochemical properties. Synthesis of polycrystalline NNMO (PC-NNMO) materials through conventional calcination of solid precursors remains the prevailing method, where heating occurs in a dry environment with air or O 2 . On the other hand, the molten salt method, where precursors are submerged in molten salt medium during calcination, emerged in recent years to be a scalable technique for more controlled crystal growth and uniform morphology in a variety of materials. Here, we utilize the molten salt method to synthesize single crystalline NNMO (SC-NNMO) materials with enhanced electrochemical properties. The SC-NNMO material exhibits an initial specific discharge capacity of 95 mAh g –1 at a 0.1C rate, retaining approximately 88.5% of its capacity after 100 cycles over a wide voltage range of 2.0–4.2 V. Furthermore, SC-NNMO maintains a capacity retention of 83.9% after 300 cycles at a 1C rate compared to 66.6% for PC-NNMO, indicating excellent long-term cycling stability. This stability is further confirmed by the performance of an SC-NNMO//hard carbon full cell, which retains 90.3% of its capacity after 200 cycles at 1C within a voltage window of 1.9–4.1 V. The enhancement in stability of the SC-NNMO sample is attributed to the single crystalline structure suppressing the undesired P2–O2 phase transition at high voltage. This study also presents an easy, efficient, and straightforward molten salt process for SC-NNMO material synthesis, offering valuable insights into the potential application of such methodology for the large-scale, cost-effective production of various sodium-layered transition metal oxide positive electrode materials for SIBs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗