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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hydrometallurgical Recycling of Black Mass of Spent Lithium-Ion Batteries Using Methanesulfonic Acid: Leaching, Kinetic Studies, and Potential for Total Recovery of Valuable Components

Methanesulfonic acid (MSA) exhibits several advantageous properties rendering it a promising candidate for circular hydrometallurgical processes. These properties include a high acidity (pKa = - 1.9) comparable to that of classical mineral acids as well as biodegradability, high stability, and high solubility of metal-MSA complexes in aqueous solutions. In this study, MSA was employed as a lixiviant for the leaching of metals (lithium, nickel, cobalt, and manganese) from the black mass of spent lithium-ion batteries (LIBs). The effect of various parameters, including MSA concentration, H 2 O 2 concentration, temperature, and pulp density, was systematically investigated. Under the optimized conditions (1.5 M MSA, 0.2 M H 2 O 2 , 60 °C, and 50 g/L pulp density), quantitative leaching of lithium was achieved within 30 min, while for nickel and cobalt it was after 2 h, and 4 h for manganese leaching. The leaching kinetics of Li, Ni, Co, and Mn were studies using the shrinking particle models (SPM) and the Avrami model. The results indicated that the Avrami model provided the best fit to the kinetic data, with apparent activation energies of 46.81 kJ/mol for Li, 58.61 kJ/mol for Ni, 59.69 kJ/mol for Co, and 58.86 kJ/mol for Mn, within the temperature range of 25-70 °C (except for Li, which was analyzed in the range of 25-60 °C), consistent with chemical reaction control. Subsequently, residual contaminants in the leaching residue were eliminated through pyrolysis. The quantitative leaching of metals in MSA solution (a green lixiviant), combined with the pyrolytic treatment of leaching residues, represents a circular strategy for the total recovery of valuable components from the black mass of spent LIBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Towards a quantitative understanding of bonding in supersonic single particle impacts: A three-dimensional FIB-SEM exploration

Particle bonding is crucial to coating quality in cold spray, but it has been a challenge to accurately quantify bonding even in single particle impacts. This paper uses FIB-SEM to explicitly map the particle-substrate interface for Cu-on-Cu single microparticle impacts in a full 3D rendering that spans a wide range of impact velocities. This approach permits a detailed quantification of the total bonding area and all of its associated components. In addition to revealing why prior 2D characterization efforts have missed important details about impact bonding, these data quantitatively reveal the evolution of bonding from its onset at the “critical velocity” V cr (where bonding is generally poor, ∼6 %) to its peak at around 1.3‧V cr (where almost 90 % of the particle bonds). Further increase in the velocity to 1.5‧V cr and beyond finds the onset of hydrodynamic penetration and a decrease in bonding. These data then support the development of a simple analytical model based on oxide rarefication and extrusion of bare metal through gaps in the oxide layer as driving the development of bonding. As a result, the model reproduces the experiments and provides guidance on optimization of bonding as a function of material and process parameters.

Analytical methods↗
DOE OSTI2025

Dynamic response of a freely rotating butterfly valve in the advanced test reactor − dynamic coefficients modeling

Here, in evaluating the water hammer issue pertaining to the primary-coolant-regulating butterfly valve in the Advanced Test Reactor, the dynamic fluid body interaction (DFBI) approach was implemented in the analysis covered in Part I. Although DFBI modeling accurately and simultaneously solved the dynamic motion of the valve’s disk along with the flow field of the surrounding fluid, it shed little light on the reason behind such motion. For Part II, the reacting torque of the fluid on the disk was decomposed into representations of the dynamic coefficients in terms of stiffness, damping, and added mass. These were evaluated via simulations with steady-state static (stiffness), constant angular speed (damping), and variable angular speed (added mass) disks. Substituting the dynamic coefficients into Newton’s second law enabled the response trajectories to be obtained. Stable (by average) and unstable equilibrium positions and thrust tendencies of the valve were determined based on the stiffness coefficient (or static torque), the response amplitude was dampened or enlarged by the damping coefficient (minorly affected by added mass), and the response frequency was altered by the damping and added mass coefficients. Although the dynamic coefficient approach renders slightly different trajectories, due to the averaging effect of the torque in comparison to the DFBI method, the overall trend of the response aligns with the DFBI simulation, thus confirming the conclusion in Part I that a fix to the current butterfly valve is necessary.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Mechanical separations of corn stover anatomical fractions in an integrated feedstock preprocessing system: An experimental and data-driven modeling study

High variabilities of material attributes in lignocellulosic biomass present risks for biofuel and biochemical productions and must be mitigated via preprocessing. Since almost no mechanical device is originally designed for processing biomass, how to operate existing apparatuses with efficient performance has not been investigated extensively. This work presents a study on an integrated screening and air classification to separate cobs and stalks from husks and leaves in corn stover. Prototype machine learning models were developed to assess the feasibility of predicting the process outcome based on the measurable parameters. The models trained upon limited experimental data rendered decent predictive accuracy of yield and purity. The experimental data and modeling results collectively suggest decreasing throughput leads to a higher purity. To the contrary, if throughput increases, a lower purity is likely. A possible trade-off between yield and purity of the separated streams indicates the need for optimal combinations of feedstock size, moisture, and throughput to achieve optimized separations. The results of this study also suggest the need to further improve model predictability by developing more accurate formulations for physics governing the integrated unit operations. To accomplish this, additional experimental data needs to be generated for model training.

09 - BIOMASS FUELS↗
DOE OSTI2025

Structure and spectroscopy of graphite monofluoride

The structure of graphite monofluoride, (CF) n , has been debated since its discovery in 1934. In this work, we investigate a commercial graphite monofluoride by vibrational spectroscopy (infrared, Raman and the first inelastic neutron scattering spectra of this material). The spectroscopy shows that the material contains unreacted graphite and the partially fluorinated product dicarbon fluoride, (C 2 F) n , We evaluate the previously proposed $P\bar{6}m2$ and $P\bar{3}m1$ structures using computational methods and find F···F contacts render the $P\bar{6}m2$ structure dynamically unstable. We propose two alternative structures, $Cmc2_1$ $P6_3mc$, generated by displacement of one layer relative to another and find that $Cmc2_1$ is also dynamically unstable. The calculations are validated by comparison of calculated and observed INS spectra

Density functional theory↗
DOE OSTI2026

Stable yet hydrophilic graphene oxide nanomembranes by zwitterionic reduction for dye desalination

Holey graphene oxide (HGO) nanosheets have emerged as a promising membrane platform for dye desalination, and they must be reduced to enhance hydrophobicity and stability for long-term underwater operation, which usually decreases water and salt permeance. Herein, we develop a facile method to stabilize HGO nanosheets while retaining their hydrophilicity by reducing them with sulfobetaine amine (SBAm, a superhydrophilic zwitterion), achieving high water and salt permeance and a high salt/dye separation factor. Specifically, HGO nanosheets react with SBAm via an amine-epoxide reaction, rendering reduced HGO nanosheets containing superhydrophilic zwitterions. The effects of in-plane pores, zwitterion content, and layer thickness on the membrane chemistry, structure, and salt/dye separation properties of the SBAm-reduced HGOs (SHGOs) are thoroughly examined. The membrane achieves a Na 2 SO 4 /Direct red separation factor of up to 850, surpassing the state-of-the-art GO membranes. Moreover, hollow fiber membrane modules based on SHGOs are fabricated and exhibit stable performance in multi-cycle tests over 100 h of operation with mixed dye-salt solutions, demonstrating the scalability of our approach and its potential for practical applications.

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

Mesh-based super-resolution of fluid flows with multiscale graph neural networks

A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized meshes of elements (or cells) directly. To facilitate mesh-based GNN representations in a manner similar to spectral (or finite) element discretizations, a baseline GNN layer (termed a message passing layer, which updates local node properties) is modified to account for synchronization of coincident graph nodes, rendering compatibility with commonly used element-based mesh connectivities. Furthermore, the architecture is multiscale in nature, and is comprised of a combination of coarse-scale and fine-scale message passing layer sequences (termed processors) separated by a graph unpooling layer. The coarse-scale processor embeds a query element (alongside a set number of neighboring coarse elements) into a single latent graph representation using coarse-scale synchronized message passing over the element neighborhood, and the fine-scale processor leverages additional message passing operations on this latent graph to correct for interpolation errors. Demonstration studies are performed using hexahedral mesh-based data from Taylor–Green Vortex and backward-facing step flow simulations at Reynolds numbers of 1600 and 3200. Through analysis of both global and local errors, the results ultimately show how the GNN is able to produce accurate super-resolved fields compared to targets in both coarse-scale and multiscale model configurations. Reconstruction errors for fixed architectures were found to increase in proportion to the Reynolds number. Geometry extrapolation studies on a separate cavity flow configuration show promising cross-mesh capabilities of the super-resolution strategy.

Backward-facing step↗
DOE OSTI2025

Computational diagnostics and characterization of combustion recession in diesel sprays

While low-temperature combustion (LTC) strategies have been found to mitigate nitrogen oxides and particulate matter emissions in diesel engines, studies have also associated LTC with an increase in unburnt hydrocarbons. With more recent studies on diesel after end-of-injection (AEOI), combustion recession is identified as a phenomenon where at near nozzle region, high-temperature ignition (HTI) combustion can propagate back to the nozzle tip consuming the unburnt hydrocarbons AEOI. Current literature has suggested that combustion recession is controlled by auto-ignition. However, high-fidelity simulations and detailed analysis of such a mechanism are missing. In this study, comprehensive Large Eddy Simulations of a reacting spray at “Spray A” conditions are performed, where detailed analysis of combustion recession concerning flame morphology and propagation modes are included. In particular, this study demonstrated for the first time that while combustion recession is mainly auto-ignition dominated (consistent with the literature), a cool flame was found to deflagrate towards the richer regions of the mixture, promoting mixing and increasing the mixture temperature. This leads to HTI kernels, which then grow and develop as deflagrative waves, therefore sustaining the combustion recession process. The study also detailed the extinction mechanism of combustion: the entrainment wave will overlean the near-nozzle mixtures, rendering it unable to support HTI, which leads to the extinction of the upstream flame AEOI in lower reactivity mixtures. Combustion recession is also observed to be contingent on the chemical and diffusion processes, even at low scalar dissipation rates. Finally, a new criterion for combustion recession based on chemical explosive mode is proposed and validated with previous combustion recession index to quantify the extent of HTI in near-nozzle mixtures AEOI. The newly developed metric combined with a previous experimentally-based metric can provide simple but valuable measurements of the degree and propensity of the upstream flame AEOI.

CEMA↗
DOE OSTI2024

Virtual texture analysis to understand microstructure effects on deformation twinning and detwinning behavior in BCC metals

Understanding and predicting deformation twinning contributions to plastic deformation in BCC metals has been a long-standing challenge due to the interplay with dislocation slip and non-Schmid effects that render an asymmetry under tension and compression. This paper uses molecular dynamics simulations to understand the effect of unloading and grain orientation on deformation twinning in a nanocrystalline Fe (nc-Fe) system as a model BCC metal. The nc-Fe system is loaded under uniaxial stress tension and compression to understand the effect of grain orientation (Schmid effects) on deformation twinning behavior and the tension/compression asymmetry (non-Schmid effects). A new virtual texture analysis “VirTex” tool is used to understand the role of grain orientation on the nucleation of twins and their contributions to the observed stress–strain response. For certain grain orientations, the twinnability is observed to be different in tension and compression. In addition, the flow stress accommodation from twins in certain grains is observed to be different in tension and compression and different from that for the grains. Subsequent unloading leads to detwinning in the deformed microstructures, where the extent of detwinning depends on the strain from which the system is unloaded and on the morphology of the twin. Lastly, the simulations are carried out to analyze the role of the Schmid factor on the twinnability and asymmetry in tension and compression.

Kannan, Aadhithyan [University of Connecticut, Sto↗
DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
DOE OSTI2024

Understanding interfacial crystallization dynamics on carbon fiber reinforced polypropylene composite manufacturing

Reinforcing polymers with discontinuous fibers improves mechanical properties, such as strength and stiffness, and in some cases achieve isotropic properties, rendering them suitable for various engineering applications. Matrix materials are generally highly engineered thermosets (e.g. crosslinked epoxies), bonded to the fiber periphery by proprietary surface and sizing chemistries. Semicrystalline thermoplastic matrices are less utilized due to poor fiber-matrix bonding resulting in inefficient interfacial load-transfer in reinforced composites. However, flexibility with melt-processing or molding conditions can be leveraged to promote non-covalent interfacial bonding between matrix and fiber via crystallization of the matrix onto fiber surface. In the present study, we utilize a co-mingle chopped carbon and isotactic polypropylene fibers to form isotropic composites, tailoring interfacial immobilized matrix or interphase morphology to optimize performance through precise control of thermal processing/molding windows. Calorimetry and optical microscopy were employed to investigate the impact of carbon fiber at various volume fractions (10, 20, and 30 %) on isotactic polypropylene crystallization and mechanical performance. Variations in mechanical properties correspond to the structural evolution of the interfacial region and are correlated to underlying microstructural attributes using wide-angle X-ray scattering, thermal analysis, and low-field nuclear magnetic resonance spectroscopy. These results provide a practical framework for the manufacturing of thermoplastic matrix composites. In conclusion, the results presented provide a guide for the strategic optimization of interphase design, showcasing tailorable tensile strengths which outperform any isotactic polypropylene carbon fiber composites previously reported in literature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials

Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.

Xu, Pei [Clemson Univ., SC (United States)]↗
DOE OSTI2025

A robust fourth-order finite-difference discretization for the strongly anisotropic transport equation in magnetized plasmas

We propose a second-order temporally implicit, fourth-order-accurate spatial discretization scheme for the strongly anisotropic heat transport equation characteristic of hot, fusion-grade plasmas. Following Du Toit et al. (2018), the scheme transforms mixed-derivative diffusion fluxes (which are responsible for the lack of a discrete maximum principle) into nonlinear advective fluxes, amenable to nonlinear-solver-friendly monotonicity-preserving limiters. The scheme enables accurate multi-dimensional heat transport simulations with up to seven orders of magnitude of heat-transport-coefficient anisotropies with low cross-field numerical error pollution and excellent algorithmic performance, with the number of linear iterations scaling very weakly with grid resolution and grid anisotropy, and scaling with the square-root of the implicit timestep. We propose a multigrid preconditioning strategy based on a lower-order approximation that renders the scheme efficient and scalable under grid refinement. Several numerical tests are presented that display the expected spatial convergence rates and strong algorithmic performance, including fully nonlinear magnetohydrodynamics simulations of kink instabilities in a Bennett pinch in 2D helical geometry and of ITER in 3D toroidal geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Non-photosynthetic lineages sibling to Cyanobacteria associate with eukaryotes in the open ocean

Margulisbacteria are elusive uncultivated bacteria that have illuminated evolutionary transitions in the progenitor of Cyanobacteria, the latter being a critically important phylum that underpins oxygenic photosynthesis. The non-photosynthetic Margulisbacteria were discovered in a sulfidic spring and later in other habitats. Currently, this candidate phylum partitions into the Riflemargulisbacteria, primarily from sediments and groundwater, the Termititenax from insect gut microbiomes, and the Marinamargulisbacteria, from marine samples. We found that Marinamargulisbacteria amplicons were unusually distributed in size-fractionated samples from the sunlit photic and dark twilight zones of the ocean. Further, sequencing of wild marine protists rendered genomic information for distinct marinamargulisbacterial clades co-associated with uncultivated, non-photosynthetic Stramenopila and Opisthokonta protists. Phylogenomic analyses combining these data and available metagenome-assembled genomes (MAGs) and single-amplified genomes (SAGs) from sorted bacteria revealed new Marinamargulisbacteria lineages. The lineages delineate by their environment, forming clades comprising freshwater, marine pelagic, or sediment/hypoxic taxa. In conclusion, the remarkable diversity of Margulisbacteria indicates success in colonizing various habitats, potentially in a conserved strategy involving eukaryotic cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Uniform secondary current distribution at disk electrodes under Tafel kinetics enabled by concentric current-shielding rings

Non-uniform secondary current distribution at rotating disk electrodes (RDE) is a common problem when using resistive electrolyte media or large applied currents. In a recent publication, we have shown that auxiliary electrodes such as the ring of a rotating ring disk electrode (RRDE) can help suppress current non-uniformities at the disk enabling reliable electroanalytical measurements. However, this previous work considered linear kinetics where current distribution non-uniformities were moderate. In the present contribution, we consider current distribution non-uniformities encountered under Tafel kinetics. We show, for the case of 2,5-dihydroxy-1,4-benzoquinone (DHBQ) reduction, that optimally chosen ring conditions serve to provide effective shielding at the disk edge rendering the overall disk current distribution to be uniform. Numerical modeling and scaling analysis (using the Wagner number) are presented to aid a user in determining the optimal ring current density for achieving uniform disk current distribution under Tafel kinetics. This approach is especially useful when studying soluble-soluble redox transitions for which, unlike deposit distribution in electrodeposition, the current distribution non-uniformity is not visually apparent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hydrogenolysis Versus Hydrocracking for Polyolefin Upcycling

Global plastic production has reached 413.8 million metric tons in 2024 and is forecasted to surpass 1.2 billion metric tons by 2050. Polyolefins, mainly polyethylene (PE) and polypropylene (PP), dominate single-use packaging and account for approximately 55% of global plastic waste. The chemical inertness that makes these materials desirable for commercial applications also renders them persistent in the environment. Current recycling technologies have proven to be insufficient to divert plastic waste from landfills or environmental loss due to technical limitations and poor economic incentives. Conventional mechanical recycling is a form of downcycling, in which the polymer remelting process results in products with inferior material properties and reduced market value. Pyrolysis, a thermochemical route used for the chemical recycling of plastic wastes into refinery feedstock, requires severe conditions, typically 400–600 °C in an oxygen-free environment. The high temperature drives up energy costs and produces a wide range of poorly defined products, including undesirable light gases and heavy tars that pose challenges for downstream processing. As an alternative, catalytic chemical recycling offers a promising route for converting waste polyolefins back into value-added hydrocarbons—such as fuels, lubricant base oils, and other chemical feedstocks—at temperatures typically below 300 °C. This opinion article focuses on two dominant pathways in heterogeneous catalysis that are used to cleave the C–C bonds of polyolefins: hydrogenolysis and hydrocracking. Both catalytic pathways have the potential to selectively convert polyolefin waste into valuable fuels and chemical feedstocks under mild conditions. Notably, while hydrocracking primarily yields branched hydrocarbon products, hydrogenolysis predominantly produces linear hydrocarbons. Here, we compare the mechanisms and catalyst designs for hydrocracking and hydrogenolysis, analyze critical technical challenges from catalyst stability to process engineering, and provide an outlook on how these complementary pathways can be used to repurpose plastic waste into valuable products.

Zhang, Ruoxi [Iowa State Univ., Ames, IA (United S↗
DOE OSTI2025

Insights Into Thermal Runaway Mechanisms: Fast Tomography Analysis of Metal Agglomerates in Lithium-Ion Batteries

Thermal Runaway (TR) in lithium-ion batteries (LIB) is a critical technological and social concern. Whilst such events are rare, TR is characterized by uncontrollable heating leading to catastrophic failures. To deepen the understanding of the failure process and subsequently develop more accurate TR prediction models and as a result safer battery systems, we present in this work high-speed X-ray tomography for in-depth investigations of the copper current collector melting and agglomeration during TR. The melting process presents valuable real-time internal information about heat evolution during TR, previously challenging to access but crucially important for validating TR models. In this work, controlled failure studies combined with high-speed X-ray tomography were performed on two different commercial LIB models, subjecting them to both external heating and nail penetration to induce TR. Through real-time observation via high-speed tomography, followed by segmentation, rendering, and analysis, the formation of copper agglomerates was qualitatively and quantitatively characterized and visualized for the first time. Agglomerates tended to form either from the battery's outermost layers or centrally, depending on the method of TR initiation, and gives an indirect insight into the internal temperature evolution and distribution. Moreover, an initial comparative analysis between the battery models also revealed differences in agglomerate size, which has been linked to the thicker copper current collectors of one of the cell models. We further discuss the impact of larger copper agglomerates on heat distribution and safety. This study not only sheds light on the intricate dynamics of TR in LIBs but also underscores the pivotal role of 'gold-standard' imaging techniques in advancing battery safety, crucial for the robust modeling of TR and the future design of electric vehicle safety systems.

25 ENERGY STORAGE↗
DOE OSTI2025

Derivation of physical equations for high-speed laser welding using large language models

It is challenging to formulate complex physical phenomena that occur in a manufacturing process, particularly when the available data are limited, rendering conventional data-driven approaches ineffective. This study aims to predict humping onset in high-speed laser welding by introducing a novel framework, namely text-to-equations generative pre-trained transformer (T2EGPT). This method leverages the capabilities of large language models (LLMs), in combination with sparse experimental data and enriched literature data, to derive an interpretable and generalizable equation for predicting humping initiation. By capturing key correlations among physical parameters, T2EGPT generates a compact and dimensionless expression that accurately predicts hump formation. The equation reveals that humping arises from the interplay between inertia-driven backward melt flow and capillary-driven surface stabilization, where inertial forces drive molten metal backward and capillary forces resist surface deformation. Furthermore, compared to traditional data-driven models, T2EGPT demonstrates enhanced predictive accuracy and cross-material transferability. More broadly, this study highlights the potential of LLMs to integrate textual information with data-driven discovery, enabling the extraction of physical laws in data-scarce scientific domains.

36 MATERIALS SCIENCE↗