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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Concavity-based local erosion and sphere-size-based local dilation applied to lithium-ion battery electrode microstructures for particle identification

Performance metrics of lithium-ion batteries can be extracted from the analysis of electrode microstructures nanoscale imaging. The characterization workflow can involve a challenging particle identification, or instance segmentation, step. In this work, we propose a new identification method based on an original transformation: a sphere-size-based local dilation followed by a concavity-based local erosion, that is local morphology closing. The new transformation is much more efficient than the global morphology closing, with correct identification achieved with only 1.7 % dilation volume and 2.6 % erosion volume on a test geometry, compared to 39.2 % and more than 50 %, respectively, with its global counterpart. The new method has been then benchmarked versus other identification algorithms (watershed and pseudo coulomb repulsive field) on a real electrode microstructure with equal or better segmentation achieved.

25 ENERGY STORAGE↗
DOE OSTI2026

The remarkable inefficiency of stratocumulus

Marine stratocumulus clouds play a central role in Earth's climate system by reflecting incoming solar radiation and exerting a strong cooling effect. Their organization into open and closed mesoscale cellular morphologies can be thought of as an example of bistable dynamics driven by aerosol–cloud interactions and mesoscale processes. From the perspective of non-equilibrium thermodynamics, these structures are an example of a far-from-equilibrium open system that continuously produces and exports entropy. While entropy production has been studied in idealized deep convective systems, it has not yet been quantified for shallow clouds. Here, we compute and decompose the internal entropy production of open- and closed-cell stratocumulus using an ensemble of large-eddy simulations. We show that the overall entropy production of stratocumulus is low, reflecting the limited vertical extent and corresponding reduced ability to utilize the energy fluxes at the system's boundaries. Moist processes dominate the overall irreversibility, which, combined with their low entropy production, leads to a mechanical efficiency about an order of magnitude smaller than in deep convective systems. Although the dominant irreversible processes differ between open- and closed-cell regimes, the distributions of total entropy production largely overlap across the ensemble, limiting the ability to distinguish the dynamics of individual cases based solely on total entropy production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Self-Assembly of Anisotropic Particles on Curved Surfaces

The surface curvature of membranes, interfaces, and substrates plays a crucial role in shaping the self-assembly of particles adsorbed on these surfaces. However, little is known about the interplay between particle anisotropy and surface curvature and how they couple to alter the free energy landscape of particle assemblies. Using molecular dynamics simulations, we investigate the effect of prescribed curvatures on a quasi-2D assembly of anisotropic patchy particles. By varying curvature and surface coverage, we uncover a rich geometric phase diagram, with curvature inducing ordered structures entirely absent on planar surfaces. Large spatial domains of ordered structures can contain hidden microdomains of orientational textures imprinted by the surface on the assembly. The dynamical landscape is also reshaped by surface curvature, with a glass-like state emerging at modest densities and high curvature. Changes to the symmetry of the surface curvature are found to result in distinct structures, including phases with mesoscale ordering. In conclusion, our findings show that the coupling between surface curvature and particle geometry opens an unexplored space of morphologies and structures that can be exploited for material design.

Fluids↗
DOE OSTI2026

Sifting for a Stream: The Morphology of the $300S$ Stellar Stream

Stellar streams are sensitive laboratories for understanding the small-scale structure in our Galaxy’s gravitational field. Here, we analyze the morphology of the $300S$ stellar stream, which has an eccentric, retrograde orbit and thus could be an especially powerful probe of both baryonic and dark substructures within the Milky Way. Due to extensive background contamination from the Sagittarius stream (Sgr), we perform an analysis combining Dark Energy Camera Legacy Survey photometry, $Gaia$ DR3 proper motions, and spectroscopy from the Southern Stellar Stream Spectroscopic Survey (S 5 ). We redetermine the stream coordinate system and distance gradient, then apply two approaches to describe $300S$ ’s morphology. In the first, we analyze stars from $Gaia$ using proper motions to remove Sgr. In the second, we generate a simultaneous model of $300S$ and Sgr based purely on photometric information. Both approaches agree within their respective domains and describe the stream over a region spanning 33° . Overall, $300S$ has three well-defined density peaks and smooth variations in stream width. Furthermore, $300S$ has a possible gap of ~4.7 and a kink. Dynamical modeling of the kink implies that $300S$ was dramatically influenced by the Large Magellanic Cloud. This is the first model of $300S$ ’s morphology across its entire known footprint, opening the door for deeper analysis to constrain the structures of the Milky Way.

Milky Way galaxy↗
DOE OSTI2025

Band Gap Engineering and Metastable Phase Discovery in Cu 2 BaSnS 4– x Se x Nanocrystals via Topotactic Anion Exchange

Earth-abundant multinary inorganic chalcogenides, such as Cu 2 BaSnS 4–x Se x (CBTSSe), have emerged as promising absorbers for next-generation solar cells. However, accessing colloidal CBTSSe nanocrystals, including metastable polymorphs with more favorable optoelectronic properties, remains a critical synthetic challenge. Herein, we report the first use of topotactic anion exchange to access metastable crystal structures in soft chemistry syntheses. By varying the stoichiometry of selenium precursors, we directly synthesize Cu 2 BaSnS 4–x Se x (0 ≤ x ≤ 4) nanocrystals with a broad band gap tunability of 1.51–2.04 eV. At x = 4, a new metastable trigonal phase of Cu 2 BaSnSe 4 is isolated. This phase arises via an in-situ topotactic anion exchange from sulfur-rich Cu 2 BaSnS 4–x Se x intermediates, while retaining both the cation sublattice and overall nanocrystal morphology. The trigonal Cu 2 BaSnSe 4 exhibits a nearly direct band gap that is 280 meV lower than its thermodynamically stable orthorhombic counterpart, aligning more closely with the optimal band gap for single-junction solar cells. In contrast, a post-synthetic anion exchange route leads to the thermodynamically preferred orthorhombic polymorph, pointing to the critical nature of the in-situ transformation. Our findings open a versatile pathway for both polymorphic and morphological control in colloidal nanocrystals, expanding the synthetic design space for new optoelectronic materials.

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

Adaptive continuity-preserving simplification of street networks

While street network data are nearly universally available, their representation is usually transportation-based. However, for many types of analyses, e.g., urban morphology or network science, unprocessed transportation-based street network data is unsuitable, making a cumbersome manual simplification process necessary. To address this challenge, in this paper we propose an algorithm for simplification of street networks, based on the detection of network portions that need to be simplified, and continuity-preserving heuristics that generate new geometries. The algorithm, released in the open-source Python package neatnet, facilitates the generation of morphological networks and generalises to various geographical contexts without a need to alter the parameters, while offering better performance than other available solutions.

Fleischmann, Martin [Charles University, Prague, C↗
DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unraveling the pathway towards superionic transport in polymer electrolytes

Ionic transport in polymers is critical for Li-ion batteries, fuel cells, flow batteries and many other energy storage and conversion technologies. A significant enhancement of ion conductivity in polymers may be achieved through an increase in the polarity of side chains and their self-organization into specific morphologies, which can potentially act as percolated ionic structures. However, higher polarity increases attractive interactions within a polymer matrix and slows down its segmental dynamics, which conversely hinders ionic transport. To overcome this tradeoff, we designed the functionalization of a Li salt-doped polymer matrix by tailored amounts of zwitterionic (ZI) groups. Our results suggest the emergence of a self-assembled percolation conductivity regime above a specific ZI concentration, in which ion hopping decouples from segmental dynamics by up to ten orders of magnitude. Consequently, in the highly concentrated ZI regime, our polymeric materials exhibit in their glassy state energy barriers for ion hopping similar to, or even smaller than, those reported for superionic ceramics. Our study also reveals that ion dynamics in the poly(zwitterion) with all monomers carrying ZI groups is significantly faster than that of a monomeric ZI compound, although the latter has much faster structural relaxation. Furthermore, this result highlights the crucial role played by the local morphology on the ion transport of polymer electrolytes and opens a new pathway for the design of superionic polymers, significantly expanding the current limited portfolio of solid-state electrolytes for energy applications.

Ion conductivity↗
DOE OSTI2026

Modeling the contributions to acoustic nonlinearity from complex dislocation networks using 3D dislocation dynamics

Nonlinear ultrasonic parameters are highly sensitive to microstructural features that affect macroscale material behavior, providing a nondestructive means to characterize their evolution. Although dislocations are known to be a strong source of acoustic nonlinearity, establishing quantitative links between the acoustic nonlinearity parameter (β), measured via Second Harmonic Generation, and dislocation morphology—such as dislocation length and density—remains an open challenge. This work advances the numerical modeling of dislocation–β relationships using 3D dislocation dynamics (DD) simulations in two approaches: a “static” method computing strain and stress fields from dislocation configurations in the absence of external loading, and a “quasi-static” method to estimate β from the curvature of dislocation lines under applied load. First, the static method is combined with finite element analysis to investigate a recent assertion that heterogeneous initial strain fields can induce higher harmonic generation in a linear elastic medium; the present results do not corroborate this outcome. Then, the quasi-static method is applied to multiple-dislocation scenarios through parametric studies, revealing behaviors not predicted by analytical models, such as the competing interactions of edge and screw dislocations and the significant influence of applied stress on β. Finally, the simulations are used to model SHG experimental results and validate the hypothesis that β can decrease during plastic deformation, despite increasing dislocation density. As the DD code used here is open-source, it provides a practical platform for future investigation into microstructure–β relationships important to the interpretation of SHG results.

Materials science↗
DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Latent Catalysis as a Platform for Accessing Diverse Material Properties in Vat Photopolymerization 3D Printing

Vat photopolymerization (VP) 3D printing is an attractive strategy to manufacture customized polymer parts. The properties of printed materials are limited by the need to employ a low viscosity liquid resin and achieve rapid polymerization kinetics. To circumvent this limitation, dual‐cure methods have been developed using reagents embedded in the liquid resin formulation; however, the reagent‐based approach requires the discovery and optimization of new chemistry for each desired material. Here, in this work, we demonstrate a catalytic, dual‐cure platform that enables access to both Nylon‐6 and polyester interpenetrating networks through VP 3D printing under a universal approach. Structure–reactivity relationships of the latent NHC catalysts led to the identification of a magnesium chloride–NHC adduct as a latent catalyst that is orthogonal to radical polymerization and can be unmasked at elevated temperatures post‐printing to initiate ring‐opening polymerization of lactones and lactams. This strategy results in access to semicrystalline materials, which are a challenging morphology to access via VP 3D printing, that have attractive mechanical properties and can be printed at high resolution. This work represents the first photochemical‐based 3D printing of Nylon‐based materials and demonstrates the value of catalytic approaches to access new material properties in VP 3D printing.

Colliver, Cali N. [University of North Carolina, C↗
DOE OSTI2025

UV + Damp Heat Induced Power Losses in Fielded Utility N-Type Si PV Modules

A recent trend in commercial PV modules is a transition to n-type silicon cells, including passivated emitter rear totally diffused (n-PERT), tunnel oxide passivated contact (TOPCon), and silicon heterojunction (SHJ). There is evidence via lab studies that some of these cells are more susceptible to UV induced degradation (UVID), yet there is a lack of confirmation that such degradation occurs in the field. Current IEC standards designed to screen for early module failures require only minimal UV exposure (15 kWh/m2 280-400 nm, ~2-3 months equivalent outdoor exposure). Here, we investigate fielded n-PERT silicon (Si) modules from a commercial utility that show power losses of ~2%/year. We present a comprehensive picture of the physics and chemistry of degradation supported by both module and cell electronic characterization (EL, PL, IV, EQE, and DLIT) and materials-level morphological and chemical analysis (SEM, EDS, XPS, FTIR, and HPLC). All sampled site modules show short circuit current (Isc) and open circuit voltage (Voc) losses when compared to unfielded spares, with the most severely degraded also having losses in fill factor (FF). We identify two different degradation modes contributing to overall power loss: (1) external quantum efficiency (EQE) measurements show losses in the blue range of the spectra, indicative of cell surface recombination losses, and (2) variations in high series resistance (Rs) at the cell level that are correlated with compositional differences in cell metallization. Using unfielded spares, we were able to reproduce Voc, Isc, and EQE losses via a minimum UV stress of 67.5 kWh/m2 (280-400 nm), 4.5x the exposure currently required in IEC 61215-2 (MQT 10). Degradation continued with additional UV dosage equivalent to the fielded modules (405 kWh/m2 total), with power loss leveling out at an average of 6.1%. Subsequent 1000 h of 85% RH/85degrees C damp heat testing showed that cells exposed to UV underwent additional severe series resistance degradation, even those without the susceptible paste composition seen in the field, whereas non-UV exposed cells saw little change. We attribute this to higher concentrations of acetic acid generated on the UV exposed area of the module, leading to degradation of the gridline/cell interface and high Rs. This study is unique in that it reproduces field observed utility scale UVID with an accelerated test and supports the need for standards development for longer UV exposure combined with other stress factors to catch materials interplay within a module package.

14 SOLAR ENERGY↗
DOE OSTI2026

Citric Acid Functionalized Natural Fibers to Enhance Thermal Stability and Moisture Resistance in Polylactic Acid Composites

Cellulosic fibers can impart many unique benefits into composite applications, such as reduced weight or structural reinforcement; however, these materials also increase hygroscopicity and decrease thermal stability, restricting broader applications. The present work adapted an experimental process for functionalizing the cellulose surface using citric acid (CA) for three fibers: a 100% cellulose bleached soft Kraft pulp (e.g., creafill) and two natural fibers with similar composition but different fiber morphology, flax fiber and banana fiber. The process uses CA with a sodium hypophosphite (SHP) catalyst to chemically functionalize fiber surfaces, and the reaction mechanism was investigated through Fourier Transform Infrared Spectroscopy (FTIR), which suggested a grafting mechanism rather than a surface-based crosslinking between neighboring sites. Functionalized fibers were compounded into polylactic acid (PLA) at 20 wt.% to better understand how this functionalization might impact critical performance properties like thermal stability, crystallization, thermal mechanical properties, and water uptake of these composites. The study demonstrated varying levels of efficacy for the functionalization of cellulosic fibers with CA/SHP and the fiber with the most open microstructure, e.g., banana fiber, exhibited the largest change in its properties with a 38% reduction in water uptake compared to untreated banana fiber composites. Parallel evaluation of the functionalization process for different fibers demonstrates the importance of fiber morphology on surface modification and can enable their use in composites by demonstrating the efficacy of this potentially low-cost, low-toxicity method for reducing hygroscopicity and improving thermal stability.

citric acid↗
DOE OSTI2026

Reconstruction and Dissolution of Copper Catalysts during Electrocatalytic Nitrate Reduction

Copper-based electrocatalysts are widely explored for electrochemical nitrate remediation, yet their stability under operating conditions remains poorly understood. While nanoscale and subnanoscale Cu motifs are known to restructure during the nitrate reduction reaction (NO 3 RR), how these transformations translate into irreversible material loss remains unclear. Here, we quantify Cu dissolution during NO 3 RR as a function of catalyst architecture and electrolyte chemistry using two model systems: single-atom Cu (Cu 1 ) and Cu nanoparticles (Cu NP ). Time-resolved leaching measurements, integrated with in situ X-ray absorption spectroscopy (XAS), reveal measurable Cu loss for both catalysts during NO 3 RR. Dissolution is concentrated at the initiation of electrolysis, coinciding with rapid restructuring, and depends strongly on morphology, with Cu NP consistently exhibiting greater Cu loss than Cu 1 . In situ XAS reveals that Cu 1 forms transient metallic clusters under reduction that largely redisperse upon returning to open-circuit voltage, whereas Cu NP undergoes reduction of an oxidized surface layer accompanied by sustained Cu loss during electrolysis. Notably, the presence of nitrate significantly intensifies restructuring and Cu loss, highlighting the critical role of electrolyte composition. Furthermore, these findings establish a direct link between electrochemical restructuring and Cu dissolution, unveiling electrolyte-dependent interactions as key determinants of catalyst durability in electrochemical nitrate conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Patchy nanoparticles by atomic stencilling

Stencilling, in which patterns are created by painting over masks, has ubiquitous applications in art, architecture and manufacturing. Modern, top-down microfabrication methods have succeeded in reducing mask sizes to under 10 nm, enabling ever smaller microdevices as today’s fastest computer chips. Meanwhile, bottom-up masking using chemical bonds or physical interactions has remained largely unexplored, despite its advantages of low cost, solution-processability, scalability and high compatibility with complex, curved and three-dimensional (3D) surfaces. Here we report atomic stencilling to make patchy nanoparticles (NPs), using surface-adsorbed iodide submonolayers to create the mask and ligand-mediated grafted polymers onto unmasked regions as ‘paint’. We use this approach to synthesize more than 20 different types of NP coated with polymer patches in high yield. Polymer scaling theory and molecular dynamics (MD) simulation show that stencilling, along with the interplay of enthalpic and entropic effects of polymers, generates patchy particle morphologies not reported previously. These polymer-patched NPs self-assemble into extended crystals owing to highly uniform patches, including different non-closely packed superlattices. We propose that atomic stencilling opens new avenues in patterning NPs and other substrates at the nanometre length scale, leading to precise control of their chemistry, reactivity and interactions for a wide range of applications, such as targeted delivery, catalysis, microelectronics, integrated metamaterials and tissue engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Trihydrogen Formation on Gold Nanoparticles in Strong Laser Fields

The tri-hydrogen cation (H+ 3 ) plays a central role in proton-transfer chemistry, astrochemical pathways, and hydrogen plasma environments, acting as a key indicator of ultrafast proton rearrangement. Although H+ 3 formation is studied extensively in the gas phase, its surface-mediated generation and its sensitiv- ity to nanoparticle morphology remain largely unexplored. Gold nanoparticles (AuNPs), which can localize surface charge and sustain strong electric fields, offer an ideal platform to probe such nonequilibrium reaction pathways. Us- ing reaction nanoscopy, we spatially map H+ 3 production on AuNPs exposed to intense femtosecond laser fields. By comparing spherical and faceted nanopar- ticles, we demonstrate how morphology modulates charge density and governs reaction efficiency. We find that sharp features on faceted particles concen- trate charge more effectively, promoting molecular fragmentation and enabling proton rearrangement and migration that enhance H+ 3 yields. This work opens new directions for exploiting strong-field interactions at metal interfaces to drive nanoscale reactivity and photocatalysis.

Dagar, Ritika [SLAC National Accelerator Laborator↗
DOE OSTI2025

Wave ripples formed in ancient, ice-free lakes in Gale crater, Mars

Symmetrical wave ripples identified with NASA’s Curiosity rover in ancient lake deposits at Gale crater provide a key paleoclimate constraint for early Mars: At the time of ripple formation, climate conditions must have supported ice-free liquid water on the surface of Mars. These features are the most definitive examples of wave ripples on another planet. The ripples occur in two stratigraphic intervals within the orbitally defined Layered Sulfate Unit: a thin but laterally extensive unit at the base of the Amapari member of the Mirador formation, and a sandstone lens within the Contigo member of the Mirador formation. In both locations, the ripples have an average wavelength of ~4.5 centimeters. Internal laminae and ripple morphology show an architecture common in wave-influenced environments where wind-generated surface gravity waves mobilize bottom sediment in oscillatory flows. Their presence suggests formation in a shallow-water (<2 meters) setting that was open to the atmosphere, which requires atmospheric conditions that allow stable surface water.

Science & Technology - Other Topics↗
DOE OSTI2025

Origin of enhanced performance when Mn-rich rocksalt cathodes transform to δ -DRX

Most Mn-rich cathodes are known to undergo phase transformation into structures resembling spinel-like ordering upon electrochemical cycling. Recently, the irreversible transformation of Ti-containing Mn-rich disordered rock-salt cathodes into a phase — named δ — with nanoscale spinel-like domains has been shown to increase energy density, capacity retention, and rate capability. However, the nature of the boundaries between domains and their relationship with composition and electrochemistry are not well understood. In this work, we discuss how the transformation into the multi-domain structure results in eight variants of Spinel domains, which is crucial for explaining the nanoscale domain formation in the δ -phase. We study the energetics of crystallographically unique boundaries and the possibility of Li-percolation across them with a fine-tuned CHGNet machine learning interatomic potential. Energetics of 16 d vacancies reveal a strong affinity to segregate to the boundaries, thereby opening Li-pathways at the boundary to enhance long-range Li-percolation in the δ structure. Defect calculations of the relatively low-mobility Ti show how it can influence the extent of Spinel ordering, domain morphology and size significantly; leading to guidelines for engineering electrochemical performance through changes in composition.

Anand, Shashwat↗