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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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NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗
DOE OSTI2025

Anion sublattice design enables superionic conductivity in crystalline oxyhalides

Solid-state batteries are attractive energy storage systems as a result of their inherent safety, but their development hinges on advanced solid-state electrolytes (SSEs). Most SSEs remain largely confined to single-anion systems (e.g., sulfides, oxides, halides, and polymers). Through mixed-anion design strategy, we develop crystalline Li 3 Ta 3 O 4 Cl 10 (LTOC) and its derivatives with excellent ionic conductivities (up to 13.7 millisiemens per centimeter at 25°C) and electrochemical stability. The LTOC structure features mixed-anion spiral chains, consisting of corner-shared oxygen and terminal chlorine atoms, which induces continuous “tetrahedron-tetrahedron” Li-ion migration pathways with low energy barriers. Additionally, LTOC demonstrates holistic cathode compatibility, enabling solid-state batteries operation at 4.9 volts versus Li/Li + and low temperature, down to −50°C. In conclusion, these findings describe a promising class of superionic conductors for high-performance solid-state batteries.

Zhao, Feipeng [Western University, London, ON (Can↗
DOE OSTI2025

Uncovering Structure–Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning

Anion exchange membranes (AEMs) play a vital role in the performance of water electrolyzers and fuel cells, yet their discovery and optimization remain challenging due to the complexity of structure–property relationships. In this study, we introduce a machine learning framework that leverages conditional graph neural networks (cGNNs) and descriptor-based models and a hybrid graph neural network (HGARE) to predict and interpret ionic conductivity. The descriptor-based pipeline employs principal component analysis (PCA), ablation, and SHAP analysis to identify factors governing anion conductivity, revealing electronic, topological, and compositional descriptors as key contributors. Beyond prediction, dimensionality reduction and clustering are performed by employing t-SNE and KMeans as well as SOM, which reveal distinct membranes clusters, some of which were enriched with high anion conductivity. Among graph-based approaches, the graph convolutional (GCN) achieved strong predictive performance, while the Hybrid Graph Autoencoder-Regressor Ensemble (HGARE) achieved the highest accuracy. Additionally, atom-level saliency maps from GCN provide spatial explanations for conductive behavior, revealing the importance of polarizable and flexible regions. This work contributes to the accelerated and data-driven design of high-performance AEMs.

Naghshnejad, Pegah [Department of Chemical Enginee↗
DOE OSTI2025

Mastering the Copolymerization Behavior of Ethyl Cyanoacrylate as Gel Polymer Electrolyte for Lithium‐metal Battery Application

Polymers with strong electron-withdrawing groups (e.g., cyano-containing polymers) are attractive for a wide range of applications due to their high dielectric constant and outstanding electrochemical stability. However, the polymerization of such monomers is difficult to control with trace of water affording instant reactions, and copolymerization with other monomers without using strong acid is even more challenging. The present study demonstrates a facile approach enabling efficient and controllable copolymerization of ethyl cyanoacrylate (ECA) without adding undesired additives, achieving mechanically robust and high ion-conduction gel polymer electrolyte (GPE) for safe and long cycle-life lithium-metal batteries (LMBs). The incorporated dual-lithium salts, i.e., lithium difluoro(oxalato)borate (LiDFOB) and lithium bis(trifluoromethanesulphonyl)imide (LiTFSI) not only facilitate radical polymerization of ECA monomers by suppressing their anionic polymerization, but also promote the formation of high-ionic conducting GPE. The incorporated methyl methacrylate (MMA) monomer accelerates the radical polymerization of ECA (confirmed by DFT calculations), achieving controlled copolymerization of ECA-based copolymers. Here, the mechanically robust polymer network made by the ECA copolymer enables LMBs with both LFP cathodes and high-voltage LCO cathodes (4.5 V) operatable at different temperatures with ultra-long cycle life at 1 C (capacity retention of 81.1 % and 83.8 %, respectively, over 1000 cycles).

Min, Weixing [Nankai University, Tianjin (China)]↗
DOE OSTI2024

Iridium Surface Oxide Affects the Nafion Interface in Proton-Exchange-Membrane Water Electrolysis

Proton-exchange-membrane water electrolyzer (PEMWE) catalyst layers consist of aggregates of catalyst particles (typically iridium) and ionomer (typically Nafion). Prior work suggests that the oxide form of Ir affects the kinetics of the oxygen-evolution reaction. However, because most catalyst-benchmarking studies are conducted ex situ in liquid electrolytes, it remains unclear how the ionomer is influenced by the catalyst oxide and affects overall cell performance. Using a suite of experimental techniques, we conduct fundamental investigations into model ink (catalyst and ionomer dispersed in solution) and thin-film systems to inform cell-level overpotential analysis as a function of three forms of Ir (metallic Ir m , oxyhydroxide IrOOH, and oxide IrO 2 ). Furthermore, nafion on Ir m has a high degree of phase separation and higher swelling, likely improving the ionic conductivity. Additionally, Nafion binds most strongly to IrOOH, likely yielding reduced kinetic overpotentials. These findings highlight the intricacies of the ionomer/Ir interface and provide insight into all catalyst-layer systems.

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

Tuning Solvation Dynamics of Electrolytes at Their Eutectic Point Through Halide Identity

Deep eutectic solvents (DESs) are regarded as highly promising solvent systems for redox flow batteries. DESs, composed of choline halides (ChX, X = F − , Cl − , Br − , I − ) and ethylene glycol (EG), exhibit distinct physicochemical properties at their eutectic points, including halide-dependent phase behavior, viscosity, polarity, conductivity, and solvation dynamics. In this study, we investigate the effects of the halide identity on the solvation properties of ChX:EG mixtures at varying mol % of ChX salt content. The solvatochromic polarity based on ET(30) measurements indicates higher polarity for larger halides (I − > Br − ) than for smaller halides (Cl − > F − ), which exhibit larger compensating solvation shells. The ionic conductivity follows the trend of the solvent fluidity (the inverse of the viscosity), namely ChCl > ChBr > ChI > ChF, influenced by the ion mobility and solvodynamic radii. Measurements of the liquidus temperatures (T L ) reveal that the system with ChCl exhibits the deepest eutectic point (at ~20 mol % ChCl), while ChBr and ChI have shallower minima at ~10 mol % ChBr and ~3 mol % ChI, respectively. ChF does not display a eutectic transition but instead appears to readily supercool at salt concentrations above 30 mol % ChF. Consistent with the phase transition measurements, femtosecond transient absorption spectroscopy shows that in the ChCl system, the solvation dynamics become faster with an increasing salt concentration up to ~16.67 mol %, after which the dynamics slow down with further increases in the salt content. The ChF-based system exhibits similar behavior, though with slower dynamics. In contrast, the solvation dynamics of the systems containing ChBr and ChI monotonously slow down with an increasing salt concentration, in agreement with the phase transition measurements, which show that the eutectic points occur at low salt concentrations. These measurements suggest that the solvent composition and, in particular, the identity of the halide anion play a significant role in the solvation behavior of these ethylene-glycol-based DESs, offering a foundation for tuning the DES properties for specific applications.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Revisiting the Role of Mobile Ion Size for the Activation Barrier of Charge Transport in Single Ion Conducting Polymers

The development of polymer electrolytes for energy storage and conversion technologies requires a fundamental understanding of the material parameters controlling the energy barriers for ion transport. In glassy polymers, these activation barriers are usually extracted by using Arrhenius procedures. However, our recent studies of single ion conducting polymers reveal that this traditional Arrhenius description provides anomalously small prefactors, an issue that is widely disregarded in the literature. Here, to address it, an alternative approach was introduced for extracting the effective activation barrier E by imposing a physically valid limit (∼10 –13 s) for the ion hopping attempt time. Two consequences of this recent approach are that in polymer electrolytes (i) E is significantly (∼30–40%) lower than the barrier estimated using traditional Arrhenius fits and (ii) E displays significant temperature dependence even in the glassy state. Under these circumstances, we are revisiting the role of ion size, glass transition temperature T g , and dielectric and elastic constants of the polymer matrix on the effective activation barriers for ion transport as extracted using the recently proposed approach and highlight the differences from the picture that previously emerged based on simplistic apparent Arrhenius analysis. To this end, we investigate cation transport in three families of single ion conducting trifluoromethane sulfonimide-based polyanions with varied T g . Our results indicate that E decreases with mobile cation size and is highly sensitive to changes in the dielectric permittivity of the matrix, even for large cations. These insights call for revisions of many earlier results based on apparent Arrhenius fits as the proposed approach can provide more accurate guidance for the design of polymer electrolytes with enhanced ionic conductivity.

energy barrier↗
DOE OSTI2026

Ultrafast Reactive Laser Sintering of Highly Conductive Garnet-Type LLZTO Solid Electrolytes

Rapid and scalable fabrication of garnet-type solid electrolytes remains a major challenge for the practical deployment of lithium metal batteries. Here, we report reactive laser sintering (RLS) as an ultrafast and potentially scalable strategy for fabricating garnet-type Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) solid electrolytes. RLS of LLZTO enables simultaneous reaction and densification, achieving ∼95% relative density while minimizing lithium loss and suppressing secondary phase formation. Compared to conventional furnace sintering, RLS promotes enhanced grain growth and improved densification, leading to improved ionic conductivity (0.36 ± 0.08 mS cm −1 ) while maintaining comparable activation energies for Li + transport. Structural characterization by X-ray diffraction (XRD), Raman spectroscopy, and solid-state 6 Li/ 7 Li NMR confirms the formation of cubic garnet LLZTO with homogeneous microscale elemental distribution. In addition, nanoindentation measurements demonstrate that RLS preserves the mechanical properties of the garnet framework despite ultrafast localized thermal processing. By integrating simultaneous reaction and densification with tunable microstructural control, reactive laser sintering provides a promising manufacturing pathway for high-performance garnet solid electrolytes toward next-generation solid-state batteries.

CO2 laser↗
DOE OSTI2025

Superionic Surface Li-Ion Transport in Carbonaceous Materials

Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. Here, in this study, we have discovered an ultrafast Li-ion transport phenomenon on the surface of carbonaceous materials with limited reversible Li insertion capacity and high surface area. An ionic conductivity of 18.1 mS cm –1 at room temperature is observed in lithiated Ketjen black (KB), far exceeding those of most solid-state ion conductors. Theoretical calculations reveal low diffusion barriers for the surface Li species. As a result, lithiated KB functions effectively as an interlayer between Li and solid-state electrolytes (SSEs) to mitigate dendrite growth. Further, lithiated KB acts as a high-performance mixed ionic–electronic conductor and replaces solid electrolytes to enhance graphite anode performance, demonstrating full utilization with ∼85% capacity retention over 300 cycles. The discovery of this surface-mediated ultrafast Li-ion transport mechanism provides new directions for the design of solid-state ion conductors and solid-state batteries.

Li metal batteries↗
DOE OSTI2025

Contorted acene ribbons for stable and ultrasensitive neural probes

Organic materials that conduct both electrons and ions are integral to implantable bioelectronics because of their conformable nature. There is a dearth of these materials that are highly sensitive to cations, which are the majority ions on the surface of neurons. This manuscript offers a solution using an extended ribbon structure that is defect-free, providing high electronic mobility along its fused backbone, while the edge structure of these ribbons promotes high ionic conductivity. We incorporated these mixed ion/electron conductors into neural probes and implanted them in a rodent brain where they offer a suite of useful properties: high cation sensitivity, stability over several weeks after implantation, and biocompatibility. These materials represent an innovative class of implantable biosensors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

Molecular Dynamics Simulations of Liquid and Polymer Electrolytes for Energy Storage Devices

Advancing beyond current lithium-ion technology is necessary in order to enable energy storage devices for electric airplanes. Electrolyte stability is a key limiting factor, yet the design of improved electrolytes remains a formidable challenge. Molecular dynamics (MD) simulations are a powerful tool for studying electrolytes, since they can be used to evaluate structural, thermodynamic, and transport properties, and can provide molecular-level detail often inaccessible to experimental techniques. Our computational materials groups at the NASA Ames Research Center has developed models and methods to accurately simulate both liquid and polymer electrolytes.We report the results from atomistic MD simulations of several electrolyte materials, with lithium salts dissolved in ionic liquids, dimethoxyethane (DME), and polyethylene oxide (PEO). For improved accuracy, we employ polarizable models, where each atom is given an environment-dependent atomic dipole. The simulations accurately predict bulk transport properties, including viscosity, diffusion, and ionic conductivity, in quantitative agreement with available experimental data. Moreover, the simulations provide important insights into the solvation structure of the lithium ions.We also report the results from coarse-grained MD simulations of polyanion electrolytes. In order to more efficiently capture the longer length- and time-scales of these systems, we employ a generic bead-spring model. These simulations provide important insight into how the polymer chain architecture and ionic interaction strengths affect the ionic aggregation behavior and cation dynamics. Despite the simplicity of the model, the simulations yield qualitative agreement with experimental data for similar systems.

Abbott, Lauren J.↗
DOE OSTI2024

Regenerative Solid Interfaces Enhance High-Performance All- Solid-State Lithium Batteries

All-solid-state Lithium batteries (ASSLBs) have gained widespread attention in recent years. However, their performance is still largely limited by the poor transport properties and stability of the solid-solid interfaces (SSIs). In this study, we report a new strategy for creating a reversible interface with good conductivity and self-adaptive mechanical properties for high-performance all-solid-state lithium batteries (ASSLBs). The interface is formed in situ from nanosized lithium iodide (LiI), a component of the solid-state electrolyte (SSE), which flows plastically along the SSE interfaces under high pressure due to its high ductility. Moreover, LiI segregates and enriches along the Li/SSE interfaces, reversibly enhancing Li plating/stripping due to its lithophilicity and high ionic conductivity. This dynamic LiI interface enables stable operation of metallic Li anode (>1000 h) at high current densities and elevated temperatures, and long-term cycling of all-solid-state Li-sulfur batteries (>250 cycles) with high sulfur utilization rate (>1400 mAh g-1) and areal capacity (>2 mAh cm-2). This study reveals a unique role of LiI in building robust SSIs and provides new insights into the design of novel SSEs and interfaces for high-performance ASSLBs.

Yu, Zhaoxin↗
DOE OSTI2025

Molecular Interlocking Multidimensional Modulations of Cathode‐Electrolyte Interface for Constructing High Energy Density Quasi‐Solid‐State Batteries

Gel polymers are regarded as a promising candidate electrolyte for lithium-metal quasi-solid-state batteries, primarily due to their high ionic conductivity and solid-liquid synergistic properties. However, challenges such as interfacial side reactions, limitations in Li + transport caused by interfacial issues, and leaching of transition metals from the cathode have yet to be effectively solved. Herein, a novel gel electrolyte modulation strategy based on electrostatic filler assembly is proposed to address the issues of ineffective capacity utilization and inadequate cycling stability of high-energy-density cathode materials in solid-state lithium-ion batteries. It constructs a 3D interpenetrating charge-bridge network that effectively tackles the phase-separation challenge between fillers and electrolytes at the molecular level. Meanwhile, the molecular interlocking structure effectively inhibits the electrolyte erosion. More critically, it optimizes and stabilizes the cathode-electrolyte interface film, which facilitates the conduction of Li + -ions through a size-sieving mechanism. Consequently, this strategy enables effective adaptation across diverse high-energy-density cathode materials with satisfactory capacity performance (170.4 mAh g −1 at 4.5 V/1 C for LiNi 0.6 Co 0.2 Mn 0.2 O 2 and 194.0 mAh g −1 at 4.3 V/1 C for LiNi 0.9 Co 0.08 Mn 0.02 O 2 ). In conclusion, this investigation offers a straightforward and effective reference for addressing the critical challenges of ionic transport and interface stabilization in the design of gel electrolytes.

cathode-electrolyte interface↗
NASA NTRS2010

The Earliest Ion Channels in Protocellular Membranes

Cellular membranes with their hydrophobic interior are virtually impermeable to ions. Bulk of ion transport through them is enabled through ion channels. Ion channels of contemporary cells are complex protein molecules which span the membrane creating a cylindrical pore filled with water. Protocells, which are widely regarded as precursors to modern cells, had similarly impermeable membranes, but the set of proteins in their disposal was much simpler and more limited. We have been, therefore, exploring an idea that the first ion channels in protocellular membranes were formed by much smaller peptide molecules that could spontaneously selfassemble into short-lived cylindrical bundles in a membrane. Earlier studies have shown that a group of peptides known as peptaibols is capable of forming ion channels in lipid bilayers when they are exposed to an electric field. Peptaibols are small, non-genetically encoded peptides produced by some fungi as a part of their system of defense against bacteria. They are usually only 14-20 residues long, which is just enough to span the membrane. Their sequence is characterized by the presence of non-standard amino acids which, interestingly, are also expected to have existed on the early earth. In particular, the presence of 2-aminoisobutyric acid (AIB) gives peptaibols strong helix forming propensities. Association of the helices inside membranes leads to the formation of cylindrical bundles, typically containing 4 to 10 monomers. Although peptaibols are excellent candidates for models of the earliest ion channels their structures, which are stabilized only by van der Waals forces and occasional hydrogen bonds between neighboring helices, are not very stable. Although it might properly reflect protobiological reality, it is also a major obstacle in studying channel behavior. For this reason we focused on two members of the peptaibol family, trichotoxin and antiamoebin, which are characterized by a single conductance level. This indicates that their structures are unique and stable. In addition, it is also believed that the trichotoxin channel displays some selectivity between potassium and chloride ions. This makes trichotoxin and antiamoebin ideal models of the earliest ion channels that could provide insight into the origins of ion conductance and selectivity. In the absence of crystal structure of the trichotoxin and antiamoebin channels, we propose their molecular models based on experimentally determined number of monomers forming the bundles. We use molecular dynamics simulations to validate the models in terms of their conductance and selectivity. On the basis of our simulations we show that the emergence of channels built of small, alpha-helical peptides was protobiologically plausible and did not require highly specific amino acid sequences, which is a convenient evolutionary trait. Despite their simple structure, such channels could possess properties that, at the first sight, appear to require markedly larger complexity. To this end, we will discuss how the amino acid sequence and structure of primitive channels give rise to the phenomena of ionic conductance and selectivity across the earliest cell walls, which were essential functions for the emergence and early evolution of protocells. Furthermore, we will argue that even though architectures of membrane proteins are not nearly as diverse as those of water-soluble proteins, they are sufficiently flexible to adapt readily to the functional demands arising during evolution.

Mijajlovic, Milan↗
DOE OSTI2026

Simply Fabricatable Reference Electrode for Studying Li Metal Interfaces Operando

Reference electrodes are essential for studying Li metal interfaces, yet their integration into coin cells remains challenging due to geometric constraints and assembly complexity. Here, we present a simple three-electrode coin cell design in which a thin Cu film is evaporated directly onto a trilayer PP/PE/PP separator, forming a conductive and flexible in situ current collector. The microporous Cu preserves separator porosity and pressure uniformity, avoiding artifacts introduced by wire- or mesh-type references. It is easily activated by Li plating and provides stable potentials for over 10 days in ether-based electrolytes without affecting Coulombic efficiency (>99%) or Li deposition morphology. Operando measurements using this design reveal electrode-specific impedance evolution and voltage responses otherwise obscured in two-electrode setups, uncovering a correlation between the SEI resistance and electrolyte ionic conductivity. This microporous Cu reference electrode offers a robust and accessible platform for characterizing reactive interfaces under realistic cycling conditions.

Sacci, Robert [ORNL] (ORCID:0000000200735221)↗
DOE OSTI2024

Selective ion transport through hydrated micropores in polymer membranes

Abstract Ion-conducting polymer membranes are essential in many separation processes and electrochemical devices, including electrodialysis 1 , redox flow batteries 2 , fuel cells 3 and electrolysers 4,5 . Controlling ion transport and selectivity in these membranes largely hinges on the manipulation of pore size. Although membrane pore structures can be designed in the dry state 6 , they are redefined upon hydration owing to swelling in electrolyte solutions. Strategies to control pore hydration and a deeper understanding of pore structure evolution are vital for accurate pore size tuning. Here we report polymer membranes containing pendant groups of varying hydrophobicity, strategically positioned near charged groups to regulate their hydration capacity and pore swelling. Modulation of the hydrated micropore size (less than two nanometres) enables direct control over water and ion transport across broad length scales, as quantified by spectroscopic and computational methods. Ion selectivity improves in hydration-restrained pores created by more hydrophobic pendant groups. These highly interconnected ion transport channels, with tuned pore gate sizes, show higher ionic conductivity and orders-of-magnitude lower permeation rates of redox-active species compared with conventional membranes, enabling stable cycling of energy-dense aqueous organic redox flow batteries. This pore size tailoring approach provides a promising avenue to membranes with precisely controlled ionic and molecular transport functions.

Science & Technology - Other Topics↗
NASA NTRSDate not supplied

J-OCTA Applications to in Space Manufacturing and Nanofluidic Technologies

We consider examples of J-OCTA software application to physics-based analysis of in-space manufacturing technologies. Firstly, we consider an atomistic model of thermal welding at the polymer-polymer interface for applications to 3D manufacturing by following diffusion of polymer chains. We show that each component of the polymer blend has its own characteristic time of diffusion and that both strain–stress and shear viscosity curves agree with experimental data. Next, nonlinear shrinkage of the metal part during manufacturing by bound metal deposition under microgravity, is considered using a multi-scale physics-based approach that spans from atomistic dynamics to full-part shrinkage. At the smallest scale we use atomistic dynamics to simulated sintering of a few Ti6Al4V nanoparticles, study sintering mechanisms and estimate important parameters including grain boundary width and diffusion rates of the atoms at the surface that are used at the other scales of modelling. Finally, we analyse selective ionic conduction through an artificial nanopore in a graphene sheet motivated by applications to desalination and energy harvesting. We build distributions of the number and orientation of water molecules in the ion’s hydration shells providing novel insight into the ion-water-carbon interaction at the nanopore.

molecular dynamics↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗