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At least 145 records · Page 8

Topology optimization for the full-cell design of porous electrodes in electrochemical energy storage devices

In this paper, we introduce a density-based topology optimization framework to design porous electrodes for maximum energy storage. We simulate the full cell with a model that incorporates electronic potential, ionic potential, and electrolyte concentration. The system consists of three materials, namely pure liquid electrolyte and the porous solids of the anode and cathode, for which we determine the optimal placement. We use separate electronic potentials to model each electrode, which allows interdigitated designs. As a result, a penalization is required to ensure that the anode and cathode do not touch, i.e., causing a short circuit. We compare multiple 2D designs generated for different fixed conditions, e.g. material properties. A 3D design with complex channel and interlocked structure is also created. All optimized designs are far superior to the traditional monolithic electrode design with respect to energy storage metrics. We observe up to a 750% increase in energy storage for cases with slow effective ionic diffusion within the porous electrode.

25 ENERGY STORAGE

Applications of SPICE for modeling miniaturized biomedical sensor systems

This paper proposes a model for a miniaturized signal conditioning system for biopotential and ion-selective electrode arrays. The system consists of three main components: sensors, interconnections, and signal conditioning chip. The model for this system is based on SPICE. Transmission-line based equivalent circuits are used to represent the sensors, lumped resistance-capacitance circuits describe the interconnections, and a model for the signal conditioning chip is extracted from its layout. A system for measurements of biopotentials and ionic activities can be miniaturized and optimized for cardiovascular applications based on the development of an integrated SPICE system model of its electrochemical, interconnection, and electronic components.

Microelectrodes

Elemental Fractionation During Rapid Accretion of the Moon Triggered by a Giant Impact

Recently, Ida et al. made an N-body simulation of lunar accretion from a protolunar disk formed by a giant impact. One of their important conclusions is that the accretion time of the Moon is as short as one month. Such rapid accretion is a necessary consequence of the high surface density of a lunar mass disk accreting just beyond the Roche limit (about 3Re); the Safronov accretion time (a few days) is even shorter. The energy of accretion always exceeds the gravitational binding energy of newly arriving matter. Hence, without an energy sink, the accreting body is thermally unstable. For the Earth and other planets, radiation acts as the sink. However, in such a short accretion time, the Moon cannot radiate the accretional energy. Even radiating at a silicate cloudtop temperature of roughly 2000 K, it would take more than 100 yr to radiatively cool the Moon. The plausible alternative heat sinks are heat capacity, latent heat of vaporization, and thermal escape of the gas to space (i.e., hydrodynamic blowoff). The latter becomes plausible for the Moon because the scale height at 2000 K (about 300 km) is a significant fraction of the lunar radius. The early stages of lunar (or "lunatesimal") growth release relatively little energy and can occur simply by heating the material, especially if the accreting material is originally cold. However, the material is unlikely to be cold, because the disk itself is hot and cooling time is long, while the lunar accretion time iss very short. Therefore, the moon is likely to accrete condensed material just after it condenses. Accordingly, the newly accreted material will be on the verge of vaporization and will have very little heat capacity to spare. The immediate heat sink is the latent heat of vaporization. Most of the vapor will escape from the moon, because the thermal energy in the gas can be used to drive escape. However, vaporization is generally incomplete. the latent heat of vaporization exceeds the energy of accretion. Viewed globally, the accretional energy is about half the energy required to vaporize the entire Moon. Thus to first approximation, half of the Moon-forming material can be vaporized and lost during accretion. During this process, we would expect preferential loss of relatively volatile elements. Escape will retard the rate of accretion. To test these ideas, we computed detailed models of the thermal state of the Moon during accretion. We pay special attention to the structure of the silicate atmosphere and its loss rate by calculating the chemical species at equilibrium. We used the PHEQ program which includes 12 elements (H,O,C,Mg,Si,Fe,Ca, Al, Na,Ti, and N.) and 272 compounds (including ionic compounds). Because of the large heats of vaporization and ionization, the adiabatic atmosphere is nearly isothermal and massive escape is expected. The pressure of the atmosphere is determined by the balance between vaporization of a accreting material and escape. If the accretion time is one month, a 0.3 bar atmosphere is expected. Elemental fractionation depends strongly on the temperature of the accreting material. The initial temperature of the material can be estimated from the condition of gravitational instability in the protolunar disk. As shown by Ida et al, accretion starts when gravitational instability occurs when more than 99% of the material condenses. At this point, all of Ca, Al, Si, Mg, and Fe, and 95% of Na (probably K also), are in condensed phases. If the moon is formed from the accretion of such material, volatile elements such as Na, and K are retained by the moon only early in accretion. At later times, K and Na are lost and a fraction of the MG, Si and Fe is lost. However, refractory elements such as Ca and Al are retained and so achieve a mild degree (factor 2) of superabundance.

Abe, Y.

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

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.

Materials discovery

Modelling intelligent behavior

An introductory discussion of the related concepts of intelligence and consciousness suggests criteria to be met in the modeling of intelligence and the development of intelligent materials. Methods for the modeling of actual structure and activity of the animal cortex have been found, based on present knowledge of the ionic and cellular constitution of the nervous system. These have led to the development of a realistic neural network model, which has been used to study the formation of memory and the process of learning. An account is given of experiments with simple materials which exhibit almost all properties of biological synapses and suggest the possibility of a new type of computer architecture to implement an advanced type of artificial intelligence.

Green, H. S.

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE

Site Disorder Drives Cyanide Dynamics and Fast Ion Transport in Li 6 PS 5 CN

Halide argyrodite solid-state electrolytes of the general formula Li 6 PS 5 X exhibit complex static and dynamic disorder that plays a crucial role in ion transport processes. Here, we unravel the rich interplay between site disorder and dynamics in the plastic crystal argyrodite Li 6 PS 5 CN and the impact on ion diffusion processes through a suite of experimental and computational methodologies, including temperature-dependent synchrotron powder X-ray diffraction, AC electrochemical impedance spectroscopy, 7 Li solid-state NMR, and machine learning-assisted molecular dynamics simulations. Sulfide and (pseudo)halide site disorder between the two anion sublattices unilaterally improves long-range lithium diffusion irrespective of the (pseudo)halide identity, which demonstrates the importance of site disorder in dictating bulk ionic conductivity in the argyrodite family. Furthermore, we find that anion site disorder modulates the presence and time scales of cyanide rotational dynamics. Ordered configurations of anions enable fast, quasi-free rotations of cyanides that occur on time scales of 10 11 Hz at T = 300 K. In contrast, we find that cyanide dynamics are slow or frozen in Li 6 PS 5 CN when site disorder between the cyanide and sulfide sublattices is present at T = 300 K. We rationalize the observed differences in cyanide dynamics in the context of elastic dipole interactions between neighboring cyanide anions and local strain induced by the configurations of site disorder that may impact the energetic landscape for cyanide rotational dynamics. Through this study, we find that anion disorder plays a decisive role in dictating the extent and time scales of both lithium ion and cyanide dynamics in Li 6 PS 5 CN.

36 MATERIALS SCIENCE

The Transition from Hydrogen Bonding to Ionization in (HCI)n(NH3)n and (HCI)n(H2O)n Clusters: Consequences for Anharmonic Vibrational Spectroscopy

Anharmonic vibrational frequencies and intensities are calculated for 1:1 and 2:2 (HCl)(sub n)(NH3)(sub n) and (HCl)(sub n)(H2O)(sub n) complexes, employing the correlation-corrected vibrational self-consistent field method with ab initio potential surfaces at the MP2/TZP computational level. In this method, the anharmonic coupling between all vibrational modes is included, which is found to be important for the systems studied. For the 4:4 (HCl)(sub n)(H2O)(sub n) complex, the vibrational spectra are calculated at the harmonic level, and anharmonic effects are estimated. Just as the (HCl)(sub n)(NH3)(sub n) structure switches from hydrogen-bonded to ionic for n=2, the (HCl)(sub n)(H2O)(sub n) switches to ionic structure for n=4. For (HCl)2(H2O)2, the lowest energy structure corresponds to the hydrogen-bonded form. However, configurations of the ionic form are separated from this minimum by a barrier of less than an O-H stretching quantum. This suggests the possibility of experiments on ionization dynamics using infrared excitation of the hydrogen-bonded form. The strong cooperative effects on the hydrogen bonding, and concomitant transition to ionic bonding, makes an accurate estimate of the large anharmonicity crucial for understanding the infrared spectra of these systems. The anharmonicity is typically of the order of several hundred wave numbers for the proton stretching motions involved in hydrogen or ionic bonding, and can also be quite large for the intramolecular modes. In addition, the large cooperative effects in the 2:2 and higher order (HCl(sub n)(H2O)(sub n) complexes may have interesting implications for solvation of hydrogen halides at ice surfaces.

Chaban, Galina M.

Direct determination of the ionic charge distribution of heavy ions in Fe-rich solar energetic particle events

The first direct determination of the iron charge distribution in the energy range 0.3-1.0 MeV/nucleon during Fe-rich and He-3-rich solar energetic particle events is reported. The deterimination was made on the basis of ISEE-3 data for the event from August 1978 to October 1979. The enrichment of He-3 iron relative to oxygen was found to be directly correlated with the enrichment of He-3 to He-4. Average ion charge distributions were computed for: (1) He-3-rich periods which were also Fe-rich and had a He-3/He-4 ratio of 0.1 and (2) periods with a He-3/He-4 ratio of less than 0.1. It is found that the mean iron charge state for He-3-rich periods was in the range 19.5 (+ or - 1.5) and only 14 (+ or - 1) for the time periods with He-3/He-4 less than ratio of 0.1. The reduced data are found to be in good agreement with the theoretical predictions of Fisk (1978), suggesting a temperature in the source region above 5 million degrees K for Fe-rich and He-3-rich solar energetic particle events.

Klecker, B.

Frontiers of Ionic Liquids in Carbon Dioxide Separation and Valorization

Ionic liquids (ILs) have emerged as highly tunable sorbents and membranes for gas separation, especially in the purification of CO 2 -containing gas streams such as air, natural gas, biogas, and syngas. Their negligible volatility, high thermal stability, and chemical versatility position them as promising alternatives to conventional amine and alkaline metal derivative-based systems, effectively addressing key challenges such as volatility, stability, and high regeneration energy. Here, this Review explores IL-derived systems for CO 2 -related gas separation across dense, porous, and supported categories. At the dense liquid level, we discuss strategies for tailoring IL properties to optimize CO 2 sorption, focusing on the correlation between IL-CO 2 interaction strength, uptake capacity, and regeneration energy. Key advancements in carbon capture, including amino-functionalized (AILs) and superbase-derived ILs (SILs), are highlighted, along with strategies such as chemical structure engineering, multiple binding site integration, alternative driving force exploration, and stability enhancement. Then, the porous liquids (PLs) scale focuses on the emerging field integrating IL properties with permanent porosity engineering, spanning ultramicropores (<5 Å) to macropores (around 100 nm). These innovations improve gas uptake capacity, accelerate transport kinetics, introduce the gating effect, and enable the coexistence of active sites with antagonistic properties within a single IL medium. At the supported IL scale, the discussion shifts to IL- and ionic pair-modified sorbents and membranes, emphasizing the modulation of cations and anions, confinement effects from porous supports, and the IL–interface interaction to enhance CO 2 separation performance, particularly in diluted gas streams. Beyond separation, this Review highlights IL-based integrated processes for CO 2 capture and conversion into value-added chemicals via thermocatalytic, electrocatalytic, and photocatalytic pathways. At each scale, advanced computational and experimental tools for IL design are also discussed, providing insights into stability enhancement, sorption efficiency, and process integration. The Review concludes by addressing existing challenges and outlining future directions for IL-driven innovations in gas separation technologies.

Qiu, Liqi [Univ. of Tennessee, Knoxville, TN (Unit

Electrocatalysis in CO 2 -Binding Organic Liquids with an Iron Porphyrin

Direct electrochemical upgrading of CO 2 in capture media is an attractive approach to carbon capture that can bypass the energy requirement for the thermal release of pure CO 2 . Here we investigate the electrocatalytic activity of iron(tetraphenylporphyrin) in the presence of organic solvents that convert into ionic liquids upon exposure to CO 2 . Four different solvent systems were tested, all of which capture CO 2 in the form of an alkyl carbonate (or carbamate) anion and an acidic ammonium cation. The electrocatalytic selectivity exhibited a strong dependence on the acidity of the capture medium, with the most basic solvent affording a high selectivity for production of CO instead of H 2 . Experimental and computational studies support a canonical mechanism in which the catalyst reacts with free CO 2 in solution, as opposed to a reaction with the alkyl carbonate that is present in high concentration. Kinetic analysis indicates that the rate-limiting step is changed from C–O protonolysis in traditional solvents to the binding of CO 2 in the capture media. Quantitative 13 C– 13 C EXSY revealed that the dissociation of the alkyl carbonate into free, solvated CO 2 is very rapid (~15 s –1 ) compared to the interconversion of HCO 3 – /CO 2 in aqueous solution. These results underscore the need to understand the mechanism and kinetics for both the release of captured CO 2 and its electrocatalytic conversion.

Carbon Dioxide

Disentangling cation effects on ion mobility and structure in ionic liquid electrolytes

Ionic liquids (ILs) are low-temperature molten salts, where ion transport is primarily governed by ion–ion interactions. Yet, the impact of organic IL cations on critical electrolyte properties such as ion dissociation and overall transport behavior in lithium-salt-doped ILs remains poorly understood. Moreover, despite their critical role in designing IL-based electrolytes for energy storage applications, ion–ion interactions and ion-specific transport under an applied electrical potential are seldom quantified, largely due to the unique experimental and computational challenges involved. Herein, we compare transport properties obtained using 1 H, 7 Li, and 19 F pulsed-field gradient nuclear magnetic resonance (NMR) and electrophoretic NMR (eNMR) with those measured by electrochemical impedance spectroscopy. Non-equilibrium molecular dynamics (MD) simulations and eNMR confirm the presence of negatively charged [Li(TFSI) n ] (1−n) aggregates that migrate towards the positive electrode, resulting in negative lithium transference numbers. Equilibrium MD simulations reveal a vehicular Li ion transport mechanism facilitated by long-lived aggregates with Li + cations strongly bound to multiple TFSI − anions. Finally, we observe an inverse relationship between the apparent charge of the TFSI − anion in the neat IL, which is dictated by the IL cation, and Li + transport in the salt-doped systems. This highlights the opportunity to tune electrolyte performance by tailoring cation chemistry.

Li-ion batteries

Revised ion temperatures for Voyager plasma measurements in the Io plasma torus

A calculation error in previous computations of ion temperatures in the Io plasma torus of the Jovian magnetosphere from Voyager plasma-science-experiment measurements is reported, and its effects on subsequently published studies are evaluated. It is found that the temperatures reported by Bagenal et al. (1980) and Bagenal and Sullivan (1981) for Jupiter and by Bridge et al. (1981) for Saturn are half the correct values, with major effects on ionic-species scale heights, plasma-density maps, and flux-tube content estimations. The temperatures given by Bridge et al. (1979) and McNutt et al. (1981) are not affected by the error. A corrected isodensity contour map is presented, and uncertainties in the measurement of ion temperatures are discussed.

Bagenal, F.

Emulation of Synaptic Plasticity in WO 3 ‐Based Ion‐Gated Transistors

Neuromorphic systems, inspired by the human brain, promise significant advancements in computational efficiency and power consumption by integrating processing and memory functions, thereby addressing the von Neumann bottleneck. This paper explores the synaptic plasticity of a WO3-based ion-gated transistor (IGT) in [EMIM][TFSI] and a 0.1 mol L −1 LiTFSI in [EMIM][TFSI] for neuromorphic computing applications. Cyclic voltammetry (CV), transistor characteristics, and atomic force microscopy (AFM) force–distance (FD) profiling analyses reveal that Li + brings about ion intercalation, together with higher mobility and conductance, and slower response time (τ). WO 3 IGTs exhibit spike amplitude-dependent plasticity (SADP), spike number-dependent plasticity (SNDP), spike duration-dependent plasticity (SDDP), frequency-dependent plasticity (FDP), and paired-pulse facilitation (PPF), which are all crucial for mimicking biological synaptic functions and understanding how to achieve different types of plasticity in the same IGT. The findings underscore the importance of selecting the appropriate ionic medium to optimize the performance of synaptic transistors, enabling the development of neuromorphic systems capable of adaptive learning and real-time processing, which are essential for applications in artificial intelligence (AI).

36 MATERIALS SCIENCE

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. Further, in this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Ultra-Fast Non-Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing

Low-current multilevel programmability with inherent non-volatility and high stability of resistance states is required for both multi-bit memory storage and deep learning accelerators but is difficult to achieve. Here, in a resistive switching system, this work realizes >512 (>9 bits) distinct non-volatile conductance levels with stable retention for each state with current levels down to the nanoampere range, highly promising for potential integration with small processing nodes with ultra-low power consumption requirements. This is achieved by demonstrating a new thin film design concept that encompasses three key features: an ultra-thin epitaxial oxygen ionic switching layer that provides a tunable energy barrier at the bottom electrode, an overcoat amorphous layer that acts as an ion migration barrier for stable state retention, and a partial conductive filament as a localized electronic transport channel to the epitaxial switching layer. A large dynamic resistance range of up to seven orders of magnitude is achieved with reset-free transitions among intermediate states, and programmability is demonstrated with ultra-fast (20 ns) pulses. Artificial neural network (ANN) simulations, based on the experimental performance and its non-idealities, demonstrate close-to-ideal inference accuracies for various Modified National Institute of Standards and Technology (MNIST) data sets.

36 MATERIALS SCIENCE

Minimal implicit-solvent coarse-grained simulation of Pluronic block copolymers with ionic liquids

Pluronic block copolymers, composed of poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO) in a triblock structure (PEO–PPO–PEO), are well known for their amphiphilic character and ability to self‐assemble into micelles in aqueous solution. The addition of ionic liquids (ILs) can further modulate the core–shell structures of these copolymers, influencing their stability, critical micellization temperature, and size. However, fully atomistic simulations often become prohibitively expensive due to the size and complexity of these systems. In this work, coarse‐grained simulations using a minimal implicit‐solvent model were performed to examine how two classes of ILs, namely, 1‐alkyl‐3‐methylimidazolium ([C n C 1 im]) and 1‐alkyl‐3‐methylpyrrolidinium ([C n C 1 pyrr]), change the micellization of Pluronic block copolymers in aqueous solution. The effects of IL concentration and alkyl group length were investigated, and the model greatly improved the efficiency of simulating large‐scale micelle systems. Furthermore, the numerical simulations are qualitatively compared with experimental investigations. Our results show that adding ILs expands the micelle core by embedding IL tails among the PPO blocks, thereby increasing overall micelle size. Less polar ILs generally induce more pronounced micellar growth. However, the effect of IL tail length on conformation and micellar packing is non‐monotonic. Up to moderate chain lengths (around C8–C10), the IL tails can extend sufficiently to increase local separation within the micelle; at longer tail lengths, enhanced hydrophobic clustering and steric hindrance cause the tails to bend or fold, capping further expansion. In addition, although block copolymer chains tend to pack more closely in the presence of longer‐tailed ILs, the random coil size of an individual polymer chain does not necessarily shrink. Meanwhile, these insights provide a deeper understanding of how Pluronic/IL systems interact, informing applications in drug delivery, cosmetics, food, and environmental engineering. Finally, our minimal implicit‐solvent model can be applied to larger systems and longer timescales, substantially reducing computational cost while reproducing key structural trends observed experimentally.

Atomistic simulations