The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La0.7Ca0.3MnO3 as a representative VC-NDR material and comparing it with LaCoO3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

Thermal management for high power electronics prioritizes minimizing thermal resistances between the heat source and the ultimate heat sink. This often sacrifices thermal capacitance, resulting in overheating under transient thermal loads. Phase change composites (PCCs) composed of conductive scaffolds infiltrated with phase change materials when added in series between a heat source and a heat sink increase the effective thermal capacitance at the cost of increased thermal resistance. The impact on thermal impedance (Zth) depends on both the heat-pulse period and the heat sink's effective heat transfer coefficient (heff). In this study, Zth of a PCC slab was measured experimentally under isolated square-wave heat pulses of 4.25 W cm-2 and on-times between 0.01-2000 s. The heff was tuned from 500 to 5000 W m-2 K-1 (approximately representing natural convection of water to forced convection in water) by using layers of insulation between the PCC and a cold plate. As heff decreases, the magnitude of Zth suppression due to melting effects increased in magnitude and range of on-times. Importantly, the heff was observed to dictate the pulse length over which a PCC decreased the thermal impedance relative to a solid copper block under equal volume basis. PCCs decrease junction temperatures while simultaneously reducing size and weight of systems under conditions where (1) the input power is short relative to steady-state conditions and (2) high heff are not available due to engineering constraints.

There is growing interest in correlated oxides that can switch between volatile resistance states when an electrical bias is applied, functioning as artificial neurons in neuromorphic computing systems. Most devices typically rely on first-order insulator-metal transitions (IMT). However, recent discoveries have shown that devices made of a second-order spin-transition material, such as LaCoO3 (LCO), can exhibit different or improved functionalities. Despite their significance, the microscopic details surrounding the formation of conductive channels have still been unreported. In this study, the spatiotemporal details of channel formation are revealed by using a combination of infrared (IR) and Raman microscopy. Comparison of LCO and materials such as VO2 reveals critical differences with important ramifications for computing. First, the findings indicate that LCO channels are narrower and more efficient than VO2, but they are also more sensitive to electric fields and disorder. Channels are found to repeatedly hop between different locations under steady-state oscillations, a behavior not previously reported. Additionally, memory effects at high bias are observed. The experiments, along with finite element simulations (FES), suggest that the spin transition in LCO may significantly influence channel nucleation, leading to an increased sensitivity of neuronal devices to disorder and electrode geometry. We discuss how the inherent stochasticity and memory effects could enable functionalities in neuromorphic computing.

Vanadium dioxide (VO2) is of interest for adaptive electronic applications such as neuromorphic neuristor devices and variable emissivity or tunable thermal control materials, thanks to its key property-a metal-insulator transition (MIT) at 68 degrees C that is accompanied by a dramatic change in electrical and optical properties. To improve performance in these roles, it is critical to develop approaches to engineer transport properties and the MIT behavior. While many documented techniques exist to modulate the MIT and film resistivities via lattice strain and chemical doping, less is known about the effects of ion irradiation on the intrinsic properties of VO2, despite the ability to control the spatial distribution of irradiation beams and the prevalence of high energy ion implantation in the semiconductor industry. The impact of irradiation of different acceleration energies on the responses of VO2 is of specific interest, as charged particle energy generally impacts both the resulting defect profile and corresponding transport behavior. Here, we demonstrate that 2 MeV He ions at equivalent calculated displacements per atom, in two different types of films, can create remarkable changes to the nature of charge transport in VO2, especially in the low-temperature insulating phase. Simulation of resulting changes in electrical conductivity reveals that He ion irradiation offers a strategy to increase both oscillation frequency and the signal transmission. These results provide insights into the intentional design of defect populations to modulate transport for neuromorphic VO2 devices.

Transitions between rotationally ordered and disordered states in globular small molecules are associated with large entropy changes and thus hold promise as solid-state barocaloric refrigerants. However, the relationships between elements of the molecular structure and the corresponding thermodynamic properties of the phase transformation between ordered and disordered states remain unresolved. We hypothesize that more spherical molecules, as measured by their rotational moments of inertia, exhibit larger relative increases in their rotational degrees of freedom as they transition to rotationally disordered states. We probe this by isolating the impact of rotational moments of inertia from more dominant factors, including intermolecular bonding, through the selective deuteration of different functional groups of the model plastic crystal molecule neopentyl glycol. We demonstrate a decrease in the phase transition temperature of up to approximately 3 K associated with the existence of deuterated methyl groups and explain this change in terms of relative changes in the rotational moments of inertia of the compounds. This observation places bounds on the role of rotational moments of inertia in thermodynamic aspects of the phase transformation and introduces a vector for subtle modulation of the transition point for cooling applications.

Isostructural solid phases with similar lattice parameters are known to promote nucleation in salt hydrate phase change material (PCM) systems, as they serve as templates for crystal growth. However, the phases with the closest structural and chemical similarities are generally also salt hydrates and thus, are susceptible to dissolution or reaction to form phases with different hydration states. Here, we investigate an isostructural system where the candidate nucleation particle, strontium chloride hexahydrate (SrCl2 & centerdot;6H2O), reacts in the liquid PCM, calcium chloride hexahydrate (CaCl2 & centerdot;6H2O). We demonstrate that when present, crystalline SrCl2 & centerdot;6H2O dramatically reduces undercooling of CaCl2 & centerdot;6H2O. However, when present in low concentrations, SrCl2 & centerdot;6H2O will react with CaCl2 & centerdot;6H2O to form a lower hydrate phase, SrCl2 & centerdot;2H2O, which does not promote nucleation in the system. Critically, we demonstrate that it is possible to regain the nucleation particle activity by supercooling the system, recovering the active SrCl2 & centerdot;6H2O phase. This result bounds the utility of isostructural compounds as nucleation particles for salt hydrate phases and illustrates that nucleation strategies relying on isostructural systems are complicated by the solubility relationships and reactivity between the phases present.

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current-voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure-function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Phase change materials (PCMs) have tremendous capacity as passive components to recover and repurpose thermal energy from transient power systems. However, PCMs are only effective if the time scale of the thermal energy storage and retrieval rates match those required for a particular system. We develop a framework to assess the efficiency of pulsed thermal energy storage based on the concept of "thermal impedance," drawing upon an analogous approach from electrical energy storage. We experimentally characterize a 1 cm thick paraffin-infiltrated copper foam composite PCM subject to pulsed heat boundary conditions up to 1 W cm(-2) and demonstrate a decrease in thermal impedance by up to a factor of 2.5x in the regime in which melting occurs (tau on = 10(-1) to >10(2) s) relative to a reference case in which melting does not occur. This represents both a signature of the ability to extract or retrieve thermal energy via latent heat, as well as an experimentally accessible measure that provides insight into the internal dynamics of a composite PCM volume. These principles can serve to design the internal structure of composite PCM elements for pulsed thermal systems.

The measurement of thermal impedance can be used to understand the thermal performance of a thermal management system (TMS) under transient thermal loads and can isolate the role of melting in a phase change composite (PCC) systems. Accurate measurement of thermal impedance under transient thermal loads of a heterogeneous media is critical for evaluating the thermal performance of complex systems, such as those encountered in power electronic devices. The study presents a systematic framework to measure and interpret thermal impedance in heterogeneous PCCs with varying operating conditions, including duty factor, on-time, heat flux, air temperature, and thermal loads. Various reference scenarios are examined to isolate the role of melting, and a framework for directly comparing experimentally determined thermal performance characteristics in TMSs was determined. Key concepts such as developing convergence criteria in experimental testing, defining appropriate factors within the uncertainty of the measurement, and identifying sources of variability are discussed. The results highlight that including a phase change within a TMS can induce a $0.11^{\circ} \mathrm{C} \cdot \mathrm{W}^{-1}$ decrease in junction temperature at 10 s, with a decrease in junction temperature between 0.5 and 50 s. This work establishes a foundation for optimizing composite PCM-based TMS in nextgeneration power electronic systems.

Negative differential resistance (NDR) is a key electronic response enabling two‐terminal artificial neurons that can be achieved through different physical phenomena, including phase‐homogeneous current density and temperature (electro‐thermal) localizations and spatially‐localized metal‐insulator phase transitions (MITs). These two effects have been observed to occur sequentially in select electrically‐biased transition metal oxides. However, it is unknown why and under what conditions localizing behaviors precede MITs, particularly as a function of device length scale. To this end, the interplay between phase‐homogeneous electro‐thermal localizations and MITs is investigated in a 3D multiphysics simulation of a lateral thin film device, using the material properties of the prototype MIT material VO 2 . These findings demonstrate that the MIT is nucleated through dynamically localizing current density and temperature. A critical device width (≈0.7 µm in this study) is identified, below which both the electrically‐induced electro‐thermal and phase inhomogeneities cease to appear. It is demonstrated that the formation of spatial inhomogeneities directly relates to device dimensions, and demonstrate the decoupling of NDR from the MIT through device scaling relationships. These results provide insight into the material phenomena underlying the material's electrical responses, clarifying conditions under which spatial inhomogeneities form in electrically‐biased MIT materials.

Phase change materials (PCMs) are used to passively cool transient electromechanical systems by absorbing (releasing) heat over a narrow temperature range during the phase transition. Paraffins and salt hydrates, having large gravimetric and volumetric latent heats of fusion, are particularly well-suited for applications like heating, ventilation, and air conditioning (HVAC) that demand efficient thermal management. However, even with large thermal capacitances, these materials are not commonly implemented at the system level due in part to their low gravimetric and volumetric power densities. One method to increase the rates of thermal energy charging (discharging) of these low thermal conductivity materials is to create a composite system, such as a thermally conductive porous network infiltrated with PCM. In this work, we report a comparative investigation of the heat transfer rates of a 3.50 mm thick paraffin-expanded graphite (EG) and salt hydrate eutectic-EG composite slab in a cold plate heat exchanger over 100 cycles. A custom flow loop setup is used to characterize the rate of heat transfer between the working fluid and PCM-EG composite slabs. Transient heat transfer is characterized for each material at temperatures $5^{\circ} \mathrm{C}$ and $10^{\circ} \mathrm{C}$ above their melting points. This study demonstrates the improved thermal performance and system-level efficiency of PCM-EG composites, as well as their stability over hundreds of cycles. We observe similar thermal charge and discharge times for both PCM-EG composites, indicating that the heat storage rates are governed primarily by the conductive EG matrix and not the thermal conductivity of the PCM itself. Finally, we present the first reported heat absorption rate for a salt hydrate eutectic composite slab.

Artificial neurons exhibiting volatile threshold switching and action potential‐like oscillations are crucial for brain‐inspired computing. While Complimentary Metal‐Oxide‐Semiconductor (CMOS)‐based strategies require hundreds of transistors to simulate each neuron, neuronal oscillations arise spontaneously in individual electro‐thermal devices due to nonlinearities like the Mott transition in VO 2 . Despite improved understanding of the physics, quantitative connections between neuronal performance and material properties remain under‐explored, preventing predictive neuron design and rational materials selection. In this work, a physics‐aware forward design methodology is developed for interrogating a wide palette of materials with properties varying by orders of magnitude, and their performance (high frequency, high dynamical reconfigurability and low power) under external circuit and device geometry constraints is assessed. The space of viable materials is identified to be much larger than previously recognized, with candidates from a range of materials classes, including Ge, GaP and MoS 2 . CMOS‐compatible performance (such as 100 GHz oscillating frequencies) can be achieved with CMOS‐compatible node sizes (≈10 nm). Finally, combinations of material properties yielding desired neuronal performance under uncertain design constraints are considered. This work solidifies forward design principles for electro‐thermal neuron devices, a necessary pre‐condition for inverse design from desired neuronal performance to required materials properties.

Plastic crystals, many of which are globular small molecules that exhibit transitions between rotationally ordered and rotationally disordered states, represent an important subclass of colossal barocaloric effect materials. The known set of plastic crystals is notably sparse, which presents a challenge to developing predictive thermodynamic models to describe new molecular structures. To predict the transformation entropy of plastic crystals, we developed a comprehensive database of tetrahedral plastic crystal molecules (neopentane analogs) and used several types of features, including chemical functional groups, molecular symmetry, DFT-calculated vibrational entropy, and energy decomposition analysis to train a machine learning model. To select the most relevant features, we used a correlation matrix to screen out highly correlated features and ran sure independence screening and sparsifying operator (SISSO) regression on the remaining features. The SISSO regression samples over combinatorial spaces, including operations and features, to find the relationship between material properties. Using a dataset of 49 plastic crystals and 37 non-plastic crystals based on a common tetrahedral geometry, we have demonstrated the effectiveness of this strategy. Furthermore, we applied this strategy to develop a regression model to predict transition entropy and enthalpy. The top 100 models from the operation space showed that the overall distribution of performance became narrower, sacrificing the top-performing model but avoiding the worst models. Using this approach, we identified the top-performing descriptors to further clarify the underlying mechanisms of the plastic crystal transformation.

The thermal hysteresis exhibited in plastic crystal compounds greatly reduces their cyclic efficiency, limiting their potential for replacing current environmentally harmful refrigerants. A mechanistic understanding of the origins of this hysteresis has yet to be established. Here, we systematically investigate the transformation kinetics of the model plastic crystal, neopentyl glycol (NPG), through microscopic and calorimetric techniques. We reveal an asymmetry between the forward (heating) and reverse (cooling) transitions. We also demonstrate that the forward transformation is rate-limited by the rate of growth of rotationally disordered domains. In contrast, the reverse transformation is rate-limited by the nucleation of the ordered crystal domain, demonstrated by the sharp exothermic peaks in calorimetry and rapid self-nucleation phenomena observed optically. This nucleation limitation is largely responsible for the large thermal hysteresis in NPG, which we observe to be as large as 16.7 °C for an approximately 10 mg sample cooled at 0.5 °C min−1. These findings demonstrate the underlying origin of the thermal hysteresis and introduce a direction to mitigate hysteresis in plastic crystal transformations.

Building artificial neurons and synapses is key to achieving the promise of energy efficiency and acceleration envisioned for brain-inspired information processing. Emulating the spiking behavior of biological neurons in physical materials requires precise programming of conductance nonlinearities. Strong correlated solid-state compounds exhibit pronounced nonlinearities such as metal-insulator transitions arising from dynamic electron-electron and electron-lattice interactions. However, a detailed understanding of atomic rearrangements and their implications for electronic structure remains obscure. In this work, we unveil discontinuous conductance switching from an antiferromagnetic insulator to a paramagnetic metal in epsilon-Cu0.9V2O5. Distinctively, fashioning nonlinear dynamical oscillators from entire millimeter-sized crystals allows us to map the structural transformations underpinning conductance switching at an atomistic scale using single-crystal X-ray diffraction. We observe superlattice ordering of Cu ions between [V4O10] layers at low temperatures, a direct result of interchain Cu-ion migration and intrachain reorganization. The resulting charge and spin ordering along the vanadium oxide framework stabilizes an insulating state. Using X-ray absorption and emission spectroscopies, assigned with the aid of electronic structure calculations and measurements of partially and completely decuprated samples, we find that Cu 3d and V 3d orbitals are closely overlapped near the Fermi level. The filling and overlap of these states, specifically the narrowing/broadening of V 3d xy states near the Fermi level, mediate conductance switching upon Cu-ion rearrangement. Understanding the mechanisms of conductance nonlinearities in terms of ion motion along specific trajectories can enable the atomistic design of neuromorphic active elements through strategies such as cointercalation and site-selective modification.

Nucleation particles, solid phases dispersed throughout a medium to decrease the energy barrier for solidification or other reversible phase transitions, are generally selected on the basis of structural or interfacial energy considerations between the host phase and the solid phase that is crystallizing. However, the existence of chemical reactions between the nucleation particles and the host phase can obscure these underlying relationships, thereby complicating the process of selection of active nucleation particle phases. Here, we reveal the origin of nucleation activity of barium-based nucleation particles in the salt hydrate calcium chloride hexahydrate (CCH), a candidate for near room temperature thermal energy storage. We demonstrate that these compounds undergo a series of cation exchange and secondary precipitation reactions, resulting in an assemblage of solid precipitates with some degree of limited solid solution, which collectively dramatically reduce undercooling in CCH, but which obscure the identification of a single crystalline phase primarily responsible for the nucleation of crystalline CCH from the liquid. Importantly, this result illustrates a pathway to harness in situ chemical reactions to generate stable active nucleation particles in reactive phase change materials, which may not be readily synthesized by alternative methods, or which may not be active or remain stable when added in isolation.

Reversible martensitic transformations nucleate from sparse defects that lower the transformation's nucleation energy barrier. However, most defects do not serve as potent nucleation sites and, instead, can pin boundary motion and impede phase growth. Identifying potent defects from the general defect population remains an open challenge and has important implications for engineering reversible alloys. This study considers the influence of mesoscale order-disorder domains and the phase boundaries between the L21 and B2 phases on nucleation kinetics. We use solution heat treatment and secondary annealing in Ni45Co5Mn36.7In13.3 microparticles to compare an average L21 domain interfacial area density of 590.6 - 50.6 mu m-1, measured from transmission electron microscopy micrographs using the intercept method in ASTM standard E112-13. A total of 131 single particles with radii between 3.8 and 20.7 mu m were individually characterized magnetically to measure their transformation temperatures and the transformation behavior. Overall, the undercooling in each particle ranged from 11.3 to 59.4 K, with the smallest volumes having the largest magnitude and variance. The nucleation site potency distributions between the two domain sizes were statistically alike, suggesting that L21 domain size is not a critical factor in initiating nucleation at the length scales of this experiment. The implications for microlevel devices include opportunities to heat treat materials to high operational temperatures (e.g., 773 K) without impacting nucleation behavior.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTForum: Pathways toward Critical Advances in the Chemistry of Materials for Thermal Energy StoragePatrick J. Shamberger*Patrick J. ShambergerDepartment of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, United States*Email: [email protected]More by Patrick J. Shambergerhttps://orcid.org/0000-0002-8737-6064 and Svetlana A. Sukhishvili*Svetlana A. SukhishviliDepartment of Materials Science and Engineering, Texas A&M University, College Station, Texas 77843, United States*Email: [email protected], [email protected]More by Svetlana A. Sukhishvilihttps://orcid.org/0000-0002-2328-4494Cite this: ACS Appl. Eng. Mater. 2024, 2, 3, 501–502Publication Date (Web):March 22, 2024Publication History Received4 March 2024Published online22 March 2024Published inissue 22 March 2024https://pubs.acs.org/doi/10.1021/acsaenm.4c00148https://doi.org/10.1021/acsaenm.4c00148editorialACS PublicationsCopyright © Published 2024 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views239Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (930 KB) Get e-AlertscloseSUBJECTS:Energy storage,Materials,Salts,Solvates,Thermal energy Get e-Alerts