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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data for Discovery, Characterization, and Application of Chromosomal Integration Sites for Stable Heterologous Gene Expression in Rhodotorula toruloides

Rhodotorula toruloides is a non-model, oleaginous yeast uniquely suited to produce acetyl-CoA-derived chemicals. However, the lack of well-characterized genomic integration sites has impeded the metabolic engineering of this organism. Here we report a set of computationally predicted and experimentally validated chromosomal integration sites in R. toruloides . We first implemented an in silico platform by integrating essential gene information and transcriptomic data to identify candidate sites that meet stringent criteria. We then conducted a full experimental characterization of these sites, assessing integration efficiency, gene expression levels, impact on cell growth, and long-term expression stability. Among the identified sites, 12 exhibited integration efficiencies of 50% or higher, making them sufficient for most metabolic engineering applications. Using selected high-efficiency sites, we achieved simultaneous double and triple integrations and efficiently integrated long functional pathways (up to 14.7 kb). Additionally, we developed a new inducible marker recycling system that allows multiple rounds of integration at our characterized sites. We validated this system by performing five sequential rounds of GFP integration and three sequential rounds of MaFAR integration for fatty alcohol production, demonstrating, for the first time, precise gene copy number tuning in R. toruloides . These characterized integration sites should significantly advance metabolic engineering efforts and future genetic tool development in R. toruloides .

Conversion↗
DOE OSTI2025

Designs for scalable construction of hybrid quantum photonic cavities

Nanophotonic resonators are central to numerous applications, from efficient spin–photon interfaces to laser oscillators and precision sensing. A leading approach consists of photonic crystal (PhC) cavities, which have been realized in a wide range of dielectric materials. However, translating proof-of-concept devices into a functional system entails a number of additional challenges, inspiring new approaches that combine resonators with wavelength-scale confinement and high quality factors; scalable integration with integrated circuits and photonic circuits; electrical or mechanical cavity tuning; and, in many cases, a need for heterogeneous integration with functional materials such as III–V semiconductors or diamond color centers for spin–photon interfaces. Here we introduce a concept that generates a finely tunable PhC cavity at a selected wavelength between two heterogeneous optical materials whose properties satisfy the above requirements. The cavity is formed by stamping a hard-to-process material with simple waveguide geometries on top of an easy-to-process material consisting of dielectric grating mirrors and active tuning capability. We simulate our concept for the particularly challenging design problem of multiplexed quantum repeaters based on arrays of cavity-coupled diamond color centers, achieving theoretically calculated unloaded quality factors of 106, mode volumes as small as 1.2(λ/neff)3, and maintaining >60% total on-chip collection efficiency of fluorescent photons. We further introduce a method of low-power piezoelectric tuning of these hybrid diamond cavities, simulating optical resonance shifts up to ∼760 GHz and color center fluorescence tuning of 5 GHz independent of cavity tuning. These results will motivate integrated photonic cavities toward larger scale systems-compatible designs.

Greenspon, Andrew S. (ORCID:0000000296317568)↗
DOE OSTI2026

Optimizing time integration for accurate recovery of shockwave interface location in radiography

We present simulations and experiments of time integrated radiographic imaging of a moving 1D shock wave front and a quantitative method for determining the statistical error in locating the shock front as a function of integration time and noise in the radiograph. We discuss the trade-off between increasing motion blur, which leads to decreased shock front location certainty, and increasing signal-to-noise, which leads to improved image quality with increasing integration time. We find an optimum integration time between a short integration time, where noise limits the error, and a long integration time, where motion blurring limits the error. This methodology can be used to tune experimental configurations to obtain the highest quality radiograph for a given experimental configuration.

Bremsstrahlung↗
DOE OSTI2026

Optimizing district energy systems by integrating Borehole Thermal Energy Storage Using a Mixed-Integer Linear Programming g-function framework with a Multi-Timescale Rolling Horizon method

Shallow geothermal has gained increasing attention in recent years; however, a reliable framework for its accurate incorporation into large-scale energy system optimization remains lacking. This study proposes a Mixed-Integer Linear Programming (MILP) framework combined with the g-function approach to integrate Borehole Thermal Energy Storage (BTES) technology into energy system optimization. Validation against a Modelica-based reservoir network simulation demonstrates that the proposed framework effectively captures the ground thermal response under varying energy loads and accurately estimates the borefield energy supply. To enhance scalability, a Rolling Horizon with Multi-Timescale (RH-MTS) method is further introduced, reducing computational time by 73 % for the 1-year optimization model with only minor loss of optimality. The framework is demonstrated through the case study of the UC Berkeley campus. Results indicate that BTES is a cost-effective and low-carbon solution: two borefields comprising 382 boreholes can meet 8.0 % and 6.6 % of the total campus heating and cooling demand, respectively, at an average energy rate of 0.70–0.77 USD/kWh and carbon intensity of 0.54 kg-CO2/kWh. Short-term analysis reveals a 35%–65% decline in BTES energy flow after 3–6 months of continuous heating/cooling operation, while long-term simulation shows that annual energy production of BTES can vary by up to 12.0 % after four years before stabilizing. Overall, this study develops a novel optimization framework that couples physics-based g-function method with MILP optimization framework, thereby advancing methodological development for shallow-geothermal integration and providing actionable guidance for BTES deployment in district-energy systems.

Yang, Jiahui↗
DOE OSTI2024

Nanoconfined Interfaces for Highly Selective Separation of Critical Rare Earth Elements

Industrial demand for rare earth elements (REEs) has surged over the past three decades due to their unique properties that support sustainable energy and new technologies. Separating individual REEs is challenging and hazardous, typically done through liquid-liquid extraction. There is an urgent need for environmentally friendly and efficient separation technologies for REEs. Porous materials offer promising advances for sustainable REE separation via ion-selective capture. We hypothesize that REE separation can be efficiently achieved in reactive nanopores, such as Zr(IV) and Cr(III) metal-organic frameworks (MOFs), through surface functionalization. By integrating material synthesis, interfacial chemistry experiments, theory, computation, and machine learning, we gained insights into the chemical factors controlling REE speciation and their competitive adsorption on MOFs. Our findings show that these materials’ selectivity can be tuned by surface functionalization. The machine learning component addressed ion-specific diffusion based on MOF topology and chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2026

High-order cumulants and correlation functions near the critical point from molecular dynamics

We present a systematic investigation of particle-number fluctuations in the crossover region near the critical end point of a first-order phase transition using molecular dynamics simulations of the classical Lennard-Jones fluid. We extend our prior studies to third- and fourth-order cumulants in both coordinate- and momentum-space acceptances and integrated correlation functions (factorial cumulants). We find that, even near the critical point, non-Gaussian cumulants equilibrate on timescales comparable to those of the second-order cumulants, but show stronger finite-size effects. The presence of interactions and of the critical point leads to strong deviations of the cumulants from the ideal-gas baseline in coordinate space; these deviations are expected to persist in momentum space in the presence of collective expansion. In particular, the kurtosis becomes strongly negative, κσ 2 ≪ − 1 , on the crossover side of the critical point. However, this signal is significantly diluted once an efficiency cut used to distinguish protons from baryons is applied, leading to |κσ 2 | ≲ 1 even in the presence of the critical point. We discuss our results in the context of ongoing measurements of proton-number cumulants in heavy-ion collisions in RHIC-BES-II.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Materializing Inter-Channel Relationships With Multi-Density Woodcock Tracking

Volume rendering techniques for scientific visualization have recently shifted toward Monte Carlo (MC) methods for their flexibility and robustness, but their use in multi-channel visualization remains underexplored. Traditional multi-channel volume rendering often relies on arbitrary, non-physically based color blending functions that hinder interpretation. Here, we introduce multi-density Woodcock tracking, a simple extension of Woodcock tracking that leverages an MC method to produce high-fidelity, physically grounded multi-channel renderings without arbitrary blending. By generalizing Woodcock’s distance tracking, we provide a unified blending modality that also integrates blending functions from prior works. We further implement effects that enhance boundary and feature recognition. By accumulating frames in real-time, our approach delivers high-quality visualizations with perceptual benefits, demonstrated on diverse datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Advanced and functional composite materials via additive manufacturing: Trends and perspectives

Additive manufacturing (AM) has many advantages over conventional subtractive manufacturing methods. The cost-effective AM allows for precise fabrication of complex structures with less material waste, making it a popular manufacturing process in many applications. The recent development of AM materials has advanced significantly. Further, by precisely controlling material distribution and microstructural features, AM facilitates the creation of new composites with specific requirements. AM techniques have contributed considerably to integrating unique functionalities for various applications. This review emphasizes multiple categories of materials, including metal alloys, polymer-based composites, and sustainable composites, as well as applications of sensing materials and strategies and emerging artificial intelligence and machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development of a High-Rate Lithium-Air Battery Using a Gaseous CO 2 Reactant

Li-air batteries are considered a potential alternative to Li-ion batteries for transportation applications due to their high theoretical specific energy. Most works in this area focus on use of O 2 as the reactant. However, newer concepts for using gaseous reactants (such as CO 2 , which has a theoretical specific energy density of 1,876 Wh/kg) provide opportunities for further exploration. The main objective of this project was the development of a novel strategy that enables operation of Li-CO 2 batteries at high-capacity and high-rate, with a long-cycle-life. The team was able to: (1) Synthesize two novel transition metal chalcogenide (TMC) catalysts that work in synergy with ionic liquid-based electrolytes to enhance the efficiency of reactions during discharge and charge processes; (2) Fabricate high-porosity cathode electrodes with 3D printing to increase electrode surface area and gas permeability; (3) Develop a multiscale modeling framework that integrates Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties of Li-CO 2 batteries; (4) Assemble a stackable Li-CO 2 pouch-cell able to deliver a capacity of >200 mAh. These achievements were realized through an integrated approach based on materials synthesis, testing, characterization, analysis, and computation. This project produced a thorough understanding of key chemical, electronic, and kinetic parameters that govern the operation of Li- CO 2 batteries in realistic conditions. The methodologies employed, and the insight generated, will be valuable beyond advancing the field of Li-CO 2 batteries

25 ENERGY STORAGE↗
DOE OSTI2024

Mode-multiplexed photonic integrated vector dot-product core from inverse design

Photonic computing has the potential to harness the full degrees of freedom (DOFs) of the light field, including the wavelength, spatial mode, spatial location, phase quadrature, and polarization, to achieve a higher level of computing parallelism and scalability than digital electronic processors. While multiplexing using the wavelength and other DOFs can be readily integrated on silicon photonics platforms with compact footprints, conventional mode-division multiplexed (MDM) photonic designs occupy areas exceeding tens to hundreds of microns for a few spatial modes, significantly limiting their scalability. Here, we utilize inverse design to demonstrate an ultracompact photonic computing core that calculates vector dot products based on MDM coherent mixing. Our dot-product core integrates the functionalities of two-mode multiplexers and one multimode coherent mixer within a nominal footprint of 5 μm x 3 μm . We have experimentally demonstrated computing examples on the fabricated dot-product core, including complex number multiplication and motion estimation using optical flow. The compact dot-product core design enables large-scale on-chip integration in a parallel photonic computing primitive cluster for high-throughput scientific computing and computer vision tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Island influences on plant functional traits and trait–trait associations across species‐ and community‐scales

The island rule predicts gigantism or dwarfism in body size of island species relative to their mainland counterparts. However, whether other functional traits shift and whether trait–trait associations on islands differ between species and community levels remains unclear. We measured 13 carbon- and water-related functional traits in 37 shared tree species across 35 eastern Chinese islands and 66 nearby mainland plots. We examined species-level trait value shifts and associations under the island rule and compared trait associations between species and communities. Most size-related, wood-anatomical, and hydraulic traits shifted on islands, with large values decreasing and small values increasing; yet, their associations remained stable, aligning with the global trait spectrum and trait–trait coevolution. This stability, despite trait value shifts, suggests evolutionary integration of functional strategies. By contrast, island community-scale trait associations diverged from shared species-level patterns and sometimes reversed, such as positive relationships between wood density and resource-acquisitive traits. Community-level trait associations were stronger on islands, likely reflecting constrained environmental filtering and migration limitation. These contrasting patterns suggest that dominant species can restructure trait associations at the community level, with implications for ecosystem functioning and carbon storage, thereby advancing understanding of plant trait strategies in island systems.

Archipelagos↗
DOE OSTI2025

CANA v1.0.0: efficient quantification of canalization in automata networks

The biomolecular networks underpinning cell function exhibit canalization, or the buffering of fluctuations required to function in a noisy environment. We present a new major release of $\tt{CANA}$, v1.0.0, an open-source Python package for understanding canalization in automata network models, discrete dynamical systems in which activation of biomolecular entities (e.g. transcription of genes) is modeled as the activity of coupled automata. One understudied putative mechanism for canalization is the functional equivalence of biomolecular regulators (e.g. among the transcription factors for a gene). We study this mechanism using the theory of symmetry in discrete functions. We present a new exact method, $\tt{schematodes}$, for finding maximal symmetry groups among the inputs to discrete functions, and integrate it into $\tt{CANA}$. The $\tt{schematodes}$ method substantially outperforms the inexact method of previous $\tt{CANA}$ versions both in speed and accuracy. We apply $\tt{CANA}$ v1.0.0 to study symmetry in 74 experimentally supported automata network models from the Cell Collective (CC) repository. The symmetry distribution is significantly different in the CC than in random automata with the same in-degree (connectivity) and bias (average output) (Kolmogorov–Smirnov test, P ≪ .001). Its spread is much wider than in a null model (IQR 0.31 versus IQR 0.20 with equal medians), demonstrating that the CC is enriched in functions with extreme symmetry or asymmetry.

Boolean networks↗
DOE OSTI2025

Hierarchical Hybrid Multifunctional Materials through Interface Engineering

This project focuses on the development of stimuli-responsive hybrid multifunctional materials. We place emphasis on the design, synthesis, structural characterization, evaluation of functional properties (electronic, thermal and optical) of several (1-x)Cu 2 Se/(x)WBGS hierarchical bulk composites between Cu 2 Se, a narrow band gap semiconductor (NBGS), with a range of wider band gap semiconductors (WBGS) such as CuMSe 2 (M = Al, Ga, In, Fe, Cr) and Cu 4 TiSe 4 . Cu2Se is a well-studied NBGS with excellent thermoelectric properties (high electrical conductivity, large thermopower, etc.) while CuMSe 2 and Cu 4 TiSe 4 are high performance solar absorber materials (large band gap, large absorption coefficient, etc.). Our primary objectives are (i) to demonstrate the ability to integrate dissimilar functional properties such as large optical absorption coefficient and high electronic conductivity, within (1-x)Cu 2 Se/(x)WBGS composite; and (ii) to establish the correlation between the hierarchical structural entanglement of Cu 2 Se with WBGS (CuMSe 2 or Cu 4 TiSe 4 ) phase, the interactions between native electronic defects within the coexisting phases in the resulting (1-x)Cu 2 Se/(x)WBGS bulk composites , and the impacts on their electronic conductivity, thermal transport and optical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High Precision, High Frequency Printed Antennas

An emerging trend in advanced manufacturing is printed electronics and sensors. The ability to print customized electronics and sensors integrated into functional packages is a growing need within a variety of growing markets such as smart manufacturing, internet of things (IoT), and the small satellite industry. Both Oak Ridge National Laboratory (ORNL) and the MITRE Corporation have seedling research efforts evaluating the potential for future printed electronic systems. High frequency, wide-bandwidth phased array antennas (i.e. >45 GHz) open the door to new applications. However, such sensors require currently prohibitively small feature sizes for commercial 3D printing technologies along with increasing challenges with connecting the driving electronics to such features. An additional finding with related advanced manufacturing challenges is the rapid production of 3D additive connectors for integration with commercial printed circuit boards (PCBs), primarily for advanced in-circuit inspection techniques. This work is developing additive manufacturing processes for producing connected and conductive fine scale 3D features. The primary focus was on aerosol-jet printing (AJP), which has a small minimum resolution (<50 µm) but is traditionally printed flat with small height/width aspect ratios <<1, and developing controls to enable fully 3D, high aspect ratio, and unsupported features. In Phase 1 of this effort, baselines of process performance were characterized, and test coupons produced for both ultra-high frequency antennas and microstructures to support reverse engineering of PCBs. In Phase 2, these efforts will be extended for system demonstration of ultra-high frequency antenna arrays, as well as reverse engineering circuitry for dense PCBs.

42 ENGINEERING↗
DOE OSTI2025

In Situ MOF Pyrolysis Construction of Hierarchical Porous Co‐Nanoparticles/Carbon Cloth Composites for Enhanced Electromagnetic Wave Shielding and Absorption

The development of high-performance electromagnetic protection materials integrating broadband absorption and effective shielding capabilities is hindered by challenges in simultaneously optimizing multiple electromagnetic properties through conventional material designs. This study pioneers a hierarchical porous Co nanoparticle/carbon cloth (Co/CC) composite via controlled annealing of a Co-MOF precursor on carbon cloth. The Co-MOF served a dual role as both magnetic source and pore-forming agent, enabling in situ generation of uniformly dispersed Co nanoparticles and creation of abundant pores/interfaces on the CC fibers during pyrolysis. This unique architecture synergistically enhanced dielectric loss (via interfacial/dipolar polarization) and magnetic loss (via natural resonance, exchange interactions, and eddy currents), significantly improving impedance matching. The hierarchical pores further functioned as integrated “absorption–reflection” units for efficient electromagnetic energy attenuation. Consequently, the Co/CC composite annealed at 800°C (Co/CC-800) achieves minimum reflection loss (−40.69 dB) and 120% effective absorption bandwidth extension (6.16 GHz) as a filler, and exhibits superior electromagnetic interference shielding effectiveness (46.66 dB) as an integrated component. Significantly, Co/CC-800 demonstrated robust photothermal and electrothermal conversion capabilities, ensuring operational stability in ice-covered and humid harsh environments. This work pioneers a pore-structure-mediated strategy to harmonize dielectric–magnetic synergy, providing a new paradigm for designing advanced multifunctional electromagnetic protection materials.

dielectric‐magnetic synergy↗
DOE OSTI2025

Nitrogen limitation causes a seismic shift in redox state and phosphorylation of proteins implicated in carbon flux and lipidome remodeling in Rhodotorula toruloides

Background: Oleaginous yeast are prodigious producers of oleochemicals, offering alternative and secure sources for applications in foodstuff, skincare, biofuels, and bioplastics. Nitrogen starvation is the primary strategy used to induce oil accumulation in oleaginous yeast as part of a global stress response. While research has demonstrated that post-translational modifications (PTMs), including phosphorylation and protein cysteine thiol oxidation (redox PTMs), are involved in signaling pathways that regulate stress responses in metazoa and algae, their role in oleaginous yeast remain understudied and unexplored. Results: Towards linking the yeast oleaginous phenotype to protein function, we integrated lipidomics, redox proteomics, and phosphoproteomics to investigate Rhodotorula toruloides under nitrogen-rich and starved conditions over time. Our lipidomics results unearthed interactions involving sphingolipids and cardiolipins with ER stress and mitophagy. Our redox and phosphoproteomics data highlighted the roles of the AMPK, TOR, and calcium signaling pathways in regulation of lipogenesis, autophagy, and oxidative stress response. As a first, we also demonstrated that lipogenic enzymes including fatty acid synthase are modified as a consequence of shifts in cellular redox states due to nutrient availability. Conclusions: We conclude that lipid accumulation is largely a consequence of carbon rerouting and autophagy governed by changes to PTMs, and not increases in the abundance of enzymes involved in central carbon metabolism and fatty acid biosynthesis. Our systems-level approach sets the stage for acquiring multidimensional data sets for protein structural modeling and predicting the functional relevance of PTMs using Artificial Intelligence/Machine Learning (AI/ML). Coupled to those bioinformatics approaches, the putative PTM switches that we delineate will enable advanced metabolic engineering strategies to decouple lipid accumulation from nitrogen limitation.

Lipid Signalling↗
DOE OSTI2026

Innovating Distributed Embedded Energy Prize (InDEEP): A Lessons Learned Report

The U.S. Department of Energy's Water Power Technologies Office (WPTO) launched the Innovating Distributed Embedded Energy Prize (InDEEP) in March 2023 to accelerate innovation in Distributed Embedded Energy Conversion Technologies (DEEC-Tec) for ocean wave energy. Administered by the National Laboratory of the Rockies (NLR) with technical support from Sandia National Laboratories (SNL), InDEEP focused on the development of small, distributed, and embeddable energy converters (DEECs) and their integration into scalable DEEC-Tec metamaterials for marine renewable energy applications. Spanning three phases over two years, InDEEP awarded approximately $2.3 million to teams from academia, industry, and startups. Phase I emphasized conceptual design. Phase II moved into the prototyping of individual DEECs. Phase III required integration into functional DEEC-Tec metamaterial prototypes. Across 60 submissions, teams explored a wide range of energy conversion mechanisms - including piezoelectric, variable-capacitance, ionic, and inductive methods. Note, the prize did not include the design nor demonstration of ocean wave energy conversion systems. Rather, the prize only required participants to design and demonstrate individual DEECs and corresponding DEEC-Tec metamaterials. This prize utilized a mix of novel and proven techniques to attract participants from outside marine energy, including an engagement leaderboard, robust recruitment, technical expert mentorship, and a suite of technical trainings. Key insights from the competition emphasized that DEEC-Tec metamaterials must be intentionally designed to produce beneficial emergent behaviors - advantages that go beyond simply combining multiple DEEC units. Top-performing teams showed that thoughtful design of system architecture, coordinated deformation, and systems adaptabilities could unlock meaningful performance gains both at the DEEC system level and DEEC-Tec metamaterial system level. A critical realization was that many DEEC-Tec metamaterials could benefit from being designed to accept lower-frequency energy inputs and shift those into higher-frequencies per each DEEC making up the respective DEEC-Tec metamaterial. Other important takeaways included the need for rigorous and quantitative performance testing, effective integration of power conditioning electronics, and the pivotal role of material science in enabling innovative, adaptive DEEC-Tec-based energy conversion designs. InDEEP also helped establish a growing DEEC-Tec community of practitioners, attracting participants from beyond traditional marine energy sectors. Through a strong support infrastructure, InDEEP fostered early-stage innovation and laid a foundation for future DEEC-Tec-based ocean wave energy conversion solutions - positioning DEEC-Tec as a promising pathway toward scalable, resilient ocean wave energy conversion. Through focused R&D of individual DEECs and their integration into DEEC-Tec metamaterials, alongside a growing, multidisciplinary community catalyzed by InDEEP, there is a strong opportunity to drive a disruptive shift in ocean wave energy conversion design and development. This convergence of novel architectures, emergent behaviors, and collaborative innovation positions DEEC-Tec as a transformative approach, moving the field from rigid, centralized energy conversion-based designs to resilient, modular systems highly adaptable for real-world ocean wave energy conversion applications.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

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 La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 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.

active reactance↗