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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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Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

36 MATERIALS SCIENCE

Reconfigurable structural color by reversible switching of colloidal discoid liquid crystal alignment

Combining the effects of alternating-current (AC) electric fields and sedimentation reconfigures colloidal discoids between planar and homeotropic orientational alignments, thereby controlling the material’s structural color. Here, we self-assemble micrometer-size polystyrene discoids in an isopropanol-water mixture. After sedimentation, the discoids adopt a homeotropic alignment, with the minor axis perpendicular to the substrate. Adding an AC electric field (1 kHz) of ≥0.50 V switches the discoids to planar alignment—with the minor axis parallel to the substrate—within ∼100 s. Removing the field switches them back to homeotropic alignment within ∼300 s. Kinetic modeling of the field-induced torques yields good agreement with these measurements. The peak wavelength and intensity of the crystal’s diffraction response shift significantly upon reconfiguration; scattering simulation predicts these shifts. The reconfiguration is maintained for at least 10 cycles. This method is a simple, scalable avenue to reconfigure the optical properties of colloidal crystals produced from simple dielectric spheroids.

colloidal crystals

Reflection and refraction of directrons at the interface

Reflection and refraction are ubiquitous phenomena with extensive applications, yet minimizing energy loss and information distortion during these processes remains a significant challenge. This study examines the behavior of structurally stable solitons, known as directrons, in nematic liquid crystals interacting with an interface where the director field orientation changes, despite identical physical properties, external potentials, and boundary anchoring in the two regions. During reflection and refraction, the directrons maintain nearly constant structure and velocity, ensuring energy conservation and information integrity. Microscopic analyses of the director field and macroscopic evaluations of effective potential are employed to elucidate the dependence of reflection and refraction probabilities on the directron’s incident angle and the orientation difference across the interface. The findings provide valuable insights into the dynamics of solitary waves in structured liquid crystal systems, offering significant implications for the development of tunable photonic devices, reconfigurable optical systems, and nanoscale material engineering.

Science & Technology - Other Topics

Asymmetric fluctuations and self-folding of active interfaces

We study the structure and dynamics of the interface separating a passive fluid from a microtubule-based active fluid. Turbulent-like active flows power giant interfacial fluctuations, which exhibit pronounced asymmetry between regions of positive and negative curvature. Experiments, numerical simulations, and theoretical arguments reveal how the interface breaks up the spatial symmetry of the fundamental bend instability to generate local vortical flows that lead to asymmetric interface fluctuations. The magnitude of interface deformations increases with activity: In the high activity limit, the interface self-folds invaginating passive droplets and generating a foam-like phase, where active fluid is perforated with passive droplets. These results demonstrate how active stresses control the structure, dynamics, and break-up of soft, deformable, and reconfigurable liquid–liquid interfaces.

active fluid

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver

Multitarget Rydberg gates via spatial blockade engineering

Multi-target gates offer the potential to reduce gate depth in syndrome extraction for quantum error correction. Although neutral-atom quantum computers have demonstrated native multi-qubit gates, existing approaches that avoid additional control or multiple atomic species have been limited to single-target gates. We propose single-control-multi-target CZ^n gates on a single-species neutral-atom platform that require no extra control and have gate durations comparable to standard CZ gates. Our approach leverages tailored interatomic distances to create an asymmetric blockade between the control and target atoms. Using a GPU-accelerated pulse synthesis protocol, we design smooth control pulses for CZZ and CZZZ gates, achieving fidelities of up to 99.55% and $99.24\%$, respectively, even in the presence of simulated atom placement errors and Rydberg-state decay. Our approach is most effective for N=2 (CZZ) and N=3 targets (CZZZ); for larger N, increasing spatial crowding of the targets introduces significant challenges for maintaining the required blockade asymmetry. This work presents a practical path to implementing low-overhead multi-target gates in single-species neutral-atom systems, significantly reducing the resource overhead for syndrome extraction. To motivate the impact of these gates, we apply a greedy scheduling algorithm and we demonstrate that our proposed gates can reduce the number of atom reconfiguration costs by up to 50% for color code syndrome extraction of code distances greater than 5.

Stein, Samuel A.

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics

Adding multiple electrons to helicenes: how they respond?

An overview of structural responses of helicenes with increasing dimensions and complexity to stepwise electron addition reveals charge- and topology-dependent outcomes ranging from reversible to irreversible core transformations and site-specific reactivity.

Chemistry

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Elucidation of Design Criteria for V‐based Redox Mediators: Structure‐Function Relationships that Dictate Rates of Heterogeneous Electron Transfer

Redox mediators are attractive solutions for addressing the stringent kinetic stipulations required for efficient energy conversion processes. In this work, we compare the electrochemical properties of four vanadium complexes, namely [V(acac) 3 ], [V 6 O 7 (OMe) 12 ], [ n Bu 4 N] 3 [V 6 O 13 (TRIS NO2 ) 2 ], and [ n Bu 4 N] 5 [V 18 O 46 (NO 3 )] in non-aqueous solutions on glassy carbon electrodes. The goal of this study is to investigate the electron transfer kinetics and diffusivity of these compounds under identical experimental conditions to develop an understanding of structure-function relationships that dictate the physicochemical properties of vanadium oxide assemblies. Complex selection was dictated by two criteria – (1) nuclearity of the transition metal complexes (2) distribution of electron density in the native electronic configuration. In conclusion, our analyses establish that electronic communication between metal centers significantly impacts charge transfer kinetics of these vanadium-based compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Bridging Additive Manufacturing and Electronics Printing in the Age of AI

Printing techniques have been instrumental in developing flexible and stretchable electronics, including organic light-emitting diode displays, organic thin film transistor arrays, electronic skins, organic electrochemical transistors for biosensors and neuromorphic computing, as well as flexible solar cells with low-cost processes such as inkjet printing, ultrasonic nozzle, roll-to-roll coating. The rise of additive manufacturing provides even more opportunities to print electronics in automated and customizable ways. In this work, we will review the current technologies of printing electronics (including printed batteries, supercapacitors, fuel cells, and sensors), especially with 3D printing. In this age of ongoing AI revolution, the application of AI algorithms is discussed in terms of combining them with 3D printing and electronics printing for a future with automated optimization, sustainable design, and customizable and scalable manufacturing.

Chemistry

The Electron‐Density Distribution of UCl 4 and Its Topology from X‐ray Diffraction

Abstract The chemistry of electrons in actinide complexes and materials is still poorly understood and represents a serious challenge and opportunity for experiment and theory. The study of the electron density distribution of the ground state of such systems through X‐ray diffraction represents a unique opportunity to quantitatively investigate different chemical bonding interactions at once, but was considered “almost impossible” on heavy‐atom systems, until very recently. Here, we present a combined experimental and theoretical investigation of the electron density distribution in UCl_ 4 crystals and comparison with the previously reported spin density distribution from polarized neutron diffraction. All approaches provide a consistent picture in terms of electron and spin density distribution, and chemical bond characterization. More importantly, the synergy between experiments and quantum‐mechanical calculations allows to highlight the remarkable sensitivity of X‐ray diffraction to electrons in materials.

Chemistry