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DOE OSTI2023

High-strength, lightweight nano-architected silica

Continuous nanolattices are an emerging class of mechanical metamaterials that are highly attractive due to their superior strength-to-weight ratios, which originate from their spatial architectures and nanoscale-sized elements possessing near-theoretical strength. Rational design of frameworks remains challenging below 50 nm because of limited methods to arrange small elements into complex architectures. Here, we fabricate silica frameworks with ~4- to 20-nm-thick elements using self-assembly and silica templating of DNA origami nanolattices and perform in situ micro-compression testing to examine the mechanical properties. We observe strong effects of lattice dimensions on yield strength (σ $\mathcal{y}$ ) and failure mode. Silica nanolattices are found to exhibit yield strengths higher than those of any known engineering materials with similar mass density. The robust coordination of the nanothin and strong silica elements leads to the combination of lightweight and high-strength framework materials offering an effective strategy for the fabrication of nanoarchitected materials with superior mechanical properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Sub-wavelength optical lattice in 2D materials

Recently, light-matter interaction has been vastly expanded as a control tool for inducing and enhancing many emergent nonequilibrium phenomena. However, conventional schemes for exploring such light-induced phenomena rely on uniform and diffraction-limited free-space optics, which limits the spatial resolution and the efficiency of light-matter interaction. Here, we overcome these challenges using metasurface plasmon polaritons (MPPs) to form a sub-wavelength optical lattice. Specifically, we report a “nonlocal” pump-probe scheme where MPPs are excited to induce a spatially modulated AC Stark shift for excitons in a monolayer of MoSe 2 , several microns away from the illumination spot. We identify nearly two orders of magnitude reduction for the required modulation power compared to the free-space optical illumination counterpart. Moreover, we demonstrate a broadening of the excitons’ linewidth as a robust signature of MPP-induced periodic sub-diffraction modulation. Our results will allow exploring power-efficient light-induced lattice phenomena below the diffraction limit in active chip-compatible MPP architectures.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Effect of Thermodynamic and Environmental Factors on Crystallization of DNA‐Origami Superlattices

The directed self‐assembly of nanoscale materials into ordered superlattices presents a powerful strategy for creating next‐generation materials with programmable mechanical, optical, and photonic properties. Deoxyribonucleic acid (DNA) origami has emerged as a versatile scaffold for encoding nanoscale geometry and guiding the crystallization of complex 3D architectures. However, a systematic understanding of the parameters that govern the efficiency and quality of superlattice formation remains limited. In this study, we utilize octahedral DNA nanoscale frames as a model system to investigate the relative influence of key factors, including buffer composition, ionic strength, frame concentration, and thermal annealing protocols, on the size, order, and reproducibility of the resulting superlattices. Our findings provide a quantitative framework to rationally optimize DNA‐based assembly pathways. Structural characterization via small‐angle x‐ray scattering (SAXS), scanning electron microscopy (SEM), and optical microscopy validates the quality and fidelity of the assembled lattices. Moreover, by templating these DNA frameworks into inorganic replicas, we establish general design principles that extend beyond biomolecular systems, providing a foundation for the synthesis of programmable materials in broader nanofabrication contexts.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

Amorphization of Pseudocapacitive T−Nb 2 O 5 Accelerates Lithium Diffusivity as Revealed Using Tunable Isomorphic Architectures

Abstract Intercalation pseudocapacitance can combine capacitor‐like power densities with battery‐like energy densities. Such surface‐limited behavior requires rapid diffusion where amorphization can increase solid‐state diffusivity. Here intercalation pseudocapacitive materials with tailored extents of amorphization in T‐Nb 2 O 5 are first reported. Amorphization was characterized with WAXS, XPS, XAFS, and EPR which suggested a peroxide‐rich (O 2 2− ) surface that was consistent with DFT predictions. A series of tunable isomorphic architectures enabled comparisons while independently varying transport parameters. Through process of elimination, solid‐state lithium diffusion was identified as the dominant diffusive‐constraint dictating the maximum voltage sweep rate for surface‐limited kinetics ( v SLT ), termed the Surface‐Limited Threshold (SLT). The v SLT increased with amorphization however stable cycling required crystalline T‐Nb 2 O 5 . A current‐response model using series‐impedances well‐matched these observations. This perspective revealed that amorphization of T‐Nb 2 O 5 enhanced solid‐state diffusion by 12.2 % and increased surface‐limitations by 17.0 % (stable samples). This approach enabled retaining 95 % lithiation capacity at ∼800 mV s −1 (1,600 C‐rate equivalent).

25 ENERGY STORAGE↗

DOE OSTI2025

Encoding hierarchical 3D architecture through inverse design of programmable bonds

The ability to fabricate materials and devices at small scales by design has resulted in tremendous technological progress. However, the need for engineered three-dimensional (3D) nanoscale materials requires new strategies for organizing nanocomponents. Here we demonstrate an inverse design approach for the assembly of nanoparticles into hierarchically ordered 3D organizations using DNA voxels with directional, addressable bonds. By identifying intrinsic symmetries in repeating mesoscale structural motifs, we prescribe a set of voxels, termed a mesovoxel, that are assembled into target 3D crystals. The relationship between different degrees of encoded information used for voxel bonds and the fidelity of assembly is investigated using experimental and computational methods. We apply this assembly strategy to create periodic 3D nanoparticle ordered organizations, including structures with low-dimensional elements, helical motifs, a nanoscale analogue of a face-centred perovskite crystal and a distributed Bragg reflector based on a crystal with plasmonic and photonic length-scale regimes.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Substrate effects on phase formation and interfacial stability in superconducting vanadium silicide thin films

Thin films of vanadium silicide (V-silicide) in the A15 cubic phase (V 3 Si) are promising for superconducting quantum devices due to their high transition temperature and potential compatibility with scalable semiconductor fabrication. However, solid-phase synthesis often yields secondary silicide phases that degrade performance. Here, in this study, we investigate the influence of substrate properties using two silicon-on-insulator architectures: one with a polycrystalline HfO 2 buried layer (group A) and the other with amorphous SiO 2 (group B). Both systems exhibit superconductivity consistent with V 3 Si formation, yet structural analysis reveals mixed-phase films in both cases. Crucially, only group A maintains an atomically sharp and chemically stable interface, a prerequisite for phase purity. Prolonged annealing in group A reduces the unwanted V 5 Si 3 phase but also leads to the emergence of Si-rich VSi 2 , likely due to localized substrate degradation. Preliminary atomic-resolution imaging suggests that HfO 2 crystallinity may promote local phase-selective nucleation. These findings highlight the importance of substrate design in promoting phase control and maintaining interfacial integrity in superconducting silicides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Picoseconds-Limited Exciton Recombination in Metal–Organic Chalcogenides Hybrid Quantum Wells

Metal-organic species can be designed to self-assemble in large-scale, atomically defined, supramolecular architectures. Hybrid quantum wells, where inorganic two-dimensional (2D) planes are separated by organic ligands, are a particular example. The ligands effectively provide an intralayer confinement for charge carriers resulting in a 2D electronic structure, even in multilayered assemblies. Air-stable metal organic chalcogenides hybrid quantum wells have recently been found to host tightly bound 2D excitons with strong optical anisotropy in a bulk matrix. Here, we investigate the excited carrier dynamics in the prototypical metal organic chalcogenide [AgSePh] ∞ , disentangling three excitonic resonances by low temperature transient absorption spectroscopy. Our analysis suggests a complex relaxation cascade comprising ultrafast screening and renormalization, inter-exciton relaxation, and self-trapping of excitons within few picoseconds. Finally, the ps-decay provided by the self-trapping mechanism may be leveraged to unlock the material's potential for ultrafast optoelectronic applications.

2D excitons↗

DOE OSTI2025

Implementation of scalable suspended superinductors

Superinductors have become a crucial component in the superconducting circuit toolbox, playing a key role in the development of more robust qubits. Enhancing the performance of these devices can be achieved by suspending the superinductors from the substrate, thereby reducing stray capacitance. Here, we present a fabrication framework for constructing superconducting circuits with suspended superinductors in planar architectures. To validate the effectiveness of this process, we systematically characterize both resonators and qubits with suspended arrays of Josephson junctions, ultimately confirming the high quality of the superinductive elements. In addition, this process is broadly compatible with other types of superinductors and circuit designs. Furthermore, our results not only pave the way for scalable superconducting architectures utilizing superinductors but also provide the primitive for future investigation of loss mechanisms associated with the device substrate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Atomically Fine-Tuning Organic–Inorganic Carbon Molecular Sieve Membranes for Hydrogen Production

Polymeric membranes with great processability are attractive for the H 2 /CO 2 separation required for hydrogen production from renewable biomass with carbon capture for utilization and sequestration. However, it remains elusive to engineer polymer architectures to obtain desired sub-3.3 Å ultramicropores to efficiently sieve H 2 from CO 2 . Herein, we demonstrate a scalable way of carbonizing polybenzimidazole (PBI) at low temperatures, followed by vapor phase infiltration (VPI) to atomically narrow ultramicropores throughout the films, forming hybrid organic–inorganic carbon molecular sieves (CMSs). One VPI cycle (100 s) for the PBI carbonized at 500 °C remarkably increases H 2 /CO 2 selectivity from 9.6 to 83 at 100 °C, surpassing Robeson’s upper bound. The CMS demonstrates a stable H 2 /CO 2 separation performance when challenged with simulated syngas streams and can be fabricated into thin-film composite membranes, outperforming state-of-the-art membranes. Finally, the scalable approach can be ubiquitous to molecularly fine-tune ultramicropores of leading polymeric membranes to further improve their size-sieving ability and thus separation efficiency.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

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

Neuromorphic↗