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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

From dynamics to kinetics for the dissociation of •QOOH radicals derived from isopentane

Thermal dissociation rates for hydroperoxyalkyl (•QOOH) radicals are challenging to measure due to their inherent instability. Recent dynamics experiments have demonstrated that in situ synthesis of •QOOH radicals in a supersonic expansion in combination with IR action spectroscopy provides an effective route to accurately map their energy-resolved dissociation rates. Collaborative theoretical analyses provide a route for converting from those microcanonical rates to accurate temperature- and pressure-dependent thermal rate constants. Here, we present the results of this conversion for the dissociation of two isopentyl β-QOOH isomers of relevance to isopentane oxidation. The results are compared with existing literature values, as well as with results obtained in a similar fashion for related •QOOH systems. Extrapolations from benchmark calculations of barrier properties for smaller systems to accurate predictions for larger systems and multidimensional hindered rotor treatments are both shown to be important components of the theoretical analysis. For completeness, we extend the theoretical analysis to a treatment of the full RO 2 /QOOH system for isopentyl (1,1-dimethyl-propyl) radical.

Ab initio kinetics↗
DOE OSTI2025

Data-Driven Mapping of the Cesium Cadmium Bromide Phase Space Utilizing a Soft-Chemistry Approach

Soft-chemistry techniques provide a versatile approach to synthesizing inorganic materials under mild conditions, enabling access to compositions and structures that are challenging to achieve through traditional thermodynamically driven solid-state methods. However, these solution-based routes often result in phase competition, requiring precise control over reaction conditions to achieve selective product formation. While one-variable-at-a-time (OVAT) approaches have traditionally been used for phase selection, data-driven strategies are emerging as more efficient methods for navigating complex synthetic spaces. Ternary metal halides, such as cesium cadmium bromides (Cs–Cd–Br), are of growing interest due to their potential in wide and ultrawide band gap applications. Unlike the well-studied cesium lead halide phases, the compositional diversity and solution-based synthesis of ternary Cs–Cd–Br phases remain largely unexplored. This study systematically investigates the synthetic phase space of the Cs–Cd–Br system by constructing a data-driven phase map. Using a common set of precursors and a standardized experimental procedure, we successfully synthesize all four known Cs–Cd–Br phases—CsCdBr 3 , Cs 2 CdBr 4 , Cs 3 CdBr 5 , and Cs 7 Cd 3 Br 13 —each exhibiting distinct structures, morphologies, and optical properties. Our findings highlight the potential of soft-chemistry methods for expanding the library of ternary metal halides and provide key insights into the thermodynamic and kinetic factors governing phase formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗
DOE OSTI2025

Causal discovery from data assisted by large language models

Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

Causal inference↗
DOE OSTI2025

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Aridity and forest age mediate landscape scale patterns of tropical forest resistance to cyclonic storms

Abstract Cyclonic storms, or hurricanes, are expected to intensify as ocean heat energy rises due to climate change. Ecological theory suggests that tropical forest resistance to hurricanes should increase with forest age and wood density. However, most data on hurricane effects on tropical forests come from a limited number of well‐studied long‐term monitoring sites, restricting our capacity to evaluate the resistance of tropical forests to hurricanes across broad environmental gradients. In this study, we assessed whether forest age and aridity mediate the effects of hurricanes Irma and Maria in Puerto Rico, Vieques and Culebra islands. We leveraged functional trait data for 410 tree species, remotely sensed measurements of canopy height and cover, along with data on forest stand characteristics of 180 of 338 forest monitoring plots, each covering an area of 0.067 ha. The plots represent a broad mean annual precipitation (MAP) gradient from 701 to 4598 mm and a complex mosaic of forest age from 5 to around 85 years since deforestation. Hurricanes resulted in a 25% increase in basal area mortality rates, a 45% decrease in canopy height and a 21% reduction in canopy cover. These effects intensified with forest age, even after considering proximity to the hurricane path. The links between forest age and hurricane disturbances were likely due the prevalence of tall canopies. Tall forest canopies were strongly linked with low community‐weighted wood density (WD). These characteristics were on average more common in moist and wet forests (MAP >1250 mm). Conversely, dry forests were dominated by short species with high wood density (WD > 0.6 g cm −3 ) and did not show significant increases in basal area mortality rates after the hurricanes. Synthesis . Our findings show that selection towards drought‐tolerant traits across aridity gradients, such as short stature and dense wood, enhances resistance to hurricanes. However, forest age modulated responses to hurricanes, with older forests being less resistant across the islands. This evidence highlights the importance of considering the intricate links between ecological succession and plant function when forecasting tropical forests’ responses to increasingly strong hurricanes.

Vargas G., German↗
DOE OSTI2025

X-ray Diffraction Studies of Single-Crystal Materials for Broad Battery Applications

Single-crystal materials have attracted growing interest in battery research due to their well-defined crystallographic orientation, absence of grain boundaries, and enhanced mechanical and electrochemical stability. This Review provides a comprehensive overview of recent advances in the synthesis, structural evolution, and performance optimization of single-crystal electrodes and solid electrolytes. Particular focus is placed on the application of advanced X-ray diffraction (XRD) techniques, including operando synchrotron diffraction, reciprocal space mapping, and Bragg coherent diffraction imaging, which have enabled in-depth investigations of lattice strain, cation disorder, phase transitions, and defect formation. Representative case studies across Ni-rich layered oxides, spinel-type cathodes, and garnet-based electrolytes are examined to highlight the structural features unique to single crystals. Additionally, the synergistic integration of XRD with machine learning, tomography, and spectroscopy is discussed as a powerful direction for real-time analysis and predictive modeling. Furthermore, these insights provide critical guidance for the rational design of high-performance single-crystal materials in lithium, sodium, and solid-state battery systems.

25 ENERGY STORAGE↗
DOE OSTI2026

Data‐Efficient Generation of Synthetic Microstructures of Polymer‐Bonded Energetic Material With Fine‐Tuned Stable Diffusion

Among current deep learning approaches for synthetic image generation, diffusion-based models stand out in terms of algorithmic stability and ability to retain high-fidelity image features with detailed resolution. Here, in this work, we employ Dreambooth, a method for fine-tuning Stable Diffusion, on X-ray CT images of microstructure of the polymer-bonded form (PBX) of a commonly used high explosive, Pentaerythritol tetranitrate (PETN), which yields generative models for creating synthetic PBX images. The models developed here represent five classes (or ‘lots’) of microstructures and demonstrate successful generation of images of each class with high fidelity, as verified by computed classification accuracy of ∼ 94% or higher. Data augmentation afforded by such image synthesis can be used to more reliably decipher underlying statistics, build processing-structure correlations, recognize off-normal structural anomalies, and identify age-related changes. Ideas related to converting image data into appropriate density mapping and performing mesoscale simulation or surrogate modeling of detonation are also discussed.

Dreambooth↗
DOE OSTI2024

Using scalable computer vision to automate high-throughput semiconductor characterization

Abstract High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 10 4 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 10 1 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA 1−x MA x PbI 3 , 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis.

Science & Technology - Other Topics↗
DOE OSTI2026

Novel PtNi single-atom–nanocluster (SA–NC) ensembles promote Tafel kinetics and ampere-class AEM hydrogen evolution

The development of efficient, durable, and low-PGM electrocatalysts for the hydrogen evolution reaction (HER) in alkaline media is critical for next-generation electrolysis technologies. We report a facile two-step synthesis of highly dispersed PtNi and PtNi-nitride nanoclusters (NCs) (~2.3 nm) with ultralow Pt content (0.5 at.%) anchored on N-doped Vulcan carbon. Structural and compositional characterization via XAS, XPS, HAADF-STEM, HRTEM, and EDS mapping established key structure–activity relationships across varying Pt/Ni ratios and pyrolysis temperatures. The Pt0.5Ni0.5/C-750 catalyst, an ensemble of PtNi M-N-C type single-atom (SA) moieties with neighboring PtNi nanoclusters (NC), exhibited superior HER performance in alkaline media, achieving overpotentials of 30, 115, and 210 mV at 10, 100, and 500 mA cm−2, respectively. Despite at a lower Pt content, this novel SA–NC ensemble outperformed commercial Pt/C by ~36%. A standardized literature comparison with contemporary Pt- and Ru-doped analogues reveals the as-prepared Pt0.5Ni0.5/C-750 to sit at the apex of Tafel-limited kinetics and low overpotential at 100 mA cm−2. Tafel-limited Tafel slopes in both alkaline and acidic regimes confirm favorable proton recombination kinetics. Mass activities at 200 mV reached 13.8 and 18.84 A mgPt−1 in alkaline and acidic media, respectively. However, excessive nitridation (e.g., at 650 °C) adversely altered Pt electronic structure and HER kinetics. While Ni enhanced alkaline HER, acidic HER favored Ni-free analogues. Pt0.5Ni0.5/C-750 also demonstrated robust temperature responsiveness and 300-h operational stability at high current densities (0.5–1.0 A cm−2) in MEA tests. This work presents a scalable strategy for designing thermally responsive, durable, and compositionally tunable NC catalysts with neighboring SA moieties for alkaline electrolysis.

AEMWE↗
DOE OSTI2024

Decarbonizing nitrogen fertilizer production via the electrochemical nitrogen oxidation reaction

Nitric acid is an important commodity chemical with extensive applications in both agricultural and industrial sectors. However, current production methods involve a combination of the Haber–Bosch and Ostwald processes, which are both energy and carbon emission intensive. The electrochemical nitrogen oxidation reaction (NOR) to produce nitric acid or nitrates shows great potential as an environmentally friendly method for producing fertilizers under mild conditions. The key to progress in this field lies in understanding the fundamental mechanistic insights and establishing robust experimental methods, which is essential for the efficient design and synthesis of electrocatalysts for NOR. Additionally, poor gas mass transport in conventional electrochemical reactors at present lead to lower NOR activity, thereby limiting the progress in this field. In this work, we present a synergistic computational and experimental approach to map out the potential chemical and electrochemical steps and determine the energetics on PtO 2 catalyst to gain mechanistic insights into NOR. Here, this study marks the first attempt to perform NOR in a vapor-fed reactor designed using advanced (additive) manufacturing. The vapor-fed reactor significantly improved the N 2 mass transport to the catalyst, allowing us to report the highest rate for nitrate production to date at 3.3 μmol cm -2 h -1 at 2.01 V vs RHE.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

2D materials↗
DOE OSTI2026

Evolution of Electronic Properties of Graphene Nanoribbons with Progressive Carving: From Straight to Porous to Chevron Ribbons

Graphene nanoribbons (GNRs) are highly versatile materials due to their unique electronic, magnetic, and optical properties, which can be precisely tuned by controlling their width, edge structure, and topology. Here, we report the on-surface synthesis and characterization of a straight N = 15 armchair GNR with periodic annulene nanopores (15-pGNR). It serves as a structural link between two well-established GNRs: the pristine N = 15 armchair GNR without pores (15-AGNR) and the chevron GNR (cGNR). With the addition of the 15-pGNR reported in this study, these three GNRs form a rare experimentally accessible series of ribbons, in which the evolution of electronic properties can be tracked upon progressive carving of a basic 15-AGNR: first, by creating periodic nanopores to form 15-pGNR and then by extending the pore area and producing meandering cGNR. We have designed a molecular precursor for the 15-pGNR and grown the nanoribbons on single-crystal gold substrates by on-surface synthesis in ultra-high vacuum (UHV) conditions. The atomically precise structure of 15-pGNR was confirmed by scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM). The band gap of 15-pGNR was studied by scanning tunneling spectroscopy (STS) and dI/dV mapping, and the occupied electronic levels were investigated by angle-resolved photoemission spectroscopy (ARPES). A theoretical and experimental comparison of 15-pGNRs, 15-AGNRs, and cGNRs demonstrates that the introduction of periodic nanopores into 15-AGNR leads to a more than 2-fold increase in its band gap. In contrast, the band gaps of 15-pGNR and cGNR differ only by about 15%. Such band gap increase can be qualitatively understood to arise from two combined effects, the periodic perforation of the graphene lattice and the confinement effect induced by the GNR width.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Transitory Topochemical Tailoring of a van der Waals Superconductor

Topochemical intercalation is widely used to access metastable phases with novel electronic properties, but the reverse reaction (deintercalation) typically restores the original state, limiting practical use. Here, in this study, we present a topochemical approach that employs a sacrificial intercalant that thermally decomposes to irreversibly lock in the new electronic state. Using 2-aminobutane as the sacrificial intercalant, we convert the van der Waals (vdW) material 1T-TiSe 2 into a superconductor and the vdW superconductor 2H-NbSe 2 into a nonsuperconducting metal, while preserving the ability to exfoliate the resulting crystals. We find that this transitory intercalation increases the electron density in both materials and partially suppresses the CDW in TiSe 2 . By tuning the thermolysis temperature, we can systematically vary the carrier density in TiSe 2 , enabling us to map its phase diagram. The superconductivity in TiSe 2 is retained in exfoliated flakes, although with a lower critical temperature. This transitory topochemical strategy enables access to new electronic states with precisely tuned carrier densities that are otherwise inaccessible through direct solid-state synthesis.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Genesis: A Compiler Framework for Hamiltonian Simulation on Hybrid CV-DV Quantum Computers

We introduce Genesis, the first compiler designed to support Hamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable (DV) quantum computing systems. Genesis is a two-level compilation system. At the first level, it decomposes an input Hamiltonian into basis gates using the native instruction set of the target hybrid CV-DV quantum computer. At the second level, it tackles the mapping and routing of qumodes/qubits to implement long-range interactions for the gates decomposed from the first level. Rather than a typical implementation that relies on SWAP primitives similar to qubit-based (or DV-only) systems, we propose an integrated design of connectivity-aware gate synthesis and beamsplitter SWAP insertion tailored for hybrid CV-DV systems. We also introduce an OpenQASM-like domain-specific language (DSL) named CVDV-QASM to represent Hamiltonian in terms of Pauli-exponentials and basic gate sequences from the hybrid CVDV gate set. Genesis has successfully compiled several important Hamiltonians, including the Bose-Hubbard model, Z2−Higgs model, Hubbard-Holstein model, Heisenberg model and Electron-vibration coupling Hamiltonians, which are critical in domains like quantum field theory, condensed matter physics, and quantum chemistry. Our implementation is available at Genesis-CVDV-Compiler https://github.com/ruadapt/Genesis-CVDV-Compiler

Chen, Henry↗
DOE OSTI2025

Knocking out the carboxyltransferase interactor 1 (CTI1) in Chlamydomonas boosted oil content by fivefold without affecting cell growth

Summary The first step in chloroplast de novo fatty acid synthesis is catalysed by acetyl‐CoA carboxylase (ACCase). As the rate‐limiting step for this pathway, ACCase is subject to both positive and negative regulation. In this study, we identify a Chlamydomonas homologue of the plant carboxyltransferase interactor 1 (CrCTI1) and show that this protein interacts with the Chlamydomonas α‐carboxyltransferase (Crα‐CT) subunit of the ACCase by yeast two‐hybrid protein–protein interaction assay. Three independent CRISPR‐Cas9 mediated knockout mutants for CrCTI1 each produced an ‘enhanced oil’ phenotype, accumulating 25% more total fatty acids and storing up to fivefold more triacylglycerols (TAGs) in lipid droplets. The TAG phenotype of the crcti1 mutants was not influenced by light but was affected by trophic growth conditions. By growing cells under heterotrophic conditions, we observed a crucial function of CrCTI1 in balancing lipid accumulation and cell growth. Mutating a previously mapped in vivo phosphorylation site (CrCTI1 Ser108 to either Ala or to Asp), did not affect the interaction with Crα‐CT. However, mutating all six predicted phosphorylation sites within Crα‐CT to create a phosphomimetic mutant reduced this pairwise interaction significantly. Comparative proteomic analyses of the crcti1 mutants and WT suggested a role for CrCTI1 in regulating carbon flux by coordinating carbon metabolism, antioxidant and fatty acid β‐oxidation pathways, to enable cells to adapt to carbon availability. Taken together, this study identifies CrCTI1 as a negative regulator of fatty acid synthesis in algae and provides a new molecular brick for the genetic engineering of microalgae for biotechnology purposes.

Li, Zhongze [Aix‐Marseille Université, CEA, CNRS, ↗
DOE OSTI2026

Understanding the structural and morphological effects of synthesis route on NpO 2

The availability of actinide standard materials for use in nuclear safeguard applications is critical, as is thorough characterization thereof. Although accurate trace element compositions and isotopic considerations are paramount for deployment of reference standards, structural characterization is also essential towards accurately describing the chemical form and potential matrix effects in candidate materials. Here, to this end, samples of NpO 2 were synthesized via a direct denitration (DD) method and probed with powder X-ray diffraction (PXRD), Raman spectroscopy, and scanning electron microscopy (SEM) for structural and morphological characterization and comparison with NpO 2 materials produced via modified direct denitration (MDD). PXRD confirmed the bulk identity of NpO 2 , and no additional phases were identified using this method. Analysis of Raman data collected using a 532 nm excitation wavelength indicates that samples are mostly phase pure; however, some variability in spectral features is observed. Analysis of additional spectroscopic data collected with a 785 nm excitation wavelength revealed variability in the relative intensity of spectral features. Raman spectroscopy indicates that the sample is primarily NpO 2 ; however, additional signals indicate possible structural disorder, oxidized species, or potential contributions from other Np phases. To further investigate the possibility of additional phase contributions within the sample of NpO 2 , Raman spectroscopic mapping was employed to examine the homogeneity of the sample produced via DD. From this analysis, we determined that despite variability in the intensity of Raman-active vibrational modes, consistent spectra are obtained throughout the area of the sample investigated. SEM images show aggregates with variable sizes and shapes, with rounded, primary particles possessing an average diameter of approximately 100 nm. Comparison of the results of these multimodal analyses to the literature indicates that the crystal chemical, spectroscopic, and microstructural properties of NpO 2 vary based on synthesis method, even if X-ray diffraction data indicate that the bulk phase is NpO 2 .

Direct denitration↗
DOE OSTI2025

Sequential Infiltration Synthesis of Bilayer Porous Alumina Nanostructures for Broad-Angle, Broadband Antireflective Coatings

Antireflective coatings (ARCs) are thin films engineered to reduce light reflections. Delivering broadband, wide-angle performance is essential for photovoltaics, imaging, and sensing, yet truly omnidirectional antireflection remains difficult due to angle-dependent optical paths and a narrow palette of suitable refractive indices. Here, in this study, we systematically investigate an emerging class of multilayer inorganic ARCs based on conformally coated nanoporous alumina templated by intrinsically microporous polymers (PIMs) and block copolymers (BCPs). We establish a design framework that maps thickness reflectance relationships to identify thickness pairs minimizing reflection across wavelength and incidence angle. We show that deliberately separating the local reflectance minima of the top and bottom layers in bilayer nanostructures broadens the antireflective bandwidth and angular range. We show that 235-nm single-side bilayer porous alumina nanostructures achieves under 2 % reflectance from 380–750 nm for incidence angles up to 45° with less than 0.6% reflectance for incidence angles under 20°. The approach is readily extensible to additional layers or materials with refractive indices tuned via templated nanoporosity and composition, enabling practical, etch-free ARC fabrication without HF or fluorinated precursors and advancing straightforward design of broadband, wide-angle (quasi-omnidirectional) ARCs for next-generation optical systems.

antireflective coatings↗