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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Confusion-Driven Machine Learning of Structural Phases of a Flexible, Magnetic Stockmayer Polymer

We use a semisupervised, neural-network-based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by dipole–dipole and Lennard-Jones interactions. As input for the neural network, we use the particle positions and magnetic dipole moments of equilibrium polymer configurations, which we generate via replica-exchange Wang–Landau simulations. We demonstrate that by measuring the classification accuracy of neural networks, we can effectively identify transition points between multiple structural phases without any prior knowledge of their existence or location. We corroborate our findings by investigating relevant conventional order parameters. Our study furthermore examines previously unexplored low-temperature regions of the phase diagram, where we find new structural transitions between highly ordered helicoidal polymer configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Controlling Preferred Grain Orientation in Vapor-Deposited Metal-Halide Perovskite Thin Films

Metal-halide perovskites are promising semiconductor materials for light-absorber layers in solar cells due to their optimized band gap, defect tolerance, and relative ease of synthesis. In this work, we demonstrate the viability of vapor transport deposition (VTD), a solvent-free processing method, to realize polycrystalline films of both lead iodide (PbI 2 ) and methylammonium lead iodide (MAPbI 3 ) perovskites showing preferential crystallographic orientation. Notable is the difference in perovskite film texturing observed for sequentially and co-deposited VTD films of MAPbI 3 , as well as the lack of strong preferential orientation in solution-processed MAPbI 3 films. While sequential deposition of the perovskite precursors PbI 2 and methylammonium iodide (MAI) leads to a MAPbI 3 film with a limited preferential grain orientation, co-deposited MAPbI 3 films mirror the orientation of excess PbI 2 present in the film. The latter is confirmed by the similar dependence of preferential orientation for PbI 2 and co-deposited MAPbI 3 on underlayer roughness, as well as the emergence of preferential orientation in MAPbI 3 only when excess PbI 2 is present. Both MAPbI 3 and PbI 2 films show a strong preferred lattice orientation on smooth substrates, while on rougher substrates, this orientation is disrupted. Furthermore, these results are interpreted as excess PbI 2 frustrating the diffusion of MAI in the film, which would otherwise disrupt the preferred orientation, pointing to a notable difference in lattice orientation arising from processing technique and conditions.

Deposition↗
DOE OSTI2025

A direct-adjoint approach for material point model calibration with application to plasticity

Here, this paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization problem, where the constitutive model evolution equations for a single material point serve as constraints. The objective function quantifies the mismatch between the stress predicted by the model and corresponding experimental measurements. To improve calibration efficiency, a novel direct-adjoint approach is presented to compute the Hessian of the objective function, which enables the use of second-order optimization algorithms. Automatic differentiation is used for gradient and Hessian computations. Two numerical examples are employed to validate the Hessian matrices and to demonstrate that the Newton–Raphson algorithm consistently outperforms gradient-based algorithms such as L-BFGS-B.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Transient Catalytic Reaction Analysis Through Signal Defragmentation

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Reconstructing the reflectivity of liquid surfaces from grazing incidence X-ray off-specular scattering data

The capillary wave model of a liquid surface predicts both the X-ray specular reflection and the diffuse scattering around it. A quantitative method is presented to obtain the X-ray reflectivity (XRR) from a liquid surface through the diffuse scattering data around the specular reflection measured using a grazing incidence X-ray off-specular scattering (GIXOS) geometry at a fixed horizontal offset angle with respect to the plane of incidence. With this approach the entire Q z -dependent reflectivity profile can be obtained at a single, fixed incident angle. This permits a much faster acquisition of the profile than with conventional reflectometry, where the incident angle must be scanned point by point to obtain a Q z -dependent profile. The XRR derived from the GIXOS-measured diffuse scattering, referred to in this paper as pseudo-reflectivity, provides a larger Q z range compared with the reflectivity measured by conventional reflectometry. Transforming the GIXOS-measured diffuse scattering profile to pseudo-XRR opens up the GIXOS method to widely available specular XRR analysis software tools. Here the GIXOS-derived pseudo-XRR is compared with the XRR measured by specular reflectometry from two simple vapor–liquid interfaces at different surface tension, and from a hexadecyltrimethylammonium bromide monolayer on a water surface. For the simple liquids, excellent agreement (beyond 11 orders of magnitude in signal) is found between the two methods, supporting the approach of using GIXOS-measured diffuse scattering to derive reflectivities. Pseudo-XRR obtained at different horizontal offset angles with respect to the plane of incidence yields indistinguishable results, and this supports the robustness of the GIXOS-XRR approach. The pseudo-XRR method can be extended to soft thin films on a liquid surface, and criteria are established for the applicability of the approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Desmearing Bonse–Hart USANS data using Bayesian Gaussian process regression

Ultra-small-angle neutron scattering (USANS) enables access to micrometer-scale structures but is intrinsically affected by strong, anisotropic resolution smearing arising from slit-geometry optics. As a result, recovery of the intrinsic scattering intensity constitutes an ill-posed inverse problem, and commonly used iterative desmearing methods lack rigorous uncertainty quantification. We present a Bayesian desmearing framework for slit-geometry USANS based on Gaussian process regression. In this approach, the scattering intensity is modeled as a smooth random function, and the instrumental point spread function is incorporated explicitly as a forward operator. The resulting formulation yields a closed-form maximum a posteriori solution with well-defined credibility intervals. Computational benchmarks and experimental validation using combined USANS and small-angle neutron scattering (SANS) measurements demonstrate that the framework enables stable desmearing, suppresses experimental noise, and preserves physically meaningful structural features under realistic conditions.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

Risk Assessment in a Chemical Laboratory Following an Explosive Incident Involving a Novel Diazonium Compound: Retrospective Analysis and Lessons Learned

Diazonium compounds are synthetically useful in the production of dyes and textiles, however they are highly explosive under dry conditions. Explosion prevention becomes more difficult when new diazonium compounds are synthesized, because while some syntheses include a counterion to increase their stability, this is not always a reliable method to prevent an explosive incident. Due to the uncertainty surrounding the explosiveness of different diazonium compounds, it is important to understand how to safely clean up after an incident and how to determine when it is safe to return a laboratory to typical operational use, particularly when the incident involves a novel compound where a standard does not exist for instrument calibration. Here, an explosive event is discussed involving the synthesis of 4-bromo-benzenediazonium-2-carboxylate. Following the explosive incident and 3-step cleanup, which involved a precautionary neutralization step, samples were collected from the fume hood where the incident occurred. Because the incident involved an unstable, novel compound that is not commercially available and was deemed unsafe to resynthesize for instrument calibration, we assessed the risk of further explosion by analyzing for the stable decomposition products. Mass spectrometry analysis confirmed that the residue in the fume hood contained 5-bromosalicylic acid, a decomposition product of 4-bromo-benzenediazonium-2-carboxylate. Samples were taken from multiple points in the fume hood and analyzed to estimate the spatial distribution of the decomposition product. Based on this analysis, we inferred that the primary decomposition product was far more abundant than residual energetic, indicating the energetic had been consumed or neutralized to a trace quantity where the risk of further explosion was low. Furthermore, the steps presented here─specifically, initial neutralization and then analyzing the spatial distribution of expected decomposition products to assess risk when a novel explosive material is detonated in a confined space─were our approach to assess further risk following an explosion due to a novel diazonium compound without the need for any further handling or resynthesis of the energetic. Here, we present our approach and critically analyze these steps by discussing retrospective lessons learned and alternative analytical approaches.

Computer simulations↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Utilization of the LS-APGD microplasma/orbitrap-FTMS booster system for detection and isotopic analysis of neodymium nanoparticles

Detection and isotopic analysis of particle populations has seen rapid growth across several application areas, including environmental analysis, nuclear forensics, and food safety. The ability to characterize the particles' unique elemental and isotopic fingerprints could provide information related to formation, processing history, and transport. Regarding nuclear forensics, isotopic analysis of particles derived from diverse materials is often used as a tool to trace the origin and processing history. Mass spectrometric-based techniques currently used for particle population analysis often suffer from limited mass resolution, particularly when dealing with real-world samples that are affected by isobaric and polyatomic interferences from the matrix. To address these analytical challenges, we propose a novel method utilizing the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma ionization source coupled to an ultrahigh resolution Orbitrap mass spectrometer, further enhanced with the FTMS X2T Booster data acquisition and processing unit. The FTMS Booster enables acquisition of extended transient times of up to 3 s, significantly improving mass resolution, thereby reducing or even eliminating the need for prior separation of isobaric or polyatomic interferences. Additionally, the detection of low-abundance isotopes was improved by increasing the signal-to-noise (S/N) ratio. As proof of concept, this study demonstrates the feasibility of the LS-APGD/Orbitrap-FTMS X2T Booster platform for direct analysis using a suspension of well-characterized ∼120 nm neodymium particles. The quality of the isotope ratios values obtained from a few hundred particles were in good agreement with those obtained from homogeneous ionic solutions. These results highlight the potential of the LS-APGD/Orbitrap platform for rapid, accurate, and interference-resilient isotope ratio analysis of particle populations without the need for dissolution and subsequent chemical separations, offering significant advantages for nuclear forensics, safeguards, and environmental applications. The effort here also points to further paths forward, hopefully towards single particle (SP) analysis using microplasma ionization and the ultrahigh resolving power of the Orbitrap mass analyzer.

FTMS X2T booster↗
DOE OSTI2025

Phonon-Spin Scattering from Unpaired f-electrons in U atoms and It’s Influence on Thermal Transport in Uranium-doped Thorium Dioxide Single Crystals

In this work, impact of low level of uranium (U) atom substitution on thermal conductivity of thorium dioxide (ThO2) is investigated. ThO2 is an electronic insulator with a wide optical band-gap and no unpaired electrons whose thermal transport is governed by phonons. U-substitution introduces unpaired f-electrons resulting in paramagnetic behavior of U-ThO2 at room temperature, which significantly suppresses its thermal conductivity. A single crystal of U-ThO2 with graded composition of U is grown using a hydrothermal synthesis method, and thermal conductivity measurements are performed in regions with uniform composition of U at levels of 0%, 6%, 9% and 16%. Measured thermal conductivity profiles over 77–300 K temperature range are analyzed using an analytical expression for phonon-mediated thermal transport based on Klemens-Callaway model. Temperature dependent thermal conductivity is found to deviate significantly from the Rayleigh scattering trend expected for a simple substitutional point defect with a small perturbation to mass and interatomic forces. With the resonant scattering term, observed large suppression of thermal conductivity at low temperatures can be closely reproduced. Additionally, the extracted phonon-spin coupling constants imply a nonlinear relation of phonon-spin interaction intensity with respect to U doping percentage. Our study reveals how phonon-spin scattering contributed by unpaired f-electrons in U atoms influences thermal transport in the U-ThO2 system.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Large critical fields in superconducting Ti 4 ⁢Ir 2⁢ O from spin-orbit coupling

The recently synthesized 𝜂-carbide-type superconductors exhibit large critical fields. A notable example is Ti 4 ⁢Ir2⁢ O, for which the upper critical field strongly violates the Pauli paramagnetic limit, a behavior that is unusual for cubic materials that preserve inversion symmetry. Here, by combining density functional theory (DFT) and analytic modeling, we provide an explanation for this enhanced Pauli limiting field. We show that the nonsymmorphic Fd⁢-3 ⁢m symmetry implies that the electronic states near the X points exhibit strong spin-orbit coupling (SOC), which leads to a vanishing effective 𝑔 factor and enables the enhanced Pauli limiting field. Furthermore, our DFT results reveal a Van Hove singularity peak near the X points, accounting for ∼65% of the total density of states (DOS), occurring near the chemical potential. We propose that the strong SOC and enhanced DOS in the vicinity of the X points provide the origin of the observed enhancement of the critical field. This leads to a prediction that the magnetic field will lead to a strongly momentum-dependent gap suppression. As a result, the gap due to electronic states away from (near to) the X points will be rapidly (slowly) suppressed by fields.

Critical field↗
DOE OSTI2026

Buried Dirac Points in Quantum Spin Hall Insulators: Implications for Majorana Kramers Pair-Based Quantum Computing

Quantum spin Hall insulators (QSHIs) host helical electronic edge states that are protected from backscattering due to time-reversal symmetry (TRS). Despite considerable work investigating QSHI edge states, there is still an open question about their unexpected resilience to large magnetic fields where TRS is undoubtedly broken. In this work, we investigate the transport properties of helical edge states in a QSHI-superconductor (QSHI-SC) junction formed by a In⁢As(15 nm)/Ga⁢Sb(5 nm) double quantum well and a superconducting tantalum (Ta) constriction. We observe a robust conductance plateau up to 2 T, signaling resilient edge-state transport. Using a modified Landauer-Büttiker analysis, we find that the zero-field conductance is consistent with 98% Andreev-reflection probability owing to the high transparency of the (In⁢As/Ga⁢Sb)-Ta interface. Such resilience is consistent with the Dirac point for the edge states being buried in the bulk valence band. We further theoretically show that a buried Dirac point does not affect the robustness of the quasi-one-dimensional topological superconducting phase. We find that a buried Dirac point favors the hybridization of Majorana Kramers pairs (MKPs)—predicted to exist in a QSHI-SC constriction—and fermionic modes in the QSHI vacuum edge resulting in extended MKP states, highlighting the subtle role of buried Dirac points in probing MKPs.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Complex orders and chirality in the classical Kitaev-Γ model

It is well recognized that the low-energy physics of many Kitaev materials is governed by two dominant energy scales, the Ising-type Kitaev coupling 𝐾 and the symmetric off-diagonal Γ coupling. An understanding of the interplay between these two scales is therefore the natural starting point toward a quantitative description that includes subdominant perturbations that are inevitably present in real materials. This study focuses on the classical 𝐾−Γ model on the honeycomb lattice, with a specific emphasis on the region 𝐾< 0 and Γ > 0 , which is the most relevant for the available materials and which remains enigmatic in both quantum and classical limits, despite much effort. We employ large-scale Monte Carlo simulations on specially designed finite-size clusters and unravel the presence of a complex multisublattice magnetic order in a wide region of the phase diagram, whose structure is characterized in detail. We show that this order can be quantified in terms of a coarse-grained scalar-chirality order, featuring a counterrotating modulation on the two spin sublattices. Here, we also provide a comparison to previous studies and discuss the impact of quantum fluctuations on the phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Kinetic Monte Carlo simulations of aging in δ -Pu

We have developed a first-passage kinetic Monte Carlo approach for materials aging to investigate the sensitivity of void swelling to model parameters, including helium bubble density and size distribution. In addition to explicitly accounting for the spatial distribution of individual point defects, bubbles, and voids, our approach can simulate total doses equivalent to 100 years of natural aging on statistically representative volumes of materials. This technique enables us to study the effects on swelling and radiation damage evolution due to temperature and dose rate (as altered in artificially aged experiments), differences in effective interaction radii between vacancies and interstitials, and varying defect diffusion activation energies, while providing more detailed information than previous rate-equation based approaches. In conclusion, our results indicate that spatial effects that are not modeled in mean-field rate theories could play a significant role in void swelling initiation and growth for certain regimes of model parameters.

Actinides↗
DOE OSTI2025

Characterizing Absolute Orientations in DNA Self-Assembly of Single Molecules

DNA self-assembly of single molecules (i.e., dyes) with deterministic orientations is a powerful approach for engineering mo-lecular excitons. However, current determination methods of the dye orientation relative to DNA do not account for the orien-tation of the dye plane, which is a missing degree of freedom needed to define absolute three-dimensional orientations. In this work, we combine fluorescence-detected linear dichroism, defocused dipole imaging, and DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) super-resolution microscopy to determine the absolute three-dimensional orientations of single Cy5 dyes relative to host DNA duplexes which includes the dye plane orientation. The data revealed that the absorption and emission dipoles are perpendicular to the DNA duplex, and the mean dye plane is parallel to the DNA bases, which supports the notion that Cy5 dyes intercalate between DNA base pairs. The presented methodology will inspire the investigation of the dye plane orientation for controlling dye arrangement configurations beyond spontaneous π-stacking between dyes as well as achieving novel dye-DNA arrangements.

36 MATERIALS SCIENCE↗