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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Evolution of micro-pores in Ni–Cr alloys via molten salt dealloying

Porous materials with high specific surface area, high porosity, and high electrical conductivity are promising materials for functional applications, including catalysis, sensing, and energy storage. Molten salt dealloying was recently demonstrated in microwires as an alternative method to fabricate porous structures. The method takes advantage of the selective dissolution process introduced by impurities often observed in molten salt corrosion. This work further investigates molten salt dealloying in bulk Ni–20Cr alloy in both KCl–MgCl 2 and KCl–NaCl salts at 700 °C, using scanning electron microscopy, energy dispersive spectroscopy, and X-ray diffraction (XRD), as well as synchrotron X-ray nano-tomography. Micro-sized pores with irregular shapes and sizes ranging from sub-micron to several microns and ligaments formed during the process, while the molten salt dealloying was found to progress several microns into the bulk materials within 1–16 h, a relatively short reaction time, enhancing the practicality of using the method for synthesis. The ligament size increased from ~ 0.7 μm to ~ 1.3 μm in KCl–MgCl 2 from 1 to 16 h due to coarsening, while remaining ~ 0.4 μm in KCl–NaCl during 16 h of exposure. The XRD analysis shows that the corrosion occurred primarily near the surface of the bulk sample, and Cr 2 O 3 was identified as a corrosion product when the reaction was conducted in an air environment (controlled amount sealed in capillaries); thus surface oxides are likely to slow the morphological coarsening rate by hindering the surface diffusion in the dealloyed structure. 3D-connected pores and grain boundary corrosion were visualized by synchrotron X-ray nano-tomography. This study provides insights into the morphological and chemical evolution of molten salt dealloying in bulk materials, with a connection to molten salt corrosion concerns in the design of next-generation nuclear and solar energy power plants.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Substituted polynorbornene membranes: a modular template for targeted gas separations

Polynorbornenes are ideal materials for systematic structure–property investigations designed to correlate gas-transport properties to polymer structure. The modular nature of norbornene-based systems provides a facile route towards the synthesis of diverse polymeric materials. Though many valuable correlations between gas-permeability and polynorbornene structure have been summarized in a prior review (2017), many of these efforts focused heavily on the design of materials with high permeabilities. More recently, the design of next-generation polynorbornene membranes has shifted toward the development of materials that are highly selective for multiple targeted gas separations. Since 2017, tremendous progress has been realized in the preparation of stable and highly selective polynorbornenes for membrane-based gas separations. Examples include polynorbornene derivatives that exhibit very high selectivity for CO 2 /N 2 and light hydrocarbon (e.g., C 3 +/C 1 ) separations, some of which exhibit gas separation performances that approach or exceed the 2008 upper bound. In this work, we summarize prior findings, present recent developments in the field of advanced polynorbornene membrane materials for targeted gas separations, and provide an outlook for the future of this field of research.

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

Site-specific photo-crosslinking in a double crossover DNA tile facilitated by squaraine dye aggregates: advancing thermally stable and uniform DNA nanostructures

We investigated the role of dichloro-squaraine (SQ) dye aggregates in facilitating thymine–thymine interstrand photo-crosslinking within double crossover (DX) tiles, to develop thermally stable and structurally uniform two-dimensional (2D) DNA-based nanostructures. By strategically incorporating SQ modified thymine pairs, we enabled site-selective [2 + 2] photocycloaddition under 310 nm UV light. Strong dye–dye interactions, particularly through the formation of aggregates, facilitated covalent bond formation between proximal thymines. To evaluate the impact of dye aggregation on crosslinking efficiency, ten DX tile variants with varying SQ-modified thymine positions were tested. Our results demonstrated that SQ dye aggregates significantly enhanced crosslinking, driven by precise SQ-modified thymine dimer placement within the DNA tiles. Analytical techniques, including denaturing PAGE and UV-visible spectroscopy, validated successful crosslinking in DNA tiles with multiple SQ-modified thymine pairs. This non-phototoxic method offers a potential route for creating thermally stable, homogeneous higher-order DNA–dye assemblies with potential applications in photoactive and exciton-based fields such as optoelectronics, nanoscale computing, and quantum computing. Furthermore, the insights from this study establish a foundation for further exploration of advanced DNA–dye systems, enabling the design of next-generation DNA nanostructures with enhanced functional properties.

2D DNA template↗
DOE OSTI2026

Unveiling the mechanism of lithium-mediated nitrogen reduction via operando X-ray scattering in a flow cell with hydrogen oxidation

The lithium-mediated nitrogen reduction reaction (Li-NRR) is currently the most promising strategy for electrochemical ammonia synthesis. In this study, we present an operando grazing incidence wide angle X-ray scattering (GI WAXS) investigation using an improved electrochemical flow cell that enables hydrogen oxidation at the anode and thereby eliminates the need of a sacrificial proton donor. The improved cell design also increases nitrogen availability and mass transport, achieving ammonia faradaic efficiencies (FEs) up to 36%. This setup allows direct analysis of reaction intermediates and the solid electrolyte interphase (SEI) using state-of-the-art diglyme-based electrolytes. We identify lithium amide (LiNH2) as the only stable, crystalline intermediate, providing direct insight into the Li-NRR mechanism. Notably, in diglyme-based electrolytes, the SEI composition differs significantly from that in tetrahydrofuran-based systems, with reduced LiF content and the formation of previously unreported crystalline diglyme–lithium salt complexes. These species likely influence ammonia selectivity and long-term stability. Our findings highlight how the electrolyte composition and cell architecture govern Li-NRR selectivity and efficiency, offering a foundation for the rational design of next-generation SEI layers and solid electrolytes to enable scalable electrochemical ammonia synthesis.

Deissler, Niklas H. [Technical Univ. of Denmark, L↗
DOE OSTI2025

Elucidating the reversible exsolution–dissolution behaviour of high-entropy oxides in crystalline and amorphous phases

High-entropy oxides (HEOs), as a subclass of high-entropy materials (HEMs), offer a versatile platform for catalysis by leveraging entropy-stabilized solid solutions with tunable compositions, lattice structures, and electronic properties. While exsolution–dissolution of metal species in crystalline HEOs has emerged as a promising strategy for reversible active sites regeneration, the dynamic behaviour of HEOs possessing amorphous nature remains under-explored, particularly the difference with crystalline counterparts. In this work, we systematically investigate the architecture-dependent exsolution–dissolution behavior of HEOs by comparing a crystalline-phase HEO (c-HEO) and an amorphous-phase HEO (a-HEO), both comprising Ni, Mg, Cu, Zn, and Co as principal metal elements. Using a combination of in situ variable-temperature X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron microscopy, and in situ CO diffuse reflectance infrared Fourier transform spectroscopy (CO-DRIFTS), the structural evolution of the two HEO phases under redox conditions was elucidated. Both materials exhibit reversible exsolution of metallic species or alloys in reducing environments, followed by re-incorporation into the host lattice upon oxidation. Remarkably, the a-HEO demonstrates more facile and dynamic self-healing behavior, with alloy exsolution and dissolution occurring under milder conditions because of its enhanced reducibility and structural disorder. This study provides critical insights into the design of next-generation regenerable catalysts based on amorphous HEOs, highlighting the role of phase structure in governing reversible metal-site formation dynamics and catalytic performance.

Wang, Qingju [Univ. of Tennessee, Knoxville, TN (U↗
DOE OSTI2026

Tailored polyMOFs for ion transport in lithium-based battery electrolyte

Owing to their low flammability, solid-state and quasi-solid-state electrolytes are safer alternatives to liquid organic electrolytes for energy storage applications. Metal–organic frameworks (MOFs), with facile functional tunability, long-range order, and rich host–guest interactions, have been implemented as electrolyte materials in a wide range of energy storage applications. In this work, we investigate a class of MOFs called polyMOFs as quasi-solid-state electrolyte materials. Unlike MOF–polymer composites, which are physical mixtures of MOF particles and polymers, polyMOFs are composed of polymeric linkers and metal ion nodes that self-assembled into crystalline and porous framework materials. PolyMOFs thus marry the ionic transport properties of liquid electrolyte and polymers with the synthetic versatility and host–guest interactions of MOFs. We demonstrate that the functionality of the polymer backbone of the polyMOF linker can improve room-temperature ion transport in the material. The polyMOF based on poly(ethylene glycol) (PEG) exhibits greater ionic conductivity, lithium transference number, and lower activation energy than its polyethylene (PE) analog. Supported by solid-state 7 Li nuclear magnetic resonance spectroscopy, we propose these improvements are due to stronger coordination of Li + to oxide sites in PEG, allowing for dissociation of Li and its associated anion. DFT studies further reveal that the confined solvent molecule mediates Li + transport in PEG-functionalized UiO-66 via a metastable adsorption and hopping mechanism. This work lies at the interface of inorganic and polymer electrolytes, unveiling fundamental insights into the design of next-generation ion conductive materials for energy technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2021

The multi-optics high-resolution absorption x-ray spectrometer (HiRAXS) for studies of materials under extreme conditions

We report the development of a high-resolution spectrometer for extended x-ray absorption fine structure (EXAFS) studies of materials under extreme conditions. A curved crystal and detector in the spectrometer are replaceable such that a single body is employed to perform EXAFS measurements at different x-ray energy intervals of interest. Two configurations have been implemented using toroidal crystals with Ge 311 reflection set to provide EXAFS at the Cu K-edge (energy range 8.9–9.8 keV) and Ge 400 reflection set to provide EXAFS at the Ta L3-edge (9.8–10.7 keV). Key performance characteristics of the spectrometer were found to be consistent with design parameters. The data generated at the National Ignition Facility have shown an ≃3 eV spectral resolution for the Cu K-edge configuration and ≃6 eV for the Ta L3-edge configuration.

Stoupin, S. (ORCID:0000000226225270)↗
DOE OSTI2024

Embracing disorder in quantum materials design

Many of the most exciting materials discoveries in fundamental condensed matter physics are made in systems hosting some degree of intrinsic disorder. While disorder has historically been regarded as something to be avoided in materials design, it is often of central importance to correlated and quantum materials. This is largely driven by the conceptual and theoretical ease to handle, predict, and understand highly uniform systems that exhibit complex interactions, symmetries, and band structures. In this Perspective, we highlight how flipping this paradigm has enabled exciting possibilities in the emerging field of high entropy materials, focusing primarily on high entropy oxide and chalcogenide quantum materials. These materials host high levels of cation or anion compositional disorder while maintaining unexpectedly uniform single crystal lattices. The diversity of atomic scale interactions of spin, charge, orbital, and lattice degrees of freedom are found to emerge into coherent properties on much larger length scales. Thus, altering the variance and magnitudes of the atomic scale properties through elemental selection can open new routes to tune global correlated phases, such as magnetism, metal–insulator transitions, ferroelectricity, and even emergent topological responses. The strategy of embracing disorder in this way provides a much broader pallet from which functional states can be designed for next-generation microelectronic and quantum information systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. Here, this paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

17 WIND ENERGY↗
DOE OSTI2026

Quantum kinetic modeling of KEEN waves in a warm-dense regime

We report the first fully kinetic, quantum study of kinetic electrostatic electron nonlinear (KEEN) waves, showing that quantum diffraction systematically erodes the classical trapping mechanism, narrows harmonic locking to the fundamental, and hastens post-drive decay. Electrons are evolved with a second-order Strang-split 1D1V Wigner–Poisson solver that couples conservative semi-Lagrangian WENO advection to an analytic Fourier space update for the non-local Wigner term, while ions remain classical. We focus on collisionless dynamics in a weakly coupled regime, providing a controlled baseline before collisional extensions. Short, frequency-tuned ponderomotive pulses drive KEEN formation in a uniform Maxwellian plasma; as the dimensionless quantum parameter H rises from the classical limit to values relevant to warm-dense matter, doped semiconductors, and 2D electron systems, the drive threshold increases, higher harmonics are damped, trapped electron vortices diffuse, and the subplasma electrostatic energy relaxes to a lower stationary level, as confirmed by continuous wavelet analysis. These microscopic changes carry macroscopic weight. Ignition-scale capsules now compress matter to regimes where the electron de Broglie wavelength rivals the Debye length, making classical kinetic descriptions insufficient. By extending KEEN physics into this quantum domain, our results offer a potential diagnostic of non-equilibrium electron dynamics for next-generation inertial-confinement designs and high-energy-density platforms, indicating that predictive fusion modeling may benefit from the integration of kinetic fidelity with quantum effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Key metrics of progress in the NIF ignition implosions and future challenges on the path to higher yields

Successful demonstration of an implosion that meets the physics metrics for ignition on the National Ignition Facility (NIF) in August 2021 and demonstration of target energy gain G > 1 in December 2022 represented the culmination of more than a decade of research on the NIF. An in-depth analysis of the key ignition metrics achieved on the NIF over the past decade shows sustained progress toward identifying and mitigating degradation effects. Ignition implosions on the NIF have progressed from early experiments whose compressed fuel conditions were degraded by a factor of 10–20 below the ignition threshold with yields of only 1%–2% of the laser energy, to current experiments that significantly exceed the ignition threshold and in the best experiments achieve yields greater than twice the laser energy. It is very likely that NIF can get more than 20 MJ of yield and possibly 50–70 MJ yields when fully optimized at an ultraviolet laser energy of 2.6 MJ following the completion of the NIF Extended Yield Capability Project. Achieving these higher yields will also require improvements to capsules at larger size and possibly with alternate materials, as well as an improved understanding of hohlraum and capsule physics. Now that NIF has achieved ignition, the design of a Next Generation High Energy Density Facility that could achieve the National Nuclear Security Administration long-term goal of substantially higher yields than the NIF can proceed with a more confident physics base. We evaluate these opportunities including possible applications for inertial fusion energy and likely challenges to achieving this level of performance over the next decade.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Deep learning‑based metal artefact reduction in X-ray computed tomography of TRISO fuel compacts

TRISO (TRi-structural ISOtropic) – compact-type micro-particle fuels – are next generation nuclear fuel compacts designed with safety in mind. Structural integrity and characterisation before and after irradiation are important to determine the performance of the fuel compacts under real reactor conditions. X-ray computed tomography can be an important tool for non-destructive evaluation of these fuel compacts. The fuel particles are highly attenuating for X-rays which creates metal artefact, rendering the images unusable. Our artefact correction can mitigate these artefacts significantly. The proposed method works by first segmenting highly-attenuating structures (metal) and forward projecting to localise the source of artefacts in the projection domain, then, using a traditional or U-net-based deep learning architecture, contextually interpolating those regions to remove the artefacts. Finally the reconstruction from the modified projection is fused with the segmented metal. The proposed method shows a significant improvement in the image quality while significantly reducing the reconstruction time compared to the standard technique.

Rahman, Obaid [ORNL] (ORCID:0000000277810840)↗
DOE OSTI2025

Form-factor dependence of neutrino-nucleus cross sections using microscopic methods

To achieve its design goals, the next generation of neutrino-oscillation accelerator experiments requires percent-level predictions of neutrino-nucleus cross sections supplemented by robust estimates of the theoretical uncertainties involved. The latter arise from both approximations in solving the nuclear many-body problem and in the determination of the single- and few-nucleon quantities taken as input by many-body methods. To gauge the sensitivity of realistic nuclear many body methods to these few-nucleon quantities, we compute flux-averaged double-differential cross sections using the Green’s function Monte Carlo and spectral function methods as well as different parameterizations of the nucleon axial form factors based on either deuterium bubble-chamber data or lattice quantum chromodynamics calculations. The cross-section results are compared with available experimental data from the MiniBooNE and T2K collaborations. We also discuss the uncertainties associated with N → Δ transition form factors that enter the two-body current operator. We quantify the relations between neutrino-nucleus cross section and nucleon form factor uncertainties. These relations enable us to determine the form factor precision targets required to achieve a given cross-section precision.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Divide and conquer: broadly neutralizing antibody combinations for improved HIV-1 viral coverage

Successful HIV-1 prevention and therapy will require broad and potent coverage of within-host and global viral diversity. Broadly neutralizing antibody (bNAb) combination and multispecific therapeutics provide an opportunity to meet this challenge due to the complementary activity of individual antibody components. Here, we review the principles and applications of this concept. The Antibody Mediated Prevention (AMP) trials have demonstrated the high bar for neutralization potency and breadth that bNAb-mediated prevention modalities will need to achieve to have a meaningful impact on the HIV-1 epidemic. Additional clinical studies have recently shown that an even higher bar may be required for therapeutic inhibition of the diverse within-host quasispecies present in viremic and aviremic people with HIV-1 (PWH). We discuss how the complementarity of bNAbs in terms of neutralization profiles, resistance mutations and coverage of within-host quasispecies may overcome these stringent requirements and lead to effective bNAb combination or multispecific antibody based prophylactic and therapeutic strategies. The design of next-generation bNAb-based combination or multispecific therapeutics for the prevention and/or treatment of HIV-1 infection will need to leverage the complementarity of component bNAbs to maximize the potency and breadth that will be required for clinical success.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Bayesian inference for plasmonic nanometrology

Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗