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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Use of Transmission Electron Microscopy for Analysis of Aerosol Particles and Strategies for Imaging Fragile Particles

For over 25 years, transmission electron microscopy (TEM) has provided a method for the study of aerosol particles with sizes from below the optical diffraction limit to several microns, resolving the particles as well as smaller features. The wide use of this technique to study aerosol particles has contributed important insights about environmental aerosol particle samples and model atmospheric systems. TEM produces an image that is a 2D projection of aerosol particles that have been impacted onto grids and, through associated techniques and spectroscopies, can contribute additional information such as the determination of elemental composition, crystal structure, and 3D particle structures. Soot, mineral dust, and organic/inorganic particles have all been analyzed using TEM and spectroscopic techniques. TEM, however, has limitations that are important to understand when interpreting data including the ability of the electron beam to damage and thereby change the structure and shape of particles, especially in the case of particles composed of organic compounds and salts. In this paper, we concentrate on the breadth of studies that have used TEM as the primary analysis technique. Another focus is on common issues with TEM and cryogenic-TEM. Insights for new users on best practices for fragile particles, that is, particles that are easily susceptible to damage from the electron beam, with this technique are discussed. Tips for readers on interpreting and evaluating the quality and accuracy of TEM data in the literature are also provided and explained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Understanding Instability in Formamidinium Lead Halide Perovskites: Kinetics of Transformative Reactions at Grain and Subgrain Boundaries

Transformative and reconstructive reactions impart significant structural changes at particle boundaries of hybrid perovskites, which influence environmental stability and optoelectronic properties of these materials. Here, we investigate the moisture-induced transformative reactions in formamidinium based perovskites FAPbX 3 (X=I, Br) and show that the ambient stability of these materials can be adjusted from a few hours to several months. For FAPbI 3 , roles of water vapor, particle size, and light illumination on the kinetic pathways of the cubic (a) transformation to the hexagonal (d) phase are analyzed by X-ray diffraction, optical microscopy, photoluminescence and solid-state NMR spectroscopy techniques. The grain and sub-grain boundaries exhibit different α→δ-FAPbI 3 phase transformation kinetics. Our study suggests that the dynamic transformation involves the local water-induced dissolution of the cubic phase occurring at the crystal surfaces followed by precipitation of the hexagonal phase. Insights into structures and dynamics of a kinetically trapped α-|δ-FAPbI 3 are obtained by 1 H, 2 H, and 207 Pb ssNMR spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Local measurement of bulk thermal diffusivity using photothermal radiometry

Here, an experimental methodology using photothermal radiometry is developed for the accurate measurement of bulk thermal diffusivity of nuclear fuels and materials irradiated to high doses. Under these conditions, nuclear fuels, such as uranium oxide, and moderator materials, such as graphite, become friable, which requires characterization techniques that can accommodate irregularly shaped fragments. Photothermal radiometry, a good candidate for this application, involves locally heating a sample by using a laser and measuring the temperature field by monitoring blackbody radiation. The interaction volume for this study, less than a millimeter, is carefully chosen to sample a statistically significant number of large-scale structural features, such as pores and gas filled bubbles, and is small enough that the sample fragments can be treated as a thermal half-space. The thermal diffusivity standards considered in this study cover a range of thermal diffusivities representative of both fresh and spent nuclear fuels. We also consider a sample having a porous microstructure representative of large-scale structures found in materials irradiated to high doses. Our measurement methodology circumvents complex thermal wave models that address optical diffraction, nonlinear transfer function associated with blackbody radiation, and finite sample size effects. Consequently, the large measurement uncertainty associated with modeling these effects can be avoided. While the emphasis here is on nuclear fuels and materials, this measurement approach is well suited to measure thermal transport in a variety of technologically important materials associated with advanced synthesis techniques. Examples range from small, exotic single crystals grown using hydrothermal growth techniques to additively manufactured components having complex geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Highly multicolored light-emitting arrays for compressive spectroscopy

Miniaturized, multicolored light-emitting device arrays are promising for applications in sensing, imaging, computing, and more, but the range of emission colors achievable by a conventional light-emitting diode is limited by material or device constraints. In this work, we demonstrate a highly multicolored light-emitting array with 49 different, individually addressable colors on a single chip. The array consists of pulsed-driven metal-oxide-semiconductor capacitors, which generate electroluminescence from microdispensed materials spanning a diverse range of colors and spectral shapes, enabling facile generation of arbitrary light spectra across a broad wavelength range (400 to 1400 nm). When combined with compressive reconstruction algorithms, these arrays can be used to perform spectroscopic measurements in a compact manner without diffractive optics. As an example, we demonstrate microscale spectral imaging of samples using a multiplexed electroluminescent array in conjunction with a monochrome camera.

42 ENGINEERING↗
DOE OSTI2022

Fast scanning in x-ray microscopy: the effects of offset in the central stop position

Scanning of lightweight circular diffractive optics, separate from central stops and apertures, is emerging as an approach to exploit advances in synchrotron x-ray sources. We consider the effects in a scanning microscope of offsets between the optic and its central stop and find that scan ranges of up to about half the diameter of the optic are possible with only about a 10% increase in the focal spot width. Finally, for large scanning ranges, we present criteria for the working distance between the last aperture and the specimen to be imaged.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Snapshot multifocal light field microscopy

Light field microscopy (LFM) is an emerging technology for high-speed wide-field 3D imaging by capturing 4D light field of 3D volumes. However, its 3D imaging capability comes at a cost of lateral resolution. In addition, the lateral resolution is not uniform across depth in the light field dconvolution reconstructions. To address these problems, here, we propose a snapshot multifocal light field microscopy (MFLFM) imaging method. The underlying concept of the MFLFM is to collect multiple focal shifted light fields simultaneously. We show that by focal stacking those focal shifted light fields, the depth-of-field (DOF) of the LFM can be further improved but without sacrificing the lateral resolution. Also, if all differently focused light fields are utilized together in the deconvolution, the MFLFM could achieve a high and uniform lateral resolution within a larger DOF. We present a house-built MFLFM system by placing a diffractive optical element at the Fourier plane of a conventional LFM. The optical performance of the MFLFM are analyzed and given. Both simulations and proof-of-principle experimental results are provided to demonstrate the effectiveness and benefits of the MFLFM. We believe that the proposed snapshot MFLFM has potential to enable high-speed and high resolution 3D imaging applications.

He, Kuan↗
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗
DOE OSTI2021

Maskless Fourier transform holography

Fourier transform holography is a lensless imaging technique that retrieves an object's exit-wave function with high fidelity. It has been used to study nanoscale phenomena and spatio-temporal dynamics in solids, with sensitivity to the phase component of electronic and magnetic textures. However, the method requires an invasive and labor-intensive nanopatterning of a holography mask directly onto the sample, which can alter the sample properties, forces a fixed field-of-view, and leads to a low signal-to-noise ratio at high resolution. In this work, we propose using wavefront-shaping diffractive optics to create a structured probe with full control of its phase at the sample plane, circumventing the need for a mask. We demonstrate in silico that the method can image nanostructures and magnetic textures and validate our approach with a visible light-based experiment. The method enables investigation of a plethora of phenomena at the nanoscale including magnetic and electronic phase coexistence in solids, with further uses in soft and biological matter research.

Keskinbora, Kahraman↗
DOE OSTI2025

Single-shot spatiotemporal plasma density measurements with a chirped probe pulse

In this work, we present the development and demonstration of a diagnostic for the measurement of the spatial and temporal evolution of plasma density in a single shot. Single-shot Advanced Plasma Probe HolographIc REconstruction (SAPPHIRE) utilizes a chirped probe pulse, a diffractive optical element, a self-referenced interferometer, and an interference bandpass filter to achieve high-fidelity electron density measurements suitable for underdense plasmas that exhibit cylindrical symmetry. The method overcomes limitations in conventional diagnostics, such as reliance on shot-to-shot reproducibility, while capturing plasma dynamics on picosecond timescales with micron-level spatial resolution. The capabilities of SAPPHIRE are demonstrated through measurements of laser-driven plasma channels in helium–nitrogen gas jets. SAPPHIRE demonstrates the formation and expansion of plasma channels in a single shot and the propagation of supersonic ionization fronts while revealing shot-to-shot variations in the plasma profiles. Experimental results are validated against theoretical models and scaling laws, underscoring the robustness and accuracy of this technique. By enabling ultrafast, high-resolution plasma diagnostics in a single exposure, SAPPHIRE represents a transformative advancement in plasma measurement technology.

Grace, Elizabeth S. [Lawrence Livermore National L↗
DOE OSTI2024

Nonlinear encoding in diffractive information processing using linear optical materials

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

42 ENGINEERING↗
DOE OSTI2025

Neutron powder diffraction, Mossbauer Spectroscopy and Optical Spectroscopy to study magnetic and nuclear lattices in Fe-based oxychlorides Ca2FeO3Cl, Sr2FeO3Cl and Sr3Fe2O5Cl2

Data for Neutron Diffraction, Mossbauer Spectoscopy and Optical Spectroscopy are contained on all three samples (Ca2FeO3Cl, Sr2FeO3Cl, and Sr3Fe2O5Cl2). Mossbauer data are in the folder "MossbauerSpectroscopy". This contains details of files and examples to read the data. The Optical Spectroscopy are in the folder "AbsorptionData", this contains one file with explanatory headers. The neutron diffraction data are in the "NeutronDiffractionData" folder. The data was collected on the HB-2A powder diffractometer at HFIR. The autoreduced files are corrected using a vanadium standard and are in arbitrary intensity units. The RawData folder contains the uncorrected data with metadata for motor positions and temperature. All measurements were collected with a constant neuton wavelength of 2.41 Angstrom. An excel spreadsheet "NeutronDiffractionData_IPTS-29118_summary" contains details for each scan. Sr3Fe2O5Cl2 data were collected at only room temperature. Ca2FeO3Cl and Sr2FeO3Cl data were collected at 4K and room temperature. The autoreduced .dat fileformat is three columns corresponding to: Two-theta, Intensity and Intensity_error.

fe-based oxychlorides↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.

36 MATERIALS SCIENCE↗
DOE OSTI2024

All-optical image denoising using a diffractive visual processor

Abstract Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images – implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30–40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Study of the structure of exploding flat foils at superhigh current density

We have investigated the features produced in flat metal foils as a result of nanosecond explosions and phase transitions at current densities of (0.1–2) • 10 9 A/cm 2 . Thin foils made of aluminum, copper, nickel, and titanium, with thicknesses of 1–15 μm, were tested. Here, the exploded foil structure was studied using X-pinch radiography and laser shadow imaging. Al, Ti, and Cu (5 and 7 μm thick) foils had an initial two-dimensional structure. At the same time, no apparent structure was registered in 1 μm Cu and 5 μm Ni foils. Experiments on generators with different output parameters have shown that the dominant structures that developed in the explosion are either cracks or bubbles and may depend on the orientation of the initial structure in the thin foil with respect to the current direction. In addition, the energy deposited in the foil differs by a factor of about 1.5 for the orthogonal vs parallel current and initial foil structure directions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects

Abstract Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view. After their fabrication, the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes, while instantaneously erasing objects of the other data classes at the output field-of-view. Using the same framework, we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras, where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption, while the other classes of objects are irreversibly erased from the output image. The success of class-specific diffractive cameras was experimentally demonstrated using terahertz (THz) waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset, all-optically erasing the other handwritten digits. This diffractive camera design can be scaled to different parts of the electromagnetic spectrum, including, e.g., the visible and infrared wavelengths, to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction

By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methods’ demand for extensive labeled training data is experimentally burdensome. Here, we propose an unsupervised physics-informed neural network reconstruction method, PtychoPINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while improving reconstruction quality by combining the diffraction forward map with real-space constraints from overlapping measurements. In particular, PtychoPINN gains a factor of 4 in linear resolution and an 8 dB improvement in PSNR while also accruing improvements in generalizability and robustness. This blend of performance and computational efficiency offers exciting prospects for high-resolution real-time imaging in high-throughput environments such as X-ray free electron lasers (XFELs) and diffraction-limited light sources.

97 MATHEMATICS AND COMPUTING↗