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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Single-particle scattering spectroscopy: fundamentals and applications

Metallic nanoparticles supporting a localized surface plasmon resonance have emerged as promising platforms for nanoscopic labels, sensors, and (photo-) catalysts. To use nanoparticles in these capacities, and to gain mechanistic insight into the reactivity of inherently heterogeneous nanoparticles, single-particle characterization approaches are needed. Single-particle scattering spectroscopy has become an important, highly sensitive tool for localizing single plasmonic nanoparticles and studying their optical properties, local environment, and reactivity. In this review, we discuss approaches taken for collecting the scattered light from single particles, their advantages and disadvantages, and present some recent applications. We introduce techniques for the excitation and detection of single-particle scattering such as high-angle dark-field excitation, total internal reflection dark-field excitation, scanning near-field microscopy, and interferometric scattering. We also describe methods to achieve polarization-resolved excitation and detection. We then discuss different approaches for scanning, ratiometric, snapshot, and interferometric hyperspectral imaging techniques used to extract spectral information. Finally, we provide a brief overview of specialized setups for in situ measurements of nanoparticles in liquid systems and setups coupled to scanning tip microscopes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗

DOE OSTI2026

VoroClust: Scalable Clustering for Remote Sensing

Although supervised machine learning provides a powerful framework for image classification and segmentation, it requires comprehensive consistent datasets, which are not available for many remote-sensing applications. Remote-sensing datasets are expensive to collect, and each is acquired under different environmental conditions or with significant variations in system operating parameters. Unsupervised clustering algorithms analyze the structure of each dataset independently, rather than drawing on similarities with existing “training” examples, and are thus well suited for practical remote-sensing applications. We introduce VoroClust, a fast density-based unsupervised clustering algorithm applicable to high-resolution and high-dimensional data. VoroClust runs as fast as distance-based clustering methods, while capturing complex regional geometries at least as well as current-density-based methods. It uses a data-centered sphere cover to reduce computational demands, while still capturing data topology. It then propagates clusters outward from local peaks in density. We show that VoroClust provides fast state-of-the-art clustering for both high-resolution polarimetric synthetic aperture radar and high-dimensional hyperspectral imaging datasets.

42 ENGINEERING↗

DOE OSTI2024

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

DOE OSTI2023

Flexible Vanadium Dioxide Photodetectors for Visible to Longwave Infrared Detection at Room Temperature

Abstract Flexible optoelectronics is a rapidly growing field, with a wide range of potential applications. From wearable sensors to bendable solar cells, curved displays, and curved focal plane arrays, the possibilities are endless. The criticality of flexible photodetectors for many of these applications is acknowledged, however, devices that are demonstrated thus far are limited in their spectral range. In this study, flexible photodetectors are demonstrated using a VO x nanoparticle ink, with an extremely broad operating wavelength range of 0.4 to 20 µm. This ink is synthesized using a simple and scalable wet‐chemical process. These photodetectors operate at room temperature and exhibit minimal variance in performance even when bent at angles of up to 100 ° at a bend radius of 6.4 mm. In addition, rigorous strain testing of 100 bend and release cycles revealed a photoresponse with a standard deviation of only 0.55%. This combination of mechanical flexibility, wide spectral response, and ease of fabrication makes these devices highly desirable for a wide range of applications, including low‐cost wearable sensors and hyperspectral imaging systems.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Spectroscopic pressure investigation of a novel high energy system

This work here combines a novel multimodal spectroscopic technique, O–PTIR and Raman spectroscopy, which allows for the simultaneous collection of infrared and Raman spectra, with traditional diamond anvil cell techniques to explore pressure induced phase transitions in a high energy material. Two phase transitions are revealed, with the first transition emerging below 0.23 GPa, to a higher order phase, followed by a broad transition to a lower order phase. Aside from the pressure studies, hyperspectral imaging was performed, confirming the homogeneous nature of the system.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Techno-economics of hydrocarbon fuel production and recyclables recovery from landfill-destined municipal solid waste: AI-enhanced materials recovery facility design

Sustainable aviation fuels (SAF) production from cellulosic paper fractions of municipal solid waste (MSW) destined for landfills has strong potential to advance environmental, social, and economic sustainability across the aviation and waste sectors. This study proposes an artificial intelligence-enabled material recovery facility (AI-MRF) design to efficiently characterize, separate, process, and convert recovered paper waste from MSW into intermediate chemicals and SAF. The AI-MRF, designed to process 233,091 metric tons of MSW annually, integrates smart manufacturing technologies including AI, visual and hyperspectral imaging, multi-sensor data, and traditional sorting systems. Well-characterized and sorted cellulosic paper waste was utilized for chemical and fuel production scenarios, while clean plastics, metals, and glass were considered for recycling. Conversion of paper waste into intermediate sugars achieved a net present value (NPV) of up to $\$67$ million. For sugar-to-SAF production scenarios, the minimum fuel selling price (MFSP) was calculated at $\$6.11$ per gasoline gallon equivalent (GGE) when excluding recyclable revenue, and $\$4.03$ per GGE when halving recyclable revenue. The MFSP was further reduced to $\$1.96$ per GGE when accounting for SAF sales and recyclables. Nationally, this approach could yield about 2 billion GGE of hydrocarbon fuel annually from available MSW in the United States.

09 BIOMASS FUELS↗

DOE OSTI2021

Carrier gradients and the role of charge selective contacts in lateral heterojunction all back contact perovskite solar cells

Realizing photovoltaic devices that achieve the full potential of the metal halide perovskite material will require improved insight regarding the role of selective contacts and how key interfaces operate when mobile defects are present. However, measuring interface properties in typical device stacks where the perovskite layer is thin and sandwiched between two contacts has been a challenge. Here, we fabricate p-i-n and p-n lateral heterojunctions with nickel oxide/titanium oxide all back contacts, permitting us to employ a comprehensive analysis approach, including ultraviolet and X-ray photoemission spectroscopy (UPS/XPS), angle-resolved X-ray absorption spectroscopy (XAS), Kelvin probe force microscopy (KPFM), surface photovoltage (SPV), hyperspectral imaging (HSI), and time-resolved fluorescence lifetime imaging microscopy (TR-FLIM) to discern the role of selective contacts. Specifically, we tune the selectivity of the contacts, changing the gradient in the carrier concentration across the surface of the active layer, which is connected to carrier extraction at the buried interface, and thus the device functionality.

25 ENERGY STORAGE↗

DOE OSTI2022

Nano-spectroscopy of excitons in atomically thin transition metal dichalcogenides

Abstract Excitons play a dominant role in the optoelectronic properties of atomically thin van der Waals (vdW) semiconductors. These excitons are amenable to on-demand engineering with diverse control knobs, including dielectric screening, interlayer hybridization, and moiré potentials. However, external stimuli frequently yield heterogeneous excitonic responses at the nano- and meso-scales, making their spatial characterization with conventional diffraction-limited optics a formidable task. Here, we use a scattering-type scanning near-field optical microscope (s-SNOM) to acquire exciton spectra in atomically thin transition metal dichalcogenide microcrystals with previously unattainable 20 nm resolution. Our nano-optical data revealed material- and stacking-dependent exciton spectra of MoSe 2 , WSe 2 , and their heterostructures. Furthermore, we extracted the complex dielectric function of these prototypical vdW semiconductors. s-SNOM hyperspectral images uncovered how the dielectric screening modifies excitons at length scales as short as few nanometers. This work paves the way towards understanding and manipulation of excitons in atomically thin layers at the nanoscale.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

High-throughput micro-scale bandgap mapping for perovskite-inspired materials with complex composition space

Abstract To realize the full promise of high-throughput experimental workflows, the rate of sample synthesis must be matched by that of characterization. Of growing interest are contactless optical techniques that can rapidly measure material homogeneity and properties. Here, we present a hyperspectral imaging method to measure local optical bandgap distributions within samples, utilizing spatially-resolved reflectance spectra coupled with automated data analysis. We collect approximately one million optical bandgap data across the compositional space of Cs 3 (Bi x Sb 1-x ) 2 (Br y I 1-y ) 9 perovskite-inspired materials. Our results show non-monotonic bandgap variations (i.e., bandgap bowing) along six composition gradient sequences, in addition to identifying samples with multiple bandgaps in statistics. High-throughput transient absorption spectroscopy reveals that within these compositions, the depletion of the ground state carriers to excited states occurred at discrete energy levels with independent carrier dynamics, consistent with the bandgap observation and indicative of phase separation. This work demonstrates the potential for rapid optical measurements to assess material quality and homogeneity in a high-throughput experimental setting, supporting screening and recipe optimization of optoelectronic material candidates with desired carrier dynamics and optical properties.

Science & Technology - Other Topics↗

DOE OSTI2021

Gaussian process analysis of electron energy loss spectroscopy data: multivariate reconstruction and kernel control

Abstract Advances in hyperspectral imaging including electron energy loss spectroscopy bring forth the challenges of exploratory and physics-based analysis of multidimensional data sets. The multivariate linear unmixing methods generally explore similarities in the energy dimension, but ignore correlations in the spatial domain. At the same time, Gaussian process (GP) explicitly incorporate spatial correlations in the form of kernel functions but is computationally intensive. Here, we implement a GP method operating on the full spatial domain and reduced representations in the energy domain. In this multivariate GP, the information between the components is shared via a common spatial kernel structure, while allowing for variability in the relative noise magnitude or image morphology. We explore the role of kernel constraints on the quality of the reconstruction, and suggest an approach for estimating them from the experimental data. We further show that spatial information contained in higher-order components can be reconstructed and spatially localized.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Spatially resolved multimodal vibrational spectroscopy under high pressures

In this perspective, we discuss the potential impact on in situ studies under controlled environments of a novel multimodal spectroscopic technique, optical photothermal infrared + Raman spectroscopy, which enables the simultaneous collection of infrared and Raman scattering spectra, along with hyperspectral imaging and chemical imaging with wavelength-independent sub-500 nm spatial resolution. A brief review of the current literature regarding the O-PTIR technique is presented along with recent work from our own lab on determining the crystallinity of soft and inorganic materials. The results highlight the possibility of resolving differences in the crystallinity of soft materials associated with changes in material processing. Here, we also demonstrate the first reported use of a diamond anvil cell with simultaneous infrared and Raman measurements that showcases, using a high energy material as an example, the potential use of O-PTIR spectroscopy in diamond anvil cell techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Role of defects in ultra-high gain in fast planar tin gallium oxide UV-C photodetector by MBE

Here we report ultra-high responsivity of epitaxial (Sn x Ga 1–x ) 2 O 3 (TGO) Schottky UV-C photodetectors and experimentally identified the source of gain as deep-level defects, supported by first principles calculations. Epitaxial TGO films were grown by plasma-assisted molecular beam epitaxy on (–201) oriented n-type β-Ga 2 O 3 substrates. Fabricated vertical Schottky devices exhibited peak responsivities as high as 3.5 ×10 4 A/W at –5 V applied bias under 250 nm illumination with sharp cutoff shorter than 280 nm and fast rise/fall time in milliseconds order. Hyperspectral imaging cathodoluminescence (CL) spectra were examined to find the mid-bandgap defects, the source of this high gain. Irrespective of different tin mole fractions, the TGO epilayer exhibited extra CL peaks at the green band (~2.20 eV) not seen in β-Ga 2 O 3 along with enhancement of the blue emission-band (~2.64 eV) and suppression of the UV emission-band. Based on hybrid functional calculations of the optical emission expected for defects involving Sn in β-Ga 2 O 3 , V Ga –Sn complexes are proposed as potential defect origins of the observed green and blue emission-bands. Such complexes behave as acceptors that can efficiently trap photogenerated holes and are predicted to be predominantly responsible for the ultra-high photoconductive gain in the Sn-alloyed Ga 2 O 3 devices by means of thermionic emission and electron tunneling. Regenerating the V Ga –Sn defect complexes by optimizing the growth techniques, we have demonstrated a planar Schottky UV-C photodetector of the highest peak responsivity.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as three-dimensional hypercubes and digitally processes them, which causes a data bottleneck, limiting power efficiency, frame rate, and spectral-spatial resolution. This work introduces spectral kernel machines (SKMs) to overcome these bottlenecks. SKM directly compresses spectral analysis through the output photocurrent and learns from example objects to identify and classify new samples in a "sniff-and-seek" mode. We experimentally demonstrated SKMs with electrically tunable bipolar black phosphorus-molybdenum disulfide (bP-MoS2) photodiodes in the near- and mid-infrared band and silicon photoconductors in the visible band, performing versatile intelligent tasks from chemometrics to semiconductor metrology. This architecture consumed substantially less power and was more than an order of magnitude faster than existing solutions for hyperspectral image analysis, defining an intelligent imaging and sensing paradigm with intriguing possibilities.

Zhang, Dehui↗

DOE OSTI2026

HSI-Nerfstudio

Code to generate NeRFs for hyperspectral images (with support for multichannel density NeRFs, and geometry regularization NeRFs). By Scout Jarman at LANL

Jarman, Scout Cabe [Los Alamos National Laboratory↗

DOE OSTI2021

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗

DOE OSTI2021

Hyperdimensional Imaging Contrast Using an Optical Fiber

Fluorescence properties of a molecule can be used to study the structural and functional nature of biological processes. Physical properties, including fluorescence lifetime, emission spectrum, emission polarization, and others, help researchers probe a molecule, produce desired effects, and infer causes and consequences. Correlative imaging techniques such as hyperdimensional imaging microscopy (HDIM) combine the physical properties and biochemical states of a fluorophore. Here we present a fiber-based imaging system that can generate hyper-dimensional contrast by combining multiple fluorescence properties into a single fluorescence lifetime decay curve. Fluorescence lifetime imaging microscopy (FLIM) with controlled excitation polarization and temporally dispersed emission can generate a spectrally coded, polarization-filtered lifetime distribution for a pixel. This HDIM scheme generates a better contrast between different molecules than that from individual techniques. This setup uses only a single detector and is simpler to implement, modular, cost-efficient, and adaptable to any existing FLIM microscope. We present higher contrast data from Arabidopsis thaliana epidermal cells based on intrinsic anthocyanin emission properties under multiphoton excitation. This work lays the foundation for an alternative hyperdimensional imaging system and demonstrates that contrast-based imaging is useful to study cellular heterogeneity in biological samples.

47 OTHER INSTRUMENTATION↗