Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “imaging and sensing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Mid-infrared photodetection with 2D metal halide perovskites at ambient temperature

The detection of mid-infrared (MIR) light is technologically important for applications such as night vision, imaging, sensing, and thermal metrology. Traditional MIR photodetectors either require cryogenic cooling or have sophisticated device structures involving complex nanofabrication. Here, we conceive spectrally tunable MIR detection by using two-dimensional metal halide perovskites (2D-MHPs) as the critical building block. Leveraging the ultralow cross-plane thermal conductivity and strong temperature-dependent excitonic resonances of 2D-MHPs, we demonstrate ambient-temperature, all-optical detection of MIR light with sensitivity down to 1 nanowatt per square micrometer, using plastic substrates. Through the adoption of membrane-based structures and a photonic enhancement strategy unique to our all-optical detection modality, we further improved the sensitivity to sub–10 picowatt-per-square-micrometer levels. The detection covers the mid-wave infrared regime from 2 to 4.5 micrometers and extends to the long-wave infrared wavelength at 10.6 micrometers, with wavelength-independent sensitivity response. Our work opens a pathway to alternative types of solution-processable, long-wavelength thermal detectors for molecular sensing, environmental monitoring, and thermal imaging.

Li, Yanyan [Yale University, New Haven, CT (United↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2024

Direct Observation of Circularly Polarized Nonlinear Optical Activities in Chiral Hybrid Lead Halides

Circularly polarized light emission is a crucial application in imaging, sensing, and photonics. However, utilizing low-energy photons to excite materials, as opposed to high-energy light excitation, can facilitate deep-tissue imaging and sensing applications. The challenge lies in finding materials capable of directly generating circularly polarized nonlinear optical effects. In this study, we introduce a chiral hybrid lead halide (CHLH) material system, R/S-DPEDPb 3 Br 8 ·H 2 O (DPED = 1,2-diphenylethylenediammonium), which can directly produce circularly polarized second harmonic generation (CP-SHG) through linearly polarized infrared light excitation, exhibiting a polarization efficiency as high as 37% at room temperature. To understand the spin relaxation mechanisms behind the high polarization efficiency, we utilized two models, so-called D’yakonov–Perel’ (DP) and Bir–Aronov–Pikus (BAP) mechanisms. The unique zigzag inorganic frameworks within the hybrid structure are believed to reduce the dielectric confinement and exciton binding energy, thus enhancing spin polarization, especially in regions with a high excitation pump fluence based on the DP mechanism. In the case of low excitation pump fluence, the BAP mechanism dominates, as evidenced by the observed decrease in the polarization ratio from CP-SHG measurement. Using density functional theory analysis, we elucidate how the distinctive 8-coordination environment of lead bromide building blocks effectively suppresses spin–orbit coupling at the conduction band minimum. This suppression significantly diminishes spin-splitting, thereby slowing the spin relaxation rate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automated piezoresponse force microscopy domain tracking during fast thermally stimulated phase transition in CuInP 2 S 6

Real-time tracking of dynamic nanoscale processes such as phase transitions by scanning probe microscopy is a challenging task, typically requiring extensive and laborious human supervision. Smart strategies to track specific regions of interest (ROI) in the system during such transformations in a fast and automated manner are necessary to study the evolution of the microscopic changes in such dynamic systems. In this work, we realize automated ROI tracking in piezoresponse force microscopy during a fast (≈0.8 °C s –1 ) thermally stimulated ferroelectric-to-paraelectric phase transition in CuInP 2 S 6 . We use a combination of fast (1 frame per second) sparse scanning with compressed sensing image reconstruction and real-time offset correction via phase cross correlation. The applied methodology enables in situ fast and automated functional nanoscale characterization of a certain ROI during external stimulation that generates sample drift and changes local functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Perspectives of active Si photonics devices for data communication and optical sensing

Si photonics has made rapid progress in research and commercialization in the past two decades. While it started with electronic–photonic integration on Si to overcome the interconnect bottleneck in data communications, Si photonics has now greatly expanded into optical sensing, light detection and ranging (LiDAR), optical computing, and microwave/RF photonics applications. From an applied physics point of view, this perspective discusses novel materials and integration schemes of active Si photonics devices for a broad range of applications in data communications, spectrally extended complementary metal–oxide–semiconductor (CMOS) image sensing, as well as 3D imaging for LiDAR systems. We also present a brief outlook of future synergy between Si photonic integrated circuits and Si CMOS image sensors toward ultrahigh capacity optical I/O, ultrafast imaging systems, and ultrahigh sensitivity lab-on-chip molecular biosensing.

electronic band structure↗
DOE OSTI2024

Quantum Frequency Combs with Path Identity for Quantum Remote Sensing

Quantum sensing promises to revolutionize sensing applications by employing quantum states of light or matter as sensing probes. Photons are the clear choice as quantum probes for remote sensing because they can travel to and interact with a distant target. Existing schemes are mainly based on the quantum illumination framework, which requires quantum memory to store a single photon of an initially entangled pair until its twin reflects off a target and returns for final correlation measurements. Existing demonstrations are limited to tabletop experiments, and expanding the sensing range faces various roadblocks, including long-time quantum storage and photon loss and noise when transmitting quantum signals over long distances. We propose a novel quantum sensing framework that addresses these challenges using quantum frequency combs with path identity for remote sensing of signatures (“qCOMBPASS”). The combination of one key quantum phenomenon and two quantum resources—namely, quantum-induced coherence by path identity, quantum frequency combs, and two-mode squeezed light—allows for quantum remote sensing without requiring quantum memory. The proposed scheme is akin to a quantum radar based on entangled frequency-comb pairs that uses path identity to detect, range, or sense a remote target of interest by measuring pulses of one comb in the pair that never traveled to the target but that contains target information “teleported” by quantum-induced coherence by path identity from the other comb in the pair that traveled to the target but is not detected. We develop the basic qCOMBPASS theory, analyze the properties of the qCOMBPASS transceiver, and introduce the qCOMBPASS equation—a quantum analog of the well-known LIDAR equation in classical remote sensing. We also describe an experimental scheme to demonstrate the concept using two-mode squeezed quantum combs. qCOMBPASS can strongly impact various applications in remote quantum sensing, imaging, metrology, and communications. These applications include detection and ranging of low-reflectivity objects, measurement of small displacements of a remote target with precision beyond the standard quantum limit (SQL), standoff hyperspectral quantum imaging, discreet surveillance from space with low detection probability (detect without being detected), very-long-baseline interferometry, quantum Doppler sensing, quantum clock synchronization, and networks of distributed quantum sensors. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A physical method for downscaling land surface temperatures using surface energy balance theory

Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. Here, the DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2025

2024 Workshop - Remote Sensing and Fluxes Upscaling for Real-world Impact - Tutorial v1

The software-tutorial was developed within the 2024 Remote Sensing and Fluxes Upscaling for Real-world Impact workshop as part of the hands-on session. The workshop was supported by AmeriFlux, National Ecological Observatory Network (NEON) and CarbonDew. The software provides basic tools to perform the following tasks: - gather remote sensing images using Google Earth Engine API; - gather flux data; - perform basic functions, such as plotting time-series, perform QA of the data, compute vegetation indices; - perform correlation analysis between flux data and remote sensing data; - perform flux predictions based on remote sensing data integrated in different modalities.

Falco, Nicola [Lawrence Berkeley National Laborato↗
DOE OSTI2024

Deep learning models map rapid plant species changes from citizen science and remote sensing data

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Gillespie, Lauren E.↗
DOE OSTI2022

Tuning Phonon Energies in Lanthanide‐doped Potassium Lead Halide Nanocrystals for Enhanced Nonlinearity and Upconversion

Abstract Optical applications of lanthanide‐doped nanoparticles require materials with low phonon energies to minimize nonradiative relaxation and promote nonlinear processes like upconversion. Heavy halide hosts offer low phonon energies but are challenging to synthesize as nanocrystals. Here, we demonstrate the size‐controlled synthesis of low‐phonon‐energy KPb 2 X 5 (X=Cl, Br) nanoparticles and the ability to tune nanocrystal phonon energies as low as 128 cm −1 . KPb 2 Cl 5 nanoparticles are moisture resistant and can be efficiently doped with lighter lanthanides. The low phonon energies of KPb 2 X 5 nanoparticles promote upconversion luminescence from higher lanthanide excited states and enable highly nonlinear, avalanche‐like emission from KPb 2 Cl 5 : Nd 3+ nanoparticles. The realization of nanoparticles with tunable, ultra‐low phonon energies facilitates the discovery of nanomaterials with phonon‐dependent properties, precisely engineered for applications in nanoscale imaging, sensing, luminescence thermometry and energy conversion.

Zhang, Zhuolei↗
DOE OSTI2022

Tuning Phonon Energies in Lanthanide-doped Potassium Lead Halide Nanocrystals for Enhanced Nonlinearity and Upconversion

Optical applications of lanthanide-doped nanoparticles require materials with low phonon energies to minimize nonradiative relaxation and promote nonlinear processes like upconversion. Heavy halide hosts offer low phonon energies but are challenging to synthesize as nanocrystals. Here, we demonstrate the size-controlled synthesis of low-phonon-energy KPb 2 X 5 (X=Cl, Br) nanoparticles and the ability to tune nanocrystal phonon energies as low as 128 cm -1 . KPb 2 Cl 5 nanoparticles are moisture resistant and can be efficiently doped with lighter lanthanides. Further, the low phonon energies of KPb 2 X 5 nanoparticles promote upconversion luminescence from higher lanthanide excited states and enable highly nonlinear, avalanche-like emission from KPb 2 Cl 5 : Nd 3+ nanoparticles. The realization of nanoparticles with tunable, ultra-low phonon energies facilitates the discovery of nanomaterials with phonon-dependent properties, precisely engineered for applications in nanoscale imaging, sensing, luminescence thermometry and energy conversion.

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

Backbone Stiffness‐Dependent Photoluminescence of Pendant Fluorophores in Organic Nanoparticles

Fluorescent organic nanoparticles (FoNPs) with backbone stiffness‐dependent photoluminescence were synthesized via microemulsion atom transfer radical polymerization (ATRP) of 2‐(2‐bromoisobutyryloxy)ethyl methacrylate (BiBEM), ethylene glycol dimethacrylate (EGDMA), and methacrylate monomers bearing pendant fluorophores, 1‐pyrenemethyl methacrylate (PyMMA), or 4‐(1,2,2‐triphenylethenyl)benzenemethyl methacrylate (TPEMMA). The crosslinking density precisely tuned the intraparticle rigidity, enabling systematic control over emission mechanisms. Pyrene‐containing FoNPs exhibited a rigidity‐dependent transition from excimer‐dominated to monomer‐dominated fluorescence, whereas TPE‐based FoNPs displayed aggregation‐induced emission (AIE) enhancement as intramolecular motion was restricted. Solvent‐dependent studies revealed that increased polarity and viscosity, particularly in benzyl alcohol and DMSO, promoted cooperative rigidification and enhanced emission intensity through specific polymer–solvent interactions. Furthermore, the retained alkyl bromide chain ends on FoNPs enabled dual roles as initiators and crosslinkers in UV‐induced polymerization of poly(ethylene glycol) acrylates, forming luminescent FoNP–OEG hybrid gels with improved mechanical robustness. This work establishes a versatile platform for integrating tunable optical and mechanical properties into a single polymeric nanoparticle framework, offering new design principles for multifunctional soft materials and providing a platform for future sensing, imaging, and photonic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Stable Non-equilibrium Structures in Chiral Nematics under Microfluidic Flow

Cholesteric liquid crystals (CLCs) are compelling responsive materials with applications in next-generation sensing, imaging, and display technologies. While electric fields and surface treatments have been used to manipulate the molecular organization and, subsequently, the optical properties of CLCs, their response to controlled fluid flow has remained largely unexplored. Here, in this study, we investigate the influence of microfluidic flow on the structure of thermotropic CLCs that can exhibit structural coloration. We demonstrate that the shear forces that arise from microfluidic flow align the helical axis of CLCs; alignment is a prerequisite for harnessing the promising photonic properties of CLCs. Moreover, we show that microfluidic flow can generate non-equilibrium structures exhibiting photonic band gaps that are inaccessible in the stationary cholesteric phase. Our findings have implications for the use of CLCs in applications involving flow processing such as additive manufacturing.

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

Deactivating Emission in Azulene via Solvent-Induced (Anti)Aromaticity

Anti-Kasha emission is a coveted feature in optoelectronics, with promise in areas such as imaging, sensing, and the production of white-light LEDs via dual-photon emission. Despite being a rare feature in organic systems, anti-Kasha emission is readily observed in the deceptively simple molecule azulene, which possesses two bright singlet states in the UV–visible spectrum and readily emits from the S 2 state. With dominant anti-Kasha emission and decades of synthetic study, azulene is a perfect candidate for novel material fabrication; however, large gaps persist in understanding the photophysics of even the simple parent compound. These range from competing, experimentally unverified models of azulene reactivity and aromaticity to the unexplained deactivation of anti-Kasha emission in a host of azulene derivatives. Herein, we use fluorescence and transient absorption spectroscopies to explore the detailed solvent dependence of azulene photophysics. We discover a tunable reduction in the S 2 lifetime via weak complexation with aromatic solvents. Interestingly, our results are independent of polarity, highlighting the primacy of peripherally delocalized 10-π Hückel aromaticity over zwitterionic character. When the dipolar character is enhanced through chemical functionalization, we observe even greater sensitivity to solvent aromaticity and more rapid quenching, revealing the role of conical intersections in azulenes with zwitterionic excited states. Overall, this work provides essential mechanistic insight into the photophysics of azulene and reveals a simple new approach to control the excited-state aromaticity and anti-Kasha emission in this class of materials.

Absorption↗
DOE OSTI2023

All-silicon quantum light source by embedding an atomic emissive center in a nanophotonic cavity

Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Optimizing structured surfaces for diffractive waveguides

We introduce universal diffractive waveguide designs that can match the performance of conventional dielectric waveguides and achieve various functionalities. Optimized using deep learning, diffractive waveguides can be cascaded to form any desired length and are comprised of transmissive diffractive surfaces that permit the propagation of desired modes with low loss and high mode purity. In addition to guiding the targeted modes through cascaded diffractive units, we also developed various waveguide components and introduced bent diffractive waveguides, rotating the direction of mode propagation, as well as spatial and spectral mode filtering and mode splitting diffractive waveguide designs, and mode-specific polarization control. This framework was experimentally validated in the terahertz spectrum to selectively pass certain spatial modes while rejecting others. Without the need for material dispersion engineering diffractive waveguides can be scaled to operate at different wavelengths, including visible and infrared spectrum, covering potential applications in, e.g., telecommunications, imaging, sensing and spectroscopy.

Applied optics↗
DOE OSTI2022

Luminescent MOFs (LMOFs): recent advancement towards a greener WLED technology

The replacement of traditional incandescent, halogen and fluorescent lamps by white light emitting diodes (WLEDs) is expected to reduce the global electricity consumption by one-third by 2030, according to the US Department of Energy. The current WLED technology uses rare-earth element (REE) based phosphor materials, which, not only is cost-intensive but also constitutes an environmental concern. Hence, researchers are in a quest for a new-generation of opto-electronic materials that can replace the conventional phosphors in WLEDs and thus aim towards a cleaner and more energy efficient lighting technology for the future. Luminescent metal–organic frameworks (LMOFs) have recently emerged as a new sub-class of MOFs which have demonstrated enormous potential for applications in sensing, imaging, optoelectronics and in solid-state lighting (SSL) technology. LMOFs could be game changers as lighting phosphors due to advantages such as high luminescence quantum yield, tunable excitation and emission which can be achieved by rational design and optimization of metal centers, linkers, and the guest molecules, facile fabrication into devices, and structural robustness. These clear advantageous features of LMOFs make them score over other contemporary materials, and enable them to be futuristic phosphor materials for WLED technology. Here, in this feature article, we will provide an overview of the most recent developments of LMOF-based phosphor materials for SSL with a special focus on WLED technology. The emphasis will be centered around REE-free LMOFs, as the aim is to direct the attention of the readers towards a more viable and greener lighting technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗