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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 OSTI2019

Enhanced Light Extraction from Low Cost White OLEDs Fabricated on Novel Patterned Substrates” (Final Report)

Organic LEDs (OLEDs) are a very attractive emerging solid state lighting (SSL) technology. Currently, one of the main challenges is to increase the extraction of the light generated inside the OLED into the forward (viewing) hemisphere. In conventional OLEDs fabricated on a transparent flat anode coated on glass the outcoupling factor is only about 20%. About 50% of the light is lost at the metal cathode (surface plasmon excitation), and waveguided internally in the organic layers and the transparent anode. Another ~30% is waveguided in the substrate to its edges. The research addressed this issue of outcoupling by evaluating OLEDs fabricated on corrugated plastic substrates manufactured in a cost-effective approach. It demonstrated that the outcoupling from such OLEDs can exceed 50% by mitigating plasmon–related loss and internal waveguiding, even without the addition of a microlens array or a hemispherical lens, either of which extracts the light trapped in the substrate. Simulations conducted in parallel with the experimental effort demonstrated how the corrugation assists in the outcoupling. The project also evaluated the role of the corrugation pitch and height in determining the outcoupling factor and other properties of the OLEDs. The technical effectiveness and economic feasibility of the project lie in the patterning of the extracting plastic substrates in an approach that is scalable to roll-to-roll (R2R) manufacturing. R2R processes are drastically lower-cost than batch or single-unit fabrication. The corrugated plastic substrates studied are part of an integrated substrate that includes also a microlens array at the air-side of the substrate and a conductive electrode. The project that was a university-industry collaboration is of major benefit to the public as it demonstrates the feasibility of manufacturing highly efficient OLEDs for SSL in a R2R process, which would drastically reduce the manufacturing cost and increase production in the USA. SSL is efficient and environmentally-friendly. It reduces electricity consumption, and hence lighting cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Prediction of Lumen Output and Chromaticity Shift in LEDs Using Kalman Filter and Extended Kalman Filter Based Models

Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.

Lall, Pradeep↗
DOE OSTI2017

High Performance OLED Panel and Luminaire

In this project, OLEDWorks developed and demonstrated the technology required to produce OLED lighting panels with high energy efficiency and excellent light quality. OLED panels developed in this program produce high quality warm white light with CRI greater than 85 and efficacy up to 80 lumens per watt (LPW). An OLED luminaire employing 24 of the high performance panels produces practical levels of illumination for general lighting, with a flux of over 2200 lumens at 60 LPW. This is a significant advance in the state of the art for OLED solid-state lighting (SSL), which is expected to be a complementary light source to the more advanced LED SSL technology that is rapidly replacing all other traditional forms of lighting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Solid-State Lighting 2017 Suggested Research Topics Supplement: Technology and Market Context

A 2017 update to the Solid-State Lighting R&D Plan that is divided into two documents. The first document describes a list of suggested SSL priority research topics and the second document provides context and background, including information drawn from technical, market, and economic studies. Widely referenced by industry and government both here and abroad, these documents reflect SSL stakeholder inputs on key R&D topics that will improve efficacy, reduce cost, remove barriers to adoption, and add value for LED and OLED lighting solutions over the next three to five years, and discuss those applications that drive and prioritize the specific R&D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Solid-State Lighting 2017 Suggested Research Topics

A 2017 update to the Solid-State Lighting R&D Plan that is divided into two documents. The first document describes a list of suggested SSL priority research topics and the second document provides context and background, including information drawn from technical, market, and economic studies. Widely referenced by industry and government both here and abroad, these documents reflect SSL stakeholder inputs on key R&D topics that will improve efficacy, reduce cost, remove barriers to adoption, and add value for LED and OLED lighting solutions over the next three to five years, and discuss those applications that drive and prioritize the specific R&D.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Solid-State Lighting 2017 Suggested Research Topics Supplement: Technology and Market Context

A 2017 update to the Solid-State Lighting R&D Plan that is divided into two documents. The first document describes a list of suggested SSL priority research topics and the second document provides context and background, including information drawn from technical, market, and economic studies. Widely referenced by industry and government both here and abroad, these documents reflect SSL stakeholder inputs on key R&D topics that will improve efficacy, reduce cost, remove barriers to adoption, and add value for LED and OLED lighting solutions over the next three to five years, and discuss those applications that drive and prioritize the specific R&D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Solid-State Lighting 2017 Suggested Research Topics

A 2017 update to the Solid-State Lighting R&D Plan that is divided into two documents. The first document describes a list of suggested SSL priority research topics and the second document provides context and background, including information drawn from technical, market, and economic studies. Widely referenced by industry and government both here and abroad, these documents reflect SSL stakeholder inputs on key R&D topics that will improve efficacy, reduce cost, remove barriers to adoption, and add value for LED and OLED lighting solutions over the next three to five years, and discuss those applications that drive and prioritize the specific R&D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Initial Results of Accelerated Stress Testing on Single-Channel and Multichannel Drivers: Solid-State Lighting Technology Area

This report is the first in a series of studies on accelerated stress testing (AST) of drivers used for SSL luminaires, such as downlights, troffers, and streetlights. A representative group of two-stage commercial driver products was exposed to an AST environment consisting of 75°C and 75% relative humidity (7575). These drivers were a mix of single-channel drivers (i.e., a single output current for one LED primary) and multichannel drivers (i.e., separate output currents for multiple LED primaries). This AST environment was chosen because previous testing on downlights with integrated drivers demonstrated that 38% of the sample population failed in less than 2,500 hours of testing using this method. In addition to AST test results, the performance of an SSL downlight product incorporating an integrated, multichannel driver during extended room temperature operational life (RTOL) testing is also reported. A battery of measurements was used to evaluate these products during accelerated testing, including full electrical characterization (i.e., power consumption, PF, total harmonic distortion [THD], and inrush current) and photometric characterization of external LED loads attached to the drivers (i.e., flicker performance and lumen maintenance).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

The potential of ill-nitride laser diodes for solid-state lighting [Advantages of III-Nitride Laser Diodes in Solid-State Lighting]

III-nitride laser diodes (LDs) are an interesting light source for solid-state lighting (SSL). Modelling of LDs is performed to reveal the potential advantages over traditionally used light-emitting diodes (LEDs). The first, and most notable, advantage is LDs have higher efficiency at higher currents when compared to LEDs. This is because Auger recombination that causes efficiency droop can no longer grow after laser threshold. Second, the same phosphor-converted methods used with LEDs can also be used with LDs to produce white light with similar color rendering and color temperature. Third, producing white light from direct emitters is equally challenging for both LEDs and LDs, with neither source having a direct advantage. Lastly, the LD emission is directional and can be more readily captured and focused, leading to the possibility of novel and more compact luminaires. These advantages make LDs a compelling source for future SSL.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Chromophore-Based Luminescent Metal–Organic Frameworks as Lighting Phosphors

Here, energy-efficient solid-state-lighting (SSL) technologies are rapidly developing, but the lack of stable, high-performance rare-earth free phosphors may impede the growth of the SSL market. One possible alternative is organic phosphor materials, but these can suffer from lower quantum yields and thermal instability compared to rare-earth phosphors. However, if luminescent organic chromophores can be built into a rigid metal-organic framework, their quantum yields and thermal stability can be greatly improved. This Forum Article discusses the design of a group of such chromophore-based luminescent metal-organic frameworks with exceptionally high performance and rational control of the important parameters that influence their emission properties, including electronic structures of chromophore, coligands, metal ions, and guest molecule s.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Performance of Spherical Quantum Well Down Converters in Solid State Lighting

We report the color conversion performance of amber and red emitting quantum dots (QDs) on InGaN solid-state lighting (SSL) light emitting diode (LED) packages. Spherical quantum well (SQW) architectures (CdS/CdSe 1- x S x /CdS) were prepared using a library of thio- and selenourea synthesis reagents and high throughput synthesis robotics. CdS/CdSe 1- x S x QDs with narrow luminescence bands were coated with thick CdS shells (thickness = 1.6-7.5 nm) to achieve photoluminescence quantum yields (PLQY) up to 88% at amber and red emission wavelengths (λ max = 600-642 nm, FWHM < 45 nm). The photoluminescence from SQWs encapsulated in silicone and deposited on LED packages was monitored under accelerated aging conditions (oven temperature = 85 °C, relative humidity = 5-85%, blue optical power density = 3-45 W/cm 2 ) by monitoring the red photon output over several hundred hours of continuous operation. The growth of a ZnS shell on the SQW surface increases the stability under long-term operation but also reduces the PLQY, especially of SQWs with thick CdS shells. The results illustrate that the outer ZnS shell layer is key to optimizing the PLQY and the long-term stability of QDs during operation on SSL packages.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Photophysics of Thermally-Assisted Photobleaching in “Giant” Quantum Dots Revealed in Single Nanocrystals

Quantum dots (QDs) are steadily being implemented as down-conversion phosphors in market-ready display products to enhance color rendering, brightness, and energy efficiency. However, for adequate longevity, QDs must be encased in a protective barrier that separates them from ambient oxygen and humidity, and device architectures are designed to avoid significant heating of the QDs as well as direct contact between the QDs and the excitation source. In order to increase the utility of QDs in display technologies and to extend their usefulness to more demanding applications as, for example, alternative phosphors for solid-state lighting (SSL), QDs must retain their photoluminescence emission properties over extended periods of time under conditions of high temperature and high light flux. Doing so would simplify the fabrication costs for QD display technologies and enable QDs to be used as down-conversion materials in light-emitting diodes for SSL, where direct-on-chip configurations expose the emitters to temperatures approaching 100 °C and to photon fluxes from 0.1 W/mm 2 to potentially 10 W/mm 2 . Here, we investigate the photobleaching processes of single QDs exposed to controlled temperature and photon flux. In particular, we investigate two types of room-temperature-stable core/thick-shell QDs, known as “giant” QDs for which shell growth is conducted using either a standard layer-by-layer technique or by a continuous injection method. We determine the mechanistic pathways responsible for thermally-assisted photodegradation, distinguishing effects of hot-carrier trapping and QD charging. As a result, the findings presented here will assist in the further development of advanced QD heterostructures for maximum device lifetime stability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗
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↗
DOE OSTI2026

Particle trajectory representation learning with masked point modeling

Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.

Young, Samuel [Stanford Univ., CA (United States)]↗