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The SSL R&D Plan provides analysis and direction for ongoing R&D activities to advance SSL technology and increase energy savings. The R&D Plan also reviews SSL technology status and trends for both LEDs and OLEDs and offers an overview of the current DOE SSL R&D project portfolio.
The chemical processes occurring on the surface of cathode materials during battery cycling play a crucial role in determining battery’s performance. However, our understanding on such surface chemistry is far from clear due to the complexity of redox chemistry during battery charge/discharge. In this work, through intensive aberration corrected STEM investigation on eight layered oxide cathode materials, we report two important findings on the pristine oxides. First, Ni and Co show strong plane selectivity when building up their respective surface segregation layers (SSL). Specifically, Ni-SSL is exclusively developed on (200)m facet in Li-Mn-rich oxides (monoclinic C2/m symmetry) and (012)h facet in Mn-Ni equally rich oxides (hexagonal R-3m symmetry), while Co-SSL has a strong preference to (20-2)m plane with minimal Co-SSL also developed on some other planes in LMR cathodes. Structurally, Ni-SSLs tend to form spinel-like lattice while Co-SSLs are in a rock-salt-like structure. Secondly, by increasing Ni concentration in these layered oxides, Ni and Co SSLs can be suppressed and even eliminated. Our findings indicate that Ni and Co SSLs are tunable through controlling particle morphology and oxide composition, which opens up a new way for future rational design and synthesis of cathode materials.
Self-supervised learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose resimulation-based self-supervised representation learning (RS3L), a novel simulation-based SSL strategy that employs a method of resimulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and rerunning simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pretraining enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.
Here, light-emitting diode (LED) technology has advanced rapidly over the last decade, primarily driven by display and general illumination applications ("solid-state lighting (SSL) for humans"). These advancements have made LED lighting technically and economically advantageous not only for these applications, but also, as an indirect benefit, for adjacent applications such as horticultural lighting ("SSL for plants"). Moreover, LED technology has much room for continued improvement. In the near-term, these improvements will continue to be driven by SSL for humans (with indirect benefit to SSL for plants), the most important of which can be anticipated.
This project, “Building Surface Science Capacity to Serve the Automobile Industry in Southeastern Michigan” was carried out in two phases: (1) the 2009 – 2012 renovation of space in the new EMU Science Complex, which included the Surface Science Laboratory (SSL), a very vigorous research lab at EMU that carries on a variety of research projects to serve the auto and other industries in Michigan; and (2) the 2013 purchase of several pieces of equipment to further enhance the research capability of the SSL. The funding granted by the DoE was proposed to “renovate the space in the Science Complex to include SSL and purchase equipment for tribological and electrochemical impedance measurements in the lab, thus SSL will serve the auto and other industries in Michigan better.” We believe we have fully accomplished the mission.
OLEDs continue to make strides in display applications, but their commercial utilization in solid-state lighting (SSL) is lagging. An ongoing challenge, in particular for manufacturing, is the need for enhanced efficiency and hence the necessity to increase in an inexpensive approach 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 external quantum efficiency (EQE) is only ~20%. About 50% of the light is lost to internal waveguiding in the high refractive index (RI) organic + ITO anode layers and to surface plasmon polaritons (SPPs) at the organic/metal cathode interface. Another ~30% of the light is externally waveguided in the substrate to its edges. While extraction of the externally waveguided light is commonly addressed by adding a microlens array (MLA) or a scattering layer at the substrate’s air-side, light outcoupling increases by only ~1.6-1.7x (vs up to 2.5x in improving from ~20% to ~50%). The use of a hemispherical lens or an index matching fluid (IMF) at the substrate/photodetector (PD) interface increases the outcoupling by at least 2x; these approaches however, are not viable industrially, and even a MLA is sometimes undesirable due to its non-planar, scattering structure. In multi-stack tandem OLEDs, where the metal cathode is far from the emitting zone(s), the impact of photons loss to SPPs decreases. Our project addressed the ~50% loss to the internally waveguided light and SPPs. We evaluated OLEDs fabricated on patterned or planarized plastic substrates manufactured in a cost-effective approach compatible with a roll-to-roll (R2R) process. The OLEDs were either (i) patterned to various degrees depending on the pitch a and height or depth h of the pattern features or (ii) planar, with a pattern buried under a flat high RI planarization layer. We demonstrated that the outcoupling from green patterned OLEDs reaches ~50% by mitigating plasmon–related loss and internal waveguiding, even without the addition of a MLA, a hemispherical lens, or IMF. Simulations conducted in parallel with the experimental effort demonstrated how diffraction by conformally corrugated OLEDs increases the outcoupling to >60%. Structures with varying pitch values were also simulated indicating that combining domains of varying pitch could increase outcoupling to 55-60%. Experimentally, we additionally assessed the role a and h in determining not only the OLED efficiencies, but also their structural properties, i.e., the uniformity and conformality throughout the OLED stack. As planar OLEDs are preferred over corrugated devices, we studied different patterns in plastic substrates that were planarized by a high RI formulation. Planar green OLEDs on such structures showed enhanced efficiencies with EQEs larger than 60% with the addition of an IMF (to extract the substrate mode) at the substrate/Si PD interface. White OLEDs showed EQEs of 45.5%. Plastic substrates are currently less attractive than glass substrates due to drawbacks such as permeability to water vapor and oxygen, and in some cases thermal instability. Plastic substrates however, are flexible and easy to handle unlike thin flexible glass, and once transparent thin barrier films are available, they will become more attractive; they are already of interest in medical applications. Importantly, as it is easy to generate various patterns in different plastic materials, they provide excellent means for assessing and optimizing enhancing extracting structures. Such structures can also be transferred to glass substrates with some process modifications. 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 R2R manufacturing. R2R processes are of drastically lower-cost than batch or single-unit fabrication. The patterned plastic can be a part of an integrated substrate either plastic or glass, which includes also a MLA or a planar layer with embedded scattering particles, as well as a conductive metal mesh/electrode design. SSL is environmentally-friendly and as OLED SSL becomes more efficient it will reduce electricity consumption, and hence lighting cost, as well as produce less expensive attractive lighting fixtures. Our university-industry collaboration is hence of major benefit to the public as it demonstrates the feasibility of manufacturing optimized extracting substrates for highly efficient OLEDs for SSL in a future R2R process, which would drastically reduce the manufacturing cost and increase production in the USA. Moreover, newly developed methods by our team allow low-cost roll manufactured substrates to be transferred to flexible or rigid glass substrates, which solves the plastic substrate barrier issues, and when combined with device encapsulation will increase the OLEDs’ environmental stability.
Provides analysis and direction for ongoing R&D activities to advance SSL technology and increase energy savings, reviewing SSL technology status and trends for both LEDs and OLEDs and offering an overview of the current DOE SSL R&D project portfolio.
To push the boundaries of efficiency and cost in solid state lighting (SSL), narrow band red emitting down conversion phosphors must be improved. These materials are critical to both the color rendering ability and optical output of phosphor converted light emitting diodes (LEDs). Improvements in these performance parameters will have a significant impact on the adoption of SSL in retail, office, and home lighting applications. Quantum Dots (QDs) are widely acknowledged as a cost-effective front-runner among developing downconverting technologies because they emit narrow band luminescence that is tunable across the visible spectrum. Current syntheses of quantum dots, however, utilize expensive and toxic reagents and often involve several time-intensive purification steps. To address this, we have introduced a novel, cost-effective synthetic scheme, whereby the final quantum dot structures can be accessed with fewer synthetic steps using a library of inexpensive, easily-synthesized precursors. With the help of automated high throughput synthesis robotics, Columbia, LBNL, and OSRAM have optimized red and amber CdS/CdSe/CdS QD architectures for SSL. QDs with improved shelling layers have been identified that meet the industry state of the art performance on chip under high humidity accelerated aging tests. This is remarkable considering the time frame of this project compared to the time and resources allocated for the development of the industrial standard. Furthermore, although particle stability to optical charging is often stressed in improving QLED performance, we have identified effective ZnS particle over-coating as the most important factor determining particle survivability and device performance in down-converting LEDs.
Drivers used to operate light-emitting diode (LED) loads form an integral part of the solid-state lighting (SSL) system. While understanding of LED failure modes such as luminous flux depreciation and chromaticity shifts has increased greatly over the past five years, there is still much uncertainty about general failure modes in SSL drivers due to the almost infinite variety of driver typologies. This report, which is the third in a series of three reports on driver robustness, summarizes overall findings from up to 7,500 hours of accelerated stress testing (AST) on two-stage SSL drivers that could be used to operate troffers and similar fixtures. The earlier reports in this series provided initial AST results for both single-channel and multichannel drivers and an interim update on the performance of multichannel drivers in AST.
The spiral spin liquid (SSL) is a highly degenerate state characterized by a continuous contour or surface in reciprocal space spanned by a spiral propagation vector. Although the SSL state has been predicted in a number of various theoretical models, very few materials are so far experimentally identified to host such a state. Via combined single-crystal wide-angle and small-angle neutron scattering, we report observation of the SSL in the quasi-two-dimensional delafossite-like AgCrSe 2 . We show that it is a very close realization of the ideal Heisenberg J 1 –J 2 –J 3 frustrated model on the triangular lattice. By supplementing our experimental results with microscopic spin-dynamics simulations, we demonstrate how such exotic magnetic states are driven by thermal fluctuations and exchange frustration.
Geometrically frustrated magnetic systems, such as those based on the Shastry-Sutherland lattice (SSL), offer a rich playground for exploring unconventional magnetic states. The delicate balance between competing interactions in these systems leads to the emergence of novel phases. We present the characterization of Er 2 Be 2 GeO 7 , an SSL compound with Er 3+ ions forming orthogonal dimers separated by nonmagnetic layers whose structure is invariant under the 𝑃$\bar{4}21$𝑚 space group. Neutron scattering reveals an antiferromagnetic dimer structure at zero field, typical of Ising spins on that lattice and consistent with the anisotropic magnetization observed. However, magnetization measurements exhibit fractional plateaus at 1/4 and 1/2 of saturation, in contrast to the expected 1/3 plateau of the SSL Ising model. By comparing the energy of candidate states with ground-state lower bounds we show that this behavior requires spatially anisotropic interactions, leading to an anisotropic Shastry-Sutherland Ising model symmetric under the 𝐶𝑚𝑚2 space group. This anisotropy is consistent with the small orthorhombic distortion observed with single-crystal neutron diffraction. The other properties, including thermodynamics, which have been investigated theoretically using tensor networks, point to small residual interactions, potentially due to further couplings and quantum fluctuations. This study highlights Er 2 Be 2 GeO 7 as a promising platform for investigating exotic magnetic phenomena.
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. The measured state variable has been related to the underlying damage using physics-based models. 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.
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 nonclimate 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 the 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. Currently, 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. The 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% Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand-hour LM-80 test data for various LEDs are 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 are estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable are related to the underlying damage using physics-b- sed models. Life prediction of L70 life for the LEDs used in SSL luminaires from KF- and EKF-based models are also compared with the TM-21 model predictions and experimental data.
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. The measured state variable has been related to the underlying damage using physics-based models. 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.
This document is a report of observations and results obtained from a lighting demonstration project conducted under the U.S. Department of Energy (DOE) GATEWAY Demonstration Program. The program supports demonstrations of high-performance solid-state lighting (SSL) products in order to develop empirical data and experience with in-the-field applications of this advanced lighting technology. The DOE GATEWAY Demonstration Program focuses on providing a source of independent, third-party data for use in decision-making by lighting users and professionals; this data should be considered in combination with other information relevant to the particular site and application under examination. Each GATEWAY Demonstration compares SSL products against the incumbent technologies used in that location. Depending on available information and circumstances, the SSL product may also be compared to alternate lighting technologies. Though products demonstrated in the GATEWAY program may have been prescreened for performance, DOE does not endorse any commercial product or in any way guarantee that users will achieve the same results through use of these products. This report reviews the installation and use of LED PAR38 lamps to light a collection of toned albument photographic prints at the J. Paul Getty Museum in Malibu, California. Research results provided by the Getty Conservation Institute are incorporated and discussed.
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.
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.
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.