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This is an updated edition that examines critical opportunities to positively impact energy savings, greenhouse gas emissions, human well-being, and the economy through Solid-State Lighting (SSL) R&D. The document summarizes stakeholder input from DOE-hosted roundtable meetings, workshops, a Request for Information, and other sources.
I arrived in Berkeley in October 1983 as a post-doc, and my appointment was at the Space Sciences Laboratory (SSL). During that time the group was very large, with multiple activities led by Charlie himself and also by Senior Fellows such as John Lacy, Dan Jaffe, and Al Betz at the top of the hill at Space Sciences. Another significant contingent of the Townes group was housed in Birge Hall on campus, led by Reinhard Genzel when he was an Assistant Professor in the Physics Department. Although the group encompassed two separate locations, it functioned as one large group. Either we rode with Charlie up and down the hill, or (if we were concerned about our safety!) we took the bus.
Overview of SSL projects currently funded by DOE, and those previously funded but since completed. Each profile includes a brief technical description, as well as information about project partners, funding, and the research period. This report is updated annually.
Illustrated overview of the DOE SSL Portfolio’s programs and activities.
Provides an overview of funding mechanisms for research partners and projects that align with the SSL R&D plan.
The 2018 Solid-State Lighting Project Portfolio provides an overview of all SSL projects that have been funded by DOE since 2000. Projects that were active during 2017 are found in the main body of this report, and all historic projects can be found in the appendix. Within these sections, project profiles are sorted by technology type (i.e., LED or OLED) and then by performer name. A profile is provided on each project. Each profile includes a brief technical description, as well as information about project partners, funding, and the research period. This report is updated annually, although the research described in the Portfolio changes periodically as new projects are initiated and existing ones are concluded.
Abstract Graph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.
The goal of this investigation was to evaluate potential energy impacts of circadian lighting design recommendations gaining attention in a variety of common applications such as offices and classrooms. The renewed focus on health along with advances in SSL technology capabilities has underscored that there is still much to learn regarding the relationship between light and human physiology. The energy implications of designing to address these possible physiological effects are not yet fully understood. Beyond the fact that the basic metric of luminous efficacy (lumens per watt) does not cover these other effects, the emerging science seems to indicate that addressing a holistic view of the human needs in most applications may mean a need for increased light and associated energy use by electric lighting systems. Within the two applications considered, lumen output, spectral characteristics, surface reflectance distribution and desk orientation were varied to explore the magnitude of potential effects. Meeting current IES illuminance recommendations did not satisfy existing EML and CS recommendations for any of the simulations. In some cases, satisfying circadian metric recommendations required average illuminance that was more than double IES recommendations, which may negatively impact lighting quality as well. Using results from 45 unique simulation conditions, it was estimated that lighting energy use may increase between 10% and 100% due to increased luminaire light levels used to meet circadian lighting design recommendations listed in current building standards such as WELL v2 Q2 2019, UL Design Guideline 24480, and CHPS Core Criteria 3.0.
Metal halides are an important class of optoelectronic materials combining exceptional optical and electronic properties. An inherent advantage of metal halides is their solution synthesis and processability, which render them as low-cost and environmentally friendly materials for a range of applications from photovoltaics and photodetection to solid-state lighting (SSL). Here, in this study, we synthesized three previously unreported lead-free organic–inorganic hybrid copper halides: (OA) 4 CuX 5 (X = Br, I; OA + = C 8 H 17 NH 3+ , n-octylammonium cation) and (HA) 2 CuI 3 (HA + = C 6 H 13 NH 3 + , n-hexylammonium cation), all of which exhibit broadband emissions arising from self-trapped excitons (STEs). Among these compounds, (OA) 4 CuI 5 demonstrates tunable dual-band white-light emission with a high color rendering index value of 91 at room temperature. Temperature-dependent photoluminescence measurements and first-principles calculations reveal distinct behaviors between the two emission states in (OA) 4 CuI 5 . These findings highlight the potential of copper halide compounds for optoelectronic applications, particularly in the development of environmentally friendly solid-state lighting technologies.
Light extraction from OLEDs remains a challenge. While consumer demand for OLEDs in display technology continues to grow as prices decline, commercial use of OLEDs in solid state lighting (SSL) applications is lagging due to low light extraction, which results in low efficiency. Previous studies have been directed toward increasing the light extraction factor η out , but cost effective approaches that will enable high throughput and potential upscaling, as well as elucidation of the extraction process, are still lagging. One promising approach is the use of buried light extraction enhancing patterns that are planarized with a high refractive index (RI) layer. When adding substrate mode extracting means, the highest external quantum efficiencies (EQEs) were achieved using a complex approach of planarized vacuum nano holes. Here we present η out -enhancing planarized extraction structures (PES) in plastic. While plastic substrates currently present long-term stability issues and other drawbacks, as we show, they provide a tool for simple, inexpensive, and rapid generation of various enhancing structures that importantly can be transferred to the preferred rigid and flexible glass substrates. Moreover, plastic substrates are attractive for biomedical applications. The successful structures we present are easily scalable periodic corrugations, e.g., with pitch a ~ 4.25 µm and pitch/depth ratio a/h ~2.4 planarized by a layer of RI ~1.9, resulting in maximal EQEs exceeding 60% for a green OLED and 48.5% for a white OLED. Comparable increased EQEs were achieved for structures with a ~ 15 and 16 µm, and a/h ~ 2.1 and 4, respectively. Enhanced extraction was also observed with a very simply fabricated quasiperiodic nanohole array, and surprisingly, with a random shallow design. The latter is at variance with simulations based on diffraction only. Results are discussed in terms of a, h, and structural parameters together with scattering matrix simulations to assess the effect of various PES parameters and the nanoparticle-embedded planarizing layer on η out . The experiments and simulations indicate contributions of diffraction, reflection, and scattering to EQE enhancement.
In this paper, we use a field theory approach to study the effects of permanent dipoles on interpenetration and free energy changes as a function of distance between two identical planar polymer brushes. Melts (i.e., solvent-free) and solvated brushes made up of polymers grafted on nonadsorbing substrates are studied. In particular, the weak coupling limit of the dipolar interactions is considered, which leads to concentration-dependent pairwise interactions, and the effects of orientational order are neglected. It is predicted that a gradual increase in the dipole moment of the polymer segments can lead to attractive interactions between the brushes at intermediate separation distances. Finally, because classical theory of polymer brushes based on the strong stretching limit (SSL) and the standard self-consistent field theory (SCFT) simulations using the Flory’s χ parameter always predicts repulsive interactions at all separations, our work highlights the importance of dipolar interactions in tailoring and accurately predicting forces between polar polymeric interfaces in contact with each other.
Solid State Lighting (SSL) based devices are predicted to play a crucial role in the coming years. Development of W-LED, which have an edge over traditional lighting sources due to their compact size, higher reliability, shock resistance, interesting design possibilities, higher transparency and an extremely long lifetime. Over the fifteen trivalent lanthanide ions, Dy{sup 3+} ions doped glasses are most appropriate for white light generation because of the fact that it exhibits two intense emission bands corresponds to the {sup 4}F{sub 9/2}→{sup 6}H{sub 15/2} (magnetic dipole) and {sup 4}F{sub 9/2}→{sup 6}H{sub 13/2} (electric dipole) transitions at around 480-500 nm and 580-600 nm pertaining to blue and yellow regions respectively. In this work, the developments of Dy3+ doped in several glass structures for white emitting materials application have reviewed. Properties of Dy{sup 3+} doped in glasses were discussed for use as a solid state lighting materials application.
MgCr 2 O 4 is one of the best-known realizations of the pyrochlore-lattice Heisenberg antiferromagnet. The strong antiferromagnetic exchange interactions are perturbed by small further-neighbor exchanges such that this compound may in principle realize a spiral spin liquid (SSL) phase in the zero-temperature limit. However, a spin Jahn-Teller transition below T N ≈ 13 K yields a complicated long-range magnetic order with multiple coexisting propagation vectors. We present neutron scattering and thermomagnetic measurements of MgCr 2 O 4 samples under applied hydrostatic pressure up to P = 1.7 GPa demonstrating the existence of multiple close-lying nearly degenerate magnetic ground states. We show that the application of hydrostatic pressure increases the ordering temperature by around 0.8 K per GPa and increases the bandwidth of the magnetic excitations by around 0.5 meV per GPa. We also evidence a strong tendency for the preferential occupation of a subset of magnetic domains under pressure. In particular, we show that the $k$ = (0,0,1) magnetic phase, which is almost negligible at ambient pressure, dramatically increases in spectral weight under pressure. This modifies the spectrum of magnetic excitations, which we interpret unambiguously as spin waves from multiple magnetic domains. Moreover, we report that the application of pressure reveals a feature in the magnetic susceptibility above the magnetostructural transition. We interpret this as the onset of a short-range ordered phase associated with $k$ = (0,0,1), previously not observed in magnetometry measurements.
The effects of axial boundary conductivity on the formation and stability of a magnetized free Stewartson-Shercliff layer (SSL) in a short Taylor-Couette device are reported. As the axial field increases with insulating endcaps, hydrodynamic Kelvin-Helmholtz-type instabilities set in at the SSLs of the conducting fluid, resulting in a much reduced flow shear. With conducting endcaps, SSLs respond to an axial field weaker by the square root of the conductivity ratio of endcaps to fluid. Flow shear continuously builds up as the axial field increases despite the local violation of the Rayleigh criterion, leading to a large number of hydrodynamically unstable modes. In conclusion, numerical simulations of both the mean flow and the instabilities are in agreement with the experimental results.
Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.
Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.
The effects of axial boundary conductivity on the formation and stability of a magnetized free Stewartson-Shercliff layer (SSL) in a short Taylor-Couette device are reported. As the axial field increases with insulating endcaps, hydrodynamic Kelvin-Helmholtz-type instabilities set in at the SSLs of the conducting fluid, resulting in a much reduced flow shear. With conducting endcaps, SSLs respond to an axial field weaker by the square root of the conductivity ratio of endcaps to fluid. Flow shear continuously builds up as the axial field increases despite the local violation of the Rayleigh criterion, leading to a large number of hydrodynamically unstable modes. Numerical simulations of both the mean flow and the instabilities are in agreement with the experimental results.
SAND2022-6991 O gait is an extension to zeek, an open-source network analyzer that adds metadata to the existing connection and SSL logs. Most of this metadata is already exposed by the zeek script API. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.