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Abstract Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since January 2024 are reviewed.
Here, consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since July 2024 are reviewed.
Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined, and new entries since January 2025 are reviewed.
Consolidated tables showing an extensive listing of the highest independently confirmed efficiencies for solar cells and modules are presented. Guidelines for inclusion of results into these tables are outlined and new entries since July 2025 are reviewed.
Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.
Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.
Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .
Computer experiments often employ space-filling strategies to create surrogate models with strong predictive performance. The impact of model parameter estimation for Gaussian process surrogates, however, is often overlooked. Obtaining a better initial estimate of the covariance lengthscale parameter, θ, can greatly improve the resulting Gaussian process fit through more effective sequential acquisitions during active learning. In this work, we propose a novel initial design maximizing the Bayesian D-optimality criterion of the Gaussian process lengthscale parameter. Previously published results have shown the emphasis on lengthscale estimation to be promising, but relied on an empirically driven design creation process. Our Bayesian D-optimal designs are rooted in information theory and lead to more informative sequential acquisitions by improving lengthscale estimation. In many cases, these gains eventually result in better surrogates than those seeded with space-filling initial designs. Furthermore, Bayesian D-optimal designs can be tailored to either isotropic or anisotropic covariance structures, and the Bayesian framework enables the inclusion of prior knowledge in the design process, offering greater flexibility and adaptability. Through several simulation studies, we demonstrate the advantages of Bayesian D-optimal designs in terms of both lengthscale estimation accuracy and predictive performance during active learning.
Abstract Materials simulations involving strongly correlated electrons pose fundamental challenges to state‐of‐the‐art electronic structure methods but are hypothesized to be the ideal use case for quantum computing algorithms. To date, no quantum computer has simulated a molecule of a size and complexity relevant to real‐world applications, despite the fact that the variational quantum eigensolver (VQE) algorithm can predict chemically accurate total energies. Nevertheless, because of the many applications of moderately sized, strongly correlated systems, such as molecular catalysts, the successful use of the VQE stands as an important waypoint in the advancement toward useful chemical modeling on near‐term quantum processors. In this paper, we take a significant step in this direction. We lay out the steps, write, and run parallel code for an (emulated) quantum computer to compute the bond dissociation curves of the TiH, LiH, NaH, and KH diatomic hydride molecules using the VQE. TiH was chosen as a relatively simple chemical system that incorporates d orbitals and strong electron correlation. Because current VQE implementations on existing quantum hardware are limited by qubit error rates, the number of qubits available, and the allowable gate depth, recent studies using it have focused on chemical systems involving s and p block elements. Through VQE + UCCSD calculations of TiH, we evaluate the near‐term feasibility of modeling a molecule with d‐orbitals on real quantum hardware. We demonstrate that the inclusion of d‐orbitals and the use of the UCCSD ansatz, which are both necessary to capture the correct TiH physics, dramatically increase the cost of this problem. We estimate the approximate error rates necessary to model TiH on current quantum computing hardware using VQE + UCCSD and show them to likely be prohibitive until significant improvements in hardware and error correction algorithms are available.
Non-classical features like interference are already being harnessed to control the output of chemical reactions. However, quantum entanglement which is an equally enigmatic many-body quantum correlation can also be used as a powerful resource yet has eluded explicit attention. In this report, an experimental scheme under the crossed beam molecular dynamical setup, with the F + HD reaction, is proposed aiming to study the possible influence of entanglement within reactant pairs on the angular features of the product distribution. The aforesaid reaction has garnered interest recently, as an unusual horseshoe shape pattern in the product (HF) distribution was observed, which has been attributed to the coupling of spin and orbital degrees of freedom. An experimental scheme is proposed aiming to study the possible influence of entanglement on the necessity for the inclusion of such spin–orbit characteristics, under circumstances wherein the existence of entanglement and spin–orbit interaction is simultaneously detectable. The attainable results are further numerically simulated highlighting specific patterns corresponding to various possibilities. Finally, such studies if extended can provide unforeseen mechanistic insight into analogous reactions, too, from the lens of quantum information.
Standard materials are often used to obtain spectra that can be compared to those from unknown samples. Spectra measured from these known substances are also used as a means of computing sensitivity factors to allow quantification by X-ray photoelectron spectroscopy (XPS) of less well-defined materials. Spectra from known materials also provide line shapes suitable for inclusion in spectral models which, when fitted to spectra, permit the chemical state for a sample to be assessed. Both types of information depend on isolating photoemission signals from the inelastically scattered signal. In this Insight note, technical issues associated with the use of XPS of as received Fe 3 O 4 powder sample surface are discussed. The Insight note is designed to show how linear algebraic techniques applied to data collected from a sample marketed as pure Fe 3 O 4 powder are used to verify that XPS has been performed on chemistry representative of the sample. The methods described in this Insight note can further be utilized in elucidating complex XPS data obtained from thin films formed or evolved during cyclic/non-steady use of complex (electro)catalyst surfaces, especially in the presence of contaminants.
Stress distribution maps in polycrystalline materials are needed to reveal stress pathways caused by short‐and long‐range interactions of crystallites. Stress induced changes to wavenumbers in Raman spectroscopy is a well‐known phenomenon that occurs when a crystalline material undergoes elastic strain. Using spatially resolved Raman spectroscopy, we have developed a technique to experimentally measure grain‐scale residual stresses across a sample of polycrystal quartz Tiger's Eye. The results of this technique are validated using multiple criteria, including evaluation of the residual stress map itself and the accuracy of the measurements taken. The residual map of Tiger's Eye shows striking similarity to observed characteristics of other polycrystalline residual stress distributions, revealing heterogenous areas of high and low magnitude stresses. The stress magnitudes are consistent with residual stress magnitudes previously measured from polycrystalline quartz. Characterization of Tiger's Eye quartz also revealed the geometric nature of the iron oxide inclusions, not previously observed in Tiger's Eye quartz. With this technique, we present a residual stress map of Tiger's Eye quartz that shows with accuracy a heterogeneous stress state with inter‐ and intra‐granular detail.
Photovoltaic systems may underperform expectations for several reasons, including inaccurate initial estimates, suboptimal operations and maintenance, or component degradation. Accurate assessment of these loss factors aids in addressing root causes of underperformance and in realizing accurate expectations and models. The performance loss rate (PLR) is a commonly cited high‐level metric for the change in system output over time, but there is no precise, standard definition. Herein, an annualized definition of PLR that is inclusive of all loss factors and that can capture nonlinear changes to performance over time is proposed. The importance of distinguishing between recoverable and nonrecoverable losses which underly PLR is highlighted.
Underspecified composition ranges often lead to alloys with unpredictable mechanical performance. To better understand the changes in microstructure and mechanical performance associated with variations of key elements, three versions of P92 are formulated within, or close to, the specified allowable for N, B, and C ranges. Chromium and Si are also varied to influence N solubility. Different service conditions (i.e., temperature and stress) are explored. It is observed that >80% decrease in creep life occurs at 625 °C and 155 MPa for the highest B and N containing alloy. Multiscale characterization reveals key changes due to the trace element variation. The high B and N containing alloy forms deleterious BN precipitates with morphology that promotes crack nucleation and damage accumulation, but this alloy additionally forms higher fractions of beneficial MX precipitates. The alloy with the lowest B and N concentrations but greater C content shows the best creep performance—a consequence of the refined M 23 C 6 carbide precipitate population and the absence of large-scale inclusions or BN precipitates. Calculations of creep activation energy reveal that the high B and N containing alloy is more prone to damage accumulation which causes an early onset of accelerating creep and greater minimum creep rate.
Abstract There is a growing need for disposal of high‐level nuclear waste. To reduce uncertainty associated with brine availability to repository excavations in salt formations, a collaboration between Sandia, Los Alamos, and Lawrence Berkeley National Laboratories is performing a series of borehole‐scale coupled process tests. Here, we report on the first round of the Brine Availability Test in Salt (BATS) project, a “shakedown” experiment called Phase 1s. Experimental testing included placing a resistive heater, a 260‐W radiative heater, and a 750‐W radiative heater within previously drilled horizontal boreholes at the Waste Isolation Pilot Plant (WIPP) while monitoring temperature and water inflow. The experiments successfully achieved the targeted temperature of 120 °C when using the 750‐W radiative heater. Simulations using FEHM (Finite Element Heat and Mass transfer code) and TOUGH‐FLAC (Transport Of Unsaturated Groundwater and Heat–Fast Lagrangian Analysis of Continua) were able to accurately predict the coupled thermo–hydro–mechanical–chemical response of salt, matching the observed temperature and brine production. Due to the extremely low permeability of salt, these systems take many years to reach steady state when perturbed by mining activities. Long‐term numerical simulations are used to develop the initial pressure and saturation conditions. The inclusion of a damaged rock zone with higher permeability around the borehole also affects the saturation and pressure distributions and plays an important role in dissipating the potential for thermal pressurization. Knowledge gained from this round of experimentation and simulation will be used to conduct the next BATS project experiment in new boreholes at WIPP.
The Additively‐Manufactured, System‐Integrated Tip (AMSIT) project is leveraging the flexibility of 3D printing to integrate several technologies in a wind turbine blade tip, while reducing the levelized cost of electricity (LCOE) produced. The design integration is demonstrated for a 200‐kW–scale turbine with 13‐m blades, with the outer 15% of the blade replaced with a 3D‐printed design. Aerodynamic performance is enhanced without increasing swept area through inclusion of a winglet and surface texturing, both challenging for traditional manufacturing. Longevity and durability are improved through integrated lightning and leading edge erosion protection. Increased power, reduced repair frequency, and ease of repair through blade modularity all contribute to reduced LCOE. The analysis is also extended to modern MW‐scale designs to estimate the impact of the technology at scale, demonstrating the potential to reduce LCOE significantly for modern onshore turbines, with even higher potential savings offshore.
Aluminum alloy A380 is one of the major high-pressure die-cast (HPDC) alloys fabricated in the die casting sector. However, microstructural features such as shrinkage and gas porosity, entrapped oxide inclusions, change in the morphology of eutectic Si particles as a function of casting location, and the presence of multiple second phase particulates, especially Fe-bearing phases in HPDC A380 alloy results in limited ductility, thus, affecting its structural application. However, through proper modification of the as-cast microstructure, it is possible to improve the mechanical performance of alloy A380. In this study, we report the effect of friction stir processing (FSP), a well-known severe plastic deformation (SPD) and thermomechanical processing tool for selective microstructural modification, on A380. FSP effectively refines the as-cast microstructure and breaks down the coarse Si particles, creating a homogenized distribution of equiaxed Si particles in the aluminum matrix. Additionally, FSP eliminates porosity that acts as crack initiation sites, leading to improved mechanical strength and ductility.
The rapid growth of Cloud Computing (CC) has increased the variety of computing resources, storage, and communication services that pose significant new challenges for the efficient use of cloud resources. The cost-efficient allocation of cloud resources has become a decisive premise for the adoption of CC services. The cost-efficient selection and scheduling of these resources to meet the demands of a scientific workflow is a challenging problem that is exacerbated by the inclusion of multiple CC providers. In this paper, we present a novel strategy for the cost-efficient selection of CC resources using Lagrange relaxation. Our approach is based on preselection of resources and demand decomposition to create a series of smaller sub-problems, which allow the estimation of the best cost-structures and selection of CC service providers for a subset of the time period of the planning horizon. Decomposition of the demand is achieved through the boundary analysis of a continuous relaxation of the problem. Using the metrics defined in terms of the cost and time of completion, we demonstrate excellent performance in relation to optimal solutions. Our method reduced the computational time from hours to seconds for a representative 36-month problem and provided high-quality solutions (< 0.05% relative error). Given the importance of selecting resources and scheduling complex scientific workflows, we believe that this novel strategy will be beneficial for many researchers and users of cloud computing resources.