Towards holistic approaches for the interpretation of X-ray Thomson Scattering experiments
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Boehme, Maximilian [Lawrence Livermore National La↗
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HOLISTIC ENERGY EFFICIENCY ANALYSIS OF ELECTRIFIED OFF-HIGHWAY MATERIAL HANDLER: FROM DRIVE CYCLE CHARACTERIZATION TO POWERTRAIN, HYDRAULIC, AND THERMAL SYSTEM PERFORMANCE
Three complexities surrounding the operation and testing of hybrid electric, heavy-duty nonroad machines have been addressed experimentally and using 1D simulation. Their resolutions have been intertwined with the development of a prototype machine that was proven to reduce fuel consumption in excess of 20%. A real-world drive cycle that leveraged hydraulic cylinder position was developed and utilized to ensure accurate reproduction of hydraulic work between the baseline and hybrid machines, while simultaneously maintaining less than 5% RMS error in position for main load handling functions. The newly developed, machine-specific drive cycle also contributed towards making equivalent comparisons in energy consumption between machine types through composite performance metrics that were extrapolated over a typical shift duration. Lastly, this work addressed thermal management energy consumption, a topic of increasing popularity when discussing electrified vehicles, by proposing a 1.4% energy savings through special mechanization and control of cooling system components.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Holistic Transportation and Energy Modeling
This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Aqueous-based recycling of perovskite photovoltaics
Abstract Cumulative silicon photovoltaic (PV) waste highlights the importance of considering waste recycling before the commercialization of emerging PV technologies 1,2 . Perovskite PVs are a promising next-generation technology 3 , in which recycling their end-of-life waste can reduce the toxic waste and retain resources 4,5 . Here we report a low-cost, green-solvent-based holistic recycling strategy to restore all valuable components from perovskite PV waste. We develop an efficient aqueous-based perovskite recycling approach that can also rejuvenate degraded perovskites. We further extend the scope of recycling to charge-transport layers, substrates, cover glasses and metal electrodes. After repeated degradation–recycling processes, the recycled devices show similar efficiency and stability compared with the fresh devices. Our holistic recycling strategy reduces by 96.6% resource depletion and by 68.8% human toxicity (cancer effects) impacts associated with perovskite PVs compared with the landfill treatment. With recycling, the levelized cost of electricity also decreases for both utility-scale and residential systems. This study highlights unique opportunities of perovskite PVs for holistic recycling and paves the way for a sustainable perovskite solar economy.
Science & Technology - Other Topics↗
A Model-Based Systems Engineering Approach for Effective Decision Support of Modern Energy Systems Depicted with Clean Hydrogen Production
A holistic approach to decision-making in modern energy systems is vital due to their increase in complexity and interconnectedness. However, decision makers often rely on narrowly-focused strategies, such as economic assessments, for energy system strategy selection. The approach in this paper helps considers various factors such as economic viability, technological feasibility, environmental impact, and social acceptance. By integrating these diverse elements, decision makers can identify more economically feasible, sustainable, and resilient energy strategies. While existing focused approaches are valuable since they provide clear metrics of a potential solution (e.g., an economic measure of profitability), they do not offer the much needed system-as-a-whole understanding. This lack of understanding often leads to selecting suboptimal or unfeasible solutions, which is often discovered much later in the process when a change may not be possible. This paper presents a novel evaluation framework to support holistic decision-making in energy systems. The framework is based on a systems thinking approach, applied through systems engineering principles and model-based systems engineering tools, coupled with a multicriteria decision analysis approach. The systems engineering approach guides the development of feasible solutions for novel energy systems, and the multicriteria decision analysis is used for a systematic evaluation of available strategies and objective selection of the best solution. The proposed framework enables holistic, multidisciplinary, and objective evaluations of solutions and strategies for energy systems, clearly demonstrates the pros and cons of available options, and supports knowledge collection and retention to be used for a different scenario or context. The framework is demonstrated in case study evaluation solutions for a novel energy system of clean hydrogen generation.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration
The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.
47 OTHER INSTRUMENTATION↗
Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize
Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
AI/ML↗
Cost-Benefit Analysis For Indonesia Building Sector: Whole-Building Cooling Solutions
The Net Zero World (NZW) Initiative Collaborative Work Program with the Government of Indonesia (GoI) includes technical assistance and investment mobilization facilitation to accelerate deployment of energy efficiency technologies and solutions for the building sector. A February 2023 U.S.–Indonesia Joint Workshop on Decarbonizing the Building Sector yielded a NZW Indonesia Building Decarbonization Working Group (NZW IBDWG) with four sub-working groups (SWG): SWG-A National Center, SWG-B Capacity Building, SWG-C Investment and Financing, and SWG-D Pilot Projects. Technical analysis of whole-building cooling solutions for tropical climates of Indonesia was conducted by SWG-A to quantify energy savings, carbon dioxide reductions, and comfort improvements offered by 12 passive or low-energy cooling strategies: ceiling fans with and without thermostat setbacks; cool roofs; cool walls; exterior awnings; exterior shades; interior shades; insulated roofs; insulated walls; low-e windows; solar window films; and natural ventilation. Leveraging the results from SWG-A, cost-benefit analysis (CBA) was conducted by SWG-C to assess the consumer and national costs and impacts associated with these 12 cooling solutions. The evaluation involved estimating life-cycle costs (LCC), payback period (PBP), net present values (NPV), annual electricity burden change for low-income households, and reduced national annual power-sector generation demand by 2030, 2040, 2050, and 2060. This evaluation can help guide Indonesia’s Just Energy Transition Partnership (JETP) investments in policies and programs to advance research, development, deployment, and commercial adoption (RDDCA) of efficient residential building sector cooling technologies and solutions in Indonesia. Four key energy conservation measures (ECM) have been identified to reduce air-conditioning (AC) energy demand in single-family housing in Indonesia: ceiling fan with temperature setback (to 28.1 °Celcius from 25 °C); insulated walls; insulated roof; and cool roof. This study found that low-income households with AC installations in Indonesia currently face a high energy cost burden of approximately 10%. However, by implementing a ceiling fan with temperature setback, this burden could decrease to 2.5% today and further reduce to 1.3% by the year 2060. The PBP for a ceiling fan with temperature setback is one year, indicating one of the lowest LCC and best NPV. In the planned upcoming phase of CBA, a series of building cooling improvement scenarios can be further defined, incorporating more than one ECM in combination with socio-economic factors evaluated in the initial CBA phase. Additionally, the analysis of ECM effects in multifamily housing can be expanded. This broader national analysis aims to encompass a holistic and comprehensive system-level perspective, including factors such as avoided power sector infrastructure investments, domestic job creation, domestic manufacturing job creation, and gross domestic product (GDP) growth.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Planning Roadmap for DER Integration in India: Industry Best Practices and Resource Guide
Ensuring safe, reliable, cost-effective DER integration at scale requires holistic planning, broad stakeholder engagement, and should address key development areas such as standards adoption, equipment testing and certification, interoperability and cybersecurity, interconnection procedures, and advanced forecasting and DER management. Each of these areas currently represent significant challenges for utilities, regulators, OEMs, developers, and even consumers worldwide. India has already seen significant growth of DERs and has announced targets for substantial growth yet to come, with the potential for DERs to make up a non-negligible portion of the country's overall generation capacity. As such, it is of critical importance that Indian stakeholders consider the potential impacts of wide-spread adoption of DERs and take preemptive action related to the five development areas listed here, among others. India should consider strategies including the adoption of key DER standards related to interconnection, testing, and cybersecurity; enabling effective and secure communication channels for DER interoperability; testing and certifying DER equipment in accredited testing laboratories; building robust, streamlined interconnection procedures; and revamping legacy system planning structures to incorporate DERs in a holistic planning framework.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Floating Wind Array Ontology and Modeling Framework
While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.
17 WIND ENERGY↗
FORCE Regression Testing
Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software
97 MATHEMATICS AND COMPUTING↗
Effect of Antagonistic Binder–Catalyst Interactions on Catalytic Activity in Lithium–Sulfur Batteries
Lithium-sulfur (Li-S) batteries are a promising next-generation energy storage solution, as they can reduce reliance on critical transition metals while offering high energy densities. However, their deployment is hindered by low sulfur utilization and the formation/diffusion of lithium polysulfides (LiPSs). While transition-metal catalysts and polymeric binders have been independently developed to enhance redox kinetics and LiPS adsorption, their mutual compatibility has remained largely unexplored. We show here that binder-catalyst interactions can significantly impact catalytic performance. Employing TiO 2 as a generic catalyst, the electrochemical performance is shown to depend strongly on the binder environment. TiO 2 paired with lithiated polyacrylic acid (LiPAA) shows benign interactions, resulting in enhanced cycle life. In contrast, pairing TiO 2 with protonated PAA produces antagonistic interactions that hinder Li 2 S growth. A mechanistic analysis unveils that the carboxylic H atom in PAA promotes COO − coordination to Ti sites, occupying catalytic centers and suppressing LiPS adsorption, increasing charge transfer and diffusion resistances. This phenomenon is observed across multiple catalysts, indicating that COOH-functionalized binders may broadly hinder catalytic activity. Overall, this study underscores the need for holistic cathode design and identifies binder-catalyst compatibility as an important parameter for high-performance Li-S batteries.
25 ENERGY STORAGE↗
In-device Battery Failure Analysis
Lithium-ion batteries are indispensable power sources for a wide range of modern electronic devices. However, battery lifespan remains a critical limitation, directly affecting the sustainability and user experience. Conventional battery failure analysis in controlled lab settings may not capture the complex interactions and environmental factors encountered in real-world, in-device operating conditions. Here, this study analyzes the failure of commercial wireless earbud batteries as a model system within their intended usage context. Through multiscale and multimodal characterizations, the degradations from the material level to the device level are correlated, elucidating a failure pattern that is closely tied to the specific device configuration and operating conditions. The findings indicate that the ultimate failure mode is determined by the interplay of battery materials, cell structural design, and the in-device microenvironment, such as temperature gradients and their fluctuations. This holistic, in-device perspective on environmental influences provides critical insights for battery integration design, enhancing the reliability of modern electronics.
36 MATERIALS SCIENCE↗
Effective Corrosion-Resistant Single-Atom Alloy Catalyst on HfO 2 -Passivated BiVO 4 Photoanode for Durable (≈800 h) Solar Water Oxidation
Green hydrogen (H 2 ) production from solar water splitting necessitates photoelectrodes with superior photoelectrochemical (PEC) activity and durability. However, surface defects and photocorrosion instability—especially at high potentials—limit PEC performance and stability. Herein, the prototypical bismuth vanadate (BiVO 4 ) photoanode is used to demonstrate a holistic approach to improve photocurrent density and long-term stability. In this approach, high surface-area nanostructuring of BiVO 4 is combined with barium (Ba) doping with semi-crystalline hafnium oxide (HfO 2 ) surface passivation and single-atom nickel platinum (NiPt) catalysts. The introduction of Ba 2+ ions into BiVO 4 increases the concentration of conductive V 4+ ions or the ratio of V 4+ ions to oxygen vacancies, avoiding V 5+ dissolution during water oxidation. The semi-crystalline HfO 2 , which serves as a passivation layer, prevents BiVO 4 photocorrosion by suppressing harmful chemical reactions when holes are transferred to the electrolyte. The synergistic use of isolated single-atom and Ni-Pt coordination improves charge transfer at the photoanode/electrolyte interface, leading to enhanced PEC kinetics and stability. As a result, a photoelectrode is demonstrated with ≈6.5 mA cm -2 at 1.23 V versus a reversible hydrogen electrode (RHE) and continuous operation for 800 h with a negligible degradation rate. This work provides a promising approach to improve photoanodes for PEC H 2 production.
08 HYDROGEN↗
Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: A case study in maize
Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.
59 BASIC BIOLOGICAL SCIENCES↗
The water–climate nexus: Intersections across sectors
Water security and climate change are important priorities for communities and regions worldwide. The intersections between water and climate change extend across many environmental and human activities. This Primer is intended as an introduction, grounded in examples, for students and others considering the interactions between climate, water, and society. In this Primer, we summarize key intersections between water and climate across four sectors: environment; drinking water, sanitation, and hygiene; food and agriculture; and energy. We begin with an overview of the fundamental water dynamics within each of these four sectors, and then discuss how climate change is impacting water and society within and across these sectors. Emphasizing the relationships and interconnectedness between water and climate change can encourage systems thinking, which can show how activities in one sector may influence activities or outcomes in other sectors. We argue that to achieve a resilient and sustainable water future under climate change, proposed solutions must consider the water–climate nexus to ensure the interconnected roles of water across sectors are not overlooked. Toward that end, we offer an initial set of guiding questions that can be used to inform the development of more holistic climate solutions.
54 ENVIRONMENTAL SCIENCES↗
Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.
3D city modeling↗
A valuation framework for customers impacted by extreme temperature-related outages
Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI), such as increased mortality rates, property damage, and reduced productivity, due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. In conclusion, these findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.
24 POWER TRANSMISSION AND DISTRIBUTION↗